(gentle music) >> Welcome to the new abnormal. Yes, you know, the pandemic, it did accelerate the shift to digital, but it's also created disorder in our world. I mean, every day it seems that companies are resetting their office reopening playbooks. They're rethinking policies on large gatherings and vaccination mandates. There's an acute labor shortage in many industries, and we're seeing an inventory glutton in certain goods, like bleach and hand sanitizer. Airline schedules and pricing algorithms, they're all unsettled. Is inflation transitory? Is that a real threat to the economy? GDP forecasts are seesawing. In short, the world is out of whack and the need for fast access to quality, trusted and governed data has never been greater. Can coherent data strategies help solve these problems, or will we have to wait for the world to reach some type of natural equilibrium? And how are companies, like Google, helping customers solve these problems in critical industries, like financial services, retail, manufacturing, and other sectors? And with me to share his perspectives on data is a long-time CUBE alum, Bruno Aziza. He's the head of data analytics at Google. Bruno, my friend, great to see you again, welcome. >> Great to see you, thanks for having me, Dave. >> So you heard my little narrative upfront, how do you see this crazy world of data today? >> I think you're right. I think there's a lot going on in the world of data analytics today. I mean, certainly over the last 30 years, we've all tried to just make the life of people better and give them access more readily to the information that they need. But certainly over the last year and half, two years, we've seen an amazing acceleration in digital transformation. And what I think we're seeing is that even after three decades of investment in the data analytics world, you know, the opportunity is still really out wide and is still available for organizations to get value out of their data. I was looking at some of the latest research in the market, and, you know, only 32% of companies are actually able to say that they get tangible, valuable insights out of their data. So after all these years, we still have a lot of opportunity ahead of us, of course, with the democratization of access to data, but also the advent in machine learning and AI, so that people can make better decisions faster than their competitors. >> So do you think that the pandemic has heightened that sort of awareness as they were sort of forced to pivot to digital, that they're maybe not getting enough out of their data strategies? That maybe their whatever, their organization, their technology, their way they were thinking about data was not adequate and didn't allow them to be agile enough? Why do you think that only 32% are getting that type of value? >> I think it's true. I think, one, digital transformation has been accelerated over the last two years. I think, you know, if you look at research the last two years, I've seen almost a decade of digital acceleration, you know, happening. But I also think that we're hitting a particular time where employees are expecting more from their employers in terms of the type of insights that can get. Consumers are now evolving, right? So they want more information. And I think now technology has evolved to a point where it's a lot easier to provision a data cloud environment so you can get more data out to your constituents. So I think the connection of these three things, expectation of employees, expectation of customers to better customer experiences, and, of course, the global environment, has accelerated quite a bit, you know, where the space can go. And for people like me, you know, 20 years ago, nobody really cared about databases and so forth. And now I feel like, you know, everybody's, you know, understands the value that we can get out of it. And we're kind of getting, you know, in the sexy territory, finally, data now is sexy for everyone and there's a lot of interest in the space. >> You and I met, of course, in the early days of Hadoop. And there were many things about Hadoop that were profound and, of course, many things that, you know, just were overly complex, et cetera. And one of the things we saw was this sort of decentralization. We thought that Hadoop was going to send five megabytes of code to petabytes of data. And what happened is everything, you know, came into this centralized repository and that centralized thinking, the data pipeline organization was very centralized. Are you seeing companies rethink that? I mean, has the cloud changed their thinking? You know, especially as the cloud expands to the edge, on-prem, everywhere. How are you seeing organizations rethink their regimes for data? >> Yeah, I think, you know, we've seen over the last three decades kind of the pendulum, right, from really centralizing everything and making the IT organization kind of the center of excellence for data analytics, all the way to now, you know, providing data as a self-service, you know, application for end-users. And I think what we're seeing now is there's a few forces happening. The first one is, of course, multicloud, right? So the world today is clearly multicloud and it's going to be multicloud for many, many years. So I think not only are now people