>>Hey, welcome back everyone. We're live here in the cube in Las Vegas Raiders reinvent 2021. I'm Jeffrey hosted the key we're in person this year. It's a hybrid event online. Great action. Going on. I'm rolling. Vice-president of ADF analytics. David is great to see you. Thanks for coming on. >>It's great to be here, John. Thanks for having me again. >>Um, so you've got a really awesome job. You've got serverless, you've got analytics. You're in the middle of all the action for AWS. What's the big news. What are you guys announcing? What's going on? >>Yeah, well, it's been an awesome reinvent for us. Uh, we've had a number of several us analytics launches. So red shift, our petabyte scale data warehouse, EMR for open source analytics. Uh, and then we've also had, uh, managed streaming for Kafka go serverless and then on demand for Kinesis. And then a couple of other big ones. We've got RO and cell based security for AWS lake formation. So you can get really fine grain controls over your data lakes and then asset transactions. You can actually have a inserts, updates and deletes on data lakes, which is a big step forward. >>Uh, so Swami on stage and the keynote he's actually finishing up now. But even last night I saw him in the hallway. We were talking about as much as about AI. Of course, he's got the AI title, but AI is the outcome. It's the application of all the data and this and a new architecture. He said on stage just now like, Hey, it's not about the old databases from the nineties, right? There's multiple data stores now available. And there's the unification is the big trend. And he said something interesting. Governance can be an advantage, not an inhibitor. This is kind of this new horizontally scalable, um, kind of idea that enables the vertical specialization around machine learning to be effective. It's not a new architecture, but it's now becoming more popular. People are realizing it. It's sort of share your thoughts on this whole not shift, but the acceleration of horizontally scalable and vertically integrated. Yeah, >>No, I think the way Swami put it is exactly right. What you want is the right tool for the right job. And you want to be able to deliver that to customers. So you're not compromising on performance or functionality of scale, but then you wanted all of these to be interconnected. So they're, well-integrated, you can stay in your favorite interface and take advantage of other technologies. So you can have things like Redshift integrated with Sage makers, you get analytics and machine learning. And then in Swami's absolutely right. Governance is actually an enabler of velocity. Once you've got the right guardrails in place, you can actually set people free because they can innovate. You don't have to be in the way, but you know that your data is protected. It's being used in the way that you expect by the people that you are allowing to use that data. And so it becomes a very powerful way for customers to set data free. And then, because things are elastic and serverless, uh, you can really just match capacity with demand. And so as you see spikes in usage, the system can scale out as those dwindle, they can scale back down, and it just becomes a very efficient way for customers to operate with data at scale >>Every year it reinvented. So it was kind of like a pinch me moment. It's like, well, more that's really good technology. Oh my God, it's getting easier and easier. As the infrastructure as code becomes more programmable, it's becoming easier, more Lambda, more serverless action. Uh, you got new offerings. How are customers benefiting for instance, from the three new offerings that you guys announced here? What specifically is the value proposition that you guys are putting out there? Yeah, so the, >>Um, you know, as we've tried to do with AWS over the years, customers get to focus on the things that really differentiate them and differentiate their businesses. So we take away in Redshift serverless, for example, all of the work that's needed to manage clusters, provision them, scale them, optimize them. Uh, and that's all been automated and made invisible to customers, the customers to think about data, what they want to do with it, what insights they can derive from it. And they know they're getting the most efficient infrastructure possible to make that a reality for them with high performance and low costs. So, uh, better results, more ability to focus on what differentiates their business and lower cost structure over time. >>Yeah. I had the essential guys on it's interesting. They had part of the soul cloud. Continuous is their word for what Adam was saying is clouds everywhere. And they're saying it's faster to match what you want to do with the outcomes, but the capabilities and outcomes kind of merging together where it's easy to say, this is what we want to do. And here's the outcome it supports that's right with that. What are some of the key trends on those outcomes that you see with the data analytics that's most popular right now? And kind of where's that, where's that going? >>Yeah. I mean, I think what we've seen is that data's just becoming more and more critical and top of mind for customers and, uh, you know, the pandemic has also accelerated that we found that customers are really looking to data and analytics and machine learning to find new opportunities. How can they, uh, really expand their business, take advantage of what's happening? And then the other part is how can they find efficiencies? And so, um, really everything that we're trying to do is we're trying to connect it to business outcomes for customers. How can you deepen your relationship with your customers? How can you create new customer experiences and how can you do that more efficiently, uh, with more agility and take advantage of, uh, the ability to be flexible. And you know, what is a very unpredictable world, as we've seen, >>I noticed a lot of purpose-built discussion going on in the keynote with Swami as well. How are you creating this next layer of what I call purpose-built platform like features? I mean, tools are great. You see a lot of tools in the data market tools are tools of your hammer. You want to look for a nail. We see people over by too many tools and you have ultimately a platform, but this seems to be a new trend where there's this connect phenomenon was showing me that you've got these platform capabilities that people can build on top of it, because there's a huge ecosystem of data tools out there that you guys have as partners that want to snap together. So the trend is things are starting to snap together, less primitive, roll your own, which you can do, but there's now more easier ways. Take me through that. Explain that, unpack that that phenomenon role rolling your own firm is, which has been the way now to here. Here's, here's some prefabricated software go. >>Yeah. Um, so it's a great observation and you're absolutely right. I mean, I think there's some customers that want to roll their own and they'll start with instances, they'll install software, they'll write their own code, build their own bespoke systems. And, uh, and we provide what the customers need to do that. But I think increasingly you're starting to see these higher level abstractions that take away all of that detail. And mark has Adam put it and allow customers to compose these. And we think it's important when you do that, uh, to be modular. So customers don't have to have these big bang all or nothing approaches you can pick what's appropriate, uh, but you're never on a dead end. You can always evolve and scale as you need to. And then you want to bring these ideas of unified governance and cohesive interfaces across so that customers find it easy to adopt the next thing. And so you can start off say with batch analytics, you can expand into real time. You can bring in machine learning and predictive capabilities. You can add natural language, and it's a big ecosystem of managed services as well as third parties and partners. >>And what's interesting. I want to get your thoughts while I got you here, because I think this is such an important trend and historic moment in time, Jerry chin, who one of the smartest VCs that we know from Greylock and coin castles in the cloud, which kind of came out of a cube conversation here in the queue years ago, where we saw the movement of that someone's going to build real value on AWS, not just an app. And you see the rise of the snowflakes and Databricks and other companies. And he was pointing out that you can get a very narrow wedge and get a position with these platforms, build on top of them and then build value. And I think that's, uh, the number one question people ask me, it's like, okay, how do I build value on top of these analytic packages? So if I'm a startup or I'm a big company, I also want to leverage these high level abstractions and build on top of it. How do you talk about that? How do you explain that? Because that's what people kind of want to know is like, okay, is it enabling me or do I have to fend for myself later? This is kind of, it comes up a lot. >>That's a great question. And, um, you know, if you saw, uh, Goldman's announcement this week, which is about bringing, building their cloud on top of AWS, it's a great example of using our capabilities in terms of infrastructure and analytics and machine learning to really allow them to take what's value added about Goldman and their position to financial markets, to build something value, add, and create a ton of value for Goldman, uh, by leveraging the things that we offer. And to us, that's an ideal outcome because it's a win-win for us in Goldman, but it's also a win for Goldman and their customers. >>That's what we call the Supercloud that's the opportunity. So is there a lot of Goldmans opportunities out there? Is that just a, these unicorns, are these sites? I mean, how do you, I mean, that's Goldman Sachs, they're huge. Is there, is this open to everybody? >>Absolutely. I mean, that's been one of the, uh, you know, one of the core ideas behind AWS was we wanted to give anybody any developer access to the same technology that the world's largest corporations had. And, uh, that's what you have today. The things that Goldman uses to build that cloud are available to anybody. And you can start for a few pennies scale up, uh, you know, into the petabytes and beyond >>When I was talking to Adams, Lipski when I met with him prior to re-invent, I noticed that he was definitely had an affinity towards the data, obviously he's Amazonia, but he spent time at Tableau. So, so as he's running that company, so you see that kind of mindset of the data advantage. So I have to ask you, because it's something that I've been talking about for a while and I'm waiting for it to emerge, but I'm not sure it's going to happen yet. But what infrastructure is code was for dev ops and then dev sec ops, there's almost like a data ops developing where data as code or programmable data. If I can connect the dots of what Swami's saying, what you're doing is this is like a new horizontal layer of data of freely available data with some government governance built in that's right. So it's, data's being baked into everything. So data is any ingredient, not a query to some database, it's gotta be baked into the apps, that's data as code that's. Right. So it's almost a data DevOps kind of vibe. >>Yeah, no, you're absolutely right. And you know, you've seen it with things like ML ops and so on. It's all the special case of dev ops. But what you're really trying to do is to get programmatic and systematic about how you deal with data. And it's not just data that you have. It's also publicly available data sets and it's customers sharing with each other. So building the ecosystem, our data, and we've got things like our open data program where we've got publicly hosted data sets or things like the AWS data exchange where customers can actually monetize data. So it's not just data as code, but now data as a monetizeable asset. So it's a really exciting time to be in the data business. >>Yeah. And I think it's so many too. So I've got to ask you while I got you here since you're an expert. Um, okay. Here's my problem. I have a lot of data. I'm nervous about it. I want to secure it. So if I try to secure it, I'm not making it available. So I want to feed the machine learning. How do I create an architecture where I can make it freely available, but yet maintain the control and the comfort that this is going to be secure. So what products do I buy? >>Yeah. So, uh, you know, a great place to start at as three. Um, you know, it's one of the best places for data lakes, uh, for all the reasons. That's why we talked about your ability scale costs. You can then use lake formation to really protect and govern that data so you can decide who's allowed to see it and what they're allowed to see, and you don't have to create multiple copies. So you can define that, you know, this group of partners can see a, B and C. This group can see D E and F and the system enforces that. And you have a central point of control where you can monitor what's happening. And if you want to change your mind, you can do that instantly. And all access can be locked down that you've got a variety of encryption capabilities with things like KMS. And so you can really lock down your data, but yet keep it open to the parties that you want and give them specifically the access that you want to give them. And then once you've done that, they're free to use that data, according to the rules that you defined with the analytics tools that we offer to go drive value, create insight, and do something >>That's lake formation. And then you got a Thena querying. Yes, we got all kinds of tooling on top of it. >>It's all right. You can have, uh, Athena query and your data in S3 lake formation, protecting it. And then SageMaker is integrated with Athena. So you can pull that data into SageMaker for machine learning, interrogate that data, using natural language with things like QuickSight Q a like we demoed. So just a ton of power without having to really think too deeply about, uh, developing expert skill sets in this. >>So the next question I want to ask you is because that first part of the great, great, great description, thank you very much. Now, 5g in the edges here, outpost, how was the analytics going on that as edge becomes more pervasive in the architecture? >>Yeah, it's going to be a key part of this ecosystem and it's really a continuum. So, uh, you know, we find customers are collecting data at the edge. They might be making local ML or inference type decisions on edge devices, or, you know, automobiles, for example. Uh, but typically that data with some point will come back into the cloud, into S3 will be used to do heavy duty training, and then those models get pushed back out to the edge. And then some of the things that we've done in Athena, for example, with federated query, as long as you have a network path, and you can understand what the data format or the database is, you can actually run a query on that data. So you can run real-time queries on data, wherever it lives, whether it's on an edge device, on an outpost, in a local zone or in your cloud region and combine all of that together in one place. >>Yeah. And I think having that data copies everywhere is a big thing deal. I've got to ask you now that we're here at reinvent, what's your take we're back in person last year was all virtual. Finally, not 60,000 people, like a couple of years ago, it's still 27,000 people here, all lining up for the sessions, all having a great time. Um, all good. What's the most important story from your, your area that people should pay attention to? What's the headline, what's the top news? What should people pay attention to? >>Yeah, so I think first off it is awesome to be back in person. It's just so fun to see customers and to see, I mean, you, like, we've been meeting here over the years and it's, it's great to so much energy in person. It's been really nice. Uh, you know, I think from an analytics perspective, there's just been a ton of innovation. I think the core idea for us is we want to make it easy for customers to use the right tool for the right job to get insight from all of their data as cost effectively as possible. And I think, uh, you know, I think if customers walk away and think about it as being, it's now easier than ever for me to take advantage of everything that AWS has to offer, uh, to make sense of all the data that I'm generating and use it to drive business value, but I think we'll have done our jobs. Right. >>What's the coolest thing that you're seeing here is that the serverless innovation, is it, um, the new abstraction layer with data high level services in your mind? What's the coolest thing. Got it. >>It's hard to pick the coolest that sticks like kicking the candies. I mean, I think the, uh, you know, the continued innovation in terms of, uh, performance and functionality in each of our services is a big deal. I think serverless is a game changer for customers. Uh, and then I think really the infusion of machine learning throughout all of these systems. So things like Redshift ML, Athena ML, Pixar, Q a just really enabling new experiences for customers, uh, in a way that's easier than it ever has been. And I think that's a, that's a big deal and I'm really excited to see what customers do with it. >>Yeah. And I think the performance thing to me, the coolest thing that I'm seeing is the graviton three and the gravitron progression with the custom stacks with all this ease of use, it's just going to be just a real performance advantage and the costs are getting lowered. So I think the ECE two instances around the compute is phenomenal. No, >>Absolutely. I mean, I think the hardware and Silicon innovation is huge and it's not just performance. It's also the energy efficiency. It's a big deal for the future reality. >>We're at an inflection point where this modern applications are being built. And in my history, I'm old, my birthday is today. I'm in my fifties. So I remember back in the eighties, every major inflection point when there was a shift in how things were developed from mainframe client server, PC inter network, you name it every time the apps change, the app owners, app developers all went to the best platform processing. And so I think, you know, that idea of system software applications being bundled together, um, is a losing formula. I think you got to have that decoupling large-scale was seeing that with cloud. And I think now if I'm an app developer, whether whether I'm in a large ISV in your ecosystem or in the APN partner or a startup, I'm going to go with my software runs the best period and where I can create value. That's right. I get distribution, I create value and it runs fast. I mean, that's, I mean, it's pretty simple. So I think the ecosystem is going to be a big action for the next couple of years. >>Absolutely. Right. And I mean, the ecosystem's huge and I think, um, and we're also grateful to have all these partners here. It's a huge deal for us. And I think it really matters for customers >>What's on your roadmap this year, what you got going on. What can you share a little bit of a trajectory without kind of, uh, breaking the rules of the Amazonian, uh, confidentiality. Um, what's, what's the focus for the year? What do you what's next? >>Well, you know, as you know, we're always talking to customers and, uh, I think we're going to make things better, faster, cheaper, easier to use. And, um, I think you've seen some of the things that we're doing with integration now, you'll see more of that. And, uh, really the goal is how can customers get value as quickly as possible for as low cost as possible? That's how we went to >>Yeah. They're in the longterm. Yeah. We've always say every time we see each other data is at the center of the value proposition. I've been saying that for 10 years now, it's actually the value proposition, powering AI. And you're seeing because of it, the rise of superclouds and then the superclouds are emerging. I think you guys are the under innings of these emerging superclouds. And so it's a huge treading, the Goldman Sachs things of validation. So again, more data, the better, sorry, cool things happening. >>It is just it's everywhere. And the, uh, the diversity of use cases is amazing. I mean, I think from, you know, the Australia swimming team to, uh, to formula one to NASDAQ, it's just incredible to see what our >>Customers do. We see the great route. Good to see you. Thanks for coming on the cube. >>Pleasure to be here as always John. Great to see you. Thank you. Yeah. >>Thanks for, thanks for sharing. All of the data is the key to the success. Data is the value proposition. You've seen the rise of superclouds because of the data advantage. If you can expose it, protect it and govern it, unleashes creativity and opportunities for entrepreneurs and businesses. Of course, you got to have the scale and the price performance. That's what doing this is the cube coverage. You're watching the leader in worldwide tech coverage here in person for any of us reinvent 2021 I'm John ferry. Thanks for watching.

