(gentle music) >> Welcome back to theCUBE's virtual program, covering VMware Explorer, 2022. The first time since 2019 that the VMware ecosystem is gathered in person. But in the post isolation economy, hybrid is the new format, cube plus digital, we call it. And so we're really happy to welcome Cal Al-Dhubaib who's the founder and CEO and AI strategist of Pandata. And Jonathan Seckler back in theCUBE, the senior director of product marketing at Dell Technologies. Guys, great to see you, thanks for coming on. >> Yeah, thanks a lot for having us. >> Yeah, thank you >> Cal, Pandata, cool name, what's it all about? >> Thanks for asking. Really excited to share our story. I'm a data scientist by training and I'm based here in Cleveland, Ohio. And Pandata is a company that helps organizations design and develop machine learning and AI technology. And when I started this here in Cleveland six years ago, I had people react to me with, what? So we help demystify AI and make it practical. And we specifically focus on trustworthy AI. So we work a lot in regulated industries like healthcare. And we help organizations navigate the complexities of building machine learning and AI technology when data's hard to work with, when there's risk on the potential outcomes, or high cost in the consequences. And that's what we do every day. >> Yeah, yeah timing is great given all the focus on privacy and what you're seeing with big tech and public policy, so we're going to get into that. Jonathan, I understand you guys got some hard news. What's your story around AI and AutoML? Share that with us. >> Yeah, thanks. So having the opportunity to speak with Cal today is really important because one of the hardest things that we find that our customers have is making that transition of experimenting with AI to making it really useful in real life. >> What is the tech underneath that? Are we talking VxRail here? Are you're talking servers? What do you got? >> Yeah, absolutely. So the Dell validated design for AI is a reference framework that is based on the optimized set of hardware for a given outcome. That includes it could be VxRail, VMware, vSphere and Nvidia GPUs and Nvidia software to make all of that happen. And for today, what we're working with is H2O.ai's solution to develop automatic machine learning. So take just that one more step to make it easier for customers to bring AI into production. >> Cool. >> So it's a full stack of software that includes automated machine learning, it includes NVIDIA's AI enterprise for deployment and development, and it's all built on an engineering validated set of hardware, including servers and storage and whatever else you need >> AI out of the box, I don't have to worry about cobbling it all together. >> Exactly. >> Cal, I want to come back to this trusted AI notion. A lot of people don't trust AI just by the very nature of it. I think about, okay, well how does it know it's a cat? And then you can never explain, it says black box. And so I'm like, what are they do with my data? And you mentioned healthcare, financial services, the government, they know everything about me. I just had to get a real ID and Massachusetts, I had to give all my data away. I don't trust it. So what is trusted AI? >> Well, so let me take a step back and talk about sobering statistics. There's a lot of different sources that report on this, but anywhere you look, you'll hear somewhere between 80 to 90% of AI projects fail to yield a return. That's pretty scary, that's a disappointing industry. And why is that? AI is hard. Versus traditional software, you're programming rules hard and fast. If I click this button, I expect A, B, C to happen. And we're talking about recognizing and reacting to patterns. It's not, will it be wrong? It's, when it's wrong, how wrong will it be? And what are it cost to accept related to that? So zooming back in on this lens of trustworthy AI, much of the last 10 years the development in AI has looked like this. Let's get the data, let's race to build the warehouses, okay we did that, no problem. Next was race to build the algorithms. Can we build more sophisticated models? Can we work with things like documents and images? And it used to be the exclusive domain of deep tech companies. You'd have to have teams of teams building the software, building the infrastructure, working on very specific components in this pipeline. And now we have this explosion of technologies, very much like what Jonathan was talking about with validated designs. So it removes the complexities of the infrastructure, it removes the complexities of being able to access the right data. And we have a ton of modeling capabilities and tools out there, so we can build a lot of things. Now, this is when we start to encounter risk in machine learning and AI. If you think about the models that are being used to replicate or learn from language like GPT-3 to create new content, it's training data set is everything that's on the internet. And if you haven't been on the internet recently, it's not all good. So how do you go about building technology to recognize specific patterns, pick up patterns that are desirable, and avoid unintended consequences? And no one's immune to this. So the discipline of trustworthy AI is building models that are easier to interrogate, that are useful for humans, and that minimize the risk of unintended consequences. >> I would add too, one of the good things about the Pandata solution is how it tries to enforce fairness and transparency in the models. We've done some studies recently with IDC, where we've tried to compare leaders in AI technology versus those who are just getting started. And I have to say, one of the biggest differences between a leader in AI and the rest of us is often that the leaders have a policy in place to deal with the risks and the ethics of using data through some kind of machine oriented model. And it's a really important part of making AI usable for the masses. >> You certainly hear a lot about, AI ultimately, there's algorithms which are built by humans. Although of course, there's algorithms to build algorithms, we know that today. >> Right, exactly. >> But humans are biased, there's inherent bias, and so this is a big problem. Obviously Dell, you have a giant observation space in terms of customers. But I wonder, Cal, if you can share with us how you're working with your customers at Pandata? What kind of customers are you working with? What are they asking? What problems are they asking you to solve? And how does it manifest itself? >> So when I like to talk about AI and where it's useful, it usually has to do with taking a repetitive task that humans are tasked with, but they're starting to act more like machines than humans. There's not much creativity in the process, it's handling something that's fairly routine, and it ends up being a bottleneck to scaling. And just a year ago even, we'd have to start approaching our clients with conversations around trustworthy AI, and now they're starting to approach us. Really example, this actually just happened earlier today, we're partnering with one of our clients that basically scans medical claims from insurance providers. And what they're trying to do is identify members that qualify for certain government subsidies. And this isn't as straightforward as it seems because there's a lot of complexities in how the rules are implemented, how judges look at these cases. Long story short, we help them build machine learning to identify these patients that qualify. And a question that comes up, and that we're starting to hear from the insurance companies they serve is how do you go about making sure that your decisions are fair and you're not selecting certain groups of individuals over others to get this assistance? And so clients are starting to wise up to that and ask questions. Other things that we've done include identifying potential private health information that's contained in medical images so that you can create curated research data sets. We've helped organizations identify anomalies in cybersecurity logs. And go from an exploration space of billions of eventual events to what are the top 100 that I should look at today? And so it's all about, how do you find these routine processes that humans are bottlenecked from getting to, we're starting to act more like machines and insert a little bit of outer recognition intelligence to get them to spend more time on the creative side. >> Can you talk a little bit more about how? A lot of people talk about augmented AI. AI is amazing. My daughter the other day was, I'm sure as an AI expert, you've seen it, where the machine actually creates standup comedy which it's so hilarious because it is and it isn't. Some of the jokes are actually really funny. Some of them are so funny 'cause they're not funny and they're weird. So it really underscored the gap. And so how do you do it? Is it augmented? Is it you're focusing on the mundane things that you want to take humans out of the loop? Explain how. >> So there's this great Wall Street Journal article by Jennifer Strong that she published I think four years ago now. And she says, "For AI to become more useful, it needs to become more boring." And I really truly believe in that. So you hear about these cutting edge use cases. And there's certainly some room for these generative AI applications inspiring new designs, inspiring new approaches. But the reality is, most successful use cases that we encounter in our business have to do with augmenting human decisions. How do you make arriving at a decision easier? How do you prioritize from millions of options, hundreds of thousands of options down to three or four that a human can then take the last stretch and really consider or think about? So a really cool story, I've been playing around with DALL.E 2. And for those of you who haven't heard, it's this algorithm that can create images from props. And they're just painting I really wish I had bought when I was in Paris a few years ago. And I gave it a description, skyline of the Sacre-Coeur Church in Montmartre with pink and white hues. And it came up with a handful of examples that I can now go take to an artist and say paint me this. So at the end of the day, automation, it's not really, yes, there's certain applications where you really are truly getting