considering their on-prem information, but they're also looking at data across multiple clouds. And so I think that is a huge force for chief data officers to consider is that, you know, you're not going to have data centralized in one place, nicely organized, because sometimes it's going to be a factor of where you want to be as an organization. Maybe you're going to be partnering with other organizations that have data in other clouds. And so you want to have an architecture that is modern and that accommodates this idea of an open cloud. The second problem that we see is this idea around data governance, intelligent data governance, right? So the world of managing data is becoming more complex because, of course, you're now dealing with many different speeds, you're dealing with many different types of data. And so you want to be able to empower people to get access to the information, without necessarily having to move this data, so they can make quick decisions on the data. So this idea of a data fabric is becoming really important. And then the third trend that we see, of course, is this idea around data sharing, right? People are now looking to use their own data to create a data economy around their business. And so the ability to augment their existing data with external data and create data products around it is becoming more and more important to the chief data officers. So it's really interesting we're seeing a switch from, you know, this chief data officer really only worried about governance, to this we're now worried about innovation, while making sure that security and governance is taken care of. You know, we call this freedom within the framework, which is a great challenge, but a great opportunity for many of these data leaders. >> You mentioned several things there. Self-service, multicloud, the governance key, especially if we can federate that governance in a decentralized world. Data fabric is interesting. I was talking to Zhamak Dehghani this weekend on email. She coined the term data mesh. And there seems to be some confusion, data mesh, data fabric. I think Gartner's using the term fabric. I know like NetApp, I think coined that term, which to me is like an infrastructure layer, you know. But what do you mean by data fabric? >> Well, the first thing that I would say is that it's not up to the vendors to define what it is. It really is up to the customer. The problem that we're seeing these customers trying to fix is you have a diversity of data, right? So you have data stored in the data mart, in a data lake, in a data warehouse, and they all have their specific, you know, reasons for being there. And so this idea of a data fabric is that without moving the data, can you, one, govern it intelligently? And, two, can you provide landing zones for people to actually do their work without having to go through the pain of setting up new infrastructure, or moving information left and right, and creating new applications? So it's this idea of basically taking advantage of your existing environment, but also governing it centrally, and also now providing self-service capabilities so people can do their job easily. So, you know, you might call it a data mesh, you might call it a data fabric. You know, the terminology to me, you know, doesn't seem to be the barrier. The issue today is how do we enable, you know, this freedom for customers? Because, you know, I think what we've seen with vendors out there is they're trying to just take the customer down to their paradigms. So if they believe in all the answers need to be in a data warehouse, they're going to guide the customer there. If they believe that, you know, everything needs to be in a data lake, they're going to guide the customer there. What we believe in is this idea of choice. You should be able to do every single use case. And we should be able to enable you to manage it intelligently, both from an access standpoint, as well as a governance standpoint. >> So when you think about those different, and I like that, you're making it somewhat technology agnostic, so whether it's a data warehouse, or a data lake, or a data hub, a data mart, those are nodes within the mesh or the fabric, right? That are discoverable, accessible, I guess, governed. I think that there's got to be some kind of centralized governance edict, but in a federated governance model so you don't have to move the data around. Is that how you're thinking about it? >> Absolutely, you know, in our recent event, in the Data Cloud Summit, we had Equifax. So the gentleman there was the VP of data governance and data fabric. So you can start seeing now these roles, you know, created around this problem. And really when you listen to what they're trying to do, they're trying to provide as much value as they can without changing the habits of their users. I think that's what's key here, is that the minute you start changing habits, force people into paradigms that maybe, you know, are useful for you as a vendor, but not so useful to the customer, you get into the danger zone. So the idea here is how can you provide a broad enough platform, a platform that is deep enough, so the data can be intelligently managed and also distributed and activated at the point of interaction for the end-user, so they can