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah, I know. You know, we have We've come a long way in terms of workload execution, right? In terms of scale and performance and, you know, concurrent execution. We really taking the lid off sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data. Essentially, you know, data is locked in applications. It's locked in data centers that's locked in cloud cloud regions incredibly hard for for data science teams to really, you know, unlocked the true value of data. When you when you can address patterns that that exists across data set. So we're perpetuate, Ah, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's It's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data cloud mentality as well as a workload orientation towards towards managing data. Yeah, >>Anita is great here in your role at Disney, and you're in your keynote and the work. You're doing the governance work, and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure. I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data. Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on. They're no longer required. Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I followed you for a number of years. You know, your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering, you know why that was. And and the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data outside of Salesforce. You know, whether it's adobe or any other marketing data set. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, no, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Venter Day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? People are recognizing, you know, why does this matters now? It's not gonna happen overnight, And there's a step global function of very big change in mentality and orientation. You know, >>it's almost as though the SAS ification of our industries sort of repeated some of the application silos, and you build a hardened top around it. All the processes are hardened around it, and Okay, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey, and maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about, at least my inference in your your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely. So we focus on ensuring compliance across all the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that we're able Thio do this. We don't We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products. So then the platforms and platforms even involving into ecosystems. And then there's this ecosystem of of data. You guys both talked a lot about data sharing. But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, many, many years ago, you know, I saw the first glimpse off, uh, multidimensional databases that were used for reporting. Really, On IBM mainframes on debt was extraordinarily difficult. We didn't even have the words back then. In terms of data, warehouses and business. All these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which, really, you know, set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different. Right? So I I've lived the pain off this problem on sort of been had a front row seat to watching this This transpire over a very long period of time. And that's that's one of the reasons um, you know why I'm here? Because I finally seen, you know, a glimpse off, you know, also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've We've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. Way we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that air notable since you're really applied your your love of data and maybe maybe touch on culture, your data culture. You know any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and and some of the impacts that I've seen I mean, I think with the advent of the cloud, you know before, Well, how do I say that before the cloud actually became, you know, so prevalent in such a common part of the strategy that's required? It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's It's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time, you decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But you're I love the inside baseball. It's just awesome. Eso really appreciate that. So But why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, we thought it was an interesting tale to tell for anybody you know who is interested in, You know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative and context is on. Do you know we thought books titled The Rise of the Data Cloud. That's exactly what it ISS and We're trying to make the case for that mindset, that mentality, that strategy. Because all of us, you know, I think is an industry or were risk off persisting, perpetuating, You know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, you know, there is an enormous opportunity out there, The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal, guest. And what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the Snowflake Data Cloud Summit on the Cube.

>> Hello everybody. And welcome back to, theCUBE coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the Chairman and CEO of Snowflake and Anita Lynch, the Vice President of data governance at Disney streaming services. Folks Welcome. >> Thank you >> Thanks for having us Dave. >> Anita Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar movie soul. I haven't gotten it to the Mandalorian yet, your favorite. But really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the Data Cloud, because I never liked the term enterprise data warehouse. What you're doing is so different from the sort of that legacy world that I've known all these years. But start with why the Data Cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah, you know, we have a, we've come a long way in terms of workload execution. Right? In terms of scale and performance, and concurrent execution. We've really taken the lid off, sort of the physical constraints that have existed on these type of operations. But there's one problem that we're not yet solving, and that is the siloing and bunkering of data. And essentially, data is locked in applications, it's locked in data centers, it's locked in cloud, cloud regions. Incredibly hard for data science teams to really unlock the true value of data, when you can't address patterns that exist across data sets. So where we perpetuate a status we've had for forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, "Look folks, we have to start on siloing and unlocking the data, and bring it into a place, where we can access it across all these perimeters, and boundaries that have historically existed. It's very much a step level function. Like the customers have always looked at things, one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita, it was great hearing your role at Disney and in your keynote, and the work you're doing, the governance work. and you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. Maybe you can expand on some of these initiatives here, and share what you're seeing as some of the biggest challenges to success, and of course, the opportunities that you're unlocking. >> Sure. In my role leading data governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them. They can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture simply because you often have different teams at different periods and time trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders. There are time constraints and quite often, it's not always clear how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos is allow individuals to see upfront how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I'm a copy Creek because it'd be the silent killer. Frank I followed you for a number of years, you're a big thinker, you and I have had a lot of conversations about the near-term, mid-term and long-term, I wonder if you could talk about, in your keynote you're talking about eliminating silos and connecting across data sources. Which is really powerful concept but really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to, Salesforce, you know, they had discovered that we were a top three destination of Salesforce data and they were wondering why that was, and the reason is of course, that people take Salesforce data push it to snowflake because they want to overlay it with what data outside of Salesforce. Whether it's Adobe or any other marketing dataset. And then they want to run very highly scaled processes on it. But the reflexes in the world of SaaS is always like no, we're an Island, we're a planet down to ourselves. Everybody needs to come with us, as opposed to we go to a different platform to run these types of processes. It's no different for the public cloud vendor. They didn't only, they have massive moats around their storage to really prevent data from leaving their orbit. So there is natural friction in terms for this to happen. But on the other hand there is an enormous need. We can't deliver on the power and potential of data unless we allow it to come together. Snowflake is the platform that allows that to happen. We were pleased with our relationship with Salesforce because they did appreciate why this was important and why this was necessary. And we think, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come, right. When people are recognizing why this matters now. It's not going to happen overnight. It is a step while will function a very big change in mentality and orientation. >> Yeah. It's almost as though the the SaaS suffocation of our industry sort of repeated some of the application silos and you build a hardened top around it, all the processes are hardened around it and okay, here we go. And you're really trying to break that, aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so it's so important. It's the first role in your title and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean, we saw this in the early days of big data where governance was just afterthought. It was like bolted on the kind of wild wild West. I'm interested in your governance journey. And maybe you can share a little bit about what role snowflake has played there in terms of supporting that agenda and kind of what's next on that journey. >> Sure. Well, I've led data teams in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance and what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about at least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera. You've taken care of all that it's sort of transparent to them. Is that right?| >> Yea That's right absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring that we're able to do this. we don't do it alone. But governance includes not just the compliance and the privacy, it's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these are really important components of our strategy. >> I got a...So I have a question maybe each of you can answer. I sort of see this, our industry moving from products, to then, to platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing but maybe Frank, you can start, Anita you can add on to Frank's answer. You're obviously both passionate about the use of data and trying to do so in a responsible way. That's critical but it's also going to have business impact. Frank, where's this passion come from on your side. And how are you putting into action in your own organization? >> Well, you know I'm really going to date myself here, but many, many years ago, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data warehouses and business. All these terms didn't exist. People just knew that they wanted to have a more flexible in way of reporting and being able to pivot data dimensionally and all these kinds of things. And I just bought whatever this predates windows 3.1, which really, set off the whole sort of graphical, way of dealing with systems which there's now a whole generations of people that don't know any different right? So I've lived the pain of this problem and sort of had a front row seat to watching this transpire over a very long period of time. And that's one of the reasons, why I'm here, because I finally seen, a glimpse of, I also, as an industry fully, fully just unleashing and unlocking to potential. We're now in a place where the technology is ahead of people's ability to harness it. Which we've never been there before. It was always like, we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just, their heads are spinning with what's now possible, which is why you see markets evolve, very rapidly right now we were talking earlier about how you can't take past definitions and concepts and apply them to what's going on in the world. because the world's changing right in front of your eyes right now. >> So Anita maybe you could add on to what Frank just said and share some of the business impacts and outcomes that are notable since you've really applied your your love of data and maybe, maybe touch on, on culture. Data culture, any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah sure. I think for me, I fell in love with technology first and then I fell in love with data. And I fell in love with data because of the impact that data can have on both the business and the technology strategy. And so it's sort of that nexus, between all three. And in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud, before, well, how do I say that. Before the cloud actually became so prevalent and such a common part of the strategy that's required it was so difficult, you know, so painful. It took so many hours to actually be able to calculate the volumes of data that we had. Now we have that accessibility, and then on top of it, with the snowflake Data Cloud it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate. And really, truly understand our customer's interests and their preferences and then tailor the experiences that they really want and deserve for them. It's, been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you this. So in your spare time you decided to write a book, I'm loving it. I don't have a signed copy so I'm going to have to send it back and have you sign it. But, and you're, I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons, obviously we thought of as an interesting tale to tell for anybody, who is interested in what's going on, how did this come about? Who are the characters behind the scenes and all this stuff. But from a business standpoint because this is such a step function it's so non incremental, we felt like, we really needed quite a bit of real estate to really lay out what the full narrative and context is. And, we thought, the books titled the "Rise of the Data Cloud." That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy because all of us, I think as an industry, were at risk of, persisting, perpetuating where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for a look. There's an enormous opportunity out there but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank, I know you and I are going to talk again Anita, I hope we have a chance to meet face to face and talk in theCUBE live someday. You're phenomenal guests and what a great story. Thank you both for coming on. And thank you for watching. Keep it right there. You're watching the, Snowflake Data Cloud Summit, on theCUBE.

>>Hello, buddy. And welcome back to the cubes. Coverage of Snowflake Data Cloud Summer 2020. We're tracking the rise of the data cloud and fresh off the keynotes. Hear Frank's Luqman, the chairman and CEO of Snowflake, and Anita Lynch, the vice president of data governance at Disney Streaming Services. Folks. Welcome E Need a Disney plus. Awesome. You know, we signed up early. Watched all the Marvel movies. Hamilton, the new Pixar movie Soul. I haven't gotten to the man DeLorean yet. Your favorite, but I really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud because I never liked the term Enterprise Data Warehouse. What you're doing is is so different from the sort of that legacy world that I've known all these years. But start with why the data cloud? What problems are you trying to solve? And maybe some of the harder challenges you're seeing? >>Yeah. You know, we have We've come a long way in terms of workload, execution, right? In terms of scale and performance and concurrent execution. We really taking the lid off. Sort of the physical constraints that that have existed on these types of operations. But there's one problem, uh, that were not yet, uh solving. And that is the silo ing and bunkering of data essentially in the data is locked in applications. It's locked in data centers. It's locked in cloud cloud regions incredibly hard for for data science teams to really unlock the true value of data when you when you can address patterns that that exists across data set. So we're perpetuate, uh, status we've had for for ever since the beginning off computing. If we don't start Thio, crack that problem now we have that opportunity. But the notion of a data cloud is like basically saying, Look, folks, you know, we we have to start inside, lowing and unlocking the data on bring it into a place where we can access it. Uh, you know, across all these parameters and boundaries that have historically existed, it's very much a step level function. Customers have always looked at things won't workload at that time. That mentality really has to go. You really have to have a data club mentality as well as a workload orientation towards towards managing data. >>Anita is great here in your role at Disney and you're in your keynote and the work you're doing the governance work and you're you're serving a great number of stakeholders, enabling things like data sharing. You got really laser focused on trust, compliance, privacy. This idea of a data clean room is really interesting. You know, maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And, of course, the opportunities that you're unlocking. >>Sure, I mean, in my role leading data to governance, it's really critical to make sure that all of our stakeholders not only know what data is available and accessible to them, they can also understand really easily and quickly whether or not the data that they're using is for the appropriate use case. And so that's a big part of how we scale data governance. And a lot of the work that we would normally have to do manually is actually done for us through the data Clean rooms. >>Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos. And I mean, obviously you can relate to that having been in the data business for a while, but I wonder if you could elucidate on that. >>Sure, I mean data complexities air going to evolve over time in any traditional data architecture. Er, simply because you often have different teams at different periods in time trying thio, analyze and gather data across Ah, whole lot of different sources. And the complexity that just arises out of that is due to the different needs of specific stakeholders, their time constraints. And quite often, um, it's not always clear how much value they're gonna be able to extract from the data at the outset. So what we've tried to do to help break down the silos is allow individuals to see up front how much value they're going to get from the data by knowing that it's trustworthy right away. By knowing that it's something that they can use in their specific use case right away, and by ensuring that essentially, as they're continuing to kind of scale, the use cases that they're focused on their no longer required Thio make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world, >>for sure. I mean, copy creep, because it be the silent killer. Frank, I've followed you for a number of years. Your big thinker. You and I have had a lot of conversations about the near term midterm and long term. I wonder if you could talk about you know, when you're Kino. You talk about eliminating silos and connecting across data sources, which really powerful concept. But really only if people are willing and able to connect and collaborate. Where do you see that happening? Maybe What are some of the blockers there? >>Well, there's there's certainly, ah, natural friction there. I still remember when we first started to talk to to Salesforce, you know, they had discovered that we were top three destination off sales first data, and they were wondering why that was. And the reason is, of course, that people take salesforce data, push it to snowflake because they wanna overlay it with what data? Outside of Salesforce, you know, whether it's adobe or any other marketing data set and then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS is always like, No, we're an island were planning down to ourselves. Everybody needs to come with us as opposed to we We go, you know, to a different platform to run these type of processes. It's no different for the for the public club. Better day didn't mean they have, you know, massive moats around there. Uh, you know, their stories to, you know, really prevent data from from leaving their their orbit. Eso there is natural friction in, uh, in terms off for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on on the power and potential of data unless we allow it to come together. Uh, snowflake is the platform that allows that to happen. Uh, you know, we were pleased with our relationship with Salesforce because they did appreciate you know why this was important and why this was necessary. And we think you know, other parts of the industry will gradually come around to it as well. So the the idea of a data cloud has really come, right? Uh, people are recognizing, you know, why does this matter now? It's not gonna happen overnight. There's a step global function of very big change in mentality and orientation. >>Yeah. It's almost as though the SAS ification of our industry sort of repeated some of the application silos and you build a hardened top around it. All the processes are hard around. OK, here we go. And you're really trying to break that, aren't you? Yeah, Exactly. Anita. Again, I wanna come back to this notion of governance. It's so it's so important. It's the first role in your title, and it really underscores the importance of this. Um, you know, Frank was just talking about some of the hurdles, and this is this is a big one. I mean, we saw this in the early days of big data. Where governance was this after thought it was like, bolted on kind of wild, Wild West. I'm interested in your governance journey. And maybe you could share a little bit about what role snowflake has played there in terms of supporting that agenda. Bond. Kind of What's next on that journey? >>Sure. Well, you know, I've I've led data teams in a numerous, uh, in numerous ways over my career. This is the first time that I've actually had the opportunity to focus on governance. And what it's done is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >>Well, I mean a big part of what you were talking about. At least my inference in your talk was really that the business folks didn't have to care about, you know, wonder about they cared about it. But they're not the wonder about and and about the privacy, the concerns, etcetera. You've taken care of all that. It's sort of transparent to them. Is that >>yeah, right. That's right. Absolutely So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical to ensuring, you know, that were ableto do this. We don't we don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access, and it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data. We focus on the infrastructure and making sure that we've architected for scale and all of these air really important components of our strategy. >>I got. So I have a question. Maybe each of you can answer. I I sort of see this our industry moving from, you know, products toe, then two platforms and platforms, even involving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, But maybe Frank, you could start in Anita. You can add on to Frank's answer. You're obviously both both passionate about the use of data and trying to do so in a responsible way. That's critical, but it's also gonna have business impact. Frank, where's this passion come from? On your side. And how are you putting in tow action in your own organization? >>Well, you know, I'm really gonna date myself here, but, you know, uh, many, many years ago, uh, I saw the first glimpse off, uh, multidimensional databases that were used for reporting really on IBM mainframes on git was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and all these terms didn't exist. People just knew that they wanted to have, um, or flexible way of reporting and being able Thio pivot data dimensionally and all these kinds of things. And I just whatever this predates, you know, Windows 3.1, which really set off the whole sort of graphical in a way of dealing with systems which there's not a whole generations of people that don't know any different, Right? So I I've lived the pain off. This problem on sort of had a front row seat to watching this this transpire over a very long period of time. And that's that's one of the reasons you know why I'm here. Because I finally seen a glimpse off. You know, I also as an industry fully fully just unleashing and unlocking the potential were not in a place where the technology is ahead of people's ability to harness it right, which we've never been there before, right? It was always like we wanted to do things that technology wouldn't let us. It's different now. I mean, people are just heads are spinning with what's now possible, which is why you see markets evolved very rapidly right now. We were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on the world. The world's changing right in front of your eyes right now. >>Sonita. Maybe you could add on to what Frank just said and share some of the business impacts and and outcomes that are notable since you're really applied your your love of data and maybe maybe touch on culture, data, culture, any words of wisdom for folks in the audience who might be thinking about embarking on a data cloud journey similar to what you've been on? >>Yeah, sure, I think for me. I fell in love with technology first, and then I fell in love with data, and I fell in love with data because of the impact the data can have on both the business and the technology strategy. And so it's sort of that nexus, you know, between all three and in terms of my career journey and some of the impacts that I've seen. I mean, I think with the advent of the cloud you know before. Well, how do I say that before the cloud actually became, you know, so prevalent and such a common part of the strategy that's required It was so difficult, you know, so painful. It took so many hours to actually be able to calculate, you know, the volumes of data that we had. Now we have that accessibility, and then on top of it with the snowflake data cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have toe have multiple copies of the data. And I think moving beyond some of the traditional mechanisms for for measuring business impact has has only been possible with the volumes of data that we have available to us today. And it's just it's phenomenal to see the speed at which we can operate and really, truly understand our customers, interests and their preferences, and then tailor the experiences that they really want and deserve for them. Um, it's it's been a great feeling. Thio, get to this point in time. >>That's fantastic. So, Frank, I gotta ask you if you're still in your spare time. You decided to write a book? I'm loving it. Um, I don't have a signed copy, so I'm gonna have to send it back and have you sign it. But your love, the inside baseball, it's just awesome. Eso really appreciate that. So but why did you decide to write a book? >>Well, there were a couple of reasons. Obviously, uh, we thought it was an interesting tale to tell for anybody who's interested in, you know what's going on. How did this come about, You know, where the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, because this is such a step function, it's so non incremental. We felt like, you know, we really needed quite a bit of real estate to really lay out what the full narrative in context is on. Do you know, we thought books titled The Rise of the Data Cloud. That's exactly what it iss. And we're trying to make the case for that mindset, that mentality, that strategy. Uh, because all of us, you know, I think it's an industry were risk off, you know, persisting, perpetuating. Uh, you know, where we've been since the beginning off computing. So we're really trying to make a pretty forceful case for Look, there's an enormous opportunity out there. The different choices you have to make along the way. >>Guys, we got to leave it there. Frank. I know you and I are gonna talk again. Anita. I hope we have a chance to meet face to face and and talking the Cube live someday. You're phenomenal guests. And what a great story. Thank you both for coming on. Thank you. All right, you're welcome. And keep it right there, buddy. We'll be back for the next guest right after this short break and we're clear. All right. Not bad.

