(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>Mhm Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with health, a lot of our stakeholders and HP. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The Cto Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis. Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well, >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient. But at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud radar survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the EMEA markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've we've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge enterprises. The survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new go to markets uh to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call us effective. It's just not the easy ones but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. So as we see, we've talked about this for many times, more and more companies moving to multi cloud arrangements for a variety of reasons, what have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around >>right, you know, hybrid >>cloud has been the new normal. Right? And um, and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right cloud solution framework >>and this is >>focused on implementing a hybrid, multi cloud leveraging and in house developed tools and frameworks as well as platforms along with our strategic partner ecosystem, >>that is our biggest contribution >>onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis public cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. >>Now, apart from all of these >>things, it also offers features and functionalities very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities >>seamlessly >>across the >>multiple hybrid >>ecosystem protect. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you've mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh Liza in in the previous question as well. Then we looked at when we look at enterprises trying to take the cloud adoption. The big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers, for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in a easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>We're sticking with you for a second. If we unpack that growth, that business profit growth opportunity that the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework >>and they offer >>the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. >>Either it is a >>continuous service with HP is as ephemeral data platform on HP hardware, or >>Vida as a >>service based on a compose Herbal and Converse infrastructure or H P. S cloud built on >>HPC cloud, build on Cray systems >>and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a >>very similar >>metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we >>also work very closely with >>H P E >>to create a >>very compelling sourcing strategy for driving hybrid, cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well, the HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Liza is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well. The relationship between emphasis and hedge P. Is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win but we call it a win win win which is essentially win for HP win for emphasis but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will V. D. I. Uh engagement with them where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. Uh I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector where we've really done work very closely with PHP across all regions and all geography is uh to make this what I would call when when very partnership. >>I like that when when when who wouldn't want that one more question for you. Talk to me about the next, as we talked about some of those survey results and I think folks can find that survey, the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this >>opportunity. Absolutely. For election. Saw ju I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah. Mhm. Yeah.

>>Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with a lot of our stakeholders and hp. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The CTO Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient, but at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud reader survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the email markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge. Enterprises the survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new, go to markets uh, to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call as effective. It's just not the easy ones, but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. We see, we've talked about this for many times more and more companies moving to multi cloud arrangements for a variety of reasons. What have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around right, you know, hybrid cloud has been the new normal. Right? And um and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, you know, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right loud solution framework and this is focused on implementing a hybrid multi cloud leveraging an in house developed tools and frameworks as well as platforms along with our strategic Puerto rico system, that is our biggest contribution onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis Polly cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. Now, apart from all of these things, it also offers features and functionality is very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities seamlessly across the multiple hybrid ecosystem. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh lisa in in the previous question as well. When we looked at when we look at enterprises trying to take the cloud adoption, the big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in the easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>I'm sticking with you for a second. If we unpack that growth, that business profit growth opportunity that you the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today, enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework and they offer the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. Either it is a continuous service with HP is is immoral data platform on HP hardware or video as a service based on a compose Herbal and Converse infrastructure or H. P. S cloud built on HPC cloud, build on Cray systems and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a very similar metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we also work very closely with H. P. E to create a very compelling sourcing strategy for driving hybrid cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well. The HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Visa is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well, the relationship between emphasis and hedge P is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win, but we call it a win win win, which is essentially win for HPV win for emphasis, but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will be D I uh engagement with them, where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector, where we've really done work very closely with HP across all regions and all geography is uh to make this what I would call a win win win partnership. >>I like that when when when who wouldn't want that. One more question for you talk to me about the next, as we talked about some of those survey results and I think folks can find that survey the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space uh for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this opportunity. >>Absolutely. For election Soju. I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah, yeah.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle, everybody to Cuban cloud. My name is Dave Volonte, and I'll be here throughout the day with my co host, John Ferrier, who was quarantined in an undisclosed location in California. He's all good. Don't worry. Just precautionary. John, how are you doing? >>Hey, great to see you. John. Quarantine. My youngest daughter had covitz, so contact tracing. I was negative in quarantine at a friend's location. All good. >>Well, we wish you the best. Yeah, well, right. I mean, you know what's it like, John? I mean, you're away from your family. Your basically shut in, right? I mean, you go out for a walk, but you're really not in any contact with anybody. >>Correct? Yeah. I mean, basically just isolation, Um, pretty much what everyone's been kind of living on, kind of suffering through, but hopefully the vaccines are being distributed. You know, one of the things we talked about it reinvent the Amazon's cloud conference. Was the vaccine on, but just the whole workflow around that it's gonna get better. It's kind of really sucky. Here in the California area, they haven't done a good job, a lot of criticism around, how that's rolling out. And, you know, Amazon is now offering to help now that there's a new regime in the U. S. Government S o. You know, something to talk about, But certainly this has been a terrible time for Cove it and everyone in the deaths involved. But it's it's essentially pulled back the covers, if you will, on technology and you're seeing everything. Society. In fact, um, well, that's big tech MIT disinformation campaigns. All these vulnerabilities and cyber, um, accelerated digital transformation. We'll talk about a lot today, but yeah, it's totally changed the world. And I think we're in a new generation. I think this is a real inflection point, Dave. You know, modern society and the geo political impact of this is significant. You know, one of the benefits of being quarantined you'd be hanging out on these clubhouse APS, uh, late at night, listening to experts talk about what's going on, and it's interesting what's happening with with things like water and, you know, the island of Taiwan and China and U. S. Sovereignty, data, sovereignty, misinformation. So much going on to talk about. And, uh, meanwhile, companies like Mark injuries in BC firm starting a media company. What's going on? Hell freezing over. So >>we're gonna be talking about a lot of that stuff today. I mean, Cuba on cloud. It's our very first virtual editorial event we're trying to do is bring together our community. It's a it's an open forum and we're we're running the day on our 3 65 software platform. So we got a great lineup. We got CEO Seo's data Practitioners. We got a hard core technologies coming in, cloud experts, investors. We got some analysts coming in and we're creating this day long Siri's. And we've got a number of sessions that we've developed and we're gonna unpack. The future of Cloud computing in the coming decade is, John said, we're gonna talk about some of the public policy new administration. What does that mean for tech and for big tech in General? John, what can you add to that? >>Well, I think one of the things that we talked about Cove in this personal impact to me but other people as well. One of the things that people are craving right now is information factual information, truth texture that we call it. But hear this event for us, Davis, our first inaugural editorial event. Robbo, Kristen, Nicole, the entire Cube team Silicon angle, really trying to put together Morva cadence we're gonna doom or of these events where we can put out feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires with people making things happen. But it's often the people under there that are the rial newsmakers amid savory, for instance, that Google one of the most impressive technical people, he's gotta talk. He's gonna present democratization of software development in many Mawr riel people making things happen. And I think there's a communal element. We're going to do more of these. Obviously, we have, uh, no events to go to with the Cube. So we have the cube virtual software that we have been building and over years and now perfecting and we're gonna introduce that we're gonna put it to work, their dog footing it. We're gonna put that software toe work. We're gonna do a lot mawr virtual events like this Cuban cloud Cuban startup Cuban raising money. Cuban healthcare, Cuban venture capital. Always think we could do anything. Question is, what's the right story? What's the most important stories? Who's telling it and increase the aperture of the lens of the industry that we have and and expose that and fastest possible. That's what this software, you'll see more of it. So it's super exciting. We're gonna add new features like pulling people up on stage, Um, kind of bring on the clubhouse vibe and more of a community interaction with people to meet each other, and we'll roll those out. But the goal here is to just showcase it's cloud story in a way from people that are living it and providing value. So enjoy the day is gonna be chock full of presentations. We're gonna have moderated chat in these sessions, so it's an all day event so people can come in, drop out, and also that's everything's on demand immediately after the time slot. But you >>want to >>participate, come into the time slot into the cube room or breakout session. Whatever you wanna call it, it's a cube room, and the people in there chatting and having a watch party. So >>when you're in that home page when you're watching, there's a hero video there. Beneath that, there's a calendar, and you'll see that red line is that red horizontal line of vertical line is rather, it's a linear clock that will show you where we are in the day. If you click on any one of those sessions that will take you into the chat, we'll take you through those in a moment and share with you some of the guests that we have upcoming and and take you through the day what I wanted to do. John is trying to set the stage for the conversations that folks are gonna here today. And to do that, I wanna ask the guys to bring up a graphic. And I want to talk to you, John, about the progression of cloud over time and maybe go back to the beginning and review the evolution of cloud and then really talk a little bit about where we think it Z headed. So, guys, if you bring up that graphic when a W S announced s three, it was March of 2000 and six. And as you recall, John you know, nobody really. In the vendor and user community. They didn't really pay too much attention to that. And then later that year, in August, it announced E C two people really started. They started to think about a new model of computing, but they were largely, you know, chicken tires. And it was kind of bleeding edge developers that really leaned in. Um what? What were you thinking at the time? When when you saw, uh, s three e c to this retail company coming into the tech world? >>I mean, I thought it was totally crap. I'm like, this is terrible. But then at that time, I was thinking working on I was in between kind of start ups and I didn't have a lot of seed funding. And then I realized the C two was freaking awesome. But I'm like, Holy shit, this is really great because I don't need to pay a lot of cash, the Provisional Data center, or get a server. Or, you know, at that time, state of the art startup move was to buy a super micro box or some sort of power server. Um, it was well past the whole proprietary thing. But you have to assemble probably anyone with 5 to 8 grand box and go in, and we'll put a couple ghetto rack, which is basically, uh, you know, you put it into some coasting location. It's like with everybody else in the tech ghetto of hosting, still paying monthly fees and then maintaining it and provisioning that's just to get started. And then Amazon was just really easy. And then from there you just It was just awesome. I just knew Amazon would be great. They had a lot of things that they had to fix. You know, custom domains and user interface Council got better and better, but it was awesome. >>Well, what we really saw the cloud take hold from my perspective anyway, was the financial crisis in, you know, 709 It put cloud on the radar of a number of CFOs and, of course, shadow I T departments. They wanted to get stuff done and and take I t in in in, ah, pecs, bite sized chunks. So it really was. There's cloud awakening and we came out of that financial crisis, and this we're now in this 10 year plus boom um, you know, notwithstanding obviously the economic crisis with cove it. But much of it was powered by the cloud in the decade. I would say it was really about I t transformation. And it kind of ironic, if you will, because the pandemic it hits at the beginning of this decade, >>and it >>creates this mandate to go digital. So you've you've said a lot. John has pulled forward. It's accelerated this industry transformation. Everybody talks about that, but and we've highlighted it here in this graphic. It probably would have taken several more years to mature. But overnight you had this forced march to digital. And if you weren't a digital business, you were kind of out of business. And and so it's sort of here to stay. How do you see >>You >>know what this evolution and what we can expect in the coming decades? E think it's safe to say the last 10 years defined by you know, I t transformation. That's not gonna be the same in the coming years. How do you see it? >>It's interesting. I think the big tech companies are on, but I think this past election, the United States shows um, the power that technology has. And if you look at some of the main trends in the enterprise specifically around what clouds accelerating, I call the second wave of innovations coming where, um, it's different. It's not what people expect. Its edge edge computing, for instance, has talked about a lot. But industrial i o t. Is really where we've had a lot of problems lately in terms of hacks and malware and just just overall vulnerabilities, whether it's supply chain vulnerabilities, toe actual disinformation, you know, you know, vulnerabilities inside these networks s I think this network effects, it's gonna be a huge thing. I think the impact that tech will have on society and global society geopolitical things gonna be also another one. Um, I think the modern application development of how applications were written with data, you know, we always been saying this day from the beginning of the Cube data is his integral part of the development process. And I think more than ever, when you think about cloud and edge and this distributed computing paradigm, that cloud is now going next level with is the software and how it's written will be different. You gotta handle things like, where's the compute component? Is it gonna be at the edge with all the server chips, innovations that Amazon apple intel of doing, you're gonna have compute right at the edge, industrial and kind of human edge. How does that work? What's Leighton see to that? It's it really is an edge game. So to me, software has to be written holistically in a system's impact on the way. Now that's not necessarily nude in the computer science and in the tech field, it's just gonna be deployed differently. So that's a complete rewrite, in my opinion of the software applications. Which is why you're seeing Amazon Google VM Ware really pushing Cooper Netease and these service messes in the micro Services because super critical of this technology become smarter, automated, autonomous. And that's completely different paradigm in the old full stack developer, you know, kind of model. You know, the full stack developer, his ancient. There's no such thing as a full stack developer anymore, in my opinion, because it's a half a stack because the cloud takes up the other half. But no one wants to be called the half stack developer because it doesn't sound as good as Full Stack, but really Cloud has eliminated the technology complexity of what a full stack developer used to dio. Now you can manage it and do things with it, so you know, there's some work to done, but the heavy lifting but taking care of it's the top of the stack that I think is gonna be a really critical component. >>Yeah, and that that sort of automation and machine intelligence layer is really at the top of the stack. This this thing becomes ubiquitous, and we now start to build businesses and new processes on top of it. I wanna I wanna take a look at the Big Three and guys, Can we bring up the other The next graphic, which is an estimate of what the revenue looks like for the for the Big three. And John, this is I asked and past spend for the Big Three Cloud players. And it's It's an estimate that we're gonna update after earning seasons, and I wanna point a couple things out here. First is if you look at the combined revenue production of the Big Three last year, it's almost 80 billion in infrastructure spend. I mean, think about that. That Z was that incremental spend? No. It really has caused a lot of consolidation in the on Prem data center business for guys like Dell. And, you know, um, see, now, part of the LHP split up IBM Oracle. I mean, it's etcetera. They've all felt this sea change, and they had to respond to it. I think the second thing is you can see on this data. Um, it's true that azure and G C P they seem to be growing faster than a W s. We don't know the exact numbers >>because >>A W S is the only company that really