to that automated AI in action. But in my experience, most of the use cases have to do with using AI to make humans more effective, more creative, more valuable. >> I'd also add, I think Cal, is that the opportunity to make AI real here is to automate these things and simplify the languages so that can get what we call citizen data scientists out there. I say ordinary, ordinary employees or people who are at the front line of making these decisions, working with the data directly. We've done this with customers who have done this on farms, where the growers are able to use AI to monitor and to manage the yield of crops. I think some of the other examples that you had mentioned just recently Cal I think are great. The other examples is where you can make this technology available to anyone. And maybe that's part of the message of making it boring, it's making it so simple that any of us can use it. >> I love that. John Furrier likes to say that traditionally in IT, we solve complexity with more complexity. So anything that simplifies things is goodness. So how do you use automated machine learning at Pandata? Where does that fit in here? >> So really excited that the connection here through H2O that Jonathan had mentioned earlier. So H2O.ai is one of the leading AutoML platforms. And what's really cool is if you think about the traditional way you would approach machine learning, is you need to have data scientists. These patterns might exist in documents or images or boring old spreadsheets. And the way you'd approach this is, okay, get these expensive data scientists, and 80% of what they do is clean up the data. And I'm yet to encounter a situation where there isn't cleaning data. Now, I'll get through the cleaning up the data step, you actually have to consider, all right, am I working with language? Am I working with financial forecasts? What are the statistical modeling approaches I want to use? And there's a lot of creativity involved in that. And you have to set up a whole experiment, and that takes a lot of time and effort. And then you might test one, two or three models because you know to use those or those are the go to for this type of problem. And you see which one performs best and you iterate from there. The AutoML framework basically allows you to cut through all of that. It can reduce the amount of time you're spending on those steps to 1/10 of the time. You're able to very quickly profile data, understand anomalies, understand what data you want to work with, what data you don't want to work with. And then when it comes to the modeling steps, instead of iterating through three or four AutoML is throwing the whole kitchen sink at it. Anything that's appropriate to the task, maybe you're trying to predict a category or label something, maybe you're trying to predict a value like a financial forecast or even generate test. And it tests all of the models that it has at its disposal that are appropriate to the task and says, here are the top 10. You can use features like let me make this more explainable, let me make the model more accurate. I don't necessarily care about interrogating the results because the risk here is low, I want to a model that predicts things with a higher accuracy. So you can use these dials instead of having to approach it from a development perspective. You can approach it from more of an experimental mindset. So you still need that expertise, you still need to understand what you're looking at, but it makes it really quick. And so you're not spending all that expensive data science time cleaning up data. >> Makes sense. Last question, so Cal, obviously you guys go deep into AI, Jonathan Dell works with every customer on the planet, all sizes, all industries. So what are you hearing and doing with customers that are best practices that you can share for people that want to get into it, that are concerned about AI, they want to simplify it? What would you tell them? Go ahead, Cal. >> Okay, you go first, Cal. >> And Jonathan, you're going to bring us home. >> Sure. >> This sounds good. So as far as where people get scared, I see two sides of it. One, our data's not clean enough, not enough quality, I'm going to stay away from this. So one, I combat that with, you've got to experiment, you got to iterate, And that's the only way your data's going to improve. Two, there's organizations that worry too much about managing the risk. We don't have the data science expertise that can help us uncover potential biases we have. We are now entering a new stage of AI development and machine learning development, And I use those terms interchangeably anymore. I know some folks will differentiate between them. But machine learning is the discipline driving most of the advances. The toolkits that we have at our disposal to quickly profile and manage and mitigate against the risk that data can bring to the table is really giving organizations more comfort, should give organizations more comfort to start to build mission critical applications. The thing that I would encourage organizations to look for, is organizations that put trustworthy AI, ethical AI first as a consideration, not as an afterthought or not as a we're going to sweep this on the carpet. When you're intentional with that, when you bring that up front and you make it a part of your design, it sets you up for success. And we saw this when GDPR changed the IT world a few years ago. Organizations that built for privacy first to begin with, adapting to GDPR was relatively straightforward. Organizations that made that an afterthought or had that as an afterthought, it was a huge lift, a huge cost to adapt and adjust to those changes. >> Great example. All right, John, I said bring us home, put a bow on this. >> Last bit. So I think beyond the mechanics of how to make a AI better and more workable, one of the big challenges with the AI is this concern that you're going to isolate and spend too much effort and dollars on the infrastructure itself. And that's one of the benefits that Dell brings to the table here with validated designs. Is that our AI validated design is built on a VMware vSphere architecture. So your backup, your migration, all of the management and the operational tools that IT is most comfortable with can be used to maintain and develop and deploy artificial intelligence projects without having to create unique infrastructure, unique stacks of hardware, and then which potentially isolates the data, potentially makes things unavailable to the rest of the organization. So when you run it all in a VMware environment, that means you can put it in the cloud, you can put it in your data center. Just really makes it easier for IT to build AI into their everyday process >> Silo busting. All right, guys, thanks Cal, John. I really appreciate you guys coming on theCUBE. >> Yeah, it's been a great time, thanks. >> All right. And thank you for watching theCUBE's coverage of VMware Explorer, 2022. Keep it right there for more action from the show floor with myself, Dave Velante, John Furrier, Lisa Martin and David Nicholson, keep it right there. (gentle music)

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Live from San Francisco, it's the Cube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello and welcome back to The Cube coverage here live in San Francisco for Google Cloud's conference Next 2018, #GoogleNext18. I'm John Furrier with Jeff Frick, my cohost all week. Third day of three days of wall to wall live coverage. Our next guest, Carol Carpenter, Vice President of Product Marketing for Google Cloud. And Ayin Vala, Chief Data Science Foundation for Precision Medicine. Welcome to The Cube, thanks for joining us. >> Thank you for having us. >> So congratulations, VP of Product Marketing. Great job getting all these announcements out, all these different products. Open source, big query machine learning, Istio, One dot, I mean, all this, tons of products, congratulations. >> Thank you, thank you. It was a tremendous amount of work. Great team. >> So you guys are starting to show real progress in customer traction, customer scale. Google's always had great technology. Consumption side of it, you guys have made progress. Diane Green mentioned on stage, on day one, she mentioned health care. She mentioned how you guys are organizing around these verticals. Health care is one of the big areas. Precision Medicine, AI usage, tell us about your story. >> Yes, so we are a very small non-profit. And we are at the intersection of data science and medical science and we work on projects that have non-profits impact and social impact. And we work on driving and developing projects that have social impact and in personalized medicine. >> So I think it's amazing. I always think with medicine, right, you look back five years wherever you are and you look back five years and think, oh my god, that was completely barbaric, right. They used to bleed people out and here, today, we still help cancer patients by basically poisoning them until they almost die and hopefully it kills the cancer first. You guys are looking at medicine in a very different way and the future medicine is so different than what it is today. And talk about, what is Presicion Medicine? Just the descriptor, it's a very different approach to kind of some of the treatments that we still use today in 2018. It's crazy. >> Yes, so Presicion Medicine has the meaning of personalized medicine. Meaning that we hone it into smaller population of people to trying to see what is the driving factors, individually customized to those populations and find out the different variables that are important for that population of people for detection of the disease, you know, cancer, Alzheimer's, those things. >> Okay, talk about the news. Okay, go ahead. >> Oh, oh, I was just going to say. And to be able to do what he's doing requires a lot of computational power to be able to actually get that precise. >> Right. Talk about the relationship and the news you guys have here. Some interesting stuff. Non-profits, they need compute power, they need, just like an eneterprise. You guys are bringing some change. What's the relationship between you guys? How are you working together? >> So one of our key messages here at this event is really around making computing available for everyone. Making data and analytics