>> Hello everybody and welcome back to the cubes coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the chairman and CEO of Snowflake and Anita Lynch, the vice president of Data Governance at Disney Streaming Services. Folks, welcome. >> Thank you Thanks for having us, Dave, >> I need a Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar Movie saw, I haven't gotten into the Mandalorian yet, your favorite, but, (woman laughing) really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud, because I never liked the term Enterprise Data Warehouse, what you're doing is so different from the sort of that legacy world that I've known all these years, but start with why the Data Cloud, what problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah I know, you know we've come a long way in terms of workload execution, right? In terms of scale and performance, you know, concurrent execution, we really taken the lid off sort of the physical constraints that have existed on these types of operations. But there's one problem, that we're not yet solving. And that is the siloing and bunkering of data. And especially in a data is locked in application it is locked data centers, is locked in cloud regions, incredibly hard for data science teams to really, you know, unlock the true value of data. When you can address patterns that exist across a data set. So we're perpetuate, a status we've had forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, look folks, you know, we have to start unsiloing and unlocking the data, and bring it into a place, you know, where we can access it, you know, across all these parameters and boundaries that have historically existed. It's very much a step level function. Now the customers have always looked at things one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita was great hearing your role at Disney, and your keynote, and the work you're doing, the governance work, and you're serving a great number of stakeholders, enabling things like data sharing, you got really laser focused on trust, compliance, privacy. Is this idea of a data clean room is really interesting. You maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And of course, the opportunities that you're unlocking. >> Sure. I mean, in my role, leading Data Governance, it's really critical to make sure that all of our stakeholders, not only know what data is available and accessible to them, they can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so, that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually, is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos, and obviously you can relate to that, having been in the data business for awhile, but wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture, simply because you often have different teams at different periods in time, trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that, is, due to the different needs of specific stakeholders. There are time constraints, and quite often it's not always clear, how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos, is, allow individuals to see upfront how much value they're going to get from the data, by knowing that it's trustworthy right away . By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I mean copy creek, cause it'd be the silent killer. Frank I followed you for a number of years. You're a big thinker. You and I have had a lot of conversations about the near term, mid term and long term. I wonder if you could talk about, you know, when your keynote, you talked about eliminating silos, and connecting across data sources, which really powerful concept, but it really only, if people are willing and able to connect and collaborate, where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to Salesforce, you know, they had, discovered that we were a top three destination of Salesforce data and they were wondering, you know, why that was? And the reason is of course that people take Salesforce data, push it to Snowflake, because they want to overlay it with data outside of Salesforce. You know what it is Adobe or any 6other marketing dataset. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS, is always like, no, we're an Island, we're a planet onto ourselves. Everybody needs to come with us as opposed to, we go to a different platform to run these types of processes. It's no different for thee public cloud vendor. They did only, they have, you know, massive moats around, you know, their storage to, you know, to really prevent data from leaving their orbit. So there is natural friction in terms of for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on the power and potential of data, unless we allow it to come together. Snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate, you know, why this was important and why this was necessary. And we think, you know, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come. Right, people are recognizing, you know, why does this matters now. It's not going to happen overnight. It is a step what will function a very big change in mentality and orientation. You know? >> Yeah. It's almost as though the sussification of our industry sort of repeated some of the application silos, and build a heart on to and all the processes of(mummers) Okay, here we go. And you're really trying to break that aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so important. It's the first rule in your title, and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean we saw this in the early days of big data where governance was this afterthought. It was like bolted on kind of wild west. I'm interested in your governance journey. And maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda, and kind of what's next on that journey. >> Sure. Well, you know, I've led data teams, in numerous ways over my career, this is the first time, that I've actually had the opportunity to focus on governance. And what it's done, is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about, at least my inference in your talk, was really that the business folks didn't have to care about, your wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera, you've taken care of all that. It's sort of transparent to them. >> Yeah, that's right. Absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical, to ensuring you know, that we're able to do this. We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access. And it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data, we focus on the infrastructure, and making sure that we've architected for scale , and all of these are really important components of our strategy. >> So I have a question maybe each of you can answer it. I sort of see this, our industry moving from products to then, to the platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, but, but maybe Frank, you can start and Anita you can add onto Frank's answer. You obviously both passionate about the use of data and trying to do so in a responsible way, that's critical, but it's also going to have business impact. Frank, where's this passion come from on your side? And how are you putting into action in your own organization? >> Well, you know, I'm really going to date myself here, but you know many years ago, you know, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and business, all these terms didn't exist. People just knew that they want to have a more flexible way of reporting and being able to pivot data, dimensionally, all these kinds of things. And I just by whatever this predates, you know, windows 3.1, which really, you know, set off the whole sort of graphical, you know, way of dealing with systems, which there's not whole generations of people that don't know any different. Right? So I've lived the pain of this problem, and sort of had a front row seat, to watching this transpire over a very long period of time. And that's one of the reasons, you know, why I'm here because I finally seen, you know, a glimpse of, you know, I also, as an industry fully, just unleashing and unlocking the potential. We're not at a place where the technology is ahead of people's ability to harness it. Right. Which we'd never been there before. Right. It was always like we wanted to do things and technology wouldn't let us, it's different now. I mean, people are just, heads are spinning with what's now possible, which is why you see Marcus evolve, you know, very rapidly right now, we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on in the world. The world's changing right in front of your eyes right now, >> So Anita maybe you could add on to what Frank just said and share some of the business impacts, and outcomes that are notable since you've really applied your love of data and maybe touch culture, data culture. Any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah Sure. I think for me, I fell in love with technology first, and then I fell in love with data and I fell in love with data because of the impact that data can have, on both the business, and the technology strategy. And so it's sort of that nexus between all three. And in terms of my career journey and some of the impacts that I've seen. I think with the advent of the Cloud, you know, before, well, how do I say this? Before the cloud actually became so prevalent and such a common part of the strategy that's required, it was so difficult, you know, so painful. It took so many hours to actually, be able to calculate, you know, the volumes of data that we had. Now we have that accessibility. And then on top of it, with the Snowflake Data Cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think, moving beyond some of the traditional mechanisms for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate and really, truly understand our customer's interests and their preferences, and then tailor the experiences that they really want and deserve for them. It's been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you to do so in your spare time you decided to write a book am loving it. I have a signed copy, so I'm going to have to send it back and have you sign it. But, and I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons. Obviously we thought it was an interesting tale to tell for anybody, you know, who is interested in, you know, what's going on? How did this come about? You know, or the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, you know, because this is such a step function, it's so non incremental, we felt like we really needed quite a bit of real estate to really lay out, what the full narrative and context is. And, you know, we thought, you know, books titled the rise of the Data Cloud. That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy, because all of us, you know, I think as an industry we're at risk of, you know, persisting, perpetuating, you know, where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for look, you know, there's an enormous opportunity out there, but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank. I know you and I are going to talk again, Anita, I hope we have a chance to meet face to face and in the cube live someday, your phenomenal guest and what a great story. Thank you both for coming on. Thanks Dave, >> Thank you >> You're welcome to keep it right there, buddy. We'll be back with the next guest right after this short break. (upbeat music)

live from New York it's the cube covering AWS summit New York 2018 hot GUI Amazon Web Services and its ecosystem partners hello and welcome back here live cube coverage in New York City for AWS Amazon Web Services summit 2018 I'm John Fourier with Jeff Rick here at the cube our next guest is dr. Matt wood general manager of artificial intelligence with Amazon Web Services keep alumnae been so busy for the past year and been on the cubanía thanks for coming back appreciate you spending the time so promotions keep on going on you got now general manager of the AI group AI operations ai automation machine learning offices a lot of big category of new things developing and a you guys have really taken AI and machine learning to a whole new level it's one of the key value propositions that you guys now have for not just a large enterprise but down to startups and developers so you know congratulations and what's the update oh well the update is this morning in the keynote I was lucky enough to introduce some new capabilities across our platform when it comes to machine learning our mission is that we want to be able to take machine learning and make it available to all developers we joke internally that we just want to we want to make machine learning boring we wanted to make it vanilla it's just it's another tool in the tool chest of any developer and any any data data scientist and we've done that this idea of taking technology that is traditionally only within reached a very very small number of well-funded organizations and making it as broadly distributed as possible we've done that pretty successfully with compute storage and databases and analytics and data warehousing and we want to do the exact same thing for the machine learning and to do that we have to kind of build an entirely new stack and we think of that stack in in three different tiers the bottom tier really for academics and researchers and data scientists we provide a wide range of frameworks open source programming libraries the developers and data scientists use to build neural networks and intelligence they're things like tend to flow and Apache mx9 and by torch and they're really they're very technical you can build you know arbitrarily sophisticated says most she open source to write mostly open source that's right we contribute a lot of our work back to MX net but we also contribute to buy torch and to tend to flow and there's big healthy open source projects growing up around you know all these popular frameworks plus more like chaos and gluon and horror boredom so that's a very very it's a key area for for researchers and academics the next level up we have machine learning platforms this is for developers and data scientists who have data they see in the clout although they want to move to the cloud quickly but they want to be able to use for modeling they want to be able to use it to build custom machine learning models and so here we try and remove as much of the undifferentiated heavy lifting associated with doing that as possible and this is really where sage maker fits in Cersei's maker allows developers to quickly fill train optimize and host their machine learning models and then at the top tier we have a set of AI services which are for application developers that don't want to get into the weeds they just want to get up and running really really quickly and so today we announced four new services really across those their middle tier in that top tier so for Sage maker we're very pleased to introduce a new streaming data protocol which allows you to take data straight from s3 and pump it straight into your algorithm and straight onto the computer infrastructure and what that means is you no longer have to copy data from s3 onto your computer infrastructure in order to be able to start training you just take away that step and just stream it right on there and it's an approach that we use inside sage maker for a lot of our built-in algorithms and it significantly increases the the speed of the algorithm and significantly of course decreases the cost of running the training because you pay by the second so any second you can save off it's a coffin for the customer and they also it helps the machine learn more that's right yeah you can put more data through it absolutely so you're no longer constrained by the amount of disk space you're not even constrained by the amount of memory on the instance you can just pump terabyte after terabyte after terabyte and we actually had another thing like talked about in the keynote this morning a new customer of ours snap who are routinely training on over 100 terabytes of image data using sage maker so you know the ability to be able to pump in lots of data is one of the keys to building successful machine learning applications so we brought that capability to everybody that's using tensorflow now you can just have your tensor flow model bring it to Sage maker do a little bit of wiring click a button and you were just start streaming your data to your tents upload what's the impact of the developer time speed I think it is it is the ability to be able to pump more data it is the decrease in time it takes to start the training but most importantly it decreases the training time all up so you'll see between a 10 and 25 percent decrease in training time some ways you can train more models or you can train more models per in the same unit time or you can just decrease the cost so it's a completely different way of thinking about how to train over large amounts of data we were doing it internally and now we're making it available for everybody through tej matrix that's the first thing the second thing that we're adding is the ability to be able to batch process and stage make them so stage maker used to be great at real-time predictions but there's a lot of use cases where you don't want to just make a one-off prediction you want to predict hundreds or thousands or even millions of things all at once so let's say you've got all of your sales information at the end of the month you want to use that to make a forecast for the next month you don't need to do that in real-time you need to do it once and then place the order and so we added batch transforms to Sage maker so you can pull in all of that data large amounts of data batch process it within a fully automated environment and then spin down the infrastructure and you're done it's a very very simple API anyone that uses a lambda function it's can take advantage of this again just dramatically decreasing the overhead and making it so much easier for everybody to take advantage of machine load and then at the top layer we had new capabilities for our AI services so we announced 12 new language pairs for our translation service and we announced new transcription so capability which allows us to take multi-channel audio such as might be recorded here but more commonly on contact centers just like you have a left channel on the right channel for stereo context centers often record the agent and the customer on the same track and today you can now pass that through our transcribed service long-form speech will split it up into the channels or automatically transcribe it will analyze all the timestamps and create just a single script and from there you can see what was being talked about you can check the topics automatically using comprehend or you can check the compliance did the agents say the words that they have to say for compliance reasons at some point during the conversation that's a material new capability for what's the top surface is being used obviously comprehend transcribe and barri of others you guys have put a lot of stuff out there all kinds of stuff what's the top sellers top use usage as a proxy for uptake you know I think I think we see a ton of we see a ton of adoption across all of these areas but where a lot of the momentum is growing right now is sage maker so if you look at a formula one they just chose Formula One racing they just chose AWS and sage maker as their machine learning platform the National Football League Major League Baseball today announcer they're you know re offering their relationship and their strategic partnership with AWS cream machine learning so all of these groups are using the data which just streams out of these these races all these games yeah and that can be the video or it can be the telemetry of the cars or the telemetry of the players and they're pumping that through Sage maker to drive more engaging experiences for their viewers so guys ok streaming this data is key this is a stage maker quickly this can do video yeah just get it all in all of it well you know we'd love data I would love to follow up on that so the question is is that when will sage maker overtake Aurora as the fastest growing product in history of Amazon because I predicted that reinvent that sage maker would go on err is it looking good right now I mean I sorta still on paper you guys are seeing is growing but see no eager give us an indicator well I mean I don't women breakout revenue per service but even the same excitement I'll say this the same excitement that I see Perseids maker now and the same opportunity and the same momentum it really really reminds me of AWS ten years ago it's the same sort of transformative democratizing approach to which really engages builders and I see the same level of the excitement as levels are super super high as well no super high in general reader pipe out there but I see the same level of enthusiasm and movement and the middle are building with it basically absolutely so what's this toy you have here I know we don't have a lot of time but this isn't you've got a little problem this is the world's first deep learning in April were on wireless video camera we thought it D blends we announced it and launched it at reinvent 2017 and actually hold that but they can hold it up to the camera it's a cute little device we modeled it after wall-e the Pixar movie and it is a HD video camera on the front here and in the base here we have a incredibly powerful custom piece of machine learning hardware so this can process over a billion machine learning operations per second you can take the video in real time you send it to the GPU on board and we'll just start processing the stream in real time so that's kind of interesting but the real value of this and why we designed it was we wanted to try and find a way for developers to get literally hands-on with machine learning so the way that build is a lifelong learners right they they