provides a clean view of i s and pass. Whereas Microsoft and Google, they kind of hide the ball in their numbers. I mean, I don't blame them because they're behind, but they do leave breadcrumbs and clues about growth rates and so forth. And so we have other means of estimating, but it's it's undeniable that azure is catching up. I mean, it's still quite distance the third thing, and before I want to get your input here, John is this is nuanced. But despite the fact that Azure and Google the growing faster than a W s. You can see those growth rates. A W s I'll call this out is the only company by our estimates that grew its business sequentially last quarter. Now, in and of itself, that's not significant. But what is significant is because AWS is so large there $45 billion last year, even if the slower growth rates it's able to grow mawr and absolute terms than its competitors, who are basically flat to down sequentially by our estimates. Eso So that's something that I think is important to point out. Everybody focuses on the growth rates, but it's you gotta look at also the absolute dollars and, well, nonetheless, Microsoft in particular, they're they're closing the gap steadily, and and we should talk more about the competitive dynamics. But I'd love to get your take on on all this, John. >>Well, I mean, the clouds are gonna win right now. Big time with the one the political climate is gonna be favoring Big check. But more importantly, with just talking about covert impact and celebrating the digital transformation is gonna create a massive rising tide. It's already happening. It's happening it's happening. And again, this shift in programming, uh, models are gonna really kinda accelerating, create new great growth. So there's no doubt in my mind of all three you're gonna win big, uh, in the future, they're just different, You know, the way they're going to market position themselves, they have to be. Google has to be a little bit different than Amazon because they're smaller and they also have different capabilities, then trying to catch up. So if you're Google or Microsoft, you have to have a competitive strategy to decide. How do I wanna ride the tide If you will put the rising tide? Well, if I'm Amazon, I mean, if I'm Microsoft and Google, I'm not going to try to go frontal and try to copy Amazon because Amazon is just pounding lead of features and scale and they're different. They were, I would say, take advantage of the first mover of pure public cloud. They really awesome. It passed and I, as they've integrated in Gardner, now reports and integrated I as and passed components. So Gardner finally got their act together and said, Hey, this is really one thing. SAS is completely different animal now Microsoft Super Smart because they I think they played the right card. They have a huge installed base converted to keep office 3 65 and move sequel server and all their core jewels into the cloud as fast as possible, clarified while filling in the gaps on the product side to be cloud. So you know, as you're doing trends job, they're just it's just pedal as fast as you can. But Microsoft is really in. The strategy is just go faster trying. Keep pedaling fast, get the features, feature velocity and try to make it high quality. Google is a little bit different. They have a little power base in terms of their network of strong, and they have a lot of other big data capabilities, so they have to use those to their advantage. So there is. There is there is competitive strategy game application happening with these companies. It's not like apples, the apples, In my opinion, it never has been, and I think that's funny that people talk about it that way. >>Well, you're bringing up some great points. I want guys bring up the next graphic because a lot of things that John just said are really relevant here. And what we're showing is that's a survey. Data from E. T. R R Data partners, like 1400 plus CEOs and I T buyers and on the vertical axis is this thing called Net score, which is a measure of spending momentum. And the horizontal axis is is what's called market share. It's a measure of the pervasiveness or, you know, number of mentions in the data set. There's a couple of key points I wanna I wanna pick up on relative to what John just said. So you see A W S and Microsoft? They stand alone. I mean, they're the hyper scale er's. They're far ahead of the pack and frankly, they have fall down, toe, lose their lead. They spend a lot on Capex. They got the flywheel effects going. They got both spending velocity and large market shares, and so, but they're taking a different approach. John, you're right there living off of their SAS, the state, their software state, Andi, they're they're building that in to their cloud. So they got their sort of a captive base of Microsoft customers. So they've got that advantage. They also as we'll hear from from Microsoft today. They they're building mawr abstraction layers. Andy Jassy has said We don't wanna be in that abstraction layer business. We wanna have access to those, you know, fine grain primitives and eso at an AP level. So so we can move fast with the market. But but But so those air sort of different philosophies, John? >>Yeah. I mean, you know, people who know me know that I love Amazon. I think their product is superior at many levels on in its way that that has advantages again. They have a great sass and ecosystem. They don't really have their own SAS play, although they're trying to add some stuff on. I've been kind of critical of Microsoft in the past, but one thing I'm not critical of Microsoft, and people can get this wrong in the marketplace. Actually, in the journalism world and also in just some other analysts, Microsoft has always had large scale eso to say that Microsoft never had scale on that Amazon owned the monopoly on our franchise on scales wrong. Microsoft had scale from day one. Their business was always large scale global. They've always had infrastructure with MSN and their search and the distributive how they distribute browsers and multiple countries. Remember they had the lock on the operating system and the browser for until the government stepped in in 1997. And since 1997 Microsoft never ever not invested in infrastructure and scale. So that whole premise that they don't compete well there is wrong. And I think that chart demonstrates that there, in there in the hyper scale leadership category, hands down the question that I have. Is that there not as good and making that scale integrate in because they have that legacy cards. This is the classic innovator's dilemma. Clay Christensen, right? So I think they're doing a good job. I think their strategy sound. They're moving as fast as they can. But then you know they're not gonna come out and say We don't have the best cloud. Um, that's not a marketing strategy. Have to kind of hide in this and get better and then double down on where they're winning, which is. Clients are converting from their legacy at the speed of Microsoft, and they have a huge client base, So that's why they're stopping so high That's why they're so good. >>Well, I'm gonna I'm gonna give you a little preview. I talked to gear up your f Who's gonna come on today and you'll see I I asked him because the criticism of Microsoft is they're, you know, they're just good enough. And so I asked him, Are you better than good enough? You know, those are fighting words if you're inside of Microsoft, but so you'll you'll have to wait to see his answer. Now, if you guys, if you could bring that that graphic back up I wanted to get into the hybrid zone. You know where the field is. Always got >>some questions coming in on chat, Dave. So we'll get to those >>great Awesome. So just just real quick Here you see this hybrid zone, this the field is bunched up, and the other companies who have a large on Prem presence and have been forced to initiate some kind of coherent cloud strategy included. There is Michael Michael, multi Cloud, and Google's there, too, because they're far behind and they got to take a different approach than a W s. But as you can see, so there's some real progress here. VM ware cloud on AWS stands out, as does red hat open shift. You got VM Ware Cloud, which is a VCF Cloud Foundation, even Dell's cloud. And you'd expect HP with Green Lake to be picking up momentum in the future quarters. And you've got IBM and Oracle, which there you go with the innovator's dilemma. But there, at least in the cloud game, and we can talk about that. But so, John, you know, to your point, you've gotta have different strategies. You're you're not going to take out the big too. So you gotta play, connect your print your on Prem to your cloud, your hybrid multi cloud and try to create new opportunities and new value there. >>Yeah, I mean, I think we'll get to the question, but just that point. I think this Zeri Chen's come on the Cube many times. We're trying to get him to come on lunch today with Features startup, but he's always said on the Q B is a V C at Greylock great firm. Jerry's Cloud genius. He's been there, but he made a point many, many years ago. It's not a winner. Take all the winner. Take most, and the Big Three maybe put four or five in there. We'll take most of the markets here. But I think one of the things that people are missing and aren't talking about Dave is that there's going to be a second tier cloud, large scale model. I don't want to say tear to cloud. It's coming to sound like a sub sub cloud, but a new category of cloud on cloud, right? So meaning if you get a snowflake, did I think this is a tale? Sign to what's coming. VM Ware Cloud is a native has had huge success, mainly because Amazon is essentially enabling them to be successful. So I think is going to be a wave of a more of a channel model of indirect cloud build out where companies like the Cube, potentially for media or others, will build clouds on top of the cloud. So if Google, Microsoft and Amazon, whoever is the first one to really enable that okay, we'll do extremely well because that means you can compete with their scale and create differentiation on top. So what snowflake did is all on Amazon now. They kind of should go to azure because it's, you know, politically correct that have multiple clouds and distribution and business model shifts. But to get that kind of performance they just wrote on Amazon. So there's nothing wrong with that. Because you're getting paid is variable. It's cap ex op X nice categorization. So I think that's the way that we're watching. I think it's super valuable, I think will create some surprises in terms of who might come out of the woodwork on be a leader in a category. Well, >>your timing is perfect, John and we do have some questions in the chat. But before we get to that, I want to bring in Sargi Joe Hall, who's a contributor to to our community. Sargi. Can you hear us? All right, so we got, uh, while >>bringing in Sarpy. Let's go down from the questions. So the first question, Um, we'll still we'll get the student second. The first question. But Ronald ask, Can a vendor in 2021 exist without a hybrid cloud story? Well, story and capabilities. Yes, they could live with. They have to have a story. >>Well, And if they don't own a public cloud? No. No, they absolutely cannot. Uh hey, Sergey. How you doing, man? Good to see you. So, folks, let me let me bring in Sergeant Kohala. He's a He's a cloud architect. He's a practitioner, He's worked in as a technologist. And there's a frequent guest on on the Cube. Good to see you, my friend. Thanks for taking the time with us. >>And good to see you guys to >>us. So we were kind of riffing on the competitive landscape we got. We got so much to talk about this, like, it's a number of questions coming in. Um, but Sargi we wanna talk about you know, what's happening here in Cloud Land? Let's get right into it. I mean, what do you guys see? I mean, we got yesterday. New regime, new inaug inauguration. Do you do you expect public policy? You'll start with you Sargi to have What kind of effect do you think public policy will have on, you know, cloud generally specifically, the big tech companies, the tech lash. Is it gonna be more of the same? Or do you see a big difference coming? >>I think that there will be some changing narrative. I believe on that. is mainly, um, from the regulators side. A lot has happened in one month, right? So people, I think are losing faith in high tech in a certain way. I mean, it doesn't, uh, e think it matters with camp. You belong to left or right kind of thing. Right? But parlor getting booted out from Italy s. I think that was huge. Um, like, how do you know that if a cloud provider will not boot you out? Um, like, what is that line where you draw the line? What are the rules? I think that discussion has to take place. Another thing which has happened in the last 23 months is is the solar winds hack, right? So not us not sort acknowledging that I was Russia and then wish you watching it now, new administration might have a different sort of Boston on that. I think that's huge. I think public public private partnership in security arena will emerge this year. We have to address that. Yeah, I think it's not changing. Uh, >>economics economy >>will change gradually. You know, we're coming out off pandemic. The money is still cheap on debt will not be cheap. for long. I think m and a activity really will pick up. So those are my sort of high level, Uh, >>thank you. I wanna come back to them. And because there's a question that chat about him in a But, John, how do you see it? Do you think Amazon and Google on a slippery slope booting parlor off? I mean, how do they adjudicate between? Well, what's happening in parlor? Uh, anything could happen on clubhouse. Who knows? I mean, can you use a I to find that stuff? >>Well, that's I mean, the Amazons, right? Hiding right there bunkered in right now from that bad, bad situation. Because again, like people we said Amazon, these all three cloud players win in the current environment. Okay, Who wins with the U. S. With the way we are China, Russia, cloud players. Okay, let's face it, that's the reality. So if I wanted to reset the world stage, you know what better way than the, you know, change over the United States economy, put people out of work, make people scared, and then reset the entire global landscape and control all with cash? That's, you know, conspiracy theory. >>So you see the riches, you see the riches, get the rich, get richer. >>Yeah, well, that's well, that's that. That's kind of what's happening, right? So if you start getting into this idea that you can't actually have an app on site because the reason now I'm not gonna I don't know the particular parlor, but apparently there was a reason. But this is dangerous, right? So what? What that's gonna do is and whether it's right or wrong or not, whether political opinion is it means that they were essentially taken offline by people that weren't voted for that. Weren't that when people didn't vote for So that's not a democracy, right? So that's that's a different kind of regime. What it's also going to do is you also have this groundswell of decentralized thinking, right. So you have a whole wave of crypto and decentralized, um, cyber punks out there who want to decentralize it. So all of this stuff in January has created a huge counterculture, and I had predicted this so many times in the Cube. David counterculture is coming and and you already have this kind of counterculture between centralized and decentralized thinking and so I think the Amazon's move is dangerous at a fundamental level. Because if you can't get it, if you can't get buy domain names and you're completely blackballed by by organized players, that's a Mafia, in my opinion. So, uh, and that and it's also fuels the decentralized move because people say, Hey, if that could be done to them, it could be done to me. Just the fact that it could be done will promote a swing in the other direction. I >>mean, independent of of, you know, again, somebody said your political views. I mean Parlor would say, Hey, we're trying to clean this stuff up now. Maybe they didn't do it fast enough, but you think about how new parlor is. You think about the early days of Twitter and Facebook, so they were sort of at a disadvantage. Trying to >>have it was it was partly was what it was. It was a right wing stand up job of standing up something quick. Their security was terrible. If you look at me and Cory Quinn on be great to have him, and he did a great analysis on this, because if you look the lawsuit was just terrible. Security was just a half, asshole. >>Well, and the experience was horrible. I mean, it's not It was not a great app, but But, like you said, it was a quick stew. Hand up, you know, for an agenda. But nonetheless, you know, to start, get to your point earlier. It's like, you know, Are they gonna, you know, shut me down? If I say something that's, you know, out of line, or how do I control that? >>Yeah, I remember, like, 2019, we involved closing sort of remarks. I was there. I was saying that these companies are gonna be too big to fail. And also, they're too big for other nations to do business with. In a way, I think MNCs are running the show worldwide. They're running the government's. They are way. Have seen the proof of that in us this year. Late last year and this year, um, Twitter last night blocked Chinese Ambassador E in us. Um, from there, you know, platform last night and I was like, What? What's going on? So, like, we used to we used to say, like the Chinese company, tech companies are in bed with the Chinese government. Right. Remember that? And now and now, Actually, I think Chinese people can say the same thing about us companies. Uh, it's not a good thing. >>Well, let's >>get some question. >>Let's get some questions from the chat. Yeah. Thank you. One is on M and a subject you mentioned them in a Who do you see is possible emanate targets. I mean, I could throw a couple out there. Um, you know, some of the cdn players, maybe aka my You know, I like I like Hashi Corp. I think they're doing some really interesting things. What do you see? >>Nothing. Hashi Corp. And anybody who's doing things in the periphery is a candidate for many by the big guys, you know, by the hyper scholars and number two tier two or five hyper scholars. Right. Uh, that's why sales forces of the world and stuff like that. Um, some some companies, which I thought there will be a target, Sort of. I mean, they target they're getting too big, because off their evaluations, I think how she Corpuz one, um, >>and >>their bunch in the networking space. Uh, well, Tara, if I say the right that was acquired by at five this week, this week or last week, Actually, last week for $500 million. Um, I know they're founder. So, like I found that, Yeah, there's a lot going on on the on the network side on the anything to do with data. Uh, that those air too hard areas in the cloud arena >>data, data protection, John, any any anything you could adhere. >>And I think I mean, I think ej ej is gonna be where the gaps are. And I think m and a activity is gonna be where again, the bigger too big to fail would agree with you on that one. But we're gonna look at white Spaces and say a white space for Amazon is like a monster space for a start up. Right? So you're gonna have these huge white spaces opportunities, and I think it's gonna be an M and a opportunity big time start ups to get bought in. Given the speed on, I think you're gonna see it around databases and around some of these new service meshes and micro services. I mean, >>they there's a There's a question here, somebody's that dons asking why is Google who has the most pervasive tech infrastructure on the planet. Not at the same level of other to hyper scale is I'll give you my two cents is because it took him a long time to get their heads out of their ads. I wrote a piece of around that a while ago on they just they figured out how to learn the enterprise. I mean, John, you've made this point a number of times, but they just and I got a late start. >>Yeah, they're adding a lot of people. If you look at their who their hiring on the Google Cloud, they're adding a lot of enterprise chops in there. They realized this years ago, and we've talked to many of the top leaders, although Curry and hasn't yet sit down with us. Um, don't know what he's hiding or waiting for, but they're clearly not geared up to chicken Pete. You can see it with some some of the things that they're doing, but I mean competed the level of Amazon, but they have strength and they're playing their strength, but they definitely recognize that they didn't have the enterprise motions and people in the DNA and that David takes time people in the enterprise. It's not for the faint of heart. It's unique details that are different. You can't just, you know, swing the Google playbook and saying We're gonna home The enterprises are text grade. They knew that years ago. So I think you're going to see a good year for Google. I think you'll see a lot of change. Um, they got great people in there. On the product marketing side is Dev Solution Architects, and then the SRE model that they have perfected has been strong. And I think security is an area that they could really