and machine learning available for everyone. This whole idea of human-centered AI. And what we've realized is, you know, data is the new natural resource. >> Yeah. >> In the world these days. And companies that know how to take advantage and actually mine insights from the data to solve problems like what they're solving at Precision Medicine. That is really where the new breakthroughs are going to come. So we announced a program here at the event, It's called Data Solutions for Change. It's from Google Cloud and it's a program in addition to our other non-profit programs. So we actually have other programs like Google Earth for non-profits. G Suite for non-profits. This one is very much focused on harnessing and helping non-profits extract insights from data. >> And is it a funding program, is it technology transfer Can you talk about, just a little detail on how it actually works. >> It's actually a combination of three things. One is funding, it's credits for up to $5,000 a month for up to six months. As well as customer support. One thing we've all talked about is the technology is amazing. You often also need to be able to apply some business logic around it and data scientists are somewhat of a challenge to hire these days. >> Yeah. >> So we're also proving free customer support, as well as online learning. >> Talk about an impact of the Cloud technology for the non-proit because6 I, you know, I'm seeing so much activity, certainly in Washington D.C. and around the world, where, you know, since the Jobs Act, fundings have changed. You got great things happening. You can have funding on mission-based funding. And also, the legacy of brand's are changing and open source changes So faster time to value. (laughs) >> Right. >> And without all the, you know, expertise it's an issue. How is Cloud helping you be better at what you do? Can you give some examples? >> Yes, so we had two different problems early on, as a small non-profit. First of all, we needed to scale up computationally. We had in-house servers. We needed a HIPAA complaint way to put our data up. So that's one of the reasons we were able to even use Google Cloud in the beginning. And now, we are able to run our models or entire data sets. Before that, we were only using a small population. And in Presicion Medicine, that's very important 'cause you want to get% entire population. That makes your models much more accurate. The second things was, we wanted to collaborate with people with clinical research backgrounds. And we need to provide a platform for them to be able to use, have the data on there, visualize, do computations, anything they want to do. And being on a Cloud really helped us to collaborate much more smoothly and you know, we only need their Gmail access, you know to Gmail to give them access and things. >> Yeah. >> And we could do it very, very quickly. Whereas before, it would take us months to transfer data. >> Yeah, it's a huge savings. Talk about the machine learning, AutoML's hot at the show, obviously, hot trend. You start to see AI ops coming in and disrupt more of the enterprise side but as data scientists, as you look at some of these machine learnings, I mean, you must get pretty excited. What are you thinking? What's your vision and how you going to use, like BigQuery's got ML built in now. This is like not new, it's Google's been using it for awhile. Are you tapping some of that? And what's your team doing with ML? >> Absolutely. We use BigQuery ML. We were able to use a few months in advance. It's great 'cause our data scientists like to work in BigQuery. They used to see, you know, you query the data right there. You can actually do the machine learning on there too. And you don't have to send it to different part of the platform for that. And it gives you sort of a proof of concept right away. For doing deep learning and those things, we use Cloud ML still, but for early on, you want to see if there is potential in a data. And you're able to do that very quickly with BigQuery ML right there. We also use AutoML Vision. We had access to about a thousand patients for MRI images and we wanted to see if we can detect Alzheimer's based on those. And we used AutoML for that. Actually works well. >> Some of the relationships with doctors, they're not always seen as the most tech savvy. So now they are getting more. As you do all this high-end, geeky stuff, you got to push it out to an interface. Google's really user-centric philosophy with user interfaces has always been kind of known for. Is that in Sheets, is that G Suite? How will you extend out the analysis and the interactions. How do you integrate into the edge work flow? You know? (laughs) >> So one thing I really appreciated for Google Cloud was that it was, seems to me it's built from the ground up for everyone to use. And it was the ease of access was very, was very important to us, like I said. We have data scientisits and statisticians and computer scientists onboard. But we needed a method and a platform that everybody can use. And through this program, they actually.. You guys provide what's called Qwiklab, which is, you know, screenshot of how to spin up a virtual machine and things like that. That, you know, a couple of years ago you have to run, you know, few command lines, too many command lines, to get that. Now it's just a push of a button. So that's just... Makes it much easier to work with people with background and domain knowledge and take away that 80% of the work, that's just a data engineering work that they don't want to do. >> That's awesome stuff. Well congratulations. Carol, a question to you is How does someone get involved in the Data Solutions for Change? An application? Online? Referral? I mean, how do these work? >> All of the above. (John laughs) We do have an online application and we welcome all non-profits to apply if they have a clear objective data problem that they want to solve. We would love to be able to help them. >> Does scope matter, big size, is it more mission? What's the mission criteria? Is there a certain bar to reach, so to speak, or-- >> Yeah, I mean we're most focused on... there really is not size, in terms of size of the non-profit or the breadth. It's much more around, do you have a problem that data and analytics can actually address. >> Yeah. >> So really working on problems that matter. And in addition, we actually announced this week that we are partnering with United Nations on a contest. It's called Sustainable.. It's for Visualize 2030 >> Yeah. >> So there are 17 sustainable development goals. >> Right, righr. >> And so, that's aimed at college students and storytelling to actually address one of these 17 areas. >> We'd love to follow up after the show, talk about some of the projects. since you have a lot of things going on. >> Yeah. >> Use of technology for good really is important right now, that people see that. People want to work for mission-driven organizations. >> Absolutely >> This becomes a clear citeria. Thanks for coming on. Appreciate it. Thanks for coming on today. Acute coverage here at Google Could Next 18 I'm John Furrier with Jeff Fricks. Stay with us. More coverage after this short break. (upbeat music)

>> Live from San Francisco, it's theCUBE, covering Google Cloud Next 2018. Brought to you by Google Cloud and it's ecosystem partners. >> Hey, welcome back everyone. We're live in San Francisco for Google Cloud's conference Next 18, #GoogleNext18. I'm John Furrier with Dave Vellante. Our next guest is Faizan Buzdar, Senior Director at Box, box.com, collaborative file sharing in the Cloud. No stranger to Cloud. Welcome to theCUBE. >> Thank you for having me. >> So you guys have a relationship with Google. First talk about the relationship with Google, and you have some breakouts you're doing on machine learning, which I want to dig into, but. Take a step back. Take a minute to explain the relationship between Box and Google Cloud. >> So Box has partnered Google for a few years now, and we have actually two areas of key, sort of, collaboration. One is around the Google Productivity Suite that was actually announced last year. But we actually demoed it for the first time in public today. Where, if you look at a bunch of customers, like about 60% of the Fortune 500, that chose Box as their secure content layer. These guys can now go into Box and say, "Create a new Google doc, Google spreadsheet, Google slide." And it will open up. It will fire up the Google editors. You can do, get all of the benefit of the rich editing, collaboration, but your content is long-term stored in Box. So it does not leave Box. So from a security and compliance layer, if you've chosen Box, you now get to use all of the power of the Google collaboration and >> It's Google Drive inside Google Box, but natively, you guys have the control for that backend, so the user experience feels native. >> Yeah, so in this case it doesn't touch Google Drive. It's basically, it never leaves Box. So that's the key benefit if you're a Box customer. >> That's awesome. That's great for the user. Great for you guys. That's awesome. Okay, so take a step back now. What's your role there? What do you do? >> So I'm Senior Director for Product Management, and I basically look after two areas. One is our sort of best of breed integration strategies, such as the one with Google Suite or Gmail. And then the second area is machine learning, especially as machine learning relates to specific business process problems in the Enterprise. So that's one of the areas that I look after. >> So how do you use data? You talked about the integration. How are you using data to solve some of those business process problems? Maybe give some examples, and tie it back into the Google Cloud. >> So, for example, so for us, we announced a product called Box Skills last year at BoxWorks. And we're going to talk about it next month at BoxWorks, too. So, the strategy there was we will bring the best of breed machine learning to apply to your content in Box, and we will take care of all of the piping. So, I keep hearing machine learning is the new electricity. But if you talk to CIOs, it's a weird kind of electricity for them because it actually feels like I have to uproot all of my appliances in factory, and take it to where the electricity is. It doesn't feel like electricity came to my