love to learn they have an insatiable appetite for new information and new technologies and the way that they learn that is they experiment they start working and they kind of spin this flywheel where you try something out it works you fiddle with it it stops working you learn a little bit more and you want to go around around around that's been tried and tested for developers for four decades the challenge with machine learning is doing that is still very very difficult you need a label data you need to understand the algorithms it's just it's hard to do but with deep lens you can get up and running in ten minutes so it's connected back to the cloud it's good at about two stage makeup you can deploy a pre-built model down onto the device in ten minutes to do object detection we do some wacky visual effects with neural style transfer we do hot dog and no hot dog detection of course but the real value comes in that you can take any of those models tear them apart so sage maker start fiddling around with them and then immediately deploy them back down onto the camera and every developer on their desk has things that they can detect there are pens and cups and people whatever it is so they can very very quickly spin this flywheel where they're experimenting changing succeeding failing and just going round around a row that's for developers your target audience yes right okay and what are some of the things that have come out of it have you seen any cool yes evolutionary it has been incredibly gratifying and really humbling to see developers that have no machine learning experience take this out of the box and build some really wonderful projects one in really good example is exercise detection so you know when you're doing a workout they build a model which detects the exerciser there and then detects the reps of the weights that you're lifting now we saw skeletal mapping so you could map a person in 3d space using a simple camera we saw security features where you could put this on your door and then it would send you a text message if it didn't recognize who was in front of the door we saw one which was amazing which would read books aloud to kids so you would hold up the book and they would detect the text extract the text send the text to paly and then speak aloud for the kids so there's games as educational tools as little security gizmos one group even trained a dog detection model which detected individual species plug this into an enormous power pack and took it to the local dog park so they could test it out so it's all of this from from a cold start with know machine learning experience you having fun yes absolutely one of the great things about machine learning is you don't just get to work in one area you get to work in you get to work in Formula One and sports and you get to work in healthcare and you get to work in retail and and develop a tool in CTO is gonna love this chief toy officers chief toy officers I love it so I got to ask you so what's new in your world GM of AI audition intelligence what does that mean just quickly explain it for our our audience is that all the software I mean what specifically are you overseeing what's your purview within the realm of AWS yeah that's that's a totally fair question so my purview is I run the products for deep learning machine learning and artificial intelligence really across the AWS machine learning team so I get I have a lot of fingers in a lot of pies I get involved in the new products we're gonna go build out I get involved in helping grow usage of existing products I get it to do a lot of invention it spent a ton of time with customers but overall work with the rest of the team on setting the technical and pronto strategy for machine learning at AWS when what's your top priorities this year adoption uptake new product introductions and you guys don't stop it well we do sync we don't need to keep on introducing more and more things any high ground that you want to take what's what's the vision I didn't the vision is to is genuinely to continue to make it as easy as possible for developers to use Ruggiero my icon overstate the importance or the challenge so we're not at the point where you can just pull down some Python code and figure it out we're not even we don't have a JVM for machine learning where there's no there's no developer tools or debuggers there's very few visualizers so it's still very hard if you kind of think of it in computing terms we're still working in assembly language and you're seen learning so there's this wealth of opportunity ahead of us and the responsibility that I feel very strongly is to be able to continually in crew on the staff to continually bring new capabilities to mortar but well cloud has been disrupting IT operations AI ops with a calling in Silicon Valley and the venture circuit Auto ml as a term has been kicked around Auto automatic machine learning you got to train the machines with something data seems to be it strikes me about this compared to storage or compared to compute or compared to some of the core Amazon foundational products those are just better ways to do something they already existed this is not a better way to do something that are exists this is a way to get the democratization at the start of the process of the application of machine learning and artificial intelligence to a plethora of applications in these cases that is fundamentally yeah different in it just a step up in terms of totally agree the power to the hands of the people it's something which is very far as an area which is very fast moving and very fast growing but what's funny is it totally builds on top of the cloud and you really can't do machine learning in any meaningful production way unless you have a way that is cheap and easy to collect large amounts of data in a way which allows you to pull down high-performance computation at any scale that you need it and so through the cloud we've actually laid the foundations for machine learning going forwards and other things too coming oh yes that's a search as you guys announced the cloud highlights the power yet that it brings to these new capabilities solutely yeah and we get to build on them at AWS and at Amazon just like our customers do so osage make the runs on ec2 we wouldn't we won't be able to do sage maker without ec2 and you know in the fullness of time we see that you know the usage of machine learning could be as big if not bigger than the whole of the rest of AWS combined that's our aspiration dr. Matt would I wish we had more time to Chad loved shopping with you I'd love to do a whole nother segment on what you're doing with customers I know you guys are great customer focus as Andy always mentions when on the cube you guys listen to customers want to hear that maybe a reinvent will circle back sounds good congratulations on your success great to see you he showed it thanks off dr. Matt would here in the cube was dreaming all this data out to the Amazon Cloud is whether they be hosts all of our stuff of course it's the cube bringing you live action here in New York City for cube coverage of AWS summit 2018 in Manhattan we'll be back with more after this short break

>> From Boston, Massachusetts, it's the Cube, covering LiveWorx 18, brought to you by PTC. (light electronic music) >> Welcome back to Boston, everybody. This is the Cube, the leader in live tech coverage. We're covering day one of the LiveWorx conference that's hosted by PTC. I'm Dave Vellante with my cohost Stu Miniman. Professor Linda A. Hill is here. She's the Wallace Brett Donham Professor of Business Administration at the Harvard Business School. Professor Hill, welcome to the Cube. Thanks so much for coming on. >> Thank you for having me. >> So, innovation, lot of misconceptions about innovation and where it stems from. People think of Steve Jobs, well, the innovation comes from a single leader and a visionary who gets us in a headlock and makes it all happen. That's not really how innovation occurs, is it? >> No, it is not, actually. Most innovation is the result of a collaboration amongst people of different expertise and different points of view, and in fact, unless you have that diversity and some conflict, you rarely see innovation. >> So this is a topic that you've researched, so this isn't just an idea that you had. You've got proof and documentation of this, so tell us a little more about the work that you do at Harvard. >> So really over 10 years ago, I began to look at the connection between leadership and innovation, because it turns out that like a lot of organizations, the academy is quite siloed, so the people studying innovation were very separate from the ones who studied leadership, and we look at the connection between the two. When you look at that, what you discover is that leading innovation is actually different from leading change. Leading change is about coming up with a vision, communicating that vision, and inspiring people to want to fulfill that vision. Leading innovation is not about that. It's really more about how do you create a space in which people will be willing and able to do the kind of collaborative work required for innovation to happen? >> Sometimes I get confused, maybe you can help me, between invention and innovation. How should we think about those two dimensions? >> Innovation and invention. The way I think about it is an innovation is something that's both an invention, i.e. new, plus useful. So it can be an innovation or it can be creative, but unless it's useful and addresses an opportunity or a challenge that an organization faces, for me, that's not an innovation. So you need both, and that is really the paradox. How do you unleash people's talents and passions so you get the innovation or the invention or the new, and then how do you actually combine that, or harness all of those different ideas so that you get something that is useful, that actually solves a problem that the collective needs solved? >> So there's an outcome that involves changing something, adoption, as part of that innovation. >> For instance, one of the things that we're doing a lot right now is we're working with organizations, incumbents, I guess you'd call them, that have put together these innovation labs to create digital assets. And the problem is that those digital assets get created, they're new, if you will, but unless the core business will adopt them and use them, they get implemented, they're not going to be useful. So we're trying to understand, how do you take what gets created in those innovation labs, those assets, if you will, and make sure that the organization takes them in and scales them so that you can actually solve a business problem? >> Professor Hill, a fascinating topic I love digging into here. Because you see so many times, startups are often people that get frustrated inside a large company. I've worked for some very large companies, so which have had labs, or research division, and even when you carve aside time for innovation, you do programs on that, there's the corporate antibodies that fight against that. Maybe talk a little bit about that dynamic. Can large companies truly innovate? >> Yes, large companies can truly innovate. We do see it happening, it is not easy by any means, and I think part of the dilemma for why we don't see more innovation is actually our mindset about what leadership is about and who can innovate. So if I could combine a couple of things you asked, invention, often when we talk to people about what is innovation, they think about technology, and they think about new, and if I'm not a technologist and I'm not creative, then I can't play the game. But what we see in organizations, big ones that can innovate, is they don't separate out the innovators from the executors. They tell everybody, guess what, your job no matter who you are, of course you need to deal with making sure we get done what we said we'd deliver, but if we're going to delight our customers or we're ever going to really get them to be sticky with us, you also need to think about not just what should you be doing, but what could you be doing. In the literature, in the research, that's called how do you close an opportunity gap and not just a performance gap? In the organizations we look at that are innovative, that can innovate time and again, they have a very democratic notion: everybody has a role to play. So our work, Collective Genius, is called Collective Genius because what we saw in Pixar was the touchstone for that work, is that they believe everybody has a slice of genius. They're not equally big or whatever, but everybody has a contribution to make, and you need to use yours to come up with what's new and useful. A lot of that will be incremental, but some of it will be breakthrough. So I think what we see with these innovation labs and the startups, if you will, is that often people do go to start them up, of course they eventually have to grow their business, so a part of what I find myself doing now is helping startups that have to scale, figure out how to maintain that culture, those capabilities, that allowed them to be successful in the first place, and that's tough one for startups, right? >> Yeah, I think Pixar's only about a 1,500 person company and they all have creativity in wat they do. I'm wondering if there's some basic training that's missing. I studied engineering and I didn't get design training in my undergraduate studies. It wasn't until I was out in the workforce that I learned about that. What kind of mindset and training do you have to do to make sure the people are open to this? >> One of the things that I did related to this is about five years ago, I told our dean of Harvard Business School that I needed to join the board of an organization called Arts Center. I don't know if you were aware of Arts Center in Pasadena. It's the number one school of industrial design in the U.S., and people don't know about it 'cause I always laugh at them. The man who designed the Apple store is a graduate there. The man who designed Tesla car and et cetera, so they're not so good at it, but one of the things that we've all come to understand is design thinking, lean startup, these are all tools that can help you be better at innovation, but unless you create an environment around that, people are going to be willing to use those tools and make the missteps, the failures that might come with it, know how to collaborate together, even when they're a large organization, I mean it's easier when you're smaller. But unless you know how to do all that, those tools, the lean startup or digital or design thinking or whatever, ' cause I'm working with a lot of the people who do that, and deep respect for them, nothing gets done. In the end, we are human, we all need to know first off that it's worthwhile to take the risk to get done whatever it is you want to get done, so what's the purpose of the work, how's it going to change the world? The second thing is we need to share a set of values about learning because we have to understand, as you well know, you cannot plan your way to an innovation, you have to act your way. And with the startup, you act as fast as you can, right, so somebody will give you enough money before you run out of money. Same similar process you have to do in a large company, an incumbent, but of course it's more complicated. The other thing that makes it more complicated is companies are global, and the other part of it that makes it more complicated that I'm seeing like in personalized medicine: you need to build an ecosystem of different kinds, of nanotechnologists, biotechnologists, different expertise to come together. All of this, frankly, you don't learn any of it in school. I remember learning that you can't teach anyone how to lead. You actually have to help people learn how to lead themselves and technologists will frequently say to me, i don't know why, you're a leadership professor? Well, this is a technical problem. We just haven't figured out the platform right, and once we get it right, all will be. No, once you get it right, humans are still going to resist change and not know how to necessarily learn together to get this done. >> I wonder if, are there any speacial leadership skills we need for digital transformation? Really kind of the overarching theme of the show here, help connect the dots for us. >> So the leading change piece is about having a vision, communicating it, and inspiring people. What it really does turn out when we look at exceptional leaders of innovation, and all of us would agree that they've done wonderful things time and again, not just once, they understand that is collective. They spend time building a culture and capabilities that really will support people collaborating together. The first one they build is, how do we know how to create a marketplace of ideas through debate and discourse? Yeah, you can brainstorm, but eventually, we have to abrade and have conflict. They know how to have healthy debates in which people are taught terms of skills, basic stuff, not just listening and inquiring, but how to actively advocate in a constructive way for your point of view, these leaders have to learn how to amplify difference, whereas many leaders learn how to minimize it. And as the founder of Pixar once said, you can never have too many cooks in the kitchen. Many people believe you can. It's like today, you need as much talent as you can get. Your job as a leader, what are the skills you need to get those top cooks to be able to cook a meal together, not to reduce the amount of diversity. You got to be prepared for the healthy fight. >> You've pointed this out in some of your talks is that you've got to have that debate. >> Yes, you have to. >> That friction, to create innovation, but at the same time it has to be productive. I know it can be toxic to an organization, maybe talk about that a little. >> I think one of the challenges is what skills do people need to learn? One is, how do you deal with conflict when people are very talented and passionate? I think many people avoid conflict or don't know how to engage that constructively, just truly don't, and they avoid it. I find that many times organizations aren't doing what they need to do because the leadrr is uncomfortable. The other thing, and I'm going to stereotype horribly here, but I'm an introvert, that book quiet is wonderful, but one of the challenges you have if you're more introverted or if you're more technical and you tend to look at things from a technical point of view, in some ways is that you often find the people with that kind of, that's what drives them, there's a right answer, there's a rational answer we need to get through or get to, as opposed to understanding that really innovative ideas are often the combination of ideas that look like they're in conflict initially, and by definition, you need to have the naive eye and the expert working together to come up with that innovative solution, so for someone who's a technologist to think they should listen to someone who's naive about a technical problem, just the very basic mindset you have about who's going to have the idea. So that's a tricky one, it's a mindset, it's not even just a skill level, it's more, who do you think actually is valuable? Where is that slice that you need at this moment going to come from? It may not be from that expert, it may be from the one who had no point of view. I heard a story that I was collecting my data, and apparently, Steve Jobs went to see Ed Land. We're here in Boston over Polaroid, which is one of our most innovative companies, right, in the history. And he said, what do I need to learn from you? And what Land said to him is, whenever my scientist and technologist get stuck, I have some of the art students or the humanities students come in and spend time in the lab. They will ask the stupid question because they don't know it's stupid. The expert's not going to ask the stupid question, particularly the tech expert, not going to ask it. They will ask the question that gets the first principles. I think, but I wouldn't want to be held to this, the person who was telling me the story, that's partly how they came up with the instant camera. Some naive person said, why do I have to wait? Why can't I have it now? And of course, silly so-and-so, you don't know it takes this, that, and the others. Then someone else thought, why does she have to wait? I think it was really a she who asked the question, the person telling me this, and they came up with a different way. Who said it has to be done in a darkroom in that way? I think that there's certain things about our mindset independently of our skill, that get in the way of our actually hearing all the different voices we need to hear to get that abrasion going in the right way. >> Listening to those Columbo questions, you say, can sometime lead to an outcome that is radically different. There's a lot of conversation in our industry, the technology industry, about, we call it the cordially shock clock, the companies are on a cordially reporting mechanism or requirement from the SEC. A lot of complaints about that, but at the same time, it feels like at least in the tech business, that U.S. companies tend to be more innovative. But again, you hear a lot of complaints about, well, they can't think for the long term. Can you help us square that circle? >> It's funny, so one thing is you rarely ever get innovation without constraint. If you actually talk to people who are trying to innovate, there needs to be the boundaries around it in which they're doing the constraint. To be completely free rarely leads to, it is the constraint. Now we did do a study of boards to try to understand when is a board facilitating innovation and when is a board interfering with it? We interviewed CEOs and lead directors of a number of companies and wrote an article