had a lot of value it. So, um always been a big fan of their huge network and all the intelligence they have that they could bring to bear on security. >>Yeah, I think Google's problem main problem that to actually there many, but one is that they don't They don't have the boots on the ground as compared to um, Microsoft, especially an Amazon actually had a similar problem, but they had a wide breath off their product portfolio. I always talk about feature proximity in cloud context, like if you're doing one thing. You wanna do another thing? And how do you go get that feature? Do you go to another cloud writer or it's right there where you are. So I think Amazon has the feature proximity and they also have, uh, aske Compared to Google, there's skills gravity. Larger people are trained on AWS. I think Google is trying there. So second problem Google is having is that that they're they're more focused on, I believe, um, on the data science part on their sort of skipping the cool components sort of off the cloud, if you will. The where the workloads needs, you know, basic stuff, right? That's like your compute storage and network. And that has to be well, talk through e think e think they will do good. >>Well, so later today, Paul Dillon sits down with Mids Avery of Google used to be in Oracle. He's with Google now, and he's gonna push him on on the numbers. You know, you're a distant third. Does that matter? And of course, you know, you're just a preview of it's gonna say, Well, no, we don't really pay attention to that stuff. But, John, you said something earlier that. I think Jerry Chen made this comment that, you know, Is it a winner? Take all? No, but it's a winner. Take a lot. You know the number two is going to get a big chunk of the pie. It appears that the markets big enough for three. But do you? Does Google have to really dramatically close the gap on be a much, much closer, you know, to the to the leaders in orderto to compete in this race? Or can they just kind of continue to bump along, siphon off the ad revenue? Put it out there? I mean, I >>definitely can compete. I think that's like Google's in it. Then it they're not. They're not caving, right? >>So But But I wrote I wrote recently that I thought they should even even put mawr oven emphasis on the cloud. I mean, maybe maybe they're already, you know, doubling down triple down. I just I think that is a multi trillion dollar, you know, future for the industry. And, you know, I think Google, believe it or not, could even do more. Now. Maybe there's just so much you could dio. >>There's a lot of challenges with these company, especially Google. They're in Silicon Valley. We have a big Social Justice warrior mentality. Um, there's a big debate going on the in the back channels of the tech scene here, and that is that if you want to be successful in cloud, you have to have a good edge strategy, and that involves surveillance, use of data and pushing the privacy limits. Right? So you know, Google has people within the country that will protest contract because AI is being used for war. Yet we have the most unstable geopolitical seen that I've ever witnessed in my lifetime going on right now. So, um, don't >>you think that's what happened with parlor? I mean, Rob Hope said, Hey, bar is pretty high to kick somebody off your platform. The parlor went over the line, but I would also think that a lot of the employees, whether it's Google AWS as well, said, Hey, why are we supporting you know this and so to your point about social justice, I mean, that's not something. That >>parlor was not just social justice. They were trying to throw the government. That's Rob e. I think they were in there to get selfies and being protesters. But apparently there was evidence from what I heard in some of these clubhouse, uh, private chats. Waas. There was overwhelming evidence on parlor. >>Yeah, but my point is that the employee backlash was also a factor. That's that's all I'm saying. >>Well, we have Google is your Google and you have employees to say we will boycott and walk out if you bid on that jet I contract for instance, right, But Microsoft one from maybe >>so. I mean, that's well, >>I think I think Tom Poole's making a really good point here, which is a Google is an alternative. Thio aws. The last Google cloud next that we were asked at they had is all virtual issue. But I saw a lot of I T practitioners in the audience looking around for an alternative to a W s just seeing, though, we could talk about Mano Cloud or Multi Cloud, and Andy Jassy has his his narrative around, and he's true when somebody goes multiple clouds, they put you know most of their eggs in one basket. Nonetheless, I think you know, Google's got a lot of people interested in, particularly in the analytic side, um, in in an alternative, hedging their bets eso and particularly use cases, so they should be able to do so. I guess my the bottom line here is the markets big enough to have Really? You don't have to be the Jack Welch. I gotta be number one and number two in the market. Is that the conclusion here? >>I think so. But the data gravity and the skills gravity are playing against them. Another problem, which I didn't want a couple of earlier was Google Eyes is that they have to boot out AWS wherever they go. Right? That is a huge challenge. Um, most off the most off the Fortune 2000 companies are already using AWS in one way or another. Right? So they are the multi cloud kind of player. Another one, you know, and just pure purely somebody going 200% Google Cloud. Uh, those cases are kind of pure, if you will. >>I think it's gonna be absolutely multi cloud. I think it's gonna be a time where you looked at the marketplace and you're gonna think in terms of disaster recovery, model of cloud or just fault tolerant capabilities or, you know, look at the parlor, the next parlor. Or what if Amazon wakes up one day and said, Hey, I don't like the cubes commentary on their virtual events, so shut them down. We should have a fail over to Google Cloud should Microsoft and Option. And one of people in Microsoft ecosystem wants to buy services from us. We have toe kind of co locate there. So these are all open questions that are gonna be the that will become certain pretty quickly, which is, you know, can a company diversify their computing An i t. In a way that works. And I think the momentum around Cooper Netease you're seeing as a great connective tissue between, you know, having applications work between clouds. Right? Well, directionally correct, in my opinion, because if I'm a company, why wouldn't I wanna have choice? So >>let's talk about this. The data is mixed on that. I'll share some data, meaty our data with you. About half the companies will say Yeah, we're spreading the wealth around to multiple clouds. Okay, That's one thing will come back to that. About the other half were saying, Yeah, we're predominantly mono cloud we didn't have. The resource is. But what I think going forward is that that what multi cloud really becomes. And I think John, you mentioned Snowflake before. I think that's an indicator of what what true multi cloud is going to look like. And what Snowflake is doing is they're building abstraction, layer across clouds. Ed Walsh would say, I'm standing on the shoulders of Giants, so they're basically following points of presence around the globe and building their own cloud. They call it a data cloud with a global mesh. We'll hear more about that later today, but you sign on to that cloud. So they're saying, Hey, we're gonna build value because so many of Amazon's not gonna build that abstraction layer across multi clouds, at least not in the near term. So that's a really opportunity for >>people. I mean, I don't want to sound like I'm dating myself, but you know the date ourselves, David. I remember back in the eighties, when you had open systems movement, right? The part of the whole Revolution OS I open systems interconnect model. At that time, the networking stacks for S N A. For IBM, decadent for deck we all know that was a proprietary stack and then incomes TCP I p Now os I never really happened on all seven layers, but the bottom layers standardized. Okay, that was huge. So I think if you look at a W s or some of the comments in the chat AWS is could be the s n a. Depends how you're looking at it, right? And you could say they're open. But in a way, they want more Amazon. So Amazon's not out there saying we love multi cloud. Why would they promote multi cloud? They are a one of the clouds they want. >>That's interesting, John. And then subject is a cloud architect. I mean, it's it is not trivial to make You're a data cloud. If you're snowflake, work on AWS work on Google. Work on Azure. Be seamless. I mean, certainly the marketing says that, but technically, that's not trivial. You know, there are latent see issues. Uh, you know, So that's gonna take a while to develop. What? Do your thoughts there? >>I think that multi cloud for for same workload and multi cloud for different workloads are two different things. Like we usually put multiple er in one bucket, right? So I think you're right. If you're trying to do multi cloud for the same workload, that's it. That's Ah, complex, uh, problem to solve architecturally, right. You have to have a common ap ice and common, you know, control playing, if you will. And we don't have that yet, and then we will not have that for a for at least one other couple of years. So, uh, if you if you want to do that, then you have to go to the lower, lowest common denominator in technical sort of stock, if you will. And then you're not leveraging the best of the breed technology off their from different vendors, right? I believe that's a hard problem to solve. And in another thing, is that that that I always say this? I'm always on the death side, you know, developer side, I think, uh, two deaths. Public cloud is a proxy for innovative culture. Right. So there's a catch phrase I have come up with today during shower eso. I think that is true. And then people who are companies who use the best of the breed technologies, they can attract the these developers and developers are the Mazen's off This digital sort of empires, amazingly, is happening there. Right there they are the Mazen's right. They head on the bricks. I think if you don't appeal to developers, if you don't but extensive for, like, force behind educating the market, you can't you can't >>put off. It's the same game Stepping story was seeing some check comments. Uh, guard. She's, uh, linked in friend of mine. She said, Microsoft, If you go back and look at the Microsoft early days to the developer Point they were, they made their phones with developers. They were a software company s Oh, hey, >>forget developers, developers, developers. >>You were if you were in the developer ecosystem, you were treated his gold. You were part of the family. If you were outside that world, you were competitors, and that was ruthless times back then. But they again they had. That was where it was today. Look at where the software defined businesses and starve it, saying it's all about being developer lead in this new way to program, right? So the cloud next Gen Cloud is going to look a lot like next Gen Developer and all the different tools and techniques they're gonna change. So I think, yes, this kind of developer ecosystem will be harnessed, and that's the power source. It's just gonna look different. So, >>Justin, Justin in the chat has a comment. I just want to answer the question about elastic thoughts on elastic. Um, I tell you, elastic has momentum uh, doing doing very well in the market place. Thea Elk Stack is a great alternative that people are looking thio relative to Splunk. Who people complain about the pricing. Of course it's plunks got the easy button, but it is getting increasingly expensive. The problem with elk stack is you know, it's open source. It gets complicated. You got a shard, the databases you gotta manage. It s Oh, that's what Ed Walsh's company chaos searches is all about. But elastic has some riel mo mentum in the marketplace right now. >>Yeah, you know, other things that coming on the chat understands what I was saying about the open systems is kubernetes. I always felt was that is a bad metaphor. But they're with me. That was the TCP I peep In this modern era, C t c p I p created that that the disruptor to the S N A s and the network protocols that were proprietary. So what KUBERNETES is doing is creating a connective tissue between clouds and letting the open source community fill in the gaps in the middle, where kind of way kind of probably a bad analogy. But that's where the disruption is. And if you look at what's happened since Kubernetes was put out there, what it's become kind of de facto and standard in the sense that everyone's rallying around it. Same exact thing happened with TCP was people were trashing it. It is terrible, you know it's not. Of course they were trashed because it was open. So I find that to be very interesting. >>Yeah, that's a good >>analogy. E. Thinks the R C a cable. I used the R C. A cable analogy like the VCRs. When they started, they, every VC had had their own cable, and they will work on Lee with that sort of plan of TV and the R C. A cable came and then now you can put any TV with any VCR, and the VCR industry took off. There's so many examples out there around, uh, standards And how standards can, you know, flair that fire, if you will, on dio for an industry to go sort of wild. And another trend guys I'm seeing is that from the consumer side. And let's talk a little bit on the consuming side. Um, is that the The difference wouldn't be to B and B to C is blood blurred because even the physical products are connected to the end user Like my door lock, the August door lock I didn't just put got get the door lock and forget about that. Like I I value the expedience it gives me or problems that gives me on daily basis. So I'm close to that vendor, right? So So the middle men, uh, middle people are getting removed from from the producer off the technology or the product to the consumer. Even even the sort of big grocery players they have their APs now, uh, how do you buy stuff and how it's delivered and all that stuff that experience matters in that context, I think, um, having, uh, to be able to sell to thes enterprises from the Cloud writer Breuder's. They have to have these case studies or all these sample sort off reference architectures and stuff like that. I think whoever has that mawr pushed that way, they are doing better like that. Amazon is Amazon. Because of that reason, I think they have lot off sort off use cases about on top of them. And they themselves do retail like crazy. Right? So and other things at all s. So I think that's a big trend. >>Great. Great points are being one of things. There's a question in there about from, uh, Yaden. Who says, uh, I like the developer Lead cloud movement, But what is the criticality of the executive audience when educating the marketplace? Um, this comes up a lot in some of my conversations around automation. So automation has been a big wave to automate this automate everything. And then everything is a service has become kind of kind of the the executive suite. Kind of like conversation we need to make everything is a service in our business. You seeing people move to that cloud model. Okay, so the executives think everything is a services business strategy, which it is on some level, but then, when they say Take that hill, do it. Developers. It's not that easy. And this is where a lot of our cube conversations over the past few months have been, especially during the cova with cute virtual. This has come up a lot, Dave this idea, and start being around. It's easy to say everything is a service but will implement it. It's really hard, and I think that's where the developer lead Connection is where the executive have to understand that in order to just say it and do it are two different things. That digital transformation. That's a big part of it. So I think that you're gonna see a lot of education this year around what it means to actually do that and how to implement it. >>I'd like to comment on the as a service and subject. Get your take on it. I mean, I think you're seeing, for instance, with HP Green Lake, Dell's come out with Apex. You know IBM as its utility model. These companies were basically taking a page out of what I what I would call a flawed SAS model. If you look at the SAS players, whether it's salesforce or workday, service now s a P oracle. These models are They're really They're not cloud pricing models. They're they're basically you got to commit to a term one year, two year, three year. We'll give you a discount if you commit to the longer term. But you're locked in on you. You probably pay upfront. Or maybe you pay quarterly. That's not a cloud pricing model. And that's why I mean, they're flawed. You're seeing companies like Data Dog, for example. Snowflake is another one, and they're beginning to price on a consumption basis. And that is, I think, one of the big changes that we're going to see this decade is that true cloud? You know, pay by the drink pricing model and to your point, john toe, actually implement. That is, you're gonna need a whole new layer across your company on it is quite complicated it not even to mention how you compensate salespeople, etcetera. The a p. I s of your product. I mean, it is that, but that is a big sea change that I see coming. Subject your >>thoughts. Yeah, I think like you couldn't see it. And like some things for this big tech exacts are hidden in the plain >>sight, right? >>They don't see it. They they have blind spots, like Look at that. Look at Amazon. They went from Melissa and 200 millisecond building on several s, Right, Right. And then here you are, like you're saying, pay us for the whole year. If you don't use the cloud, you lose it or will pay by month. Poor user and all that stuff like that that those a role models, I think these players will be forced to use that term pricing like poor minute or for a second, poor user. That way, I think the Salesforce moral is hybrid. They're struggling in a way. I think they're trying to bring the platform by doing, you know, acquisition after acquisition to be a platform for other people to build on top off. But they're having a little trouble there because because off there, such pricing and little closeness, if you will. And, uh, again, I'm coming, going, going back to developers like, if you are not appealing to developers who are writing the latest and greatest code and it is open enough, by the way open and open source are two different things that we all know that. So if your platform is not open enough, you will have you know, some problems in closing the deals. >>E. I want to just bring up a question on chat around from Justin didn't fitness. Who says can you touch on the vertical clouds? Has your offering this and great question Great CP announcing Retail cloud inventions IBM Athena Okay, I'm a huge on this point because I think this I'm not saying this for years. Cloud computing is about horizontal scalability and vertical specialization, and that's absolutely clear, and you see all the clouds doing it. The vertical rollouts is where the high fidelity data is, and with machine learning and AI efforts coming out, that's accelerated benefits. There you have tow, have the vertical focus. I think it's super smart that clouds will have some sort of vertical engine, if you will in the clouds and build on top of a control playing. Whether that's data or whatever, this is clearly the winning formula. If you look at all the successful kind of ai implementations, the ones that have access to the most data will get the most value. So, um if you're gonna have a data driven cloud you have tow, have this vertical feeling, Um, in terms of verticals, the data on DSO I think that's super important again, just generally is a strategy. I think Google doing a retail about a super smart because their whole pitches were not Amazon on. Some people say we're not Google, depending on where you look at. So every of these big players, they have dominance in the areas, and that's scarce. Companies and some companies will never go to Amazon for that reason. Or some people never go to Google for other reasons. I know people who are in the ad tech. This is a black and we're not. We're not going to Google. So again, it is what it is. But this idea of vertical specialization relevant in super >>forts, I want to bring to point out to sessions that are going on today on great points. I'm glad you asked that question. One is Alan. As he kicks off at 1 p.m. Eastern time in the transformation track, he's gonna talk a lot about the coming power of ecosystems and and we've talked about this a lot. That that that to compete with Amazon, Google Azure, you've gotta have some kind of specialization and vertical specialization is a good one. But of course, you see in the big Big three also get into that. But so he's talking at one o'clock and then it at 3 36 PM You know this times are strange, but e can explain that later Hillary Hunter is talking about she's the CTO IBM I B M's ah Financial Cloud, which is another really good example of specifying vertical requirements