factory, right? Or appliances or whatever. So, our job, we looked at it, and we said, "Hey, we have probably one of the biggest, most valuable repositories of content, Enterprise content. How do we enable it so that companies can use that without worrying about that?" So Box Skills actually has two components to it. One is what we would call, sort of, skills that are readily available out of the box. So as an example, today we are in beta with Google Vision. And the way that the admin turns that on is literally, he goes into his admin panel and he just turns on two check boxes, chooses which folders to apply it to, maybe apply it to all of the images in the Enterprise. So if you're a marketing company, now all of your images start to show these tags, which were basically returned by Google machine learning. But to the end user, it's still Box, they're still looking at their images, it still has all of those permissioning, it's just that now, we have the capability for metadata, for humans to add metadata manually, now that metadata is being added by machine learning. But in terms of adoption for the Enterprise, we made it super simple. And then, the framework also enables you to connect with any sort of best of breed machine learning. And we look at it, if you were to sort of make a, look at it as two axis, number of users that would use it, and the amount of business value that it brings. There are some things which are horizontal, like, say, the basic Google Vision, basic Google Video, basic Google Audio. Everybody would like an audio transcript, maybe. Everybody wants some data from their images. And that's something that a bunch of users will benefit from, but it might not be immense change in business process. And then there's another example, we'll say you're a ride sharing company, and you have to scan 50,000 driving licenses in every city that you go into. And currently you have that process where people submit their photos, and then people manually add that metadata. And if now you apply Google Vision to it, and you're extracting the metadata out of that, I actually love scenarios like this. Like, enterprises often ask me like where we should start. Where we should start in terms of applying machine learning, and my sort of candid advice is don't start with curing cancer. Start with something where there is some manual data being added. It's being added at scale. And take those scenarios, such as this driving license example, and now apply machine learning to that, so where previously it would take a month for you to get the data entered for 50,000 driving licenses, now you can do it in 50 minutes. And, um, yeah. >> And what's the quality impact? I mean, presumably the machines are going to get it right more often. >> Yeah. >> But do you have any data you can share with regard to that? >> So that's, actually, that's such an awesome question. And I'll connect it to my sort of previous advice to enterprises, which is that's why I love these processes because these processes have exception handling built into them already. So humans have at minimum a 5% error rate. Sometimes a 30% error rate. So, when we looked at, you know, captioned videos and TV from like 10 years ago, we could clearly see errors in that, which humans had transcribed, right? So, most of these manual processes at scale already have two processes built in, data entry, data validation and exception handling. So the reason that I love replacing the data entry portion is that machine learning is never 100%, but to the validation process, it still looks like kind of the same thing. You still saved all of your money. Not just money, but you saved sort of the time to market. And that's also what Box does, right? Because if you use Box in combination with Google Cloud, we actually, one of the things that I didn't talk about before, we looked at all of these machine learning providers, and we came up with standard JSON formats of how to represent machine learning output. So, as an example, you could imagine that getting machine learning applied in audio is a different problem than getting machine learning applied in video, is a different problem than getting machine learning applied from images. So we actually created these visual cards, which are developer components. And you can just get, put data in that JSON format, we will take care of the end user interactivity. So as an example, if it's a video, and you have topics. Now when you click on a topic, you see a timeline, which you didn't in images because there was no timeline. >> You matched the JSON configuration for the user expectation experience. >> Exactly. So now if you're in Enterprise and you're trying to turn that on, you're now, you could already see the content preview, and now you can also see the machine learning output, but it's also interactive. So if you, if you were recording this video, and you were like, "When did he say BoxWorks?" You click on that little timeline, and you will be able to jump to those portions in the timeline. >> That's awesome. I mean, you guys doing some great work. What's next? Final question, what are you guys going to do next? You got a lot to dig in. You got the AI, machine learning, store with Google. You got the Skills with Box to merge them together. What's next? >> So I think for us, the machine learning thing is just starting, so it's sort of, you'll learn more at BoxWorks. But for us I think the biggest thing there is how do we enable companies to experience machine learning faster? Which is why when we look at this two axis image audio video, we enable organizations to experience that quickly. And it actually is like an introduction to the drug because the guy who has to process insurance claims or the car damage photos, or the drone photos, he looks at that Google Vision output, and then he says, "Oh, if I can get these ties, maybe I can get these specialized business process ties." And then now he's looking at AutoML, announced today, and, you know, the adoption of that really, really >> Autonomous driving, machine learning. It's going to happen. Great stuff. Real quick question for you. When is BoxWorks? I don't think it's on our schedule. >> Next month, yeah. >> I think it's August 28th or 29th. It's coming up, yeah, yeah. >> So I'm going to go check. I don't think theCUBE is scheduled to be there, but I'm going to make a note. Follow up. >> We'd love to have you. Check with Jeff Frick on that. I think we were talking about covering the event. It's going to be local in San Francisco area? >> Uh, Moscone, yeah. >> Moscone, okay great. Well, thanks for coming on. Machine learning, certainly the future. You got auto drive, machine learning, all kinds of new stuff happening. Machine learning changing integrations, changing software, changing operations, and building better benefits, expectations for users. Box doing a great job. Congratulations on the work you're doing. Appreciate it. >> Thanks for coming on. >> Thanks for coming on. More CUBE coverage after the short break. We're going to wrap up day one. We got a special guest. Stay with us. One more interview, and then we got all day tomorrow. Be right back. (upbeat music)

>> Live from San Francisco. It's theCUBE covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. (electronic music) >> Hey, welcome back everyone. Live here in San Francisco it's theCUBE's coverage of Google Cloud and their big conference Google Next #GoogleNext18. I'm John Furrier, Dave Vellante. Our next guest is Adam Seligman, Vice President of Developer Relations at Google. Man, making it all happen, keeping the trains on time, keeping everyone motivated, welcome to theCUBE. Thanks for joining us. >> Thanks, glad to be here. >> So, first of all, take a step back, what is your job at Developer Relations? Are you herding cats, are you feeding them great code, are you overseeing a big team? Google's been very big on open-source, you've been part of the code program going back many many years. Google's always been a steward of open-source and developers are just devouring open-source in a big way right now. What's your job? >> I look after Developer Relations. There's around 20, 22 million developers in the world and we want to make every single one of them successful and build cool things, learn new technology, be part of community. That's something that's super important. I try to rally all of Google to sort of stand for developers. >> One of the big trends we're seeing now at open-source is that it's becoming such a good norm. I remember the days when I was getting into the business back in the late '80s, early '90s. Open-source, we'd kind of steal some code here and it kind of was radical. It's so normal now, and you start to see the clean, upstream etiquette, upstream projects, everyone's contributing, co-creating for a common good, monetizing downstream has been really well defined. There's some examples of probably where that could be better but for the most part, I think people are generally seeing a positive contribution. That's a community dynamic. How do you go to the next level for developers? Because this has turned out to be quite an opportunity to one: learn, meet new people, learn new skills and take advantage of some new technologies. How do you foster that community? What are you guys doing? Because no-one wants vendors to put their fingers in these upstream projects (laughs) but they're super important, they're all participating. What's the formula? How is that evolving? How do you see that? >> Google's been an open-source for maybe 20 years. Some big contributions early days, things like GCC, foundational compiler technology. And we have whole businesses that build around open-source, Chrome on the Web, Android for mobile, and now we see kubernetes in cloud and TensorFlow and AI and new things like Knative and Istio, so I think there's a course there where open-source can really shape whole ecosystems and create a lot of opportunity and a lot of innovation. And I think the challenge in all that is to do it in a really healthy, positive, community-centric way. And I think that's some real learning we've had in the last couple of years, is great leaders like Sarah Novotny have really helped guide us and her interface with open-source communities and foster the right kind of community interactions, and that's a big focus. We're trying to bring that here also. >> So, you had a keynote coming up, I know you got a hard stop and we want to try and get as many questions as we can. But I want to ask you, what are you going to be talking about at your keynote, what's the topic? 