about that last year, and what we did find is many boards actually are seen as being inhibitors. They don't help management make the right decision. Then of course the board would say now management's the one that's too conservative, but this question about how the board, with guidance, and all of these issues have come up when you're looking at research analysts and who you play to, and I've been on corporate boards. One thing is that the CEO needs to know that the board is actually going to be supportive of his or her choices relative to how you communicate why you're making the choices you're making. So there is pressure, and I think it's real. We can't tell CEOs, no, you don't need to care about it, 'cause guess what, they do get in trouble if they don't. On the other hand, if they don't know how to make the argument for investing in terms of helping the company grow, so in the long run, innovation is not innovation for innovation's sake, it's to meet customer needs so you can grow, so you need to have a narrative that makes sense and be able to talk with people, the different stakeholders, about why you're making certain choices. I must say that I think that many times companies may be making the right choice for the long haul, and get punished in the short run, for sure that happens, but I also think that there are those companies that get a way with a lot of investment in the long haul, partly because they do, over time, deliver, and there is evidence that they're making the right choices or have built a culture where people think what they're saying might actually happen or be delivered. What's happening right now because of the convergence of industries, is I think a lot of CEOS, it's a frightening time, it is difficult to sustain success these days, because what you have to do is innovate at low cost. Going back to some other piece about boards, one of the things we've found is so many board members define innovation as being technology. Technology has a very important enabling role to play in otherwise, but they have such a narrow definition of it in a way that again, they create a culture to let the people in the innovation lab innovate, but not one where everybody understands that all of us, together, need to innovate in ways that will also prepare us to execute better. They don't see the whole culture transformation, digital transformation often requires cultural transformation for you to be able to get this stuff done, and that's what takes a long time. Takes a long time to get rid of your legacy systems and put in these new, or get that balance right, but what takes even longer is getting the culture to be receptive to using that new data capability they have and working in different ways and collaborating when they've been very siloed and they're paid to be very siloed. I think that unless you show, as a CEO, that you are actually putting all of those building blocks in place, and that's what you're about, you understand it's a transformation at that level, you're just talking to the analysts about, we're going to do x, and there's no evidence about your culture or anything else going on, how you're going to lead to attract and retain the kind of talent you need, no one's buying that, I think that that's the problem. There's not a whole story that they're telling about how this goes together and they're going to move forward on it. >> To your other point, is there data to suggest, can you quantify the relationship between diversity and innovation? >> There are some data about that, I don't have it. I find it's very funny, as you can see, I'm an African-American woman. My work is on leadership globalization and innovation. I do a lot of work on how you deliver global strategies. I often find when I'm working with senior teams, they'll ask me, would you help us with our inclusion effort? And I think it's partly because of who I am and diversity comes up in our work, and if you actually build the environments for talking about, they tend to be more inclusive about diversity of thought. Not demographic diversity, those can be separate as we well know because we know Silicon Valley is not a place where you see a lot of demographic diversity, but you might see diversity of thought. I haven't asked, it's interesting, I have had some invitations by governments, too. Japan, which has womenomics, which is a part of their policy If they need to get more women in the economy, frankly, otherwise they can't grow as an economy. It turns out that the innovation story is the business case that many businesses or business people find one that they can buy into, doesn't feel like you're doing it 'cause it's the right thing, or not that you shouldn't do the right thing, but helping them understand how you really, really make sure that the minority voice is heard, and I mean minority of thought, independent of demographic, but if you create an environment as a leader where you actually run your team so that people do feel they can speak up, as you all know. It's so often, I'll talk to people afterwards and they'll say, I didn't say what I really thought about those ideas because I didn't want to be punished or I didn't want to step in that person's territory. People are making decisions based on varying complete information everyone knows. What often happens is it gets escalated up. We had this one senior team complaining, everything is so slow here, a very big bank, not the one I'm on the board of, another very big bank we're working with. Everything's so slow, people won't do anything. So when we actually ask people, what's happening? Why aren't you making decisions? First off, decisions making rights are very fuzzy in this organization, except for at the very top, so what they say is all decisions, actually, they're made on the 34th floor. We escalate 'cause if you make a decision, they're going to turn it over anyway, so we've backed off, or we don't say what we think 'cause I don't want them to say what they think about my ideas 'cause we actually have very separate business units here. >> We might get shot. >> You might get shot. That's the reality that many people live in, so we're not surprised to see that not very many organizations can innovate time and again when we think about the reality of what our contexts are. The good news for us is that in part, millennials won't tolerate some of these environments in the same way, which is going to be a good thing. I think they're marvelous to work with, I'm not one of them obviously, but I think a lot of what they're requesting, the transparency, the understanding the connections between what they do and are they having impact, the desire to be developed and be learning, and wanting to be an organization they're not ashamed of but in fact they're very proud to be a part of what's happening there, I think that that requires businesses and leaders to behave differently. One of the businesses we studied, if the millennial wants to know who's on the front line, he or she is making a difference. They had to do finance differently to be able to show, to draw the cause and effect between what that person was doing every day and how it impacted the client's work. That ended up being a really interesting task. Or a supply chain leader, who really needed them to think very differently about supply chain so they could innovate. What he ended up doing is, instead of thinking about our customers being the pharmaceutical company, the CBS or the big hospital chain or whatever it is, think about the end customer. What would we have to do with supply chain to ensure that that end patient took his or her pill on time and got better? And when they shifted the whole meaning of the work to that individual patient in his or her home, he was able, over time, to get the whole supply chain group organization to understand, we're not doing what we need to do if we're really going to reduce diabetes in the world because the biggest problem we have is not when they go and get their medication, it's whether they actually use it properly when they're there. So when you switched it to that being the purpose of the work, the mindset that everyone had to have, that's what we're delivering on. Everyone said, oh, this is completely appropriate, we needed digital, we need different kind of data to know what's going on there. >> Don't get me started on human health. Professor Hill, for an introvert, you're quite a storyteller, and we appreciate you sharing your examples and your knowledge. Thanks so much for coming on the Cube. It was great to meet you. >> Been my pleasure, glad to know you, thank you. >> Keep it right there, everybody, Stu and I will be back right after this short break. You're watching the Cube from LiveWorx in Boston. We'll be right back. (light electronic music)

(upbeat electronic music) >> Narrator: Live from San Francisco, it's The Cube, covering Red Hat Summit 2018. Brought to you by Red Hat. >> Hey, welcome back, everyone. We are here live, The Cube in San Francisco, Moscone West for the Red Hat Summit 2018 exclusive coverage. I'm John Furrier, the cohost of The Cube. I'm here with my cohost, John Troyer, who is the co-founder of Tech Reckoning, an advisory and community development firm. Our next guest is Jonathan Donaldson, Technical Director, Office of the CTO, Google Cloud. Former Cube Alumni. Formerly was Intel, been on before, now at Google Cloud for almost two years. Welcome back, good to see you. >> Good to see you too, it's great to be back. >> So, had a great time last week with the Google Cloud folks at KubeCon in Denmark. Kubernetes, rocking the world. Really, when I hear the word de facto standard and abstraction layers, I start to get, my bells go off, let me look at that. Some interesting stuff. You guys have been part of that from the beginning, with the CNCF, Google, Intel, among others. Really created a movement, congratulations. >> Yeah, thank you. It really comes down to the fact that we've been running containers for almost a dozen years. Four billion a week, we launch and collapse. And we know that at some point, as Docker and containers really started to take over the new way of developing things, that everyone is going to run into that scalability wall that we had run into years and years and years ago. And so Craig and the team at Google, again, I wasn't at Google at this time, but they had a really, let's take what we know from internally here and let's take those patterns and let's put them out there for the world to use, and that became Kubernetes. And so I think that's really the massive growth there, is that people are like, "Wow, you've solved a problem, "but not from a science project. "It's actually from something "that's been running for a decade." >> Internally, that's called bore. That's tools that Google used, that their SRE cyber lab engineers used to massively provision manage. And they're all software engineers, so it's not like they're operators. They're all Google engineers. But I want to take a minute, if you can, to explain. 'Cause you're new to Google Cloud. You're in the industry, you've been around, you helped form the CNCF, which is the Cloud Native Foundation. You know cloud, you know tech. Google's changed a lot, and Google Cloud specifically has a narrative of, they're one big cloud and they have an application called Google stuff and enterprises are different. You've been there now for almost a year or more. >> Jonathan: Little over a year, yeah. >> What's Google Cloud like right now? Break the myths down around Google Cloud. What's the current status? I know personally, a lot of cloud DNA is coming in from the industry. They've been hiring, making some great progress. Take a minute to explain the Google Cloud. >> Yeah, so it's really interesting. So again, it comes back from where you started from. So Google itself started from a scale consumer SAS type of business. And so that, they understood really well. And we still understand, obviously, uptime and scalability really, really well. And I would say if you backtrack several years ago, as the enterprise really started to look at public clouds and Google Cloud itself started to spin up, that was probably not, they probably didn't understand exactly all of the things that an enterprise would need. Really, at that point in time, no one cloud understood any of the enterprise specifically. And so what they did is they started hiring in people like myself and others that are in the group that I'm in. They're former CIOs of large enterprise companies or former VPs of engineering, and really our job in the Office of the CTO for Google Cloud is to help with the product teams, to help them build the products that enterprises need to be able to use the public cloud. And then also work with some of those top enterprise customers to help them adopt those technologies. And so I think now that if you look at Google Cloud, they understand enterprise really, really well, certainly from the product and the technology perspective. And I think it's just going to get better. >> I interviewed Jennifer Lynn, I had a one-on-one with her. I didn't publish it, it was more of a briefing. She runs Product Management, all on security side. >> Jonathan: Yeah, she's fantastic. >> So she's checking the boxes. So the table stakes are set for Google. I know you got to do some basic things to catch up to get in the cloud. But also you have partnerships. Google Next is coming up, The Cube will be there. Red Hat's a partner. Talk about that relationship with Red Hat and partners. So you're very partner-centric with Google Cloud. >> Jonathan: We are. >> And that's important in the enterprise, but so what-- >> Well, there tends to be two main ares that we focus on, from what we consider the right way to do cloud. One of them is open source. So having, which again, aligns perfectly with Red Hat, is putting the technologies that we want customers to use and that we think customers should use in open source. Kubernetes is an example, there's Istio and others that we've put out that are examples of those. A lot of the open source projects that we all take for granted today were started from white papers that we had put out at one point in time, explaining how we did those things. Red Hat, from a partner perspective, I think that that follows along. We think that the way that customers are going to consume these technologies, certainly enterprise customers are, through those partners that they know and trust. And so having a good, flourishing ecosystem of partners that surround Google Cloud is absolutely key to what we do. >> And they love multicloud too. >> They love multicloud. >> Can't go wrong with it. >> And we do too. The idea is that we want customers to come to Google Cloud and stay there because they want to stay there, because they like us for who we are and for what we offer them, not because they're locked into a specific service or technology. And things like Kubernetes, things like containers, being open sourced allows them to take their tool chains all the way from their laptop to their own cloud inside their own data center to any cloud provider they want. And we think hopefully they'll naturally gravitate towards us over time. >> One of the things I like about the cloud is that there's a flywheel, if you will, of expertise. Like I look at Amazon, for instance. They're getting a lot of metadata of the kinds of workloads that are on their cloud, so they can learn from that and turn that into an advantage for them, or not, or for their customers, and how they could do that. That's their business decision. Google has a lot of flywheel action going on. A lot of Android devices connected in the Google system. You have a lot of services that you can bring to bear in the cloud. How are you guys looking at, say, from a security standpoint alone, that would be a very valuable service to have. I can tap into all the security goodness of Google around what spear phishing is out there, things of that nature. So are you guys thinking like that, in terms of services for customers? How does that play out? >> So where we, we're very consistent on what we consider is, privacy is number one for our customers, whether they're consumer customers or whether they're enterprise customers. Where we would use data, you had mentioned a lot of things, but where we would use some data across customer bases are typically for security things, so where we would see some sort of security impact or an attack or something like that that started to impact many customers. And we would then aggregate that information. It's not really customer information. It's just like you said, metadata, themes, or trends. >> John Furrier: You're not monetizing it. >> Yeah, we're not monetizing it, but we're actually using it to protect customers. But when a customer actually uses Google Cloud, that instance is their hermetically sealed environment. In fact, I think we just came out recently with even the transparency aspects of it, where it's almost like the two key type of access, for if our engineers have to help the customer with a troubleshooting ticket, that ticket actually has to be opened. That kind of unlocks one door. The customer has to say, "Yes," that unlocks the other door. And then they can go in there and help the customer do things to solve whatever the problem is. And each one of those is transparently and permanently logged. And then the customer can, at any point in time, go in and see those things. So we are taking customer privacy from an enterprise perspective-- >> And you guys are also a whole building from Google proper, like it's a completely different campus. So that's important to note. >> It is. And a lot of it just chains on from Google proper itself. If you understood just how crazy and fanatical they are about keeping things inside and secret and proprietary. Not proprietary, but not allowing that customer data out, even on the consumer side, it would give a whole-- >> Well, you got to amplify that, I understand. But what I also see, a good side of that, which is there's a lot of resources you're bringing to bear or learnings. >> Yeah, absolutely. >> The SRE concept, for instance, is to me, really powerful, because Google had to build that out themselves. This is now a paradigm, we're seeing a cloud scale here, with the Cloud Native market bringing in all-new capabilities at scale. Horizontally scalable, fully synchronous, microservices architecture. This future is a complete game-changer on functionality at the different scale points. So there's no longer the operator's room, provisioning storage here. >> And this is what we've been doing for years and years and years. That's how all of Google itself, that's how search and ads and Gmail and everything runs, in containers all orchestrated by Borg, which is our version of Kubernetes. And so we're really just bringing those leanings into the Google Cloud, or learnings into Google Cloud and to our customers. >> Jonathan, machine learning and AI have been the big topic this week on OpenShift. Obviously that's a big strength of Google Cloud as well. Can you drill down on that story, and talk about what Google Cloud is bringing on, and machine learning on OpenShift in general? Give us a little picture of what's running. >> Yeah, so I think they showed some of the service broker stuff. And I think, did they show some of the Kubeflow stuff, which is taking some machine learning and Kubernetes underneath OpenShift. I think those are very, very interesting for people that want to start getting into using AutoML, which is kind of roll-your-own machine learning, or even the voice or vision APIs to enhance their products. And I think that those are going to be keys. Easing the adoption of those, making them really, really easy to consume, is what's going to drive the significant ramp on using those types of technologies. >> One of the key touchpoints here has been the fact that this stuff is real-world and production-ready. The fact that the enterprise architecture now rolling out apps within days or weeks. One of those things that's now real is ML. And even in the opening keynote, they talked about using a little bit of it to optimize the scheduling and what sessions were in which rooms. As you talk to enterprises, it does seem like this stuff is being baked into real enterprise apps today. Can you talk a little bit about that? >> Sure, so I certainly can't give any specific examples, because what I think what you're saying is that a lot of enterprises or a lot of companies are looking at that like, "Oh, this is our new secret sauce." It always used to be like they had some interesting feature before, that a competitor would have to keep up with or catch up with. But I think they're looking at machine learning as a way to enhance that customer experience, so that it's a much more intimate experience. It feels much more tailored to whomever is using their product. And I think that you're seeing a lot of those types of things that people are starting to bake into their products. We've, again, this is one of these things where we've been using machine learning for almost 10 years inside Google. Things like for Gmail, even in the early days, like spam filtering, something just mundane like that. Or we even used it, turned it on in our data centers, 'cause it does a really good job of lowering the PUE, which is the power efficiency in data centers. And those are very mundane things. But we have a lot of experience with that. And we're exposing that through these products. And we're starting to see people, customers gravitate to grab onto those. Instead of having to hard code something that is a one to many kind of thing, I may get it right or I may have to tweak it over time, but I'm still kind of generalizing what the use cases are that my customers want to see, once they turn on machine learning inside their applications, it feels much more tailored to the customer's use cases. >> Machine learning as a service seems to be a big hot button that's coming out. How are you guys looking at the technical direction from the cloud within the enterprise? 