and serving. You know, an audience subject. I think you have some thoughts on this. >>Actually, I lost my thought. E >>think the other piece of that is data. I mean, to the extent that you could build an ecosystem coming back to Alan Nancy's premise around data that >>billions of dollars in >>their day there's billions of dollars and that's the title of the session. But we did the trillion dollar baby post with Jazzy and said Cloud is gonna be a trillion dollars right? >>And and the point of Alan Answer session is he's thinking from an individual firm. Forget the millions that you're gonna save shifting to the cloud on cost. There's billions in ecosystems and operating models. That's >>absolutely the business value. Now going back to my half stack full stack developer, is the business value. I've been talking about this on the clubhouses a lot this past month is for the entrepreneurs out there the the activity in the business value. That's the new the new intellectual property is the business logic, right? So if you could see innovations in how work streams and workflow is gonna be a configured differently, you have now large scale cloud specialization with data, you can move quickly and take territory. That's much different scenario than a decade ago, >>at the point I was trying to make earlier was which I know I remember, is that that having the horizontal sort of features is very important, as compared to having vertical focus. You know, you're you're more healthcare focused like you. You have that sort of needs, if you will, and you and our auto or financials and stuff like that. What Google is trying to do, I think that's it. That's a good thing. Do cook up the reference architectures, but it's a bad thing in a way that you drive drive away some developers who are most of the developers at 80 plus percent, developers are horizontal like you. Look at the look into the psyche of a developer like you move from company to company. And only few developers will say I will stay only in health care, right? So I will only stay in order or something of that, right? So they you have to have these horizontal capabilities which can be applied anywhere on then. On top >>of that, I think that's true. Sorry, but I'll take a little bit different. Take on that. I would say yes, that's true. But remember, remember the old school application developer Someone was just called in Application developer. All they did was develop applications, right? They pick the framework, they did it right? So I think we're going to see more of that is just now mawr of Under the Covers developers. You've got mawr suffer defined networking and software, defined storage servers and cloud kubernetes. And it's kind of like under the hood. But you got your, you know, classic application developer. I think you're gonna see him. A lot of that come back in a way that's like I don't care about anything else. And that's the promise of cloud infrastructure is code. So I think this both. >>Hey, I worked. >>I worked at people solved and and I still today I say into into this context, I say E r P s are the ultimate low code. No code sort of thing is right. And what the problem is, they couldn't evolve. They couldn't make it. Lightweight, right? Eso um I used to write applications with drag and drop, you know, stuff. Right? But But I was miserable as a developer. I didn't Didn't want to be in the applications division off PeopleSoft. I wanted to be on the tools division. There were two divisions in most of these big companies ASAP. Oracle. Uh, like companies that divisions right? One is the cooking up the tools. One is cooking up the applications. The basketball was always gonna go to the tooling. Hey, >>guys, I'm sorry. We're almost out of time. I always wanted to t some of the sections of the day. First of all, we got Holder Mueller coming on at lunch for a power half hour. Um, you'll you'll notice when you go back to the home page. You'll notice that calendar, that linear clock that we talked about that start times are kind of weird like, for instance, an appendix coming on at 1 24. And that's because these air prerecorded assets and rather than having a bunch of dead air, we're just streaming one to the other. So so she's gonna talk about people, process and technology. We got Kathy Southwick, whose uh, Silicon Valley CEO Dan Sheehan was the CEO of Dunkin Brands and and he was actually the c 00 So it's C A CEO connecting the dots to the business. Daniel Dienes is the CEO of you I path. He's coming on a 2:47 p.m. East Coast time one of the hottest companies, probably the fastest growing software company in history. We got a guy from Bain coming on Dave Humphrey, who invested $750 million in Nutanix. He'll explain why and then, ironically, Dheeraj Pandey stew, Minuteman. Our friend interviewed him. That's 3 35. 1 of the sessions are most excited about today is John McD agony at 403 p. M. East Coast time, she's gonna talk about how to fix broken data architectures, really forward thinking stuff. And then that's the So that's the transformation track on the future of cloud track. We start off with the Big Three Milan Thompson Bukovec. At one oclock, she runs a W s storage business. Then I mentioned gig therapy wrath at 1. 30. He runs Azure is analytics. Business is awesome. Paul Dillon then talks about, um, IDs Avery at 1 59. And then our friends to, um, talks about interview Simon Crosby. I think I think that's it. I think we're going on to our next session. All right, so keep it right there. Thanks for watching the Cuban cloud. Uh huh.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back to the Cube. Lisa Martin at AWS Reinvent 19. This is Day three of the Cubes coverage. We have two sets here. Lots of cute content are joined by Justin Warren, the founder and chief analyst at Pivot nine. Justin. How's it going? Great, right? You still have a voice? Three days? >>Just barely. I've been I've been trying to take care of it. >>Impressed. And you probably have talked to at least half of the 65,000 attendees. >>I'm trying to talk to as many as I can. >>Well, we're gonna talk to another guy here. Joining us from data ICU is well, Novak, the solutions architect will be the Cube. >>Thanks for having me. >>You have a good voice too. After a three day is that you >>have been doing the best I can. >>Yeah, he's good. So did ICU. Interesting name. Let's start off by sharing with our audience. Who did a coup is and what you guys do in technology. >>Yes. So the Entomology of date ICU. It's like hi cooze for data. So we say we take your data and, you know, we make poetry out of it. Make your data so beautiful. Wow, Now, But for those who are unaware Day like it was an enterprise data science platform. Eso we provide a collaborative environment for we say coders and clickers kind of business analyst and native data scientists to make use of organizations, data bill reports and Bill productive machine learning base models and deploy them. >>I'm only the guy's been around around for eight years. Eight years. Okay, >>so start up. Still >>mourning the cloud, the opportunity there That data is no longer a liability. It's an asset or should be. >>So we've been server based from the start, which is one of our differentiators. And so by that we see ourselves as a collaborative platform. Users access it through a Web browser, log into a shared space and share code, can share visual recipes, as we call them to prepare data. >>Okay, so what customers using the platform to do with machine learning is pretty hot at the moment. I think it might be nearing the peak of the life cycle pretty hot. Yeah, what a customer is actually actually doing on the platform, >>you know, So we really focus on enabling the enterprise. So, for example, G has been a customer for some time now, and Sergey is a great prototypical example on that. They have many disparate use cases, like simple things like doing customer segmentation for, you know, marketing campaigns but also stuff like Coyote predicted maintenance. So use cases kind of run the gamut, and so did ICU. Based on open source, we're enabling all of G's users to come into a centralized platform, access their data manipulated for whatever purposes. Maybe >>nobody talked about marketing campaigns for a second. I'm wondering. Are, is their integration with serum technologies? Or how would a customer like wanting to understand customer segmentation or had a segment it for marketing campaign? How would they work in conjunction with a serum and data ICU, for example? >>It's a great question. So again, us being a platform way sit on a single server, something like an Amazon ec2 instance, and then we make connections into an organization's data sources. So if using something like Salesforce weaken seamlessly, pull in data from Salesforce Yuka manipulated in date ICU, but the same time. Maybe also have some excel file someone you know me. I can bring that into my data to work environment. And I also have a red shift data table. All those things would come into the same environment. I can visualize. I can analyze, and I can prepare the data. I see. >>So you tell you it's based on open source? I'm a longtime fan of over. It's always been involved in it for longer than I care to remember. Actually, that's an interesting way t base your product on that. So maybe talk us through how you how you came to found the company based on basic an open source. What? What led to that choice? What? What was that decision based on? >>Yeah, for sure. So you talked about how you know the hype cycle? A. I saw how hot is a I and so I think again, our founders astutely recognize that this is a very fast moving place to be. And so I'm kind of betting on one particular technology can be risky. So instead, by being a platform, we say, like sequel has been the data transformation language do jour for many days now. So, of course, that you can easily write Sequel and a lot of our visual data Transformations are based on the sequel language, but also something like Python again. It's like the language de jour for machine law machine learning model building right now, so you can easily code in python. Maintain your python libraries in date, ICU And so by leveraging open source, we figured we're making our clients more future proof as long as they're staying in date ICU. But using data ICU to leverage the best in breed and open source, they'll always be kind of where they want to be in the technological landscape by supposed to locked into some tech that is now out of date. >>What's been the appetite for making data beautiful for a legacy enterprise, like a G E that's been around for a very long time versus a more modern either. Born in the Cloud er's our CEO says, reborn in the cloud. What are some of the differences but also similarities that you see in terms of we have to be able to use emerging tech. Otherwise someone's gonna come in behind us and replace us. >>Yeah, I mean, I think it's complicated in that there's still a lot of value to be had in someone says, like a bar chart you can rely on right, So it's maybe not sexy. But having good reporting and analytics is something that both you know, 200 year old enterprise organizations and data native organizations startups needs. At the same time, building predicted machine learning models and deploying those is rest a p i n points that developers can use in your organization to provide a data driven product for your consumers. Like that's amore advanced use case that everyone kind of wants to be a part of again data. Who's a nice tool, which says Maybe you don't have developers who are very fluent in turning out flashed applications. We could give you a place to build a predictive model and deploy that predictive model, saving you time to write all that code on the back end. >>One of the themes of the show has been transformation, so it sounds like data ICU would be It's something that you can dip your toes in and start to get used to using. Even if you're not particularly familiar with Time machine learning model a model building. >>Yeah, that's exactly right. So a big part of our product and encourage watchers to go try it out themselves and go to our website. Download a free version pretrial, but is enablement. So if you're the most sophisticated applied math PhD there is, like, Who's a great environment for you to Code and Bill predictive models. If you never built the machine learning model before you can use data ICU to run visual machine learning recipes, we call them, and also we give you documentation, which is, Hey, this is a random forest model. What is a random forest model? We'll tell you a little bit about it. And that's another thing that some of these enterprises have really appreciated about date I could. It is helping up skill there user base >>in terms of that transformation theme that Justin just mention which we're hearing a lot about, not visit this show. It's a big thing, but we hear it all the time, right? But in terms of customers transformation, journey, whatever you wanna call it, cloud is gonna be an essential enabler of being able to really love it value from a I. So I'm just wondering from a strategic positioning standpoint. Is did ICU positioned as a facilitator or as fuel for a cloud transformation that on enterprise would undergo >>again? Yes, great point. So for us, I can't take the credit. This credit goes to our founders, but we've thought from the start the clouds and exciting proposition Not everyone is. They're still in 2019. Most people, if not all of them, want to get there. Also, people want too many of our clients want the multi cloud on a day. Like who says, If you want to be on prim, if you want to be in a single cloud subscription. If you want to be multi cloud again as a platform, we're just gonna give you connection to your underlying infrastructure. You could use the infrastructure that you like and just use our front end to help your analyst get value. They can. I >>think I think a lot of vendors across the entire ecosystem around to say the customer choice is really important, and the customers, particularly enterprise customers, want to be able to have lots of different options, and not all of them will be ready to go completely. All in on cloud today. They made it may take them years, possibly decades, to get there. So having that choice is like it's something that it would work with you today and we'll work with you tomorrow, depending on what choices you make. >>It's exactly right. Another thing we've seen a lot of to that day, like who helps with and whether it's like you or other tools. Like, of course, you want best in breed, but you also want particularly for a large enterprise. You don't want people operating kind of in a wild West, particularly in like the ML data science space. So you know we integrate with Jupiter notebooks, but some of our clients come to us initially. Just have I won't say rogues that has a negative connotation. But maybe I will say Road road data Scientists are just tapping into some day the store. They're using Jupiter notebooks to build a predictive model, but then to actually production allies that to get sustainable value out of it like it's to one off and so having a centralized platform like date ICU, where you can say this is where we're going to use our central model depository, that something where businesses like they can sleep easier at night because they know where is my ML development happening? It's happening in one ecosystem. What tools that happening with, well, best in breed of open source. So again, you kind of get best of both worlds like they like you. >>It sounds like it's more about the operations of machine learning. It is really, really important rather than just. It's the pure technology. Yes, that's important as well, and you need to have the data Sinus to build it, but having something that allows you to operationalize it so that you can just bake it into what we do every day as a business. >>Yeah, I think in a conference like this all about tech, it's easy to forget what we firmly believe, which is a I and maybe tech. More broadly, it's still human problems at the core, right? Once you get the tech right, the code runs corrected. The code is written correctly. Therefore, like human interactions, project management model deployment in an organization. These are really hard, human centered problems, but so having tech that enables that human centric collaboration helps with that, we find >>Let's talk about some of the things that we can't ever go to an event and not talk about. Nut is respected data quality, reliability and security. Understood? I could facilitate those three cornerstones. >>Yeah, sure. So, again, viewers, I would encourage you to check out the date. ICU has some nice visual indications of data quality. So an analyst or data scientists and come in very easily understand, you know, is this quality to conform to the standards that my organization has set and what I mean by standards that could be configured. Right? So does this column have the appropriate schema? Does it have the appropriate carnality? These are things that an individual might decide to use on then for security. So Data has its own security mechanisms. However, we also to this point about incorporating best Retek. We'll work with whatever underlying security mechanisms organizations organizations have in place. So, for instance, if you're using a W s, you have, I am rolls to manage your security. Did ICU comport those that apply those to the date ICU environment or using something like on prime miss, uh, duke waken you something like Kerberos has the technology to again manage access to resources. So we're taking the best in breed that this organization already has invested time, energy and resources into and saying We're not trying to compete with them but rather were trying to enable organizations to use these technologies efficiently. >>Yeah, I like that consistency of customer choice. We spoke about that just before. I'm seeing that here with their choices around. Well, if you're on this particular platform will integrate with whatever the tools are there. People underestimate how important that is for enterprises, that it has to be ahead. Virginia's environment, playing well with others is actually quite important. >>Yeah, I don't know that point. Like the combination of heterogeneity but also uniformity. It's a hard balance to strike, and I think it's really important, giving someone a unified environment but still choice. At the same time. A good restaurant or something like you won't be able to pick your dish, but you want to know that the entire quality is high. And so having that consistent ecosystem, I think, really helps >>what are, in your opinion, some of the next industries that you see there really right to start Really leveraging machine learning to transfer You mentioned g e a very old legacy business. If we think of you know what happened with the ride hailing industry uber, for example, or fitness with Saletan or pinchers with visible Serge, what do you think is the next industry? That's like you guys taking advantage of machine learning will completely transform this and our lives. >>I mean, the easy answer that I'll give because it's easy to say it's gonna transform. But hard to operationalize is health care, right? So there is structured data, but the data quality is so desperate and had a row genius s, I think you know, if organizations in a lot of this again it's a human centered problem. If people could decide on data standards and also data privacy is, of course, a huge issue. We talked about data security internally, but also as a customer. What day to do I want you know, this hospital, this health care provider, to have access to that human issues we have to result but conditional on that being resolved that staring out a way to anonymous eyes data and respect data privacy but have consistent data structure. And we could say, Hey, let's really set these a I M L models loose and figure out things like personalized medicine which were starting to get to. But I feel like there's still a lot of room to go. That >>sounds like it's exciting time to be in machine learning. People should definitely check out products such as Dead Rock you and see what happens. >>Last question for you is so much news has come out in the last three days. It's mind boggling sum of the takeaways, that of some of the things that you've heard from Andy Jassy to border This'll Morning. >>Yeah, I think a big thing for me, which was something for me before this week. But it's always nice to hear an Amazon reassures the concept of white box. Aye, aye. We've been talking about that a date ICU for some time, but everyone wants performance A. I R ml solutions, but increasing. There's a really appetite publicly for interpret ability, and so you have to be responsible. You have to have interpret belay I and so it's nice to hear a leader like Amazon echo that day like you. That's something we've been talking about since our start. >>A little bit validating them for data ICU, for sure, for sure. Well, thank you for joining. Just to be on the kid, the suffering. And we appreciate it. Appreciate it. All right. For my co host, Justin Warren, I'm Lisa Martin and your work to the Cube from Vegas. It's AWS reinvent 19.