do their job a lot easier? And that's really what we're about, is how do you make data simpler? How do you make, you know, the process of getting to insight a lot more fluid without changing habits necessarily, both on the IT side and the business side? >> I want to get to specifics on what Google is doing, but the last sort of uber-trends I want to ask you about 'cause, again, we've known each other for a long time. We've seen this data world grow up. And you're right, 20, 30 years ago, nobody cared about database. Well, maybe 30 years ago. But 20 years ago, it was a boring market, right now it's like the hottest thing going. But we saw, you know, bromide like data is the new oil. Well, we found out, well, actually data is more valuable than oil 'cause you can use, you know, data in a lot of different places, oil you can use once. And then the term like data as an asset, and you said data sharing. And it brings up the notion that, you know, you don't want to share your assets, but you do want to share your data as long as it can be governed. So we're starting to change the language that we use to describe data and our thinking is changing. And so it says to me that the next 10 years, aren't going to be like the last 10 years. What are your thoughts on that? >> I think you're absolutely right. I think if you look at how companies are maturing their use of data, obviously the first barrier is, "How do I, as a company, make sure that I take advantage of my data as an asset? How do I turn, you know, all this information into a sustainable, competitive advantage, really top of mind for organizations?" The second piece around it is, "How do I create now this innovation flywheel so that I can create value for my customers, and my employees, and my partners?" And then, finally, "How do I use data as the center of a product that I can then further monetize and create further value into my ecosystem?" I think the piece that's been happening that people have not talked a lot about I think, with the cloud, what's come is it's given us the opportunity to think about data as an ecosystem. Now you and I are partnering on insights. You and I are creating assets that might be the combination of your data and my data. Maybe it's an intelligent application on top of that data that now has become an intelligent, rich experience, if you will, that we can either both monetize or that we can drive value from. And so I think, you know, it's just scratching the surface on that. But I think that's where the next 10 years, to your point, are going to be, is that the companies that win with data are going to create products, intelligent products, out of that data. And they're just going to take us to places that, you know, we are not even thinking about right now. >> Yeah, and I think you're right on. That is going to be one of the big differences in the coming years is data as product. And that brings up sort of the line of business, right? I mean the lines of business heads historically have been kind of removed from the data group, that's why I was asking you about the organization before. But let's get into Google. How do you describe Google's strategy, its approach, and why it's unique? >> You know, I think one of the reasons, so I just, you know, started about a year ago, and one of the reasons for why I found, you know, the Google mission interesting, is that it's really rooted at who we are and what we do. If you think about it, we make data simple. That's really what we're about. And we live that value. If you go to google.com today, what's happening? Right, as an end-user, you don't need any training. You're going to type in whatever it is that you're looking for, and then we're going to return to you highly personalized, highly actionable insights to you as a consumer of insights, if you will. And I think that's where the market is going to. Now, you know, making data simple doesn't mean that you have to have simple infrastructure. In fact, you need to be able to handle sophistication at scale. And so simply our differentiation here is how do we go from highly sophisticated world of the internet, disconnected data, changing all the time, vast volume, and a lot of different types of data, to a simple answer that's actionable to the end-user? It's intelligence. And so our differentiation is around that. Our mission is to make data simple and we use intelligence to take the sophistication and provide to you an answer that's highly actionable, highly relevant, highly personalized for you, so you can go on and do your job, 'cause ultimately the majority of people are not in the data business. And so they need to get the information just like you said, as a business user, that's relevant, actionable, timely, so they can go off and, you know, create value for their organization. >> So I don't think anybody would argue that Google, obviously, are data experts, arguably the best in the world. But it's interesting, some of the uniqueness here that I'm hearing in your language. You used the word multicloud, Amazon doesn't, you know, use that term. So that's a differentiation. And you sell a cloud, right? You sell cloud services, but you're talking about multicloud. You sell databases, but, of course, you host other databases, like Snowflake. So where do you fit