'Cause you have three classes of enterprise. You have the early adopters, the power, front, cutting-edge. Then you have the fast followers, then you have everybody else. The everybody else and fast followers, they know about Kubernetes, some might not even, "What is Kubernetes?" So you have kind of-- >> Jonathan: "What containers?" >> A level of progress where people are. How are you guys looking at addressing those three areas, because you could blow them away with TensorFlow as a service. "Whoa, wowee, I'm just trying to get my storage LUNs "moving to a cloud operation system." There's different parts of this journey. Is there a technical direction that addresses these? What are you guys doing? >> So typically we'll work with those customers to help them chart the path through all those things, and making it easy for them to use and consume. Machine learning is still, unless you are a stats major or you're a math major, a lot of the algorithms and understanding linear algebra and things like that are still very complex topics. But then again, so is networking and BGP and things like OSPF back a few years ago. So technology always evolves, and the thing that you can do is you can just help pull people along the continuum there, by making it easy for them to use and to provide a lot of education. And so we work with customers on all ends of the spectrum. Even if it's just like, "How do I modernize my applications, "or how do I even just put them into the cloud?" We have teams that can help do that or can educate on that. If there are customers that are like, "I really want to go do something special "with maybe refactoring my applications. "I really want to get the Cloud Native experience." We help with that. And those customers that say, "I really want to find out this machine learning thing. "How can I actually make that an impactful portion of my company's portfolio?" We can certainly help with that. And there's no one, and typically you'll find in any large enterprise, because there'll be some people on each one of those camps. >> Yeah, and they'll also want to put their toe in the water here and there. The question I have for you guys is you got a lot of goodness going on. You're not trying to match Amazon speed for speed, feature for feature, you guys are picking your shots. That is core to Google, that's clear. Is there a use case or a set of building blocks that are highly adopted with you guys now, in that as Google gets out there and gets some penetration in the enterprise, what's the use, what are the key things you see with successes for you guys, out of the gate? Is there a basic building? Amazon's got EC2 and S3. What are you guys seeing as the core building blocks of Google Cloud, from a product standpoint, that's getting the most traction today? >> So I think we're seeing the same types of building blocks that the other cloud providers are, I think. Some of the differences is we look at security differently, because of, again, where we grew up. We do things like live migration of virtual machines, if you're using virtual machines, because we've had to do that internally. So I think there are some differences on just even some of the basic block and tackling type of things. But I do think that if you look at just moving to the cloud, in and of itself is not enough. That's a stepping stone. We truly believe that artificial intelligence and machine learning, Cloud Native style of applications, containers, things like service meshes, those things that reduce the operational burdens and improve the rate of new feature introduction, as well as the machine learning things, I think that that's what people tend to come to Google for. And we think that that's a lot of what people are going to stay with us for. >> I overheard a quote I want to get your reaction to. I wrote it down, it says, "I need to get away from VPNs and firewalls. "I need user and application layer security "with un-phishable access, otherwise I'm never safe." So this is kind of a user perspective or customer perspective. Also with cloud there's no perimeters, so you got phishing problems. Spear phishing's one big problem. Security, you mentioned that. And then another quote I had was, "Kubernetes is about running frameworks, "and it's about changing the way "applications are going to be built over time." That's where, I think, SRE and Istio is very interesting, and Kubeflow. This is a modern architecture for-- >> There's even KubeVirt out there, where you can run a VM inside a container, which is actually what we do internally too. So there's a lot of different ways to slice and dice. >> Yeah, how relevant is that, those concepts? Because are you hearing that as well on the customers? 'Cause that's pain point, but also the new modern software development's future way to do things. So there's pain point, I need some aspirin for that. And then I need some growth with the new applications being built and hiring talent. Is that consistent with how you guys see it? >> So which one should I tackle? So you're talking about. >> John Furrier: VPN, do the VPNs first. >> The VPNs first, okay. >> John Furrier: That's my favorite one. >> So one of the most, kind of to give you the backstory, so one of the most interesting things when I came to Google, having come from other large enterprise vendors before this, was there's no VPNs. We don't even have it on our laptop. They have this thing called BeyondCorp, which is essentially now productized as the Identity-Aware Proxy. Which is, it actually takes, we trust no one or nothing with anything. It's not the walled garden style of approach of firewall-type VPN security. What we do is, based upon the resource you're going to request access for, and are you on a trusted machine? So on one that corporate has given you? And do you have two-factor authentication that corporate, not only your, so what you have and what you know. And so they take all of those things into awareness. Is this the laptop that's registered to you? Do you have your two-factor authentication? Have you authenticated to it and it's a trusted platform? Boom, then I can gain access to the resources. But they will also look for things like if all of a sudden you were sitting here and I'm in San Francisco, but something from some country in Asia pops up with my credentials on it, they're going to slam the door shut, going, "There's no way that you can be in two places at one time." And so that's what the Identity-Aware Proxy or BeyondCorp does, kind of in a nutshell. And so we use that everywhere, internally, externally. And so that's one of the ways that we do security differently is without VPNs. And that's actually in front of a lot of the GCP technologies today, that you can actually leverage that. So I would say we take-- >> Just rethinking security. >> It's rethinking security, again, based upon a long history. And not only that, but what we use internally, from our corporate perspective. And now to get to the second question, yeah. >> Istio, Kubeflow, is more of the way software gets run. One quote from one of the ex-Googlers who left Google then went out to another company, she goes, she was blown away, "This is the way you people ship software?" Like she was a fish out of water. She was like, "Oh my god, where's Borg?" "We do Waterfall." So there's a new approach that opens doors between these, and people expect. That's this notion of Kubeflow and orchestration. So that's kind of a modern, it requires training and commitment. That's the upside. Fix the aspirin, so Identity Proxy, cool. Future of software development architecture. >> I think one of the strong things that you're going to see in software development is I think the days of people running it differently in development, and then sandbox and testing, QA, and then in prod, are over. They want to basically have that same experience, no matter where they are. They want to not have to do the crossing your fingers if it, remember, now it gets reddited or you got slash-dotted way back in the past and things would collapse. Those days of people being able to put up with those types of issues are over. And so I think that you're going to continue to see the development and the style of microservices, containers, orchestrated by something that can do auto scaling and healing, like Kubernetes. You're going to see them then start to use that base layer to add new capabilities on top, which is where we see Kubeflow, which is like, hey, how can I go put scalable machine learning on top of containers and on top of Kubernetes? And you even see, like I said, you see people saying, "Well, I don't really want to run "two different data planes and do the inception model. "If I can lay down a base layer "of Kubernetes and containers, then I can run "bare metal workloads against the bare metal. "If I need to launch a virtual machine, "I'll just launch that inside the container." And that's what KubeVirt's doing. So we're seeing a lot of this very interesting stuff pop. >> John Furrier: Yeah, creativity. >> Creativity. >> Great, talk about your role in the Office of the CTO. I know we got a couple of minutes left. I want to get out there, what is the role of the CTO? Bryan Stevens, formerly a Red Hat executive. >> Yeah, Bryan's our CTO. He used to run a big chunk of the engineering for Google Cloud, absolutely. >> And so what is the office's charter? You mentioned some CIOs, former CIOs are in there. Is it the think tank? Is it the command and control ivory tower? What's the role of the office? >> So I think a couple of years ago, Diane Greene and Bryan Stevens and other executives decided if we want to really understand what the enterprise needs from us, from a cloud perspective, we really need to have some people that have walked in those shoes, and they can't just be Diane or can't just be Bryan, who also had a big breadth of experience there. But two people can't do that for every customer for every product. And so they instituted the Office of the CTO. They tapped Will Grannis, again, had been in Boeing before, been in the military, and so tapped him to build this thing. And they went and they looked for people that had experience. Former VPs of Engineering, former CIOs. We have people from GE Oil and Gas, we have people from Boeing, we have people from Pixar. You name it, across each of the different verticals. Healthcare, we have those in the Office of the CTO. And about, probably, I think 25 to 30 of us now. I can't remember the exact numbers. And really, what our day to day life is like is working significantly with the product managers and the engineering teams to help facilitate more and more enterprise-focused engineering into the products. And then working with enterprise customers, kind of the big enterprise customers that we want to see successful, and helping drive their success as they consume Google Cloud. So being the conduit, directly into engineering. >> So in market with customers, big, known customers, getting requirements, helping facilitate product management function as well. >> Yeah, and from an engineering perspective. So we actually sit in the engineering organization. >> John Furrier: Making sure you're making the good bets. >> Jonathan: Yes, exactly. >> Great, well thanks for coming on The Cube. Thanks for sharing the insight. >> Jonathan: Thanks for having me again. >> Great to have you on, great insight, again. Google, always great technology, great enterprise mojo going on right now. Of course, The Cube will be at Google Next this July, so we'll be having live coverage from Google Next here in San Francisco at that time. Thanks for coming on, Jonathan. Really appreciate it, looking forward to more coverage. Stay with us for more of day three, as we start to wrap up our live coverage of Red Hat Summit 2018. We'll be back after this short break. (upbeat electronic music)

>> Announcer: Live, from Las Vegas, it's the Cube. Covering Dell Technologies World 2018. Brought to you by Dell EMC and it's ecosystem partners. >> And welcome back live here on the Cube as we continue our coverage of Dell Technologies World 2018. We're live at the Sands and it is now my pleasure for the first time today to say, "My partner's Stu Miniman". Stu, how you been? >> Awesome John, great to be talking on the Cube with you and our great guest. >> It's been too long, that's for sure! Good to see you again. We're joined by Manuvir Das who's the senior vice-president GM of unstructured data at Dell EMC. Thanks for being with us. We appreciate it. >> It's my pleasure to be here. >> Let's talk about your world, this exponential growth, this unpredictable growth, all this unstructured data, and now it's worth something, right? >> Yes, it is. >> So people are turning out to realize what an asset, what a resource that it's become. >> In fact, it's funny you say "Asset" because, the whole theme and tagline for my team is data capital, because I think people are coming to realize that just like you have capital assets in your factories and your people, data is part of the capital of a company now, right? And you really need to turn it into something, so I think it's an exciting time. For my team, we basically work in file and object storage, Isilon has been a market leader for some time. We have an object storage product called ECS, and we are the market leader, and we have been for some time, but it's a very interesting time because data is growing so greatly now that the leader today can be a nobody five years from now, right? So, what I tell my team every day is how are we going to disrupt ourselves before we get disrupted so that we can still be the leader five years from now when that exponential growth has continued, right? So that's what we do every day. We think about what are the ways, on behalf of the customer, the world of unstructured storage is being disrupted and how are we going to be there on the other side with the customer? >> Manuvir, I'm wondering if you could bring us inside the customers 'cuz we've been talking about unstructured data at least five years. Data data everywhere, but how is it changing customers' lives? How is it changing their businesses? What's the update on the customer's viewpoint on unstructured data? >> So, we have an interesting perspective because our business really grew up with working with specific verticals. Like media, entertainment, life sciences, automotive, and we see that there. For example, in the automotive industry, we have a lot of customers that are doing Adax and so they're basically driving vehicles around, capturing videos of a child running across the street, of a bicycle on the road, and they're bringing all that data back to our storage system so that they can train their software that runs the car, so that when the situation happens in real life, the software does the right thing, right? In media entertainment now, with 4K streaming and more and more digitized movies, how do you produce these movies, right? How does Pixar and everybody produce these movies? In life sciences, it's all about Genomix. It's started in the research domain with Genomix, and now it's going into hospitals where people want to use Genomix to make real-time decisions about what to do for their patients. And these are all ways in which these existing industries are really using unstructured data now as their lifeblood to change their business. So, it's an interesting change, really. >> You mentioned five years from now, we want to make sure that we're the disruptor, and I read a number that by five years out, all data or unstructured data, will be 93% of all data, So that kind of growth when you see that mushroom occurring, does that put a little fear, does that put the challenge? >> It's a great opportunity, right? It's a great opportunity, and it's an opportunity not just for us at Dell EMC, right? It's an opportunity for everybody. The public cloud vendors, start-ups, and our only interest is, let's take the customer to the right place, and if we can participate in that in some way, I believe there are opportunities so big, that we will be happy with whatever share of it we have. As long as the customer gets what they need. And I think every customer's going to have some mix of On-Premises storage, appliances, software defined storage, storage in the public cloud. It'll all sort of come together and I think we have a role to play, right? If I may, there's really four things we see about how the data is changing say from now to five years from now. The first thing is Flash, right? Flash is no longer just for high-end storage. Flash is everywhere in storage. The second thing is the public cloud. More and more data going into the public cloud. The fourth thing is analytics. Who is going to pay to store all of this data unless they can actually put it to use, right? And so, how do you provide the analytics? And then the fourth thing is archiving because the truth of the matter is, when you expand your data at that scale, most of the data is not useful at any point in time. It may be useful tomorrow, but it's not useful today. So how do you use technologies like optic storage to really economically store the bulk of the data? So these are the four trends we see, flash, the public cloud, analytics, and advent of object, and everything we're doing at Dell EMC with unstructured storage, is to embrace these four trends. So that we can come out the other side with the customer on these. >> There was one thing that caught my ear during the keynote this morning, there were many things that stood out, but one of them I could have sworn Michael said something about Isilon. We've googled cloud, and I'm googling, I'm looking around, obviously object storage, I think very much in the public crowd, seeing growth elsewhere, but maybe you can explain what that is and what's going on. >> So this is an Isilon file storage, is now available for customer's of Google in the Google Cloud. So, for years our customers have used Isilon On-Premise because it's really the only solution in the market where you can get a very large file system that performs well. You don't have this technology in the public cloud today. And, we have all these customers who run their workloads on prem using Isilon. And they're looking to use the computer in the public cloud, but if they don't have Isilon there, they would have to re-write their application. So, we worked out a model with Google where we host our Isilon gear, our physical Isilon clusters in the same place where Google has their cloud, and their compute, so now a customer can run their application, on the compute VMs of Google cloud, but they have some millisecond access to an Isilon cluster that is dedicated to them that is right there. So in this way, they can take the journey to the public cloud, but not have to change their application because they still use Isilon. So, this is the model we came up with. We just launched it, so that's what Michael was referring to. We're very excited about it. I think it's really an opportunity for the customer to embrace the cloud, and the thing I'm trying to tell every customer that we have because we've been on premises for a long time, is "Look, I believe in the public cloud." I actually worked on building one of those at Microsoft years ago, and all we want is, we want to help the customers get to the public cloud by giving them the same technology there, that they've had On-Premise. And, so I think everybody wins. >> Yeah, that's exciting. Thank you so much for going through that. Your team, it's up there in Seattle, so much going on. Have there been customers on beta on this? Is this available now? Give some of the speeds and feeds. >> So, in the cloud terminology, people usually start with a what they call a tech preview or an early adoptive program, so that's where we're at right now. We have the first customer already running operational on it, and they will talking about it in some of our breakout sessions as well, so we're in the early the adoptive program right now, so anybody's that's interested can join the early adoptive program. It's fully operational, but we're sort of controlling how many customers adopt it first, and then we expand from there. >> Look at the, if you would, the risk side of this, with all that data, it's a treasure trove in some respects to a company, but it's also a very alluring target. So from a security standpoint, what kind of emphasis are you shifting towards that, knowing that you have that much more waterfront to cover now? >> I think it is the key issue really, and so that's why, even John in this move to the public cloud, that we just talked about, we have not done this as some kind of multi-tenant, shared kind of environment. Every customer has a dedicated environment, their Isilon is in a cage, everybody can't get into that cage, and all the security protocols are in place. So that's a very important aspect of this. And then whether it's in the cloud or On-Premise, they're constantly raising the bar on the security protections. How does the data flow internally between the servers in the cluster? Everything's encrypted, authenticated, protected, so I think that is really the key issue going forward. Well, I know it's a challenge, but as you said, it's also an opportunity. So, good luck meeting the challenge, and I hope five years from now, we're still talking about you. >> Yes, we'll see where we are. >> Hey, how do you like the music, by the way? >> Uh, it's great. >> It's a little loud. >> That's right, a little loud. >> Manuvir, thanks for being with us. >> Sure my pleasure. >> We'll continue from a floor that's starting to get a little more energy to it. Here live at the Sands, we are at Dell Technologies World 2018.