>> from San Matteo. It's the Cube covering Scaler. Innovation Day. Brought to You by Scaler >> The Run Welcome to this special on the Ground Innovation Day. I'm John for a host of The Cube. We're here at scale. His headquarters in San Mateo, California Hardest Silicon Valley. But here the cofounder and CEO Steve, It's Irwin Ski and Jeff Low product marketing director. Thanks for having us. Thanks for having us. Thank you. But a great day so far talked Teo, the other co founders and team here. Great product opportunity. You guys been around for a couple of years, Got a lot of customers, Uh, just newly minted funded syriza and standard startup terms. That seems early, but you guys are far along, you guys, A unique architecture. What's so unique about the architecture? >> Well, thinks there's really three elements of the architecture's designed that I would highlight that differentiates us from our competitors. Three things that really set us apart. I think the biggest the 1st 1 is our use of a common our database. This is what allows us to provide a really superior search experience even though we're not using keyword indexing. Its purpose built for this problem domain and just provides us with great performance in scale. The second thing I would highlight would be the use of well, essentially were a cloud native solution. We have been architected in such a way that we can leverage the great advantage of cloud the scale, ability that cloud gives you the theological city. That cloud gives you andare. Architecture was built from the ground up to leverage that, uh and finally I would point out the way that we do our data. Um, the way that we don't silo data by data type, essentially any type of observe ability, data, whether it's logs or tracing or metrics. All that data comes into this great platform that we were in that provides a really great superior query performance over, >> and we talked earlier about Discover ability. I want to just quickly ask you about the keyword indexing and the cloud native. To me, that seems to be a two big pieces because a lot of the older all current standards people who are state of the art few years ago, 10 years ago, keyword index thing was a big part of it, and cloud native was still emerging except for those folks that were born the clouds. So >> this is a dynamic. How important is that? Oh, it's It's just critical. I mean, here, when we go to the white board, I love to talk about this in a little more detail in particular. So let's let's talk about keyword indexing, right? Because you're right. This is a lot of the technology that people leverage right now. It's what all of our competitors do in keyword indexing. Let's let's look at this from the point of view of a log ingestion pipeline. So in your first stage, you have your input, right? You've got your raw logs coming in. The first thing you do after that typically is parse. You're goingto parse out whatever fields you want from your logs. Now, all of our competitors, after they do that, they do in indexing step. Okay, this has a lot of expense to it. In fact, I'm going to dig into that after the log content is index. It's finally available for search. Where will be returned as a search result. Okay, this one little box, this little index box actually has a lot of costs associated with it. It contributes to the bloat of storage. It contributes to the cost of the overall product. In fact, that's why I love our competitors. Charge you based on how much you're indexing now, even how much you're ingesting. When you look at the cost for indexing, I think you can break it down into a few different categories. First of all, building the index. There's certain costs with just taking this data, building the index and storing it. Computational storage, memory, everything okay, But you build the index in order to get superior query performance, Right? So that kind of tells you that you're going to have another cost. You're going tohave an optimization cost. Where the index is that you're building are dependent on the queries that your users want to conduct, right, because you're trying to make sure you get as good of query performance as possible. So you have to take a look at the career. Is that your user performing the types of logs that you're coming in and you have to decide what indexing that you want to do? Okay. And that cost is shouldered by the burden of the customers. Um, okay, but nothing static in this world. So at some point your logs are going to change. The type of logs here in Justin is going to change. Maybe your query is goingto change. And so you have another category of costs, which is maintenance, right? You're going to have to react to changes in your infrastructure. It's used the type of logs you're ingesting, and basically, this is just creates a whole big loop where you have to keep an eye on your performance. You have to be constantly optimizing, maintaining and just going around in the circle. Right? And for us, we just thought that was ridiculous because all this costs is being born by the customer. And so when we designed the system, we just wanted to get rid of that. >> That's the classic shark fin. You see a fin on anything great whites going to eat you up or iceberg. You see that tip you don't see what's underneath? This seems to be the key problem, because the trend is more data. New data micro services gonna throw off new data type so that types is going up a CZ. Well, that's what that does that consistent with what you got just >> that's consistent. I mean, what we hear from our customers is they want flexibility, right? These are customers that are building service oriented, highly scalable applications on top of new infrastructure. They're reacting to changes everywhere, so they want to be able to not have to, you know, optimize their careers. They're not goingto want to maintain things. They just want to search product that works. That works over everything that they're ingesting. >> So, good plan. You eliminate that fly wheel of cost right for the index. But you guys, you were proprietary columnist, Or that's the key on >> your That's a Chiana and flexibility on data types. Yes, it does. And here, let me draw a little something to kind of highlight that because, you know, of course, it's a it begs the question. Okay, we're not doing keyword indexing. What do you do? What we do actually is leverage decades of research and distribute systems on commoner databases, and I'll use an example on or two >> People know that the data is, well, that's super fast, like a It's like a Ferrari. >> Yes, it's a fryer because you're able to do much more targeted essentially analysis on the data that you want to be searching over, right? And one way to look at this is, uh, no, Let's take a look at ah, Web access lock. Okay. And when we think about this and tables, we think that each line in the table represents, ah, particular entry from the access log. Right. And your columns represent what fields you've extracted. So for example, one the fields you might extract is thie HP status code. You know, Was it, um, a success or not? Right. Or you might have the your eye, or you might have the user agent of the incoming web request. Okay. Now, if you're not using a commoner database approach to execute a quarry where you're trying to count the number of non two hundreds that you've your Web server has responded with, you'd have to load in all the data for this >> table, right? >> And that's just its overkill in a commoner database. Essentially, what you do is you organize your data such that each column essentially has saved as a separate file. So if I'm doing a search where I just want to count the number of non two hundreds. I just have to read in these bites. And when your main bottleneck, it's sloshing bites in and out of Main Ram. This just gives you orders of magnitude better performance. And we've just built this optimize engine that does essentially this at its core and doesn't really well, really fast leveraging commoner database technology. >> So it lowers the overhead. You have to love the whole table in. That's going to take time. Clearing the table is going to take time. That seems to be the update. That's exactly right. Awesome, right? Okay. All right, Jeff. So you're the director of product marketing. So you got a genius pool of co founders here? Scaler. Been there, done that ball have successful track records as tech entrepreneurs, Not their first rodeo, making it all work. Getting it packaged for customers is the challenge that you guys have you been successful at it? What does it all mean? >> Yeah, it essentially means helping them explore and discover their data a lot more effectively than they happen before, you know, With applications and infrastructure becoming much more complex, much more distributed, our engineering customers are finding it increasingly difficult to find answers And so all of this technology that we've built is specifically designed to help him do that at much greater speed, Much greater ease, much more affordably and at scale. We always like to say we're fast, easy, affordable, at scale. >> You know, I noticed in getting to know you guys and interviewing people around around company. The tagline built by engineers for engineers is interesting. One. You guys are all super nerdy and geeky, so you get attacked and you take pride in the tech in the code. But also, your buyers are also engineers because they're dealing with cloud Native Wholenother Dev ops, level of scale where they love scale people in that market love infrastructures code. This is kind of the ethos of that market, but speed scale is what they live for, and that's their competitive advantage in most cases. How do you hit that point there? What's the alignment with the customers on scale and speed? >> Yeah, you know, with the couple of things that Stephen had mentioned, you know, the columnar database on DH, he mentioned cloud native. We like to refer to that as massively parallel or true multi tendency in the cloud those 11 two things give us really to key advantages when it comes to speed. So speed on in just that goes back to what Steven was talking about with the column. In our database, we're not having a weight to build the index so weakening unjust orders of magnitude faster than traditional solutions. So whereas a conventional solution might taking minutes even up to hours to ingest large sets of data, we can literally do it in seconds. It's the data's available immediately for used in research. One of our customers, in fact, that I'm thinking of down Australia actually uses our live tail because it actually works and as they push code out to production that can actually monitor what happens and see if the changes are impacting anything positively or negatively >> and speed two truths, a tagline the marking people came up with, which is cool. I love that kind of our fallouts. We have to get the content out there and get that let the people decide. But in your business, ingestion is critical. Getting the ingestion to value time frame nailed down is table stakes. People engineers want to test stuff. It doesn't work out of the box we ingest and they don't see value. They're not gonna kind of be within next levels. Kind of a psychology of the customer. >> Yeah, You know, when you're pushing code, you know, on an hourly basis, sometimes even minutes now, the last thing you want to do is wait for your data to analyse it, especially when a problem occurs. When a problem occurs and it's impacting a customer or impacting your overall business. You immediately go into firefighting mode, and you just can't wait to have that data become available so that speed to ingest becomes critical. You don't want to wait. The other aspect on the speed topic is B to search. So we talked about the types of searches that are calling. Our database affords us a couple that, within massively parallel and true multi tendency approach, basically means that you could do very, very ad hoc searches extremely quickly. You don't have to bill the keyword index. You don't have to have two, even build a query or learn how to build queries on DH, then run and then wait for it. And maybe in the meantime, wait to get a coffee or something like that. >> I mean, we grew up in Google search. Everyone who's exactly the Web knows what searches and discoveries kind the industry word in discovering navigation. But one of the things about searches about that made Google say Greg was relevance. You guys seem to have that same ethos around data discover, ability, speed and relevance. Talk about the relevance piece, because I think that, to me is what is everyone's trying to figure out as more data comes in? You mentioned some of the advantages Steven around, you know, complexity around data types. You know, Maur data types are coming on, so Relevance sees, is what everyone's chasing. >> So one of >> the things that I think we are very good at is helping people discover what is relevant. There are solutions out there. In fact, there's a lot of solutions out there that will focus on summarizing data, letting you easily monitor with a set of metrics, or even trace a single transaction from point A to point B through a set of services. Those are great for telling you that there is a problem or that problem exist. Maybe in this one service, this one server. But where we really shine is understanding why something has happened. Why a problem has occurred. And the ability to explore and discover through your data is what helps us get to that relevancy. >> Ameren meeting Larry and Sergey back into 1998. And you know, from day one it's fine. What you looking for him? And they did their thing. So I want to just quickly have you guys explain it. I think one thing that also has come up love to get your take on it, guys, is multi tendency urine in the clouds to get a lot of scale. We're out of resource talk about the debt. Why multi tendency is an important piece and what does that specifically mean? But the customer visa be potentially competitive solutions. And what do you guys bring for the tables? That seems to be an important discussion Point >> sure know. And it is one of the key piece of our architecture. I mean, when we talk about being designed for the cloud, this is a central part of that right? When you look at our competitors, for the most part, a lot of them have taken existing open the source off the shelf technologies and kind of taking that and shoved it into this, you know, square hole of, you know, let's run in the cloud, right? And so they're building. These SAS services were essentially they pretend like everyone's got access to a lot. Resource is but under the covers there, sitting there, spinning up thes open source solutions. Instances for each of the customers each of these instances are on ly provisioned with enough ramsi pew for that customer's needs, right? And so heaven forbid you try to issue more crews than you normally do or try to use Mohr you know, storage than you normally do, because your instance will just be capped out, right? Um, and also it's kind of inefficient in that when your users aren't issue inquiries, those CPU and RAM researchers are just sitting there idle instead, what we've done is we've built a system where we essentially have a big pool of resource is we have a big pool of CPU, a big pool of ram, a big pool of disc. Everyone comes in, get access to that, so it doesn't matter what customer you are. Your queries get full access to all these si pues that we have run around right? And that's that's the core of multi tendency is that we're able to not provision for just one look for each individual customer. But we have a big pool of resource is that everyone gets the >> land that's gonna hit the availability question on. And it's also have a side effect for all those app developers who want to build a I and stuff used data and build these micro services systems. >> They're going to get >> the benefit because you have that closed loop. Are you fly? Will, if you will. >> Yeah, yeah, the fight could just add the multi tendency really gives us a lot of economies of scale, both from, you know, the over provisioning and the ability to really effectively use resources. We also have the ability to pass those savings on to our customers. So there's that affordability piece that I think is extremely important. Find answers, this architectural force that >> Stephen I want to ask you because, you know, I know the devil's work pretty well. People are they're hard core, you know. They build their own stuff. They don't want us, have a vendor. Kuo. I can do this myself. There's always comes up there. But this use cases here. You guys seem to be doing well in that environment again. Engineering led solution, which I think gives you guys a great advantage. But what's the How do you handle the objection when you hear someone say, Well, I could do it. Just go do it myself. >> What I always like to point at is, yes, you can up to a decree, right? We often hear people that use open source technologies like elk. They can get that running and they can run it up to a certain scale like a you know, tens of gigabytes per day of logs. They're fine, right? But with those technologies, once it goes above a certain scale, it just becomes a lot more difficult to run. It's one those classic things you know, getting 50% of the way. There is easy getting 80% of the way. There is a lot harder. Getting 100% is almost impossible, right? And you, as whatever company that that that you're doing whatever product you're building, do you really want to spend your engineer? Resource is pushing through that curve, getting 80%. 100% of kind of good, a great solution. No, what we always pitches like Look, we've always solve these problems. These hard problems for this problem, too may come and leverage our technology. You don't have to spend your engineering capital on that. >> And then the people who are doing that scale that you guys provide, they want, they need those engineering resource is somewhere else. So I have to ask, you just basically followed question. Which is how does the customer know whether they have a non scaleable for scaleable solution? Because some of these SAS services air masquerading as scaleable solutions. >> No, they are. I mean, we we actually encourage our customers when they're in the pre sale stage to benchmark against us. We have ah customer right now that sending us terabytes of data per day as a trial just to show that we can meet the scale that they need. We encourage those same customers to go off and ask the other competitors to do that. And, you know, the proof is in the pudding. >> And how's the results look good? Yeah. So bring on the ingest Yes, that's that's That's the sales pitch. Yes, guys, thanks so much for sharing the inside. Even. Appreciate it, Jeff. Thanks for sharing. Appreciate it. I'm John for the Cube. Here for a special innovation Days scales >> headquarters in the heart of >> Silicon Valley's sent Matteo California. Thanks for watching.