in all this? Do you see your role, as the head of data analytics, is to sort of be the chef that helps combine all these different capabilities? Or are you sort of trying to help people adopt Google products and services? How should we think about that? >> Yeah, the best way to think about, you know, I spend 60 to 70% of my time with customers. And the best way I can think about our role is to be your innovation partner as an organization. And, you know, whichever is the scenario that you're going to be using, I think you talked about open cloud, I think another uniqueness of Google is that we have a very partner friendly, you know, approach to the business. Because we realized that when you walk into an enterprise or a digital native, and so forth, they already have a lot of assets that they have accumulated over the years. And it might be technology assets, but also might be knowledge, and know-how, right? So we want to be able to be the innovation vendor that enables you to take these assets, put them together, and create simplicity towards the data. You know, ultimately, you can have all types of complexity in the backend. But what we can do the best for you is make that really simple, really integrated, really unified, so you, as a business user, you don't have to worry about, "Where is my data? Do I need to think about moving data from here to there? Are there things that I can do only if the data is formatted that way and this way?" We want to remove all that complexity, just like we do it on google.com, so you can do your job. And so that's our job, and that's the reason for why people come to us, is because they see that we can be their best innovation partner, regardless where the data is and regardless, you know, what part of the stack they're using. >> Well, I want to take an example, because my example, I mean, I don't know Google's portfolio like you do, obviously, but one of the things I hear from customers is, "We're trying to inject as much machine intelligence into our data as possible. We see opportunities to automate." So I look at something like BigQuery, which has a strong affinity in embedded machine learning and machine intelligence, as an example, maybe of that simplification. But maybe you could pick up on that and give us some other concrete examples. >> Yeah, specifically on products, I mean, there are a lot products we can talk about, and certainly BigQuery has tremendous market momentum. You know, and it's really anchored on this idea that, you know, the idea behind BigQuery is that just add data and we'll do the rest, right? So that's kind of the idea where you can start small and you can scale at incredible, you know, volumes without really having to think about tuning it, about creating indexes, and so forth. Also, we think about BigQuery as the place that people start in order to build their ecosystem. That's why we've invested a lot in machine learning. Just a few years ago, we introduced this functionality called BigQuery Machine Learning, or BigQuery ML, if you're familiar with it. And you notice out of the top 100 customers we have, 80% of these customers are using machine learning right out of, you know, BigQuery. So now why is that? Why is it that it's so easy to use machine learning using BigQuery is because it's built in. It was built from the ground up. Instead of thinking about machine learning as an afterthought, or maybe something that only data scientists have access to that you're going to license just for narrow scenarios, we think about you have your data in a warehouse that can scale, that is equally awesome at small volume as very large volume, and we build on top of that. You know, similarly, we just announced our analytics exchange, which is basically the place where you can now build these data analytics assets that we discussed, so you can now build an ecosystem that creates value for end-users. And so BigQuery is really at the center of a lot of that strategy, but it's not unlike any of the other products that we have. We want to make it simple for people to onboard, simple to scale, to really accomplish, you know, whatever success is ahead of them. >> Well, I think ecosystems is another one of those big differences in the coming decade, because you're able to build ecosystems around data, especially if you can share that data, you know, and do so in a governed and secure way. But it leads to my question on industries, and I'm wondering if you see any patterns emerging in industries? And each industry seems to have its own unique disruption scenario. You know, retail obviously has been, you know, disrupted with online commerce. And healthcare with, of course, the pandemic. Financial services, you wonder, "Okay, are traditional banks going to lose control of payment systems?" Manufacturing you see our reliance on China's supply chain in, of course, North America. Are you seeing any patterns in industry as it pertains to data? And what can you share with us in terms of insights there? >> Yeah, we are. And, I mean, you know, there's obviously the industries that are, you know, very data savvy or data hungry. You think about, you know, the telecommunication industry, you think about manufacturing, you think about financial services and retail. I mean, financial services and retailers