(click) >> Hey welcome back everybody, Jeff Frick here with the Cube we're at the Accenture Technology Vision event 2018. The actual report comes out in a couple of days. We're here at the preview event. A couple hundred people in downtown San Francisco. A lot of demos of AR and VR downstairs. It's really a center kind of highlighting the top trends that they've surveyed their community and we're excited to be here to be joined by one of the experts. He's Tony Parisi, on the Cube a long time ago we looked at 2013 amazing. He's the global head of AR and VR for Unity technology. Tony great to see you again. >> Good to see you. >> So uh, so you've been on this AR VR virtual reality thing for a while. Amazing development in this space. >> I've been working in the field for a couple decades now in one form or another and it's just been great to see with the resurgence of virtual reality. And we had experiments 20 years ago trying to turn this into a consumer ready technology. Really wasn't ready yet. With the advent of oculus and some of these other technologies, we've seen something that a consumer could afford that enterprises can afford in large numbers. You know, some thousand dollar piece of hardware connected to a personal computer that's a few thousand dollars that can drive amazing VR experiences. These same kind of immersion techniques brought into a phone where you can take a smartphone and just look through it like a magic window into this extended reality where you're seeing 3D graphics that persist in the environment around you. And these are all working toward the future where we're going to have all this 3D amazingness. Digital magic in front of us. And it's just incredible to see how far we've come in all these years and how it's just about to be both consumer ready and be deployed into businesses for all kinds of different productivity opportunities. >> Yeah it's interesting >> Amazing - that a center role them all up into one. They went with the extended reality. Cause there's a lot of confusion, is it augmented reality, virtual reality you know, how much of it is virtual stuff overlayed to reality, how much reality is brought back into the virtual space. But at the end of the day it's a lot of blending. It's going to depend on the application. >> Definitely depends on the application. If you just take marketing terms and put them aside because everyone's got their own talking about this and agreed there's a little bit of confusion right now. If you just look at the common element, it is 3D graphics. It is graphics that represent objects, environments, places, people in a way that's much more realistic. It's much more intuitive for an enuser to grasp that touches us in our brains, in a place that a flat screen doesn't so that we remember it better, or learn it more effectively. That's what all of these different techniques have in common. So, you know, call it what you want. Accenture's rolling forward with all of that. My company Unity's supplying the core technology to power all of that across 30 platforms and you look at the whole industry. Game platforms, which is where we were born. And now in the VR and AR it's you know, a dozen platforms at least that look like they could be viable. The common element for anybody who's developing is that it's 3D graphics and they're going to make investments in certain kinds of software, certain kinds of application design and techniques and knowledge that's going to transfer among all of these different kinds of hardware platforms. Because we know at the end of the day there'll be a handful there will be 3 or 4 that end up dominating just like in any other part of the computer industry with any other digital technology. So, you know we're moment because it's so early where the technology um, we don't know how to talk about it yet. But I think if you look back, probably the same thing was true of mobile and the PC at the time if you were in the middle of it. - Right >> People call it the PC, or they called it the internet, or they called it the web, or then they called it mobile or a smartphone. You know, there's just all these terms for it but that, you know, that'll be in the the rear view mirror in a couple years. And we'll just all take this for granted as, oh there's 3D stuff now in front of us. Or there's a 3D place I go into in a VR headset. >> Yeah even we were at Baobab Studios last week and, you know, even in entertainment right? Early early television replicated just a stage right? And early movies replicated just a stage before they figured out what they could do with the medium. Same thing here and it's interestingly common, how much is interactive kind of game-like. How much of it's narrative storytelling like a movie. And he's like, don't think of it that way. It's a completely different medium with a completely different opportunity to tell stories, to do things in a completely different way. >> One hundred percent. That team at Baobab is amazing. They use Unity a lot to create their experiences. And they're the first to tell you, we don't quite know what it's going to look like in a few years. We're trying lot's of things, we are going to start from some, you know touchstones, some places we already know. Game design, linear storytelling. But this is a different beast and we don't know what we're going to get. Baobab is a great example of a company that's not afraid to experiment. They're going to try and put you, I mean, what they do is they make Pixar kind of quality, high production value animated content like a Pixar movie but you're in it in VR. You can look all around, you can see the entire action unfolding around you. And more than that, they've made you a part of the story. They make you a character. Usually a secondary character. So the whole burden is not on you as the viewer to have to figure this story out. But someone who can help the story along so you feel fully involved. And if you play that forward, if you think about where that's going to go in a few years, we may be the folks who are making the stories up. I mean, it starts with just kind of being a secondary character, but as we learn this as users, as we learn how to do this, we may start making the stories up and being a much more active part of it. But, somehow still having that sweet spot where we're giving the director and the content creator the final say in sort of how this world is being created. Uh, Brett Letter the famous director did Lawnmower Man if you know his work in a 90s work in a movie about VR. He's back in the world here also doing VR again. And he likens this to world building. He thinks VR creation for entertainment is much more like creating a Disney theme park. A world that you can inhabit and be part of and have fun for hours, days at a time versus telling one story from start to finish. So I mean, think about it what's going to happen in the next couple years. It's mind blowing where this could go. And we really don't know, none of us could predict. >> So you're deep into it Tony. I wonder if you could share a story of maybe some applications that you're seeing in production or kind of in development. Where people are not thinking, you know, men like me obviously we know entertainment, we know games, we know some of the industrial stuff like walking a shop floor and seeing the RPMs of a machine. But what are some of the applications that we don't know that you see coming down the pipe. >> Well if you just think about take one industry, like the auto industry. Right? I think you can imagine like you said if you're somewhat versed in this. The idea that you could use virtual reality to design a car instead of what they do today which is they use some 3D design packages but they still build a physical prototype of the car out of clay and, you know, companywide a Ford, or a Volkswagon, or a company like that will spend millions for every new car building these physical prototypes. They want to replace that with purely digital and virtual processes at some point which is going to save them a heck of a lot of time and money and materials cost. Right? But now you just take that one example and you take that car design and do the entire life cycle of that car to when it's assembled, manufactured, you can train people in VR how to do that. When it's getting rolled out onto a show floor, to people who are selling it. All the way to when that car is a self driving car that you put somebody into and they get in the cabin of that thing and the cabin because there's no driver in there, you now have a lot of room in there. Right? It's an entertainment center. So these kind of augmented and virtual reality technologies could potentially touch every phase in the lifecycle of not just the development, but the deployment, and the ongoing operation of a motor vehicle as we know it. So that's going to radically transform things sometime in the next 5 to 10 years. >> Alright Tony I'm going to let you go, the party's underway. We got the autonomous robots are playing in the band. In 5 years from now we can't wait 5 years because I don't even know what's going to be here. >> I hope we do this again. >> It'll be crazy different. - Well in advance of 5 years from now. >> So thanks for talking with me a few minutes. Tony Parisi, Jeff Rick, you're watching the Cube from the Accenture Technology Vision 2018. Thanks for watching. (upbeat digital music)

>> Narrator: From San Francisco, it's The Cube. Covering Samsung Developer Conference 2017, brought to you by Samsung. (upbeat music) >> Okay, welcome back, everyone. Live here, in San Francisco, for day two. We're winding down day two of two days of wall to wall coverage, The Cube's exclusive coverage of Samsung Developer Conference. I'm John Furrier the founder, co-founder of SiliconANGLE Media (mumbles) the Cube our next guest Larry Cutler, co-founder and CTO of Baobab Studios. >> You got it right. >> And you're co-founder and CEO Maureen Fan was at our Grace Hopper event. >> Larry: Yes. >> Well thanks for joining us. >> Thank you for having me. >> So you guys are doing great. Love the story-telling, gaming, tech culture coming together here at Samsung. >> Yep. >> And seeing the future. The future, if you connect the dots, is augmented reality voice-activated, headphones that can be hacked and programmed and customized. You guys are doing some interesting things, you've got some good awards. Take a minute to talk about your studios. What are you guys doing, what's the aha moment for you guys. What should people know? >> So we at Baobab Studios, we're reinventing animated storytelling using VR. And so what we've, our aha moment was feeling that you could completely immerse yourself in the world in VR. And when we started thinking about well what is so special about an animated film, it's really about transporting you to faraway worlds, and making those worlds so real that you could like reach out and touch them. And then if you think about VR, that's kind of the same thing, where VR's promise is being able to transport you to incredible places and to make you really feel like you're in those worlds and you're part of those worlds. >> And to relax and chill a little bit too. >> Well relax and chill as well. >> Or entertainment or to be thrilled. >> So in our case we wanted to take all the experience that we have telling stories at places like Pixar and Dreamworks and apply that into this new medium. And so we found that VR is an incredibly powerful medium, and it really is one that's unlike film and unlike games. So, you're talking about bringing together the expertise of people from films, and people from games. That's critical to what we're doing, and yet at the same time, the experience is something that's totally different. >> Yeah we were commenting with some of our guys in our office how Twitch was experimenting with using the comments to drive the game behavior. You start to see new kinds of game systems develop around the storytelling as more of a behavioral dynamic, not the boring game I figured out, or the multi-player game with my same friends, or whatever that's going on, you're seeing a whole new level of creativity going on, one. Two, the other question is how much does it cost to make these things, 'cause Dreamworks and these guys are spending a lot of dough to do animation. >> Larry: Yes. >> I mean it's not cheap, so how do you guys crack the code on keeping it low, not low-budget, but low-cost and also intelligent is that the secret sauce? >> Yes well that's certainly super important for us. You know we're a start-up company, so obviously we need to be able to have a pipeline that if we run efficiently and you know for us, I think what we've done is we've brought together some of the best people, you know. So we've hired our friends, we've hired people who are really experts in the field, and we're really creating a next-generation VR platform so that we can actually create these animated experiences, be able to experiment in this space. You know, sort of try out different techniques and storytelling methodologies and at the same time do it in a way that we can, you know, sort of experiment as much as possible. >> John: Talk about some of the awards you guys had, I mean, you guys done some things. Share some of the accolades you've gotten. >> Yes, so, I mean what's amazing is that we started with our first piece of content Invasion!, and we premiered it last year in April at Tribecca Film Festival, and it's really gone on to, first of all, from a consumer standpoint, it's one of the most popular downloaded pieces of content on all the VR platforms, and at the same time it's been shown at a number of different film festivals, and it recently won an Emmy, so we're very excited about that, and, you know, what we realize is that audiences just fell in love with this bunny character Chloe. So Invasion! is this story about two aliens that try to take over Earth, and they're thwarted not by humans with powerful weapons, but by cute little woodland creatures. In the first episode in Invasion! they're thwarted by this cute little bunny Chloe, and what happens is that at the very beginning of the piece, Chloe like hops up to you, and if you look down you're a little bunny in the scene, you've got bunny legs, you can hop around. And audiences, you know, the viewer just reacted in ways that you would never do in a film, like people are waving at Chloe, a lot of people reach out and try to pet Chloe, a lot of people mimic what Chloe's doing, and in short people are just treating Chloe as if she's real. And it made us realize that there's something really powerful here that is really worth kind of leaning in and digging deeper into. >> And what was the next step after that, so you went what you went, some development, was it the game, was it the character development? How do you double-down on them, I'm just kind of curious on thinking about how the, on the thinking behind it. >> It was really interesting because we, in Invasion! we were kind of the first to make you a character in the story, but at the time there really weren't hand controllers, or any of the devices so that you could actually really have sort of a more like increased role in the story. And so at the same time, people really felt that they were front and center, and they felt this sense of protecting Chloe from the aliens 'cause you're placed right in the center of the story. And so with Asteroids! when we started, we just wanted to dive deeper into that idea of you playing an active role in the story. And so in Asteroids! we focused on the two alien characters Mac and Cheez and their relationship, and you're a helper robot on the ship, so you have. >> So how do I get involved and get these stories, do I have to have a VR kit? So I don't have, I haven't bought anything yet VR-wise, so for me, how do I engage with you guys, what do I do? Do I buy Oculus Rift, or certain headsets? >> So our stuff is meant to be available for everyone. We really want to have as many people be able to see our content as possible. And so first of all, if you have a VR headset of any type, our, Invasion! is available on all those platforms, so that's the high-end headsets like the Rift and Vive, the PlayStation VR for Sony, as well as if you have a mobile phone like a Samsung phone, you can plug it into your VR. At the same time, we also have a Baobab Cardboard app, so you don't even need to have VR to see our product. So you just get an IOS or Android app, a Baobab app. >> So I'm in the market to buy a headset, I'm not price sensitive, what should I buy? What would you recommend? (Larry chuckling) >> Well there really are so many options, so you know obviously at the high >> John: What would you get? >> Well at the highest end, you know, getting a Rift or Vive really gives you the full VR experience because it's really tracking your position and orientation so you can actually walk around a space, the characters can react to you in believable ways, and it's all happening in real time. And on the other hand, the Sony PlayStation VR's are really affordable if you've got a Sony Playstation, you just buy the PSVR headset, you plug it right in and you're ready to go. I'm really excited looking forward to a world where you're not tethered to your computer, and so it's really exciting to see these stand-alone headsets that are going to come out that basically there's no tethering to a computer and yet you have that same inside-out tracking so that >> And this is the Samsung vision, right? The Samsung vision is to use their displays. >> Yes, exactly. See right now what you have, which is, which is already really exciting, is you take your Samsung phone, you plug