(upbeat techno music) >> From Sand Hill Road, in the heart of Silicon Valley, it's theCUBE. Presenting the People First Network, insights from entrepreneurs and tech leaders. >> Hello, everyone, I'm John Furrier the co-host on theCUBE, founder of SiliconANGLE Media. We are here at Sand Hill Road, at Mayfield for the 50th anniversary celebration and content series called The People First Network. This is a co-developed program. We're going to bring thought leaders, inspirational entrepreneurs and tech executives to talk about their experience and their journey around a people first society. This is the focus of entrepreneurship these days. I'm here with Navin Chaddha who's the managing director of Mayfield. Navin, you're kicking off the program. Tell us, why the program? Why People First Network? Is this a cultural thing? Is this part of a program? What's the rationale? What's the message? >> Yeah, first of all I want to thank, John, you and your team and theCUBE for co-hosting the People First Network with us. It's been a real delight working with you. Shifting to people first, Mayfield has had a long standing philosophy that people build companies and it's not the other way around. We believe in betting on great people because even if their initial idea doesn't pan out, they'll quickly pivot to find the right market opportunity. Similarly we believe when the times get tough it's our responsibility to stand behind people and the purpose of this People First Network is people like me were extremely lucky to have mentors along the way, when I was an entrepreneur and now as a venture capitalist, who are helping me achieve my dreams. Mayfield and me want to give back to other entrepreneurs, by bringing in people who are luminaries in their own fields to share their learnings with other entrepreneurs. >> This is a really great opportunity and I want to thank you guys for helping us put this together with you guys. It's a great co-creation. The observation that we're seeing in Silicon Valley and certainly in talking to some of the guests we've already interviewed and that will be coming up on the program, is the spirit of community and the culture of innovation is around the ecosystem of Silicon Valley. This has been the bedrock. >> Mm-hmm. >> Of Silicon Valley, Mayfield, one of the earliest if not the first handful of venture firms. >> Mm-hmm. >> Hanging around Stanford, doing entrepreneurship, this is a people culture in Silicon Valley and this is now going global. >> Mm-hmm. >> So great opportunity. What can we expect to see from some of the interviews? What are you looking for and what's the hope? >> Yeah, so I think what you're going to see from the interviews is, we are trying to bring around 20 plus people, and they'll be many John on the interview besides you. So there will be John Chambers, ex-chairman and CEO of Cisco. There'll be John Zimmer, president and co founder of Lyft. And there also will be John Hennessy who will be our first interview, with him, from Stanford University. And jokes apart, there'll be like 20 plus other people who will be part of this network. So I think what you're going to see is, goings always don't go great. There's a lot of learnings that happen when things don't work out. And our hope is, when these luminaries from their professions, share their learnings the entrepreneurs will benefit from it. As we all know, being an entrepreneur is hard. But sometimes, and many times, actually it's also a lonely road and our belief is, and I strongly personally also believe in it, that great entrepreneurs believe in continuous learning and are continuously adapting themselves to succeed. So our hope is, this People First Network serves as a learning opportunity from entrepreneurs to learn from great leaders. >> You said a few things I really admire about Mayfield and I want to get your reaction because I think is a fundamental for society. Building durable companies is about the long game because people fail and people succeed but they always move on. >> Mm-hmm. >> They move on to another opportunity. They move on to another pursuit. >> Mm-hmm. >> And this pay it forward culture has been a key thing for Silicon Valley. >> It absolutely has been. >> What's the inspiration behind it, from your perspective? You mentioned your experiences. Tell us a story and experience you've had? >> Yeah, so I would say, first of all, right, since we strongly believe people make products and products don't make people, we believe venture capital and entrepreneurship is about like running a marathon, it's not a sprint. So if you take a longterm view, have a strong vision and mission which is supported with great beliefs and values? You can do wonders. And our whole aim, not only as Mayfield but other venture capitalists, is to build iconic companies which are built to last which beyond creating jobs and economic wealth, can give back to the society and make the world a better place to work, live and play. >> You know one of the things that we are passionate about at theCUBE, and on SiliconANGLE Media is standing by our community. >> Mm-hmm. >> Because people do move around and I think one of the things that is key in venture capital now, than ever before is not looking for the quick hit. >> Mm-hmm. >> It's standing by your companies in good times and in bad. >> Mm-hmm. >> Because this is about people and you don't know how things might turn out, how a company might end up in a different place. We've heard some of your entrepreneurs talk about that, that the outcome was not how they envisioned it when they started. >> Mm-hmm. >> This is a key mindset for a business. >> It absolutely is, right? Let's look at a few examples. One of our most successful companies is Lyft. When we backed it at Series A, it was called Zimride. They weren't doing what they were doing, but the company had a strong vision and mission of changing the way people transport and given that, they were A plus people, as I mentioned earlier. The initial idea wasn't going to be a massive opportunity. They quickly pivoted to go after the right market opportunity. And hence, again and again, right? Like to me, it's all about the people. >> Navigating those boards is sometimes challenging and we hope that this content will help people, inspire people, help them discover their passion, discover people that they might want to work with. We really appreciate your support and thank you for contributing your network and your brand and your team in supporting our mission. >> Yeah, it's been an absolute pleasure and we hope the viewers and especially entrepreneurs can learn from the journeys of many iconic people who have built great things in their careers. >> Were here at Sand Hill Road, at Mayfield's venture capital headquarters in sunny Silicon Valley, California, Stanford, California, Palo Alto California, all one big melting pot of innovation. I'm here with John Hennessy, who's the Stanford President Emeritus, also the director of the Knight Hennessy Scholarship. Thanks for joining me today for this conversation. >> Delighted to be here, John. >> So I wanted to get your thoughts on the history of the valley. Obviously, Mayfield, celebrating their 50th anniversary and Mayfield was one of those early venture capital firms that kind of hung around the barbershop, looking for a haircut. Stanford University was that place. Early on this was the innovation spark that created the valley. A lot of other early VCs as well, but not that many in the early days and now 50 years later, so much has changed. What's your thoughts on the arc of entrepreneurship around Stanford, around Silicon Valley? >> Well, you're right, it's been an explosive force. I mean, I think there were a few companies out here on Sand Hill Road at that time. Now nearly the number of venture firms there are today. But I think the biggest change has been the kinds of technologies we build. You know, in those days, we built technologies that were primarily for other engineers or perhaps they were tandem computers being built for business interest. Now we build technologies that change people's lives, every single day and the impact on the world is so much larger than it was and these companies have grown incredibly fast. I mean, you look at the growth rate? We had the stars of the earlier compared to the Googles and Facebooks of today, it's small growth rates, so those are big changes. >> I'm excited to talk with you, because you're one of the only people that I can think of that has seen so many different waves of innovation. You've been involved in many of them yourself, one of the co-founders of MIPS, chairman of the board of Alphabet, which is Google, Google's holding company, the large holdings they have and just Stanford in general has been, you know, now with CAL, kind of the catalyst for a lot of the change. What's interesting is, you know, the Hewlett-Packards, the birthplace of Silicon Valley, that durable company view. >> Mm-hmm. >> Of how to build a company and the people that are involved is really a, still, essential part of it. Certainly happening faster, differently. When you look at the waves of innovation, is there anything that you could look at and say, hey, this is the consistent pattern that we see emerging of these waves? Is it a classic formula of engineers getting together trying to solve problems? Is it the Stanford drop out PH.d program? Is there a playbook? Is there a pattern that you see in the entrepreneurship over the years? >> You know, I think there are these waves that are often induced by big technology changes, right? The beginning of the personal computer. The beginning of the internet. The world wide web, social media. The other observation is that it's very hard to predict what the next one will be. (laughing) If it was easier to predict, there would be one big company, rather than lots of companies riding each one of these waves. The other thing I think that's fascinating about them is these waves don't create just one company. They create a whole new microcosm of companies around that technology which exploit it and bring it to the people and change people's lives with it. >> And another thing is interesting about that point is that even the failures have DNA. You see people, big venture backed company, I think Go is a great example, you think about those kinds of companies. The early work on mobile computing, the early work on processors that you were involved in MIPS. >> Mm-hmm. >> They become successful and/or may/may not have the outcomes but the people move on to other companies to either start companies. This is a nice flywheel, this is one of the things that Silicon Valley has enjoyed over the years. >> Yeah, and just look at the history of RISC technology that I was involved in. We initially thought it would take over the general purpose computing industry and I think Intel responded in an incredible way and eventually reduced the advantage. Now here we are 30 years later and 95%/98% of the processors in the world are RISC because of the rise of mobile, internet of things, dramatically changing where the processors were. >> Yeah. >> They're not on the desktop anymore, they're scattered around in very different ways. >> It's interesting, I was having a conversation with Andy Kessler, who used to be an analyst back at the time for Morgan Stanley. He then became an investor. And he was talking about, with me, the DRAM days when the Japanese were dumping DRAMs and then that was low margin business, and then Intel said, "Hey, no problem. "We'll let go of the DRAM business." but they created Pentium and then the micro processor. >> Right. >> That spawned a whole nother wave, so you see the global economy today, you see China, you see people manufacturing things at very low cost, Apple does work out there. What's your view and reaction to the global landscape? Because certainly things are changed a bit but it seems to be some of the same? What's your thoughts on the global landscape and the impact of entrepreneurs? >> It certainly is global. I mean, I think in two ways. First of all, supply chains have become completely global. Look at how many companies in the valley rely on TSMC as their primary source of silicon? It's a giant engine for the valley. But we also see, increasingly, even in young companies a kind of global, distributed engineering scheme where they'll have a group in Taiwan, or in China or in India that'll be doing part of the engineering work and they're basically outsourcing some of that and balancing their costs and bringing in other talent that might be very hard to hire right now in the valley or very expensive in the valley. And I think that's exciting to see. >> The future of Silicon Valley is interesting because you have a lot of the fast pace, it seems like ventures have shrink down in terms of the acceleration of the classic building blocks of how to get a company started. You get some funding, engineers build a product, they get a prototype, they get it out. Now it seems to be condensed. You'll see valuations of a billion dollars. Can Silicon Valley survive the current pace given the real estate prices and some of the transportation challenges? What's your view on the future of Silicon Valley? >> Well my view is there is no place like the valley. The interaction between great universities, Stanford and Cal, UCSF if you're interested in biomedical innovation and the companies makes it just a microcosm of innovation and excellence. It's challenges, if it doesn't solve it's problems on housing and transportation, it will eventually cause a second Silicon Valley to rise and challenge it and I think that's really up to us to solve and I think we're going to have to, the great leaders, the great companies in the valley are going to have to take a leadership role working with the local governments to solve that problem. >> On the Silicon Valley vision of replicating it, I've seen many people try, other regions try over the years and over the 20 years, my observation is, they kind of get it right on paper but kind of fail in the execution. It's complicated but it's nuanced in a lot of ways but now we're seeing with remote working and the future of work changing a little bit differently and all kinds of new tech from block chain to, you name it, remote working. >> Right. >> That it might be a perfect storm now to actually have a formula to replicate Silicon Valley. If you were advising folks to say, hey, if you want to replicate Silicon Valley, what would be your advice to people? >> Well you got to start with the weather. (laughing) Always a challenge to replicate that. But then the other pieces, right? Some great universities, an ecosystem that supports risk taking and smart failure. One of the great things about the valley is, you're a young engineer/computer scientist graduating, you come here. You go to a start up company, so what it fails? There's 10 other companies you can get a job with. So there's a sense of this is a really exciting place to be, that kind of innovation. Creating that, replicating that ecosystem, I think and getting all the pieces together is going to be the challenge and I think the area that does that will have a chance at building something that could eventually be a real contestant for the second Silicon Valley. >> And I think the ecosystem and community is the key word. >> And community, absolutely. >> So I'll get your thoughts on your journey. Take us through your journey. MIPS co-founder, life at Stanford, now with the Knights Scholarship Program that you're involved in, the Knight Hennessy Scholarship. What lessons have you learned from each kind of big sequence of your life? Obviously in the start up days. Take us through some of the learnings. >> Yeah. >> Whether it's the scar tissue or the success, you know? >> Well, no, the time I spent starting MIPS and I took a leave for about 18 months full-time from the university, but I stayed involved after that on a part time basis but that 18 months was an intensive learning experience because I was an engineer. I knew a lot about the technology we're building, I didn't know anything about starting a company. And I had to go through all kinds of things, you know? Determining who to hire for CEO. Whether or not the CEO would be able to scale with the company. We had to do a layoff when we almost ran out of cash and that was a grueling experience but I learned how to get through that and that was a lesson when I came back to return to the university, to really use those lessons from the valley, they were invaluable. I also became a much better teacher, because here I had actually built something in industry and after all, most of our students are going to build things, they're not going to become future academics. So I went back and reengaged with the university and started taking on a variety of leadership roles there. Which was a wonderful experience. I never thought I'd be university president, not in a million years would I have told you that was, and it wasn't my goal. It was sort of the proverbial frog in the pot of water and the temperature keeps going up and then you're cooking before you know it. >> Well one of the things you did I thought was interesting during your time in the 90's as the head of the computer science department is a lot of that Stanford innovation started to come out with the internet and you had Yahoo, you had Google, you had PH.ds and you guys were okay with people dropping out, coming back in. >> Yeah. >> So you had this culture of building? >> Yup. >> Tell us some of the stories there, I mean Yahoo was a server under the desk and the web exploded. >> Yeah, it was a server under the desk. In fact, Dave and Jerry's office was in a trailer and you go into their room and they'd have pizza boxes and Coke cans stacked around because Yahoo use was exploding and they were trying to build this portal out to serve this growing community of users. Their machine was called Akebono because they were both big sumo wrestling fans. Then eventually, the university had to say, "You guys need to move this off campus "because it's generating 3/4 of the internet traffic "at the university and we can't afford it." (laughing) So they moved off campus and of course figured out how to use advertising as a monetization model. And that changed a lot of things on the internet because that made it possible for Google to come along years later. Redo search in a way that lots of us thought, there's nothing left to do in search, there's just not a lot there. But Larry and Sergey came up with a much better search algorithm. >> Talk about the culture that you guys fostered there because this, I think, is notable, in my mind, as well as some of the things I want to get into about the interdisciplinary. But at that time, you guys fostered a culture of creating and taking things out and there was an investment group of folks around Stanford. Was it a policy? Was it more laid back? >> No, I think-- >> Take us through some of the cultural issues. >> It was a notion of what really matters in the world. How do you get impact? Because in the end that's what the university really wants to do. Some people will do impact by publishing a paper or a book but some technologies, the real impact will occur when you take it out into the real world. And that was a vision that a lot of us had, dating back to Hewlett-Packard, of course but Jim Clark at Silicon Graphics, the Cisco work, MIPS and then, of course, Yahoo and Google years later. That was something that was supported by both the leadership of the university and that made it much easier for people to go out and take their work and take it out to the world. >> Well thank you for doing that, because I think the impact has been amazing and had transcended a lot of society today. You're seeing some challenges now with society. Now we have our own problems. (laughing) The impact has been massive but now lives are being changed. You're seeing technology better lives so it's changing the educational system. It's also changing how people are doing work. Talk about your current role right now with the Knight Hennessy Scholarship. What is that structured like and how are you shaping that? What's the vision? >> Well our vision, I became concerned as I was getting ready to leave the president's office that we, as a human society, were failing to develop the kinds of leaders that we needed. It seemed to me it was true in government. It was true in the corporate world. It was even true in some parts of the nonprofit world. And we needed to step back and say, how do we generate a new community of young leaders who are going to go out, determined to do the right thing, who see their role as service to society? And their success aligned with the success of others? We put together a small program. We put together a vision of this. I got support from the trustees. I went to ask my good friend Phil Knight, talked to him about it, and I said, "Phil I have this great idea," and I explained it to him and he said, "That's terrific." So I said, "Phil I need 400 million dollars." (laughing) A month later he said, "Yes," and we were off and running. Now we've got 50 truly extraordinary scholars from around the world, 21 different birth countries. Really, some of them have already started nonprofits that are making a big difference in their home communities. Others will do it in the future. >> What are some of the things they're working on? And how did you guys roll this out? Because, obviously, getting the funding's key but now you got to execute. What are some of the things that you went through? How did you recruit? How did you deploy? How did you get it up and running? >> We recruited by going out to universities around the world, and meeting with them and, of course, using social media as well. If you want get 21 year and 22 year olds to apply? Go to social media. So that gave us a feed on some students and then we thought a lot, our goal is to educate people who will be leaders in all walks of life. So we have MBAs, we have MDs, we have PH.ds, we have JDs. >> Yeah. >> A broad cohort of people, build a community. Build a community that will last far beyond their time at Stanford so they have a connection to a community of like minded individuals long after they graduate and then try to build their leadership skills. Bringing in people who they can meet with and hear from. George Schultz is coming in on Thursday night to talk about his journey through government service in four different cabinet positions and how did he address some of the challenges that he encountered. Build up their speaking skills and their ability to collaborate with others. And hopefully, these are great people. >> Yeah. >> We just hope to push their trajectory a little higher. >> One of the things I want you is that when Steve Jobs gave his commencement speech at Stanford, which is up on YouTube, it's got zillions and zillions of views, before he passed away, that has become kind of a famous call to arms for a lot of young people. A lot of parents, I have four kids and the question always comes up, how do I get into Stanford? But the question I want to ask you is more of, as you have the program, and you look for these future leaders, what advice would you give? Because we're seeing a lot of people saying, hey you know people build their resume, they say what they think people want to hear to get into a school, you know Steve Job's point said, "Follow your passion, don't live other people's dogma" these are some of the themes that he shared during that famous commencement speech in Stanford. Your advice for the next generation of leaders? How should they develop their skills? What are some of the things that they can acquire? Steve Jobs was famous to say in interviews, "What have you built?" >> Yeah. >> "Tell me something that you've built." It's kind of a qualifying question. So this brings up the question of, how should young people develop? How should they think about, not just applying and