'Cos this is a, I won't say coming-out party for Google Cloud in particular, but clearly setting a couple stakes in the ground on what's going on. Enterprise focus, checking the boxes, table stakes are being met. And real tech: high performance, large-scale, really a good developer environment. What are you going to talk about at the keynote? >> Well, I think customers like HSBC and Target and others are coming to us, not for table stakes, they're coming to us for what's next. They're coming to us for massive-scale kubernetes, they're coming to us for AI. So, I think that the introductions we've had so far, things like the Cloud Services Platform, Istio 1.0, Knative, it really shows a bright future of service and AI-driven applications. What we're going to talk in the developer keynote, tomorrow, in day three, is really three themes: innovation, openness and open-source, and then that community theme that we were just talking about. And one area of innovation that we're going to talk about is Melody Meckfessel, who I think you talked to earlier, is going to talk about our approach to Cloud Build and integrated toolchains. We have a lot of technology we're going to open up in the DevOps space. But it's really a mentality, and this is the thing that I think is really needed coming to Google, is it's not just about pushing code down the waterfall to production, it's about building services for users and building services that the developers consume. And really flowing from code right out to running services, and then when you're done, the service is a turn on for everybody, you start routing traffic to it, you run canaries. So, it's a big step-change in how we think about continuous delivery and DevOps, we really want to land that in the keynote tomorrow. >> So I got to give some props to my partner, John Furrier, in 2010, John, you said, "Data is the new development kit." It was a while ago, and it's turned out, in my view anyway, to be true, but, Adam, it's also changed the profile of the developer. Data hackers, statisticians, mathematicians, artists. And so it's changed the way in which we think about a developer. I wonder, if you could talk about that, in terms of, how that's changed Developer Relations? >> Yufeng Guo is going to do a section AI in the keynote and he does these videos on YouTube that literally millions of people watch about how to get started on machine learning. And he's got a great line in there, which I think is attributed to him, that says, "AI is programming with data." And so I think we're in a world where all this data of user interactions and event streams and interactive things and mobile applications, we now have a lot of data to program the world on. And I think it's an incredible opportunity for developers. But the flip side, if we just restrict it to a couple thousand data scientists, it doesn't open up the world to everyone. So I think beyond that 20 million, what are the next 20 million we could pull in with AutoML? The next 20 million that can do SQL queries and can use BigQuery and do ML in BigQuery? So that's the vision of opening it up to more people, more developers. >> And the democratization of software, I mean, it's interesting, that's my background in software engineering, computer science, in the '80s you were called software engineering. Then it became software developer, then it became a software hacker. Now we're hearing words like software artisan. I interviewed Aparna, she said, "You don't need three PhDs, three degrees "in computer science, to do development anymore." The aperture's widening, big-time, because now craft is coming back to development. Because a lot of these abstractions, both on the business and tech side, are enabling different personas to come in. >> It's not legacy development anymore, it's heritage development, right?. (John laughs) I love that developers have the freedom to define their own titles and define their own tools they want to work with, and do a mix of the old and the new, and mix it up. So I think it's really important that we're not too narrow in how we define people and you don't have to be this tall to ride the ride, we really welcome everybody in to be a part of the community and if your entrance to ML is AutoML, but then eventually you graduate to TPUs, that's just fantastic. >> And how about crypto developers? They've exploded with innovation, what do you see in there? >> I could just go back to security, I think every company is really wrestling with security right now. How do they get two-factor everywhere? How do they stop phishing? How do they keep their employees safe? How do they have shielded VMs at every level of security? And it's a challenge to get developers to think about security sometimes. It's the operators that have to live with it, and so understanding your dependencies, way back up with developers are like, "Oh, I'll just use this library, "and I'll just use this library." How do you ensure you're using trusted dependencies back there, you don't have vulnerabilities you're introducing by taking dependencies in other codes. So I think there's a lot of education and best practice to share with developers to get them to care about security. >> My final question, I know you got to go. I just want to get it out there, years ago, when David and I used to hear on theCUBE, people come on, "We want to win the developers," no, they're not winnable. You don't win developers, you earn trust and you earn relationships and they might work with you and enjoy the services that they might provide to them. So I always kind of used to poo-poo that. But now with the Cloud you're seeing again, more range with developers. So, how do you keep developers happy? That might be a better question, because in order to earn and have a relationship with people who are going to be contributing IP and building IP, how do you keep harmonious relations? How do you keep people happy if you have things, like technical debt bothers people and people are like, "Oh, technical debt," you know, shipping codes, times. How do you think about that because keeping people happy is a broad answer, but in general, what's your view on keeping developers happy, harmonious, loving, working together, doing the things they love to do? >> It's a little different at Google, it's an interesting place, because there's never an "us and them" with developers, this is a company with tens of thousands of engineers on staff, most of the senior leadership team have an engineering background. So it's more like we live in the community of developers, my engineers are all over the world, living in developer communities. And so I think it really does matter how we show up and how we interact. But we sort of live it every day. So I don't think we have a hill to climb, so much as get to developers, I think we just have to have a really clear narrative, and then a really keen ear to listen to what they need and that's what I'm trying to orient them around. >> Listening, I think that's a great answer, listening. "What do you want?" you know, "What's important to you?" And then you have that perspective yourselves. Yeah, I mean, we're sort of a developer-centric company and I think the important thing is we put them at the center of everything we do, I use the word with my team, it's empathy. We have empathy for developers, you know, they have great jobs, great opportunities, but also great challenges, and as humans, can't we have empathy for them. >> I was hosting a panel one time, a night event, it was all out of fun, bunch of nerds on there were talking tech, getting on the hood, talking developers, all this stuff, range of questions, and one guy introduced himself as the, "I'm the CTO, I'm the Chief Toy Officer." (Adam laughs) Because we play with technology then we turn it into product. And you guys brought a lot of toys out here with Google, all this open-source. >> And then if we can amplify that for all the amazing talent that's in the world, at Google I/O, we host the developers' student clubs from Indonesia, and these young Indonesian women are teaching other college kids how to do android development. So, if we could bring that kind of magic to all of our assets, to the Cloud assets, I think there's this amazing, receptive community out there that could give us a bunch of whole new ideas that we don't just get in South of Market, San Francisco. >> It's inspiring to see people build things with open-source, pay it forward, contribute upstream, be part of a community, this is what it's all about, Developer Relations. Congratulations, thanks for coming on theCUBE. >> Thank you, so glad to be here, thanks guys! >> This is theCUBE paying it forward with content here from Google Next, all out in the open, co-creating with Google, Google's team, Google's customers, the best engineers, the best talent here at Google Cloud, I'm with theCUBE. I'm John Furrier, Dave Vellante, thanks for watching. Stay with us, more coverage after this short break. (electronic music)