it into a Gear VR, and the one thing that's missing is that you're not able to track both the position and orientation of where you are, and so the next generation headsets are going to have that. And the experiences will be much higher feeling. >> Alright cool, so then I, I'll actually download all of this stuff. So as the CTO of the venture, your job is to kind of look over the landscape. You have to have the 20 mile stare of the future, not screw it up, but you've got to win the present. >> Larry: Yes, we're all about being able to deliver in the present and look forward to the future. >> And that's the key, and you have that unique skill as an entrepreneur. What are you guys doing now technically and with the product, what are some of the key things that people should know about, because I mean I look at the CG and the animation world, and you see the Moore's law kind of coming that way, right, so you go wow, as someone with a live video, I would love to have a bunny and the cube set right here, right? So we see a future where I want to immerse myself with characters, not just stickers. >> We of course see that as well, so you know, obviously. >> John: Is expensive I mean it must be, 'cause in the old days what, you had the artists, monster storage, tons of compute, what's it like now? >> Well we're a really small start-up company, so we are not a, you know, 300 person organization that is producing a full animated film. We're a small team of artists and engineers working together in the same way that we had that same excitement in the early days. I started my career at Pixar in some of their earlier films, and it was that same >> John: Power work stations, you had that high-end gear. >> Yeah so it was that same excitement in those early days, like we just had to figure out like how are we going to actually create this shot, how are we going to like, you know, build this character, how are we going to like finish this on time. And we have that same exact excitement in the office. >> When were you at Pixar, were you there for the Toy Story kind of thing? >> Yeah, so I worked on the Toy Story films, and A Bug's Life and Monster's Inc., and then I went over to Dreamworks and headed up all of their character technologies on their various films. >> Okay so you guys do anything different than those guys, or are you guys more focused more simple? >> Yeah, I mean what's interesting is that this is really a new medium and it's a new skill set, because what's happened is that you are part of the world, and for us the thing that's most exciting is that by you being a character, first of all, the other characters are not just able to have, you know, sort of high-quality animation, but they're able to react to you, and so there's there A. a number of technical innovations that you need to overcome so that we can have that same high-quality character performance that I would expect from a Pixar or Dreamworks film happening, running in real time at 90 frames a second on my headset, and at the same time also be able to have those characters react to you and respond to what you're doing. And you know, so we've scratched the surface on that. So one of the things that's really interesting is how two people will actually have all these subconscious communication cues, you know, whether it's eye contact or whether it's two lovers sitting across the table and mirroring each other's behavior, that's the type of thing that we can add into our animation. And at the same time, we have stories where depending on what you do, whether you choose to participate or not, that actually affects the outcome, and affects the way that characters respond to you. And so having much smarter character performances is certainly one area that I think is really exciting. >> And that's going to be interesting for you guys because you have some structured ways to do that, and then some unstructured ways to do it with community data, machine learning, and then you can use bots in a way to help you get data, but you almost could have character developments be dynamic. >> You definitely could, and you know for us I think the thing that is always the biggest point that we come back to is story. And so on one hand we want to tell one story that's really told well, not ten different stories, and on another hand, because you're part of that story, depending on what you do, that's actually going to affect how the characters respond to you, and that could be in a really nuanced way, and so, you know, building up AI systems, and building up the smarts so that you can actually have that type of response, and yet still feel like these characters are alive and breathing. >> Well the Chloe example on Invasion! is great example, you'd love to have that automated, so when you see those magical moments, the story could evolve, you can kind of give it that programmable policy-based taping. (John laughing) >> Yeah exactly so you know, so this is the type of thing. >> We're geeking out here on the set, Larry, great to have you on, super informative content, and I love the world you're in, I think the world's changing, I think you're going to see some interesting dynamics coming, you certainly have the historical view, Pixar and Dreamworks, old school, if you call that old school. >> That's right. >> Now the new school's coming in, certainly AR looks good too. >> Yeah, we're really excited about AR as well. When we think about stories, you know, VR is about transporting you to a different world and having a story take place there. AR is about the characters being alive in your world, both are really exciting. >> Have you seen the Magic Leap demo? >> Larry: Yes, yeah. >> What's it like, can you tell us? >> I'm not allowed to tell. >> John: Is it good? >> Yes, yeah. >> So it's worth half of another half a billion, oh I can't wait. >> That I can't say, you know. I can't comment on their business, but. >> It's a freaking award chest. >> I think there's a lot of exciting things you know, AR. >> They must have a killer demo, I want to find out, I want to see this demo. Magic Leap, I want to to see your demo. All seriousness, great to have you on, and certainly come down to our Cube studios. I want to follow your story, I think you guys are on the cutting edge of a new user experience that's going to bring computer animation, computer graphics, into a new persona as a storytelling, so congratulations. Larry Cutler, how do you say the. >> Baobab. >> Baobab, B-A-O-B-A-B Studios in Redwood City, California. It's the Cube live coverage from Samsung Developer Conference, I'm John Furrier, more after this short break. (peppy music)

>> Announcer: Live, from Orlando, Florida it's the Cube, covering Grace Hopper's Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of the Grace Hopper Conference, here at the Orange County Convention Center. I'm your host, Rebecca Knight. We're joined by Maureen Fan. She is the CEO and co-founder of Baobab Studio, which is the industry's leading VR animation studio, so, welcome Maureen. >> Thank you so much for having me. >> It's excited to talk to you, because you just won an Emmy. Congratulations. >> Thank you. >> You just won an Emmy for "Invasion", so, tell us a little bit about invasion. >> It was our first piece ever and it was just an experiment to see if we could even create VR and it's a story about these adorable little bunnies and you are actually a bunny too, you look down, you have a furry, little bunny body and these aliens that come to try to take over the Earth, with their advanced technology and you and your bunny friend end up saving the entire Earth and it's starring Ethan Hawk and it just came out last year. And we're really excited, because it became the number one top downloaded VR experience across all the headsets and it's getting turned into a Hollywood Feature Film. >> Very cool, very cool >> Thank you. >> And you have another film coming out too and this is "Rainbow Crow" >> Yes. >> Tell our viewers a little bit about "Rainbow". >> So, "Rainbow Crow" is based off of a Native American legend about how the crow used to have beautiful rainbow feathers and a beautiful singing voice and it's John Legend, in our piece and how he decides to sacrifice himself, by flying into the sun to bring warmth and fire back to the Earth and in the process, loses all his beautiful feathers, becomes black and burnt and his voice becomes like the crow's voice, but it's about how beauty is within and there's also, huge themes about diversity and how if you learn to accept yourself and your differences, that's when you can accept others and that's why we specifically cast minorities and women, so, we have John Legend, Constance Wu, from "Fresh off the Boat" as a skunk character, Diego Luna, from "Rogue One", for the moth character, as well as Randy Edmunds, as a Native American elder, narrator, and we have a whole bunch of other stars to announce, soon-- >> Well we cannot wait to hear. That's already an amazing line-up. >> Thank you. >> So, when you're thinking about "Rainbow Crow" and particularly, because it's VR, which is relatively new, still experimental, I mean, the messages of diversity, does it lend itself to VR, better than, say, a standard animation film? >> Absolutely, because if you think about stories that you just watch passively, the reason why we need stories and humanity, in general is to experience characters and stories beyond those we can experience in our real lives and we think, "Oh, how would I feel if I was in the "position of that character or what would I do?" but in VR, because you are actually playing a character in a role, you actually have to decide at that point, "what would I do?" so, it's not just a experience that I just see, it's one where I'm actively experiencing it, so, I create a memory and remember afterwards and there's all these research studies at Stanford by Jeremy Bailenson, who is head of the Stanford VR lab, that shows if you are made a homeless person, inside a VR experience and you have to go through a day in the life of a homeless person or you would look in the mirror and see that you are a black woman, that you, when you get out of the headset, you act completely differently. You have so much more empathy for these people than you would normally and so, it gets you to care about these characters, in a way that you don't normally and in VR, because you're doing it in a real-time game engine, these characters can act and react to what you do, so you can turn that empathy into action and actually act upon your caring, which we call compassion, so, it really changes you in a way, that normal, traditional story-telling doesn't, so, I think that having voices and characters that are different, in front of the screen, and also, behind the screen are really important to create role models and different perspectives for all the people out in the world. >> And these are movies that are targeted at kids, children, but do you see a future in which, where there is more targeted at adults, for VR? >> Absolutely. The funny thing is, in the beginning, the VR distributors didn't think that people would want our VR animation, because they're like, "Oh, it's just going to be these hardcore boys "that just love to play games. "Are they going to want this animation?" and VR is targeted towards adults, that's why they were surprised and we were surprised when "Invasion" became the number one downloaded VR experience. It shows that the audience for our content is from little kids to grandmas and everyone in between and that's probably why it became the top downloaded experience, is because it's universally appealing and has themes that are appealing to just, every single generation, so, absolutely, but for VR to become mainstream, there needs to be more universally appealing content. Right now, the content tends to be for games, like parkour games, as well as documentaries, which are two amazing pieces of content for this medium, but for it to become mainstream, we need more universally appealing content and I'm excited about, right now, it's a new industry. This is when minorities and women in particular, can enter the space and help shape the voices and the direction of the industry. >> That is exactly where I wanted to go next. So, let's talk a little bit about Baobab Studio. It's not that old and VR is not that old and so, why are there more opportunities, would you say, for women, and minorities? >> Well, if you look at traditional animation in the traditional entertainment fields that's a very mature industry and to break into that industry, you have to either have lots and lots of money or unfair distribution advantage, but VR, there's technological disruption, which means nobody has an advantage at all, means it's a level playing field and everybody can come in and start something, so, this is a perfect opportunity, when there's low barriers to entry of coming in, for women and minorities, anyone who wants their voice heard, to start companies or to make experiences and we can set the groundwork, because there's no one telling us what we can and can't do, because no one actually knows what we can and can't do yet. >> Right, right, but yet you are still of a female, asian figurehead of a studio, that will hopefully, someday be a major studio. You're working on it, but do you find that people take you as seriously in Hollywood? I mean, what are you coming up against? >> Well, it's really interesting, because I heard for even fundraising is one of the hardest parts of starting a company and there was a Stanford Research Study that showed that if you took a deck, a pitch deck for a company and you had a male voice-over versus a female voice-over the male voice-over was, I don't remember what, it was like 50% more likely to get funded than the woman with the same exact pitch deck, so I knew from that and they also show that if you are married and wear a ring you're taken more seriously, or if you're less attractive, also, you're taken more seriously and my hypothesis and some of the hypotheses out there, is it takes away the whole entire female attraction thing, like what does it mean to be an attractive female, so, I had to go into the meetings, knowing this. I even considered wearing a ring. I considered wearing a paper bag over my head. >> A bag over you head. Exactly, exactly. >> But at the same time I felt that I need to be myself and the best thing to, there's a correlation between the perceived leadership and confidence, that I needed to just go in there and be confident in myself so, I knew that, that could work against me, but I just needed to be myself, but I had to make sure that I was really confident and really believed in what I said and honestly, besides being confident and aggressive, I also, felt comfortable, because a lot of the people I talked to, I knew from my network and I had many of my male friends and female friends who knew these VC's, do the initial introduction, so I felt more comfortable going in, for them already knowing that I had somebody else saying that I was awesome. >> Yeah, and you've had many mentors and sponsors along the way too. >> Absolutely, I would say it's one of the most important things, for my career from the very beginning. When I graduated from business school, I actually emailed my mentors and said, "Here are the things I care about for finding a job." I didn't have to go find any jobs. They actually found all these jobs. for me, set up informational interviews, for me and I just went in and did it, all the informational interviews, got the offers and just choose one of them that I wanted to be in but, even for starting my company, my co-founder, Eric Darnell was a write and director of all four "Madagascar" films and I got introduced to him, through my mentor, Glen Entis who is the co-founder of PDI Dreamworks Animation and he was my mentor through Zynga and then, Gen Entis introduced me to Alvy Ray Smith, who is the co-founder of Pixar, who also became our advisor, Alvy Ray Smith, then introduced us to Glen Keane, who is the animator for "Little Mermaid", "Alaadin". >> The power of networks. >> It was all through the network and through my mentors that I found, a lot of the opportunities that I have and they also helped my through my personal life and how to navigate being entrepreneur and I rely on them so much. >> So, beyond finding the right mentor and sponsor what else would you give, your parting words to the young Maureen fans out there? >> I think there's a tendency for society to pressure you to conform, to money, fame, beauty and you don't need to listen to that and you don't need to be bucketed. I designed my own major at Stanford and with an eBay, I took four different roles. I just kept on creating my own roles and refusing to be bucketed as a creative or a suit and you can be who you are and create a category onto yourself and so, don't feel pressured to listen to what society is telling you. The other thing, is if you are faced with pushed back for being promoted and you feel like it's maybe because you're a woman, we have a tendency as women to start blaming ourselves and thinking there's something wrong with us, versus research shows men are most likely to blame the system, don't let it affect you and bring you down, because you need to actually be confident and believe in yourself in order to rise above. >> Great. Great advice. Maureen, it's been a pleasure having you on the show. Thanks so much. >> Thank you. >> And best of luck to you. >> Thank you, so much. >> Hope you win another Emmy. >> Thank you. >> Come back and talk to us again. >> Thank you. I'm Rebecca Knight, we'll have more from Grace Hopper, just after this. (techno music)