getting in but being a candidate for some of these programs? >> Well I think the first thing is you really want to challenge yourself. You really want to engage your intellectual passions. Find something you really like to do. Find something that you're also good at because that's the thing that'll get you out of bed on weekends early, and you'll go do it. I mean, if you asked me about my career? And asked me about my number one hobby for most of my career? It was my career. I loved being a professor. I loved research, I love teaching. That made it very easy to do it with energy and excitement and passion. You know there's a great quote in Steve Job's commencement speech where he says, "I look in the mirror every morning "and if too many days in a row I find out "I don't like what I'm going to do that day, "it's time for a change." Well I think it's that commitment to something. It's that belief in something that's bigger than yourself, that's about a journey that you're going to go on with others in that leadership role. >> I want to get your thoughts on the future for young people and society and business. It's very people centric now. You're seeing a lot of the younger generation look for mission driven ventures, they want to make a difference. But there's a lot of skills out there that are not yet born, yet. There's jobs that haven't been invented yet. Who handles autonomous vehicles? What's the policy? These are societal and technology questions. What are some of things that you see that are important to focus on for some of these new skills? There's a zillion new cyber security jobs open, for instance. >> Right. I mean there's thousands and thousands of openings for people that don't have those skills. >> Well I think we're going to need two different types of people. The traditional techno experts that we've always had but we're also going to need people that have a deep understanding of technology but are deeply committed to understanding it's impact on people. One of the problems we're going to have with the rise of artificial intelligence is we're going to have job displacements. In the longterm, I'm a believer that the number of opportunities created will exceed those that get destroyed but there'll be a lot of jobs that are deskilled or actually eliminated. How are we going to help educate that cohort of people and minimize the disruption of this technology? Because that disruption is really people's live that you're playing with. >> It's interesting, the old expression of ATMs will kill the bank branch but yet, now there's more bank branches than ever before. >> Than ever before, right? >> So, I think you're right on that, I think there'll be new opportunities. Entrepreneurship certainly is changing and I want to get your thoughts. This is the number one question I get from young entrepreneurs is, how should I raise money? How should I leverage money investors and my board? As you build your early foundational successes whether you're an engineer or a team, putting that E team together, entrepreneurial team is critical and that's just not people around the table of the venture. >> Correct. >> It's the support service providers and advisors and board of directors. How should they leverage their investors and board? How should they leverage that resource and not make it contentious, make it positive? >> Make is positive, right? So the best boards are collaborative with the management team, they work together to try to move the company forward. With so many angels now investing in these young companies there's an opportunity to bring in experience from somebody who's already had a successful entrepreneurial venture and looking for really deciding who do you want your investor to be? And it's not just about who gives you the highest valuation. It's also about who'll be there when things get tough? When the cash squeeze occurs and you're about to run out of money and you're really in a difficult situation? Who will help you build out the rest of your management team? Lots of young entrepreneurs, they're excited about their technology. >> Yeah. >> They don't have any management experience. (laughing) They need help. >> Yeah. >> They need help building that team and finding the right people for the company to be successful. >> I want to get thoughts on Mayfield. The 50th anniversary, obviously, they've been around longer than me, I'm going to be 53 this year. I remember when I first pitched Yogan DeGaulle in 1990, my first venture, he passed, but, Mayfield's been around for a while. I mean, Mayfield was the name of the town around here? >> Right. >> And has a lot of history. How do you see the relationship with the ventures and Stanford evolving? Are they still solid? They're doing well? Is it evolved? There's a new program going on? I see much more integration. What's the future of venture? >> Well I think the university's still a source of many ideas, obviously the notion of entrepreneurship has spread much more broadly than the university. And lots of creative start ups are spun out of existing companies or a group of young entrepreneurs that were in Google or Facebook early and now decide they want to go do their own thing. That's certainly happens but I think that ongoing innovation cycle is still alive. It's still dependent on the venture community and their experience having built companies. Particularly when you're talking about first time entrepreneurs. >> Yeah. >> Who really don't have a lot of depth. >> My final question I want to ask you is obviously one relating, pure to my heart, is computer science. I got my degree in the 80's during the systems revolution. Fun time, a lots changed. Women in computer science, the surface area of what computer science is. >> Mm-hmm. >> It was interesting, there was a story in Bloomberg that was debunked but people were debating if the super micros was being hacked by a chip in the system. >> Right. >> And more people don't even know what computer architecture is, I was like, hey now, the drivers might able to inject malware. So you need computer architecture, a book you've written. >> Mm-hmm. >> Academically, to programming so the range of computer science has changed. The diversity has changed. What's your thoughts on the current computer science curriculums? The global programs? Where's it going and what's your perspective on that? >> So I think computer science has changed dramatically. When I was a graduate student, you could arguably take a full set of breadth courses across the discipline. Maybe only one course in AI or one course in data base if you were a hardware or systems person but you could do everything. I could go to basically any Ph.d defense and understand what was going on. No more, the field has just exploded. And the impact? I mean you have people who do bio computation, for example, and you have to understand a lot of biology in order to understand how computer science applies to that. So that's the excitement. The excitement of having computer science have this broad impact. The other thing that's exciting is to see more women, more people of color, coming into the field, really injecting new energy and new perspective into the field and I think that will stand the discipline well in the future. >> And open source has been growing. I mean if you think about what it's like now to write software, all this goodness coming in with open source, it just adds over the top. >> Yeah. >> More goodness. >> I think today a, even a young undergraduate, writing in Python, using all these open libraries, could write more code in two weeks than I could have written in a year when I was graduate student. >> If we were 21 together, sitting here you and I, today, we're 21 years old, what would we do? What would you do? >> Well I think the opportunity created by the rise of machine learning and artificial intelligence is just unrivaled. This is a technology which we have invested in for 50 or 60 years, that was disappointing us for 50 or 60 years, in terms of not meeting it's projections and then, all of a sudden, turning point. It was a radical breakthrough and we're still at the very beginning of that radical breakthrough so I think it's going to be a really exciting time. >> Diane Green had a great quote at her last Google Cloud conference. She said, "It's like butter, everything's great with it." (laughing) AI is the-- >> Yeah, it's great with it. And of course, it can be overstated but I think there really is a fundamental breakthrough in terms of how we use the technology. Driven, of course, by the amount of data available for training these neural networks and far more computational resources than we ever thought we'd have. >> John it's been a great pleasure. Thanks for spending the time with us here for our People First interview, appreciate it. >> My pleasure, John. >> I'm John Furrier with theCUBE, we are here in Sand Hill Road for the People First program, thanks for watching. (upbeat techno music)

>> Live from Las Vegas, it's theCUBE. Covering VMworld 2018. Brought to you by VMware and its eco-system partners. >> Hello, everyone. We are here live in Las Vegas for theCUBE's exclusive coverage for three days, VMworld 2018. I'm John Furrier with my co-host Stu Miniman. Our next guest is Andy Bechtolsheim who's the founder and chief development officer and chairman of Arista Networks. More importantly, he's also the co-founder of Sun Microsystems. Invested in Larry and Sergey when they were in their PhD programs. Legend in the industry. Great to have you on. Super excited to have you join this conversation. >> A pleasure to be here today. >> So, first question is, besides all the luminary things you've done in your career, what's it like working with Jayshree at Arista? >> Well, I actually met Jayshree 30 years ago when she was at AMD selling us SDDR chips at Sun Microsystems, so I guess this dates both of us, but I worked with her, of all the years when I was at Cisco, obviously, and then we both start at Arista in 2008. So we have both been there now for 10 years together. In fact, our 10-year anniversary's coming up next month. >> Jayshree's a great Cube alumni. She's an amazing person. Great technologist, we miss her. Wish she was here, having more conversations with us on the Cube, but stepping back, over your career you've seen many ways of innovation. You were involved in all of them, big ones happening. Semi-conductor computers, and now with Arista going forward and now Cloud, did you know the rocket ship of Arista was going to be this big? I mean, when you designed it at the beginning, what was the itch you were scratching, and did you know it was going to be a rocket ship? >> Well, we had some very early, what led to the founding of Arista was, we had lunch with our best friends at Google, and Larry himself told me that the biggest problem they had was not service, but actually the networking, and scaling that to the future size of their data centers, and they were going go off to build their own network, products because there was no commercial product on the market that would meet that need, so we thought with the emergence of Immersion Silicon We could make a contribution there, and the focus of the company was actually on the cloud networking from the very beginning, even though that wasn't even fell in this industry as being a major opportunity. So when we shipped our first products in 2009, 2010 many of them besides we had some business on Wall Street on latency, but the majority of the opportunity was over the cloud. >> It's interesting you mention the Google and Larry and Sergey, Larry in particular about that time in history, you go back and look at what Google was doing at that particular time, and now what they talk about at Google Cloud. They were building their own large-scale system, and there was massive scale involved. >> Yeah they had about a hundred thousand servers in the early 2004 before they went public, now they have, who knows how many millions, right? And all of course the latest technology now. So the sheer size of the cloud, the momentum the cloud has, I think was hard to forecast. We did think there was going to be a shift, but the shift was in fact more rapid than we expected. >> Andy, you talked about cloud networking, but today we still see there's such a huge discrepancy between what networking is happening in the data center and the networking that's happening in the hyperscalers. At this show, we're starting to hear about some of the multi-cloud, you had some integrations between Arista and VMware that are starting to pull some of those together. Maybe you could give us a little bit about what you're seeing between, you know, the data center and the enterprise versus the hyperscalers, when it comes to networking. >> So the data enterprise has still largely what we would call a legacy approach networking, which dates back, you know, 10, 20, 30 years, and many of those networks are still in place and progressing very slowly. But there also are enterprise customers who want to take advantage of what the cloud has done in terms of cloud networking, including the much further scalability, the much further resiliency, the much greater automation, so all of these benefits do imply equally well to the enterprise. But it is a transition for customers, you know, to fully embrace that. So the work we are doing together with VMware on integrating our cloud vision, our physical swiches with the microsegrentation is one element of that. But the bigger topic is simply an enterprise that wants to move into the future really should look at how did the cloud people build their networks, how can they run a very large data center with, you know, 10 network admins instead of, you know, hundreds of people. And especially the automation that we've been able to provide to our customers, automating updating of software, being able to bring out new releases into a running network without bringing the network down. You know, nobody could even think about doing that 10 years ago. >> Yeah, you bring up a great point about automation. In the keynote this morning, Pat Gelsinger talked about, what was it, 39 years ago he did something in intel, said we're going to do AI. Didn't quite call it AI back then, but he said, and now, we're starting to see the fruits of what come out. In the networking world, we've been talking about for decades, automating the network more. You've lived through the one gig, 10 gig, 40 gig, 400 gig you're talking about. Are we ready for automation now? Is now that moment in networking? >> I think that we were ready for 30 years, but the weird thing is, there always was a control planted in network, you know, the routing protocols, but for management there was never really a true management plan, meaning the legacy way is you dial in with S and a P into each switch and configure, your access is manually more or less, and that's really a bad way of doing it because humans do make mistakes, you end up with inconsistencies and a lot of network outages virtually has been traced to literally human mistake. So our approach with what we call Cloud Vision, which is a central point that can manage the entire base of Arista switches in a data canter, its all automated. You want to update a thing, you push a button and it happens and there's no no more dialing into a S and a P, into individual switches. >> How would you advise people who were looking at the architecture of the cloud, who are re-platforming, large enterprises have been legacy all day long, you mentioned earlier just now in the CUBE, that how the cloud guys were laying out the network was fundamental how they grew. How should, and how do people lay out the networks for cloud today? How do you see that? >> So the three big things that happened was, immersion silicon has taken over because it's, quote frankly, much more scalable than traditional chips. And that's just the hardware, right? Then the leaf-spine architecture that really our customers pioneered but is the standard in the cloud. It is use ECP for load balancing, it works. It's the most resilient, maybe the one thing, the single most important thing of the cloud is, no outages, no down time, the network works. No excuses, right? [Laughter] And our customers tell us that with our products and the leaf-spine approach, they have a better experience in terms of resiliency than any other vendor. So that's a very strong endorsement and that's as relevant to an enterprise customer as to a cloud customer. And then the automation benefit. Now, to get the automation benefit, you have to standardize on the new way of doing it, that's true, but it's just such a reduction in complexity and simplification. You can actually look at this as an Opex saving opportunity, quite frankly, and in the cloud they wouldn't have it any other way, they couldn't afford it. They're very large data centers. And they only could offer these things in a fully automatic fashion. >> Andy, I want to get your reaction to what Pat Gelsinger said on stage this morning. He said, in the old days, I'm paraphrasing, the network would dictate what the applications could do, it would enable that, and we saw an enabling capability. Now with Cloud, the apps can program the network, I'm paraphrasing that. As networks become more programmable and no outages, he made a quote, he said, the old adage was the network is the computer, the new adage is, the application is a network. >> Okay so let me sort of translate this, so. >> What's your reaction to those things? >> Sounds like an old Sun slogan, doesn't it? >> Translate that for us. >> So, the virtual networking, the NSX environment which provides security at the application level, right, it's the natural way to do network security. Cuz, you really want to be as close to the application as you can physically be, or virtually be, which is right in the VM environment. So VMware clearly has the best position in the industry to provide that level of security, which is all software, softlevel networking, you do your, you know, security policies at that level. Where we come in is, with Cloud Vision now, we have announced a way to integrate with NSX Microsegmentation, such that we can learn the policies and map them back down to the access list of the physical network to further enhance that security. So we don't actually create a separate silo for yet another policy management, we truly offer it within their policy framework, which means you have the natural segmentation between the security engineers which manages future policies and networking engineers that manage the physical network. >> Highly optimized for the environment >> Which actually works. >> Is that what you call Macrosegmentation then on the University side? >> Well we used to call it macro but it's part of their micro thing because we truly learn their policies. So if you update a policy, it gets reflected back down to cloud vision and your physical networks and it applies to physical switches, physical assets, physical servers, mainstream storage, whatnot, right? So it's a very smooth integration and we think it's a demo at this point but it will work and it's an open framework that allows us to work with VMware. >> Let me ask you a personal question. Looking at the industry, even look back in history as an illustration. TCPIP opened up remember the old OSI stack that everyone tried to do that. TCPIP opened up so much on networking, internetworking, is there a technology enabler in Cloud that you see that's going to have that kind of impact? Is it an NSX? How do customers going to deal with the multiple clouds? I mean, is there an interoperability framework coming, do you see a real disruptive technology enable that'll have that kind of impact that TCP spawned massive opportunity and wealth creation in start-ups and functionality? Is there a moment coming? >> So TCP of course was the proper layering of a network between the physical layer, layer one layer two, and the routing or the internet layer, which is layer three. And without that, this is back to the old intern argument, we wouldn't have what we have today on data. That was the only rational way to build an architecture that could actually, and I'm not sure people had a notion in 1979 when TCP was submitted that it would become that big, they probably would have picked a bigger adverse space, but it was not just the longevity but the impact it had was just phenomenal, right? Now, and that applied in terms of connectivity and how many things you have to sell with measure to talk from Point A to B. The NSX level of network management is a little different because it's much higher level. It's really a management plan, back to the point I made earlier about management plans, that allows you to integrate a cloud on your premise with what an Amazon or at IBM or the future Google and so on, in a way that you can have full visibility and you see you know exactly what's going on, all the security policies. Like, this has been a dream for people to deliver, but it requires to actually have a reasonable amount of code in each of these places. Both on your server, it's not just a protocol, it's an implementation of a co-ability, right? And, we are aware NSX is the best solution that's available today that I could see for that use-case, which is going to be very important to a large number of enterprises, many of which want to have a smooth connection between on-premise and off-premise, and in the future to add TelCo and other things to the bloody run of VMenvironment today. But that will allow them to be fully securely linked into social network. >> So you see that as a leading product in Connect. >> It's definitely a leading product. They have the most customers the most momentum the most market share, there isn't anything even close in terms of the, call it the software-defined networking layer, which is what NSX implements. And we are very proud to partner with them at the physical layer to interact with their policies. >> You think that's going to have an impact of accelerating the multi-cloud world? >> Yes because, the whole point about multi-cloud is it has to be sort of vendor-independent or, I don't know, vendor-neutral. You are going to see solutions from Amazon and Azzure to bring their own sort of public load into the premise. But that only works with their package, right? >> Yeah. >> So there will be other offerings there but in terms of true multi-cloud, I don't see any competition. >> Andy, we'd love to get your viewpoint on the future of ethernet. I hear so many people the last few years that it's like well, on the processor side Moor's Laws played out. We can't get smaller. On the ethernet side, there's not going to be the investment to be able to help get us to the next generation, there's limits in the technology, you've lived through so many of these architectural changes. Are we at the end of innovation for ethernet? >> Not at all. So, my history with ethernet dates back 40 years. So, I worked on the first three mega-ethernet 0x parts til. Then it was 10 mega-bit, hundred mega-bit, gigabit and forty hundred and now 400 coming out. So, ethernet speed transitions are really just substitutions of the previous layer to technology meaning, assuming they're more cost-effective, they do get adopted very quickly. Of course, you need the right optics, you need the right equipment, but it's a very predictable road map. I mean, I guess, it's not like adopting a new protocol, right? It's just faster. And more, and with cost efficient. So, we are on the verge of 400 gigabits becoming available in the market. It will really roll out at any kind of volume next calendar year and then it will pick up volume next year in 2000. But in the meanwhile, 100 meg ethernet- excuse me, 100 gigabit ethernet is still the fastest growing thing the industry's ever seen. Even from a million ports back in 2016, to call it five million ports last calendar year expected to what 10 million ports this year, expected 20 million ports next