>> Live. From San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. (techno music) >> Hello, everyone, welcome back to our day two of live coverage here in San Francisco, California for Google Next's conference called Next 2018, Google Next 2018 is the hashtag. I'm John Furrier with Dave Vellante. We're kickin' off day two. We just heard the keynotes, they're finishing up. Most of the meat of the keynote is out there, so we're going to just dive in and start the analysis. We got a tight schedule again, great guests, we have all the cloud-native folks comin' up from Google. We're going to hear from customers, and from partners. We're going to hear all the action. We're going to break it down for you. But first we want to do kind of a breakdown on the keynote, do analyze it and give some critical analysis, and also, things we think Google's doing great. Dave, day two, we've got three days of wall-to-wall coverage, go to the siliconangle.com for special journalism cloud series, a lot of articles hitting, a lot of CUBE videos, go to theCube.net, just check out those videos. That's our site, where all the videos are. Dave, day one, we had a great close yesterday; I thought it was phenomenal. But I thought we nailed it, today, too. And one of the things we were talkin' about in the first day close, editorially, was saying, hey, you know, this AI is super important. Today, in the keynote, more AI, more under the covers, more speed of announcements. Google kind of taking a playbook out of Amazon, let's get some announcements out there, I wouldn't say that the pace of announcements meets AWS, in terms of the announcements, but the focus is on a very few core things: AI, RollaData, Cloud-Native, Cloud Functions, Cloud Services Platform. This is the Google, that they're lifting the curtain. We're startin' to see some action. Your thoughts on the keynote... >> Well, I think you're absolutely right, I think Google realizes that it's got to compete with Amazon, from the keynote standpoint, demonstrating innovations, putting out a lot of function. I will say this, maybe it doesn't match Amazon's pace of innovation and announcements, but when you compare what these cloud-guys do with the traditional enterprise shows that we go to, there's no comparison. Even this morning, keynote day two, was drinking from a fire hose, there are dozens of announcements that Google made today. I would say just a couple of things, critical analysis, Google, everything is very scripted, as is all these shows, Amazon is very scripted as well, but they're reading everything, which I don't like, I would rather see them have a little bit more teleprompter, friendly, sort of presentation. So that's just sort of a little side comment. But the content is very good. The big themes I took away today, even though they didn't use this term, is really they're treating infrastructure as code. They're deploying infrastructure and microservices from code, as developers. So that was a theme that cut through the entire morning. Big announcement was the GA of Cloud Functions. It's been in beta, now it's Serverless, it's been in beta for a long time. And then a number of other announcements that we're going to go through and talk about, but those were some of the big highlights. But AutoML, I want to talk about that a little bit, talk a lot about developer agility. Threw out a couple of examples of customers, we heard from Chevron, we heard from Twitter, so they're starting to give examples, again, not as many Amazon, but real customers in the enterprise, customers like Mastercard, so, they're dropping some names... You're starting to see their belief manifest into actual adoption. But I'd like to ask you, John, what's your sense of the adoption bell curve, and the maturity curve, of the Google customer? >> Great question, I think for me, just kind of squinting through all of the noise, and looking at the announcements specifically, and how the portfolio of the show's going, it's very clear that Google is saying, we are here to play, we are here to win, we're going to take the long game on this cloud business. We have a ton to bring to the table, I call it the "bring out the Howitzers, the big guns." And they're doing that, they're bringing major technology, BigQuery, BigTable, Spanner, and a variety of other things, from the core Google business, bringing that out there and making it consumable; said that yesterday. Today, we looked at what's goin' on. You're seeing AI within G Suite. Leading by example, by demonstrating, look at it, this is how we use AI, you could use it, too, but not jamming AI and G Suite down the throats of the customer. AI and BigTable, I thought was pretty significant, because you can now bring machine learning and artificial intelligence, so to speak, into a data warehouse-like environment, where there's not a lot of data movement, data prep, it just happens. And then the Cloud Services Platform, the CSP, that Eyal Menor, the Vice President of Engineering, rolled out, I found interesting. The key move there was Cloud Functions. They now need to have Serverless up and running, and obviously Lambda's AWS. The uptake on the enterprise with Lambda has been significant, more than they thought. We heard that from Amazon, so I expect that Cloud Functions, and having this foundational layer with Kubernetes doubling down. The Kubernetes, Istio, and these Cloud Functions, represent that foundation. Knative open source projects, again, another arrow in their quiver around their open source contribution. This is Google, they're bringing the goods to the party, the open source party. This is an under-appreciated value proposition, in my opinion; I think a lot of people don't understand the implications of what's going to go on with this. This upstream contribution, and the downstream benefits that's going to come from their contra open source, is highly strategic. We used to call it, in the old days, "Kool-Aid injection." That's the way you ingratiate into the community with your software, ultimately the best software should win. There's not a lot of politics in open source, as there was once was, so I think that's fine. Now, to the question of migration, Google Cloud is showin' some customers up there, but I don't think they're going to, they're a long ways away from winning enterprises. What you see Google winning now is the AlphaTechies. The guys who were, and gals, who know tech, they know scale, and they can come in and appreciate the goodness of Google, they can appreciate the 10x advantages we heard from Danielle, with Spanner. These are what I call people with massive tech chops. They understand the tech, they've had problems, they need an aspirin, they need a steroid, and they need a growth hormone, right? They don't just need a pain-killer, they need solutions. These guys can make it happen. They jump in, take the machinery, and make that scale. The second level on the trajectory of their growth, on the adoption curve, is what I call, "Smart SMB, Smart enterprises." These are enterprises that have really strong technical people, where the internal conversations is not "if we should go to cloud," it's "how should we go to cloud?" And the DNA of the makeup of the technical people will decide the cloud they go with. And if it's engineering-led, meaning they have strong network operations, strong dev-team, then they have people who know what they're doing, they gravitate to Google Cloud. The third phase, which I think is not yet attainable, although aspirational, for Google, is the classic enterprise. "Man, I've been buying IT for years, oh my god, I'm like a straight-jacket of innovation, nothing's happening!" They're like, "we got to go to the cloud, how do we do it?" It's a groping for a strategy, right? So, Amazon gets those guys, because there's some things that shadow IT that Amazon can deliver, in more options, than what Google has. So I think I don't see Google knockin' that down in the short term, anytime soon. They can do plenty of business. Again, this is a trajectory that has an economy of scale to it, as an advantage, as a competitive advantage, by doing that. If Google tries to become Amazon, and meet their trajectory, the diseconomies of scale plays against Google. This is critical, Google does not want to do that, and they're not doing that, so I think the strategy of Google is right on the money. Nail the early adopters, the alpha geeks. Hit the engineering teams within the smartest companies, or small businesses, and then wait to hit that mainstream market, two, three years from now. So I think there's a multi-year journey for Google. Again, this diseconomies of scale is not what they want, they have tons of leverage in the tech, and the data, and the AI. So to me, they're right on track. They're now getting into the phase two. Smart. I give them credit for that. >> Let me pick up on a couple of things you said, and tie it into the keynotes from this morning. But I want to start with some of the conversations that you and I had last night, and around the show, with some of the GCP users. So, we've been asking them, okay, well how do you like GCP? Whaddya like? What don't you like? How does it compare with Azure? How does it compare with Amazon? And the feedback has been consistent. Tech is great, a lot of confidence in the tech. Obviously what Google's doing is they're using the tech internally, and then they're pointing it to the external world. It comes out in beta, and then they harden it, like they did today with Serverless and GOGA. The tech's great. Documentation has a little bit to be desired; we heard that as a consistence theme. Functionality not as rich in the infrastructure side as AWS, and not as enterprise app friendly as Azure, but very, very solid capabilities. This comes from people in financial services, people in healthcare, people from oil and gas. So, it's been consistent feedback that we've heard across the user base. You mentioned Knative; Knative is a new open source project, that brings Serverless to Kubernetes, and it was brought forth by Pivotal, IBM, RedHat, SAP, obviously Google, and others. Again, a big theme of the keynotes this morning was developer agility, bringing microservices, and services, and things like Kubernetes, to the developer community. Now, I want to talk about another example of a customer, Chevron. Is Google crushing it in traditional enterprise IT in the cloud? Well, no, you're bringing up the point that they're not. But, what they are doing, is doing well in places where people are solving data-oriented business problems with technology. Is that IT? It's not a traditional IT, but it's technology. Let me give you an example, Chevron was up on stage today, and they gave an example of they have thousands and thousands of docs, of topographical data points, and they use this thing called AutoML to ingest all the data into a model that they built, and visualize that data, to identify high-probability drilling zones and sites in the Gulf of Mexico. Dramatically compressed the time that it would have taken. In fact, they wouldn't have been able to do this. So they ingested the data, auto-categorized all the data to simplify it, put it into buckets, and then mapped it into their model, which was tuned over time, and identified the higher probability of sites for drilling. That's using tech to solve a business problem, drive productivity; Google crushes it with those type of data applications, really