are particularly interesting, because they're kind of both in the retail business and having to deal with this level of complexity of they have physical locations and they also have a relationship with people online, so they really want to be able to bring these two worlds together. You know, I think, you know, about those scenarios of Carrefour, for instance. It's a large retailer in Europe that has been able to not only to, you know, onboard on our platform and they're using, you know, everything from BigQuery, all the way to Looker, but also now create the data assets that enable them to differentiate within their own industry. And so we see a lot of that happening across pretty much all industries. It's difficult to think about an industry that is not really taking a hard look at their data strategy recently, especially over the last two years, and really thought about how they're creating innovation. We have actually created what we call design patterns, which are basically blueprints for organization to take on. It's free, it's free guidance, it's free datasets and code that can accelerate their building of these innovative solutions. So think about the, you know, ability to determine propensity to purchase. Or build, you know, a big trend is recommendation systems. Another one is anomaly detection, and this was great because anomaly detection is a scenario that works in telco, but also in financial services. So we certainly are seeing now companies moving up in their level of maturity, because we're making it easier and simpler for them to assemble these technologies and create, you know, what we call data-rich experiences. >> The last question is how you see the emerging edge, IoT, analytics in that space? You know, a lot of the machine learning or AI today is modeling in the cloud, as you well know. But when you think about a lot of the consumer applications, whether it's voice recognition or, you know, or fingerprinting, et cetera, you're seeing some really interesting use cases that could bleed into the enterprise. And we think about AI inferencing at the edge as really driving a lot of value. How do you see that playing out and what's Google's role there? >> So there's a lot going on in that space. I'll give you just a simple example. Maybe something that's easy for the community to understand is there's still ways that we define certain metrics that are not taking into account what actually is happening in reality. I was just talking to a company whose job is to deliver meals to people. And what they have realized is that in order for them to predict exactly the time it's going to take them from the kitchen to your desk, they have to take into account the fact that distance sometimes it's not just horizontal, it's also vertical. So if you're distributing and you're delivering meals, you know, in Singapore, for instance, high density, you have to understand maybe the data coming from the elevators. So you can determine, "Oh, if you're on the 20th floor, now my distance to you, and my ability to forecast exactly when you're going to get that meal, is going to be different than if you are on the fifth floor. And, particularly, if you're ordering at 11:32, versus if you're ordering at 11:58." And so what's happening here is that as people are developing these intelligent systems, they're now starting to input a lot of information that historically we might not have thought about, but that actually is very relevant to the end-user. And so, you know, how do you do that? Again, and you have to have a platform that enables you to have a large diversity of use cases, and that thinks ahead, if you will, of the problems you might run into. Lots and lots of innovation in this space. I mean, we work with, you know, companies like Ford to, you know, reinvent the connected, you know, cars. We work with companies like Vodafone, 700 use cases, to think about how they're going to deal with what they call their data ocean. You know, I thought you would like this term, because we've gone from data lakes to data oceans. And so there is certainly a ton of innovation and certainly, you know, the chief data officers that I have the opportunity to work with are really not short of ideas. I think what's been happening up until now, they haven't had this kind of single, unified, simple experience that they can use in order to onboard quickly and then enable their people to build great, rich-data applications. >> Yeah, we certainly had fun with that over the years, data lake or data ocean. And thank you for remembering that, Bruno. Always a pleasure seeing you. Thanks so much for your time and sharing your perspectives, and informing us about what Google's up to. Can't wait to have you back. >> Thanks for having me, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante. Appreciate you watching this CUBE Conversation, and we'll see you next time. (gentle music)