year. But this is a speed of adoption that's unheard of. And we are at Arista we are fortunate enough to be actually the market leader on gigabit adoption. We have shipped more hundred-gig ports than any vendor including Cisco for the last three years. So our ability to embrace new speeds and bring new technologies to market is, I would say, unparalleled. We have a very good track record there and we are working really hard, sort of burning the midnight oil to extend this to the 400-gig era, which is going to be another important upgrade, especially in the cloud. I should mention that the cloud is the early adopter of all the higher speeds. Those in the hundred gig will be more than 400-gig. I'm not sure too many enterprises need 400-gig but the cloud is ready to get going as soon as it's cost effective. >> Andy, for the folks that are looking at this 20 year wave coming that we're seeing kind of cloud has been talked about on stage and here on theCUBE. Oh, it's going to be a 20 year run, transforming the infrastructure. What's the in your minds eye, what do you see as the most disruptive thing that people aren't talking about in networking? What's going to be some things that might happen in the next 10 years in your mind that might happen that people aren't really aware of, that might not see it coming, any ovations on the horizon that you're excited about or people might not expect? >> Yeah well the cloud trend is fairly predictable. I would say, all the IDC, all the analysts have predicted like that are big numbers on adoption have been pretty spot on. And if you look at the annual growth rate for cloud adoption it's 40, 45, 50 and more percent. Now there's a good question of course how the big cloud winners in the end will compete against each other. You got Amazon, that's the biggest, Microsoft is actually growing purely faster than Amazon right now but they have some catching up to do. And Google working overtime to get bigger. They may differentiate in terms of their specific focus, for example, Google has a lot AI technology, internally, that they have used for their own business, and with this influence they're arguably ahead of others, and they may just bet the farm on AI and big data analytics and things like that, which are very compelling business opportunities for any enterprise customer. So the potential value that can be created deploying AI correctly is in the perhaps trillions of dollars the next 10 years, but it probably doesn't make sense for a company for most companies to build their own AI data center, that you need a huge capital expense a huge, what hardware to use, it's going to evolve very quickly. So that maybe one of the classical cases where, you won't actually start on the cloud, and the only reason ever moving on site is your well defined environment, right, so I would actually say it's the new applications that may start in the cloud, that haven't even rolled out in volume, like AI, that will may be the biggest change that people didn't expect. >> Final question, what's the future of Arista? >> We're just working really hard to, you know, be the best provider of products, making the best products for our customers, both for the cloud and for enterprise. One thing I was going to mention about Arista is that people think we're selling network boxes which is what is which we do. But the vast majority of our investment's actually software and not hardware. So we have over 90% of our R&D headcount is in software and so the right way to think about it is actually we are a software company not really a hardware company and the saying we have internally is that hardware is easy software is hard because it's actually true. Software is much much harder than building hardware these days and the EOS software sells well over 10 million slants of codes written by over thousands of man years of engineering. So it has been a tremendous journey we've been on, but we're still scratching the surface of what we can do. >> And the focus of the software obviously makes sense. Software defined is driving everything. What are the key focus areas on the software that you guys are looking at? What's the key priorities for Arista? >> We have talked about extending our business beyond the data center into the campus. We announced our very first acquisition recently which is actually a wifi company, but I can guarantee you it's going to be a very software-defined wifi network, not a legacy controller-based approach right, for enterprise, right? We're not that interested in the hardware we're interested in providing managed solutions to our customers. >> A lot of IOT action on Andy. Thanks for taking the time to come on theCUBE. Really appreciate it. Great to meet you and have you on theCUBE. Great conversation here, it's theCUBE. I'm John Furrier. Stu Miniman breaking down all the top coverage of VMworld 2018 getting the input and the commentary from industry legends and also key leaders in the innovation cloud networking. This is theCUBE. Stay with us for more after this short break. [Technical Music]

>> Live from San Francisco, it's The Cube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello, everyone. Welcome back to our live coverage. It's The Cube here, exclusive coverage of Google Cloud, Google Next 2018. I'm John Furrier, co-host with Dave Vellante, both co-founders of The Cube and SiliconANGLE, here with our special guest Diane Greene, who's the CEO of Google Cloud, legend in the industry, former CEO of VMware, among other great things. Diane, great to see you, great to have you on The Cube for the first time. >> Really fun to be here, I'm really happy to be here. >> One of the things about Google Cloud that's interesting that we've been observing is, you mentioned on stage, two years now in, you're starting to see some visibility into what Google Cloud is looking to do. They're looking to make things really easy, fast, and very developer-centric, an open source culture of inclusion, culture of openness, but hardcore performance. Talk about that vision and how that's translating as you're at the helms driving the big boat here. >> Yeah, sure. Obviously we had this amazing foundation with our modern enterprise company, Google Cloud. But what we've done with Google Cloud is we've realized that Google values engineering so much, and so do our customers. So one is, we're taking a very engineering-centric approach. People really love our open source philosophy. And then we're so double down on both security and artificial intelligence. So if you have this underlying, incredibly advanced, scaled infrastructure, high performance, security, availability, and all the goodness, and then you start taking people somewhere where they can really take advantage of AI, where they can be more secure than anywhere else and you have the engineering to help them really exploit it and to listen to the customer, it's about where they want to go, we're just getting incredible results. >> I've been following Google since the founders, Sergey and Larry, started it, it's been fun to watch. They really are the biggest Cloud ever to be built and Facebook certainly has built-- >> We have seven applications that have over a billion active users. >> Massive scale-- >> And actually, we're just this week on track to have the next one drive. >> 25 years of expertise. I've seen them move from buying servers to making their own, better airflow, just years and years of trajectory, of economies of scale, and then when Google started The Cloud a couple years ago, it's like, oh, great, everyone wants to be like Google so we'll just offer our Googleness to everyone and they're like wait, that didn't really work. People want to consume what Google has, not necessarily be Google, because not everyone can be Google. So there's a transition where Google's massive benefits are now being presented and sold, or offered as a service. This is a core strategy. What should people know about? Because people are squinting through all this market share, this company's got more revenue than that one, and if I bundle in AdWords and G Suite, you'd be the number one Cloud provider on the planet by far. So buyers are trying to figure out who's better for what. How do you talk to customers if someone says, are you behind, are you winning, how do I know if Google Cloud is better than the other Cloud? >> Well, the only way you're going to know is to kind of do a proof of concept and see what happens, you know, pull back the covers. But what we can explain to people is that we're so... One is that it's all about information. That's why I say Google's a modern enterprise company because we're about it. I said that in my keynote. We take information, we organize it, and we supercharge it. We give a lot of intelligence to it and that's what every business needs to do, and we're the best in the world at it. And then AI is this revolutionary thing going on where you can just apply it to anything. Someone made a joke about Cloud, they said it's like butter, it's better with everything. Well, The Cloud is better with everything. I think it's AI, actually. So when you combine our ability to manage data, our ability to do artificial intelligence, with our open source and then our security, not to mention the fact that the underlying infrastructure is, everybody pretty much acknowledges the most advanced technology in the world, it's a pretty unbeatable competition, I mean combination. But the thing is, we needed to bring it to market in a way that everybody could trust it and use it. One of the first things we did, which we hadn't had to do, is serving our internal customers. Have roadmaps, so customers can know what's going on, and what's coming when, that we won't ever turn something off, and all those things that an enterprise company expects and needs, for good reason. I have to say, our engineering team is loving working with external customers. Everybody said, you'll never get that engineering team caring about customers. And I knew we would because we had the same quality engineers at VMware and they loved it. And I knew it was just a matter of getting everybody to see how many interesting things that we-- >> And it's problems to solve, by the way, too. >> There's so many problems to solve and we're having even broader impact now, going to the enterprise, going to every company. >> You said in your keynote, IT is no longer a cost center, it's a key driver of business. Tech is now at the core of every product. You go back 15 years, I remember somebody said to me, have you seen what VMware can do and how fast it can spin up a server? That was cost, right? >> Yeah. >> Talk about the enterprise today. When you talk to customers, what are those problems they're solving, what are those opportunities? >> There is a class of customers, typically the internet companies, they are looking for the best infrastructure, they are looking to save cost, but they're also looking, you know, are people realizing, why should I do it all? Why don't I concentrate on my core competence? It's well known we've had Snap from day one and we were in their prospectus when they filed to go public. Then we have Twitter, we recently announced Spotify, and so forth. So those are very technically sophisticated. People, they come, they use BigQuery, they use our data analytics and our infrastructure. But then you get into the businesses, and we've taken this completely verticals approach. So they're coming to solve whatever problems it is they have. And because we have these exceptional tools and we're building platform tools, a lot of them with applied AI in every vertical, they can come to us and we can talk to them in their language and solve their problems. I talked about it in my keynote, with IT driving revenue, everybody's re-engineering how they do business. It's the most exciting time I've ever seen in the enterprise. I mean, I've always though tech was interesting, but now, it's the whole world. >> It's everywhere. You have an engineeering background, you went to MIT, studied there. If you were the lead engineer of most of these companies that are re-architecting and re-engineering, they're almost re-platforming their companies. They're allowed to think differently, it's not just an IT purchase, because they're not buying IT anymore, they're deploying platforms. >> And they're digitizing their whole business. They're using their information, they're using their data. That changes so many business processes. It changes what they can do with their customers, how they can talk to them, it changes how they can deliver anything. So it's just a radical rethink of... It's so amazing when we work deeply with the customer because they might start out talking about infrastructure and how they're going to move to The Cloud and how we can help them, and then we start talking about all the things our technologies can do for them and what's possible. And they'll kind of pause and then they'll come back and they'll go, holy cow, we are rethinking our whole company, we are redefining our mission, we're much more, you know, it's very exciting. >> I had a chance to interview some of your employees and the phrase comes up, kid in the candy store a lot. So I've got to ask you, with respect to customers, is there more of an engineering focus? As you see some of the adoption, you mentioned Twitter, Spotify, these are internet companies, these are nerds, they love to geek out, they know large scale, so not a hard sell to get them over the transom into the scale of The Cloud. As you get to the enterprise, is there a makeup, is their an orientation that attracts Google to them, and why are you winning these deals? Is it the tech, the people, the process, obviously the tech's solid, but-- >> It's a combination of all of the above. What'll happen is we'll all come in and start pitching these companies, and what we do, we really understand what they're trying to do. And then we send in the appropriate engineers for what it is they're trying to do. You get this engineer-to-engineer collaboration going that lets us know exactly how to help that company. >> They give you a list and you go, check, I've done that. Okay, next, check, check, you go down the checkbox, or is it-- >> Well, we brainstorm with them, and companies like that, because they don't necessarily understand all the technology. I always like to think what an engineering orgs does is one, it gets requirements from the customers about what they need, and we call that all the table stakes, and we get it done, and some of it's pretty hard to do. But then, the engineers, after they get to know customers, they can invent things that the customer had no idea was possible, but that solves their problem in a much more powerful way. And so, that's the magic. And that's how we're going into the market. Wherever we can, we'll take things and make it available to everybody. We're very, you know, that open source philosophy of all technology is for everybody, and it's a very nice environment to work in. >> The number one sound John and I have been talking all day about in your keynote was, security's the number one worry, AI is the number one opportunity. >> I was writing my keynote and it hit me. I'm like, oh, this is how it is. >> So please, when you talk to customers, how are you addressing that worry, and how are you addressing the opportunity? >> We're pretty proud of our security because it really is, at every layer, very deeply integrated, thought through. We don't think in terms of a firewall because if you get inside that firewall, all bets are off. So it's really everything you do needs to be looked at and you've got to make sure, and that's why the Chromebook with the hardware based two-factor authentication, and G Suite. Google, which went to that, and since we did, not a single one of our 85,000 employees have been phished. Kind of amazing. >> Yeah. >> Because it's the biggest source of attack. >> Ear phishing is the easiest way to get in. >> Yeah, but you cannot do it once you have that combination. It's all the way up there, all the way down to proprietary chips that check that the boot hasn't been tampered with every time you boot. Our new servers all have it, our Chromebooks all have it, and then everything in between. We think we have an incredibly powerful, we had to add in enterprise features like fine-grain security controls, ways to let our users manage their own encryption keys. But anyhow, we have just at a really phenomenal, and our data centers are so bulletproof. We have those catchers that'll pick up a car. We even have one of those. We had a UPS truck try and tailgate someone and got picked up in it. >> The magic of the engineering at Google. This is the value that we hear from customers, is that, we get that the technology and the engineers are there, we see the technology. But you've been involved in transformative businesses, beyond where Dave was mentioning, certainly changed IT. And it was new and transformed. Cloud's transformational as well. We were just talking earlier about the metaphor of the horse and buggy versus the car, things get automated away, which means those jobs now are gone, but new functionality. You're seeing a lot of automation machine learning, AutoML is probably one of the hottest trends going on right now. AI operations seems to be replacing what was categorically an industry, IT operations. You're starting to see IT again being disrupted. And the shifting into the value up the stack. And this is developers. >> That's the point. Because I don't feel like, yes, all those really painful jobs are going away. >> That no one wanted to do. >> That no one wanted to do anyhow. VMware was the same way. We eliminated tons of drudgery. And AI is doing it systematically across every industry but then you repurpose people. Because we still need so many people to do things. I gave the example in my keynote about the dolphin fins and using AutoML to find them and identify them. Well, that was PhD researchers and professors were looking at that. Is that what they should be doing? I don't think so. You free them up and think of the discoveries they're going to make. I mean, humans are really smart. I think all humans are, we just have to do a better job at helping them realize their potential. >> I want to talk about that, that's a great point. Culture's everything. I also interviewed some of your folks. I just wrote an article on my Forbes column about the four most powerful women in Google that aren't Diane Greene. It was some of your key lieutenants. >> That was a great piece. >> The human story came up, where you have machines and humans working together. One of the conversations was, artistry is coming back to software development. We were on this thread of modern software developers is not just your software engineer anymore. You don't need three PhDs to write code. The aperture of software development engineering and artistry and craft is coming back. What's your reaction to that? Because you're starting to see now a new level of range of software opportunities for everybody. >> Yeah, my daughter is a computer science major and she just taught at coding camp this summer, and they started from kindergarten and went up. It was amazing to hear what those kids were doing. I think a lot of applications are almost going to be like assembling lego. You have all these APIs you can put in, you have all these open source libraries, you have Serverless, so you just plop it in these little containers, and everything is taken care of for you. You're right, it's like a new age in building applications. You will still need, Google needs systems engineers but-- >> Under the hood, you've got to fix engines, mechanics. >> You guys talked about this in your article, the shifts toward creativity becomes a much more important ingredient. >> And also the human computer interface and the UX. You heard from Target, I was talking to him, they do an agile workshop for six weeks for all their developers. Their productivity, he said, an order of magnitude higher. I think the productivity of developers, in The Cloud, with all these technologies, is across the board, an order of magnitude better, at a minimum. >> Mike McNamara, the CIO of Target, was up on stage with you today. >> Yeah, he's a really impressive person. >> So I want to ask you about differentiation. You talked about open source, and specifically your contribution to open source, that's different from most Cloud players. The other thing you talked about, and I want to understand it better, is that you provide consistency with a common core set of primitives. What do you mean, and why is that important? >> Right. So when we build out all our services, we want to have one uniform way of thinking about things. So, how do you do queueing? It's common across every service. How do you do security? It's common across every service. Which means it's very intuitive and it's easy to use this system. Now, it slows you down. Software development at that layer, when you have to do that, goes more slowly. And if you have to make a change, you know, in a core primitive, everybody's got to change, right? However, you take the other side, where everybody just builds a service vertically and with disregard for how things are done, and now you've got this potpourri of ways to do things and everybody has to have specialized expertise in every service. So it really slows down the operators and the developers. You get a lot of inconsistency. So it's super high value and I have to believe people are going to start appreciating that and it's really going to be-- >> I think that's a huge problem that people don't really understand. Just as an example, if you're building out a data pipeline and tapping all these different services, you've got then different APIs for every single service that you have to become an expert at. >> That's exactly right. >> That's a real challenge. Like you said, from a software development-- >> And it's annoying. >> Yes, users who really understand this stuff are getting annoyed with it. But it's an interesting trade-off and a philosophy that you've taken that's quite a bit different from-- >> Well, Google has such a high bar for how they do things. >> That sounds foundational though. It's slower, but it's more foundational. But doesn't that accelerate the value? So the value's accelerated significantly-- >> Oh yes. >> So you go a little slower down. >> Our going a little slower makes everybody else go way faster, at a higher quality. The trade-off, it wins. >> Diane, thank you for taking the time to join us in The Cube today. >> I want to ask one final question. Culture in Google Cloud, how would you describe the DNA within Google Cloud? A lot of energy, a lot of enterprise expertise coming in big time, a lot of great stuff happening. How would you describe the DNA of Google Cloud? >> I would say just tremendous excitement because we're just moving so fast, we're scaling so fast, we're sort of barely in control, it's moving so fast. But such good things happening and the customers are loving us. It's so rewarding and everybody's increasingly taking more and more ownership and really making sure that we do super high quality work for our customers. Everybody's proud, we're all really proud. >> What's the one thing that you want people to know about that they may not know about Google Cloud, that they should definitely know about? >> Geez, you know, it's worth coming to and giving it a try. The biggest thing is how early we are, and it's the right place to be because you want the highest quality, you want the most advanced technology. And AI and security are pretty important. >> Diane Greene, the CEO of Google Cloud here inside The Cube, live in San Francisco. We're at the Moscone Center. I'm John Furrier with Dave Vellante. We'll be back with more live coverage. Stay with us for more from day one of three days of live coverage. We'll be right back.

>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)