good example. >> And AutoML drives that, and this is where, again, a machine learning, AutoML, AI operation, we mentioned that yesterday, the IT operations sector is going to be decimated. But I think the big tell sign for me is when I look at the cloud shows, Amazon definitely has competition with Google, so that anyone who says Google's way far back in the market share, which you know I think is bastardized, I think those market share numbers don't mean anything because there's so much sandbagging going on; I could look at any one and say Microsoft's just sandbagging the numbers, and Amazon not really, if Amazon could probably sandbag the numbers even more by putting revenue from their partner ecosystem. Google throws G Suite in there, but they could throw AdWords in there and say technically that's running on their cloud, and be the number one cloud. What is a good cloud? When you have a cloud, if you can make a situation where you can take a customer and get them on the cloud easily, in a simplified, accelerated way, that is a success formula. What you heard on stage today was kind of, naw, I won't say underplayed, they certainly played it up and got some applause, is Velostrata and these services. They bought a company called Velostrata in May of this past year, and what they do is essentially the migration. We had a guest on, a user yesterday, migrating from Oracle to Spanner, 10x value, major reduction in price. They didn't say 10x, but significant; we'll try to get those numbers, she wouldn't say. But what Velostrata does is allows you to migrate to existing apps in a very easy, non-disruptive way, from on-prem to the cloud. This is the killer app for the leading clouds. They need tools to move workloads and databases to their cloud, because as clients and enterprises start to do taste tests, kick the tires in cloud, they're going to want to know what's the better cloud. So, the sales motto is simply go try it before you buy it. It's cloud. You can rent it. This is the value of the cloud. So, Amazon's done an extremely awesome job at this, Google has to step up, and I think Velostrata's one of many. I think the Kubernetes piece is critical, around managing legacy workloads, and adding new cloud natives. Between Velostrata, and the Knative, and the Cloud Functions, I think Google is shoring up their offerings, and it makes them a formidable competitor for certain workloads, and those early adopters, and that Stage Two, small, medium, or Smart enterprise, as a foundational element. I think that is a tell sign, and I got to give them props for that, and again, you can get an Oracle database into cloud, you're going to win a lot of business. If you can get an app workload running on Google Cloud seamlessly, in a very easy, meaningful way, it's just going to rain money. >> So let's talk about something we just talked about, how Google's not crushing it in traditional enterprise apps, but let's talk about some-- >> For now. >> of things we heard today, where they're trying to get into that space. So they announced today support on GCP for Oracle RAC, real application clusters, and exit data, and then SAP, via a partnership with Accenture. So Accenture does crush it with Oracle and SAP. Now, here's the problem: Oracle will play its licensing games, we've seen this with Amazon, where essentially, Oracle's license costs are double in AWS, they'll do the same thing for Google, I guarantee it, than they are in Oracle's cloud. So, 2x. It's already incredibly expensive. So, Oracle's going to use its pricing strategy to lock out competitors. So, that's a big deal, but we also saw some stuff on security: Cloud Armor, automatically defending against DDoS attacks, that's a big deal. We heard about shielded VMs, so secure VMs within GCP. These are things that traditional enterprises, it's going to resonate with traditional enterprises. >> Yeah, but here's the thing, then, we have one final point. I know we're going to run over a little bit of time, here, but I wanted to get it out there. You mentioned Oracle and the licenses. It's not just about Oracle, and their costs, and that disadvantage that could happen for a lot of people, and what cloud clearly has some benefits on a lot of cost. Here's the problem, like any Mafia business, Dave, we always talk about the cloud Mafias, and the on-premise Mafias. Oracle has an ecosystem of people who make a boatload of money around these licenses. So, you have a lot of perverse incentives around keeping the old stuff around, okay? So, as the global SIs, you mentioned Accenture, Deloitte, and others, those guys may salute the Google Cloud flag and the ecosystem, but at the end of the day, it's going to come down to money for them. So, if the perverse incentive is to stay in the old ways, saying "hey, okay, if we keep the license in there I get more better billing hours and I can roll out more deployments." Because what clouds do, and what Google's actually enabling, is enabling for the automation of those systems and those services, so you're going to see a future, very quickly, where half of the work that Accenture and Deloitte get paid on is going to be gone. From weeks to minutes; months, to weeks, to minutes. This is not a good monetization playbook for Accenture, and those guys. >> Well. >> So Google has to shift a ecosystem strategy that's smart and makes people money. At the end of the day-- >> No doubt. >> That's going to be a healthy ecosystem for every dollar of Google spend, it has to be at least 5 to 15x ecosystem dollars. I just don't see it right now. >> The big consultancies love to eat at the trough, as we like to say. But let's talk about the ecosystem, because you and I, we've walked the floor a couple times now. We mentioned Accenture, Cognizant is here, RedHead is here, KPMG, Salesforce, Marketo, Tata, everybody's here. UiPath, a startup in RPA; Cohesity's here. Rubrik's here, Intel's here, everybody's here, except AWS isn't here. >> Obviously. >> (chuckles softly) And Microsoft's not here. The other point that I think is worth mentioning, is again, big theme here is internally tested and then we point it at the market. Chevron, Autotrader, Mastercard, you're starting to see these names trickle out, other traditional enterprise. They announced today a partnership with NetApp for file sharing, for NFS workloads. So you're seeing NetApp lean in to the cloud in a big way. NetApps, back! You know you were seein' that. You saw Twitter on the Google Cloud. So you're seeing more and more examples of real companies, real businesses. >> I'll just end this segment by saying one thing quickly, the high IQ people in the industry, whether it's customers, partners, or vendors, are going to have to increase their 3D chess game, because as the money shifts around, the zero-sum game in my mind, it's going to shift to the value. Things are going to get automated either way, and that could be core businesses. So, the innovative dilemma is in play for many, many people. You got to be smart, and you got to land in a position, you got to know where the puck is going to be, skate to where the puck is going to be. It's going to require the highest IQ: tech IQ, and also business IQ, to make sure that you are making money as the world turns, because those dollars are up for grabs. The dollars are shifting as the new ecosystem rolls out. If you're relying on old ways to make money, you are in for a world of hurt if you don't have a plan. So, to me, that's the big story, I think, in the cloud that Google's driving. Google's driving massive acceleration, massive value creation, massive ecosystem opportunities, but it's not your grandfather's ecosystem, it's different. So we're going to see, we're going to test people, we're going to challenge it, we're going to have conversations here in TheCube. The day two of three days of live coverage. I'm John Furrier with Dave Vellante. Stay with us as we kick off day two. We'll be right back. (techno music)

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

(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 San Jose, it's theCUBE. Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our event Big Data SV. I'm Lisa Martin, joined by Dave Vellante and we've been here all day having some great conversations really looking at big data, cloud, AI machine-learning from many different levels. We're happy to welcome back to theCUBE one of our distinguished alumni, Ron Bodkin, who's now the Technical Director of Applied AI at Google. Hey Ron, welcome back. >> It's nice to be back Lisa, thank you. >> Yeah, thanks for coming by. >> Thanks Dave. >> So you have been a friend of theCUBE for a long time, you've been in this industry and this space for a long time. Let's take a little bit of a walk down memory lane, your perspectives on Big Data Hadoop and the evolution that you've seen. >> Sure, you know so I first got involved in big data back in 2007. I was VP in generating a startup called QuantCast in the online advertising space. You know, we were using early versions of Hadoop to crunch through petabytes of data and build data science models and I saw a huge opportunity to bring those kind of capabilities to the enterprise. You know, we were working with early Hadoop vendors. Actually, at the time, there was really only one commercial vendor of Hadoop, it was Cloudera and we were working with them and then you know, others as they came online, right? So back then we had to spend a lot of time explaining to enterprises what was this concept of big data, why it was Hadoop as an open source could get interesting, what did it mean to build a data lake? And you know, we always said look, there's going to be a ton of value around data science, right? Putting your big data together and collecting complete information and then being able to build data science models to act in your business. So you know, the exciting thing for me is you know, now we're at a stage where many companies have put those assets together. You've got access to amazing cloud scale resources like we have at Google to not only work with great information, but to start to really act on it because you know, kind of in parallel with that evolution of big data was the evolution of the algorithms as well as the access to large amounts of digital data that's propelled, you know, a lot of innovation in AI through this new