>> Narrator: From theCUBE studios in Palo Alto and Boston connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this CUBE conversation here in the Palo Alto studios for theCUBE. I'm John Furrier, the host. We're here during this time where everyone's sheltering in place during the COVID-19 crisis. We're getting the interviews out and getting the stories that matter for you. It's theCUBE's mission just to share and extract the data from, signal from the noise, and share that with you. Of course the conversation here is about how the data analytics are being used. We have a great friend and CUBE alum, Bruno Kurtic, VP, founding VP of Product and Strategy for Sumo Logic, a leader in analytics. We've been following you guys, kind of going back I think many, many years, around big data, now with AI and machine learning. You guys are an industry leader. Bruno, thanks for spending the time to come on theCUBE, I know you're sheltering in place. Thanks for coming on. >> You're welcome, pleasure. >> Obviously with the crisis, the work at home has really highlighted the at-scale problem, right? We've been having many conversations on theCUBE of cybersecurity at scale, because now the endpoint protection business has been exploding, literally, a lot of pressure of malware. A convenient crime time for those hackers. You're starting to see cloud failure. Google had 18 hours of downtime. Azure's got some downtime. I think Amazon's the only one that haven't had any downtime. But everything is being at scale now, because the new work environment is actually putting pressure on the industry, not only just the financial pressure of people losing their jobs or the hiring freezes, but now the focus is staying in business and getting through this. But the pressure points of scale are starting to show. And working at home is one of them. Analytics has become a big part of it. Can you share your perspective of how people using analytics to get through this, because now the scale of the problem-solving is there with analytics. It's in charts on the virus, exponential curves, people want to know the impact of their business in all this. What's your view on this situation? >> Yeah. The world has changed so quickly. Analytics has always been important. But there are really two aspects of analytics that are important right now. A lot of our enterprises today, obviously, as you said, are switching to this sort of remote workforce. Everybody who was local is now remote, so, people are working from home. That is putting stress on the systems that support that working from home. It's putting stress on infrastructure, things like VPNs and networks and things like that because they're carrying more bits and bytes. It's putting stress on productivity tools, things like cloud provider tools, things like Office 365, and Google Drive, and Salesforce, and other things that are now being leveraged more and more as people are remote. Enterprises are leveraging analytics to optimize and to ensure that they can facilitate course of business, understand where their issues are, understand where their failures are, internal and external, route traffic appropriately to make sure that they can actually do the business they need. But that's only half of the problem. In fact, I think the other half of the problem is maybe even bigger. We as humans are no longer able to go out. We're not supposed to, and able to go shopping and doing things as we normally do, so all of these enterprises are not only working remotely, leveraging productivity tools and quote-unquote "digital technologies" to do work. They're also serving more customers through their digital properties. And so their sites, their apps, their retail stores online, and all of the digital aspects of enterprises today are under more load because consumers and customers are leveraging those channels more. People are getting groceries delivered at home, pharmaceuticals delivered at home. Everything is going through online systems rather than us going to Walgreens and other places to pick things up. Both of those aspects of scale and security are important. Analytics is important in both figuring out how do you serve your customers effectively, and how do you secure those sites. Because now that there's more load, there's more people, and it's a bigger honeypot. And then also, how do you actually do your own business to support that in a digital world? >> Bruno, that's a great point. I just want to reiterate that the role of data in all this is really fundamental and clear, the value that you can get out of the data. Now, you and I, we've had many conversations with you guys over the years. For all of us insiders, we all know this already. Data analytics, everyone's instrumenting their business. But now when you see real-life examples of death and destruction, I mean, I was reporting yesterday that leaked emails from the CDC in the United States showed that in January, they saw that people didn't have fevers with COVID-19. The system was lagging. There was no real-time notifications. This is our world. We've been living in this for this past decade, in the big data world. This is highlighting a global problem, that with notifications, with the right use of data, is a real game-changer. You couldn't get any more clear. I have to ask you, with all this kind of revelations, and I don't mean to be all gloom-and-doom, but that's the reality, highlights the fact that instrumenting and having the data analytics is a must-have. Can you share your reaction to that? >> Yeah, absolutely. You're right. Like you said, we are insiders here, and we've been espousing this world of what we internally in Sumo call the continuous intelligence, which essentially means to us and to our customers, that you collect and process all signals that are available to you as a business, as a government, as a whatever entity that is dealing with critical things. You need to process all of that data as quickly as you can. You need to mine it for insights. You need to, in an agile fashion, just like software development, you need to consume those insights, build them into your processes to improve, to react, to respond quickly, and then deliver better outcomes. The sooner you understand what the data is telling you, the sooner you can actually respond to whatever that data is telling you, and actually avoid bad outcomes, improve good outcomes, and overall, react to whatever is forcing you to react. >> I was just talking with Dave Vellante last week about this, my co-host, and also Jeff Frick, my general manager, who interviewed you in the past on theCUBE, about the transition and transformation that's happening. I want to just get your reaction to what we're seeing, and I wanted to get your thoughts on it. There's transitions and there's transformations. Yeah, we've been kind of in this data transition around analytics. You pointed out, as insiders, we've been pointing this out for years. But I think now there's more of a transformative component to this. I think it's becoming clear to everyone the role of data, and you've laid out some good things there. Now I want to ask you, on this transformation. Do you agree with it, and if you do, how does that change the roles? Because if I'm going to react to this as a business, whether small, medium, and large business, large enterprise or government, I now realize that the old world's over. I need to get to the new way. That means new roles, new responsibilities, new outcomes, new ways to measure. Can you share your thoughts on that? Do you agree with the transformation, and two, what are some of those new role changes? How should a business manager or technologist make that transformation? >> Yeah. If it was ever more clear, getting a switch, or a transformation as you say, from the old way we did business and we did technology to the new way, is only being highlighted by this crisis. If you are an enterprise, and you are trying to do everything yourself, running your own IT stacks and all of that, it is clear today that it is much more difficult to do that than if you were leveraging next generation technologies: clouds, SaaS, PaaS, and other things, because it is hard to get people even to work. I think if we have ever been in a place where this sort of transformation is a must, not a slow choice or an evolution, it is now. Because enterprises who have done that, who have done that already, are now at an advantage. I think this is a critical moment in time for us all as we all wake up to this new reality. It is not to say that enterprises are going to be switched over after this specific crisis, but what's going to happen, I believe, is that, I think the philosophies are going to change, enterprises are going to think of this as the new normal. They're going to think about, "Hey, if I don't have the data "about my business, about my customers, "about my infrastructure, about my systems, "I won't be able to respond to the next one." Because right now there's a lot of plugging the holes in the dam with fingers and toes, but we are going to need to be ready for this, because if you think about what this particular pandemic means, this isn't going to end in April or May. Because without a treatment, or without a vaccination, it's going to continue to resurface. Unless we eradicate the entire population of the virus, any new incident is going to start up like a small flare-up, and that is going to continue to bring us back into the situation. Over this time, we're going to have to continue to respond to this crisis as we are, and we need to plan for the future ones like this. That might not be a pandemic type of crisis. It could be a change in the business. It could be other types of world events, whatever it might be. But I think this is the time when enterprises are going to start adopting these types of procedures and technologies to be able to respond. >> It's interesting, Bruno, you bring up some good points. I think about all the conversations that I've had over the years with pros around "disaster recovery" and continuous operations. This is a different vector of what that means, because when you highlighted earlier, IT, it's not like a hurricane or a power outage. This is a different kind of disruption. We talked about scale. What are some of the things that you're seeing right now that businesses are being faced with, that you guys are seeing in the analytics, or use cases that have emerged from this new normal that is facing today's business with this crisis. What's changed? What is this new challenge? When you think about the business continuity and how continuous operations need to be sustained because, again, it's a different vector. It's not a blackout, it's not a hurricane. It's a different kind of disruption. It's one where the business needs to stay on more than ever. >> Yep. Correct. True. What's really interesting, and there are some relatively straightforward use cases that we're seeing. People are dealing with their