trend of deep learning that we're invested heavily in. >> I mean the epiphany of Hadoop when I first heard about it was bringing, you know, five megabytes of code to a petabyte of data as sort of the bromide. But you know, the narrative in the press has really been well, they haven't really lived up to expectations, the ROI has been largely a reduction on investment and so is that fair? I mean you've worked with practitioners, you know, all your big data career and you've seen a lot of companies transform. Obviously Google as a big data company is probably the best example of one. Do you think that's a fair narrative or did the big data hype fail to live up to expectations? >> I think there's a couple of things going on here. One is, you know, that the capabilities in big data have varied widely, right? So if you look at the way, for example, at Google we operate with big data tools that we have, they're extremely productive, work at massive scale, you know, with large numbers of users being able to slice and dice and get deep analysis of data. It's a great setup for doing machine learning, right? That's why we have things like BigQuery available in the cloud. You know, I'd say that what happened in the open source Hadoop world was it ended up settling in on more of the subset of use cases around how do we make it easy to store large amounts of data inexpensively, how do we offload ETL, how do we make it possible for data scientists to get access to raw data? I don't think that's as functional as what people really had imagined coming out of big data. But it's still served a useful function complementing what companies were already doing at their warehouse, right? So I'd say those efforts to collect big data and to make them available have really been a, they've set the stage for analytic value both through better building of analytic databases but especially through machine learning. >> And there's been some clear successes. I mean, one of them obviously is advertising, Google's had a huge success there. But much more, I mean fraud detection, you're starting to see health care really glom on. Financial services have been big on this, you know, maybe largely for marketing reasons but also risk, You know for sure, so there's been some clear successes. I've likened it to, you know, before you got to paint, you got to scrape and you got to, you put in caulking and so forth. And now we're in a position where you've got a corpus of data in your organization and you can really start to apply things like machine learning and artificial intelligence. Your thoughts on that premise? >> Yeah, I definitely think there's a lot of truth to that. I think some of it was, there was a hope, a lot of people thought that big data would be magic, that you could just dump a bunch of raw data without any effort and out would come all the answers. And that was never a realistic hope. There's always a level of you have to at least have some level of structure in the data, you have to put some effort in curating the data so you have valid results, right? So it's created a set of tools to allow scaling. You know, we now take for granted the ability to have elastic data, to have it scale and have it in the cloud in a way that just wasn't the norm even 10 years ago. It's like people were thinking about very brittle, limited amounts of data in silos was the norm, so the conversation's changed so much, we almost forget how much things have evolved. >> Speaking of evolution, tell us a little bit more about your role with applied AI at Google. What was the genesis of it and how are you working with customers for them to kind of leverage this next phase of big data and applying machine learning so that they really can identify, well monetize content and data and actually identify new revenue streams? >> Absolutely, so you know at Google, we really started the journey to become an AI-first company early this decade, a little over five years ago. We invested in the Google X team, you know, Jeff Dean was one of the leaders there, sort of to invest in, hey, these deep learning algorithms are having a big impact, right? Fei-Fei Li, who's now the Chief Scientist at Google Cloud was at Stanford doing research around how can we teach a computer to see and catalog a lot of digital data for visual purposes? So combining that with advances in computing with first GPUs and then ultimately we invested in specialized hardware that made it work well for us. The massive-scale TPU's, right? That combination really started to unlock all kinds of problems that we could solve with machine learning in a way that we couldn't before. So it's now become central to all kinds of products at Google, whether it be the biggest improvements we've had in search and advertising coming from these deep learning models but also breakthroughs, products like Google Photos where you can now search and find photos based on keywords from intelligence in a machine that looks at what's in the photo, right? So we've invested and made that a central part of the business and so what we're seeing is as we build up the cloud business, there's a tremendous interest in how can we take Google's capabilities, right, our investments in open source deep learning frameworks, TensorFlow, our investments in hardware, TPU, our scalable infrastructure for doing machine learning, right? We're able to serve a billion inferences a second, right? So we've got this massive capability we've built for our own products that we're now making available for customers and the customers are saying, "How do I tap into that? "How can I work with Google, how can I work with "the products, how can I work with the capabilities?" So the applied AI team is really about how do we help customers drive these 10x opportunities with machine learning, partnering with Google? And the reason it's a 10x opportunity is you've had a big set of improvements where models that weren't useful commercially until recently are now useful and can be applied. So you can do things like translating languages automatically, like recognizing speech, like having automated dialog for chat bots or you know, all kinds of visual APIs like our AutoML API where engineers can feed up images and it will train a model specialized to their need to recognize what you're looking for, right? So those types of advances mean that all kinds of business process can be reconceived of, and dramatically improved with automation, taking a lot of human drudgery out. So customers are like "That's really "exciting and at Google you're doing that. "How do we get that, right? "We don't know how to go there." >> Well natural language processing has been amazing in the last couple of years. Not surprising that Google is so successful there. I was kind of blown away that Amazon with Alexa sort of blew past Siri, right? And so thinking about new ways in which we're going to interact with our devices, it's clearly coming, so it leads me into my question on innovation. What's driven in your view, the innovation in the last decade and what's going to drive innovation the next 10 years? >> I think innovation is very much a function of having the right kind of culture and mindset, right? So I mean for us at Google, a big part of it is what we call 10x thinking, which is really focusing on how do you think about the big problem and work on something that could have a big impact? I also think that you can't really predict what's going to work, but there's a lot of interesting ideas and many of them won't pan out, right? But the more you have a culture of failing fast and trying things and at least being open to the data and give it a shot, right, and say "Is this crazy thing going to work?" That's why we have things like Google X where we invest in moonshots but that's where, you know, throughout the business, we say hey, you can have a 20% project, you can go work on something and many of them don't work or have a small impact but then you get things like Gmail getting created out of a 20% project. It's a cultural thing that you foster and encourage people to try things and be open to the possibility that something big is on your hands, right? >> On the cultural front, it sounds like in some cases depending on the enterprise, it's a shift, in some cases it's a cultural journey. The Google on Google story sounds like it could be a blueprint, of course, how do we do this? You've done this but how much is it a blueprint on the technology capitalizing on deep learning capabilities as well as a blueprint for helping organizations on this cultural journey to be actually being able to benefit and profit from this? >> Yeah, I mean that's absolutely right Lisa that these are both really important aspects, that there's a big part of the cultural journey. In order to be an AI-first company, to really reconceive your business around what can happen with machine learning, it's important to be a digital company, right? To have a mindset of making quick decisions and thinking about how data impacts your business and activating in real time. So there's a cultural journey that companies are going through. How do we enable our knowledge workers to do this kind of work, how do we think about our products in a new way, how do we reconceive, think about automation? There's a lot of these aspects that are cultural as well, but I think a big part of it is, you know, it's easy to get overwhelmed for companies but it's like you have pick somewhere, right? What's something you can do, what's a true north, what's an area where you can start to invest and get impact and start the journey, right? Start to do pilots, start to get something going. What we found, something I've found in my career has been when companies get started with the right first project and get some success, they can build on that success and invest more, right? Whereas you know, if you're not experimenting and trying things and moving, you're never going to get there. >> Momentum is key, well Ron, thank you so much for taking some time to stop by theCUBE. I wish we had more time to chat but we appreciate your time. >> No, it's great to be here again. >> See ya. >> We want to thank you for watching theCUBE live from our event, Big Data SV in San Jose. I'm Lisa Martin with Dave Vellante, stick around we'll be back with our wrap shortly. (relaxed electronic jingle)