authentication, VPN network issues, because everybody is low on bandwidth. Everybody is, all of these systems are at their breaking point because they're carrying more than they ever did. These are use cases that existed all along. The problem with the use cases that existed all along is that they've been slowly picking up and growing. This is the discontinuity right now. What's happened right now, all of a sudden you've got double, triple, quadruple the load, and you need to both scale up your infrastructure, scale up your monitoring, be much more vigilant about that monitoring, speed up your recovery because more is at stake, and all of those things. That's the generic use case that existed all along, but have not been in this disruptive type of operating environment. Second is, enterprises are now learning very quickly what they need to do in terms of scaling and monitoring their production, customer-facing infrastructure, what used to be in the data center, the three-tier world, adding a few notes to an application, to your website over time, worked. Right now everybody is realizing that this whole bent on building our microservices, building for scale, rearchitecting and all that stuff, so that you can respond to an instantaneous burst of traffic on your site. You want to capture that traffic, because it means revenue. If you don't capture it, you miss out on it, and then customers go elsewhere, and never come back, and all that stuff. A lot of the work loads are to ensure that the systems, the mission-critical systems, are up and running. It's all about monitoring real-time telemetry, accelerating root cause analysis across systems that are cloud systems, and so on. >> It's a great point. You actually were leading into my next question I wanted to ask you. You know, the old saying goes, "Preparation meets opportunity. Those are the lucky ones." Luck is never really there. You're prepared, and opportunity. Can you talk about those people that have been prepared, that are doing it right now, or who are actually getting through this? What does preparation look like? What's that opportunity? Who's not prepared? Who's hurting the most? Who's suffering, and what could they do differently? Are you seeing any patterns out there, that people, they did their work, they're cloud native, they're scaled out, or they have auto-scaling. What are some of the things where people were prepared, and could you describe that, and on the other side where people weren't prepared, and they're hurting. Can you describe those two environments? >> Sure. Yeah. You think about the spectrum of companies that are going through digital transformation. There are companies who are on the left side. I don't know whether I'm mirroring or not. Basically, on the left side are people who are just making that transformation and moving to serving customers digitally, and on the right side are the ones that are basically all in, already there, and have been building modern architectures to support that type of transformation. The ones that are already all the way on the right, companies like us, right? We've been in this business forever. We serve customers who are early adopters of digital, so we've had to deal with things like November 6th, primary elections, and all of our media and entertainment customers who were spiking. Or we have to deal with companies that do sporting events like World Cup or Super Bowl and things like that. We knew that our business was going to always demand of us to be able to respond to both scheduled and unscheduled disruptions, and we needed to build systems that can scale to that without many human interactions. And there are many of our customers, and companies who are in that position today, who are actually able to do business and are now thriving, because they are the ones capturing market share at this point in time. The people who are struggling are people who have not yet made it to that full transformation, people who, essentially, assume business as normal, who are maybe beginning that transformation, but don't have the know-how, or the architecture, or the technology yet to support it. Their customers are coming to them through their new digital channels, but those digital channels struggle. You'll see this, more often than not you're going to find these still running in a traditional data center than in the cloud. Sometimes they're running in the cloud where they've done just a regular lift-and-shift instead of rearchitecting and things like that. There's really a spectrum, and it's really funny and amazing how much it maps to the journey in digital transformation, and how this specific thing is essentially, what's happening right now, it looks like the business environment demands everybody to be fully digital, but not everybody is. Effectively, the ones that are not are struggling more than the ones that are. >> Yeah. Certainly, we're seeing with theCUBE, with the digital events happening on our side, all events are canceled, so they've got to move online. You can't just take a physical, old way of doing something, where there's content value, and moving it to digital. It's a whole different ball game. There's different roles, there's different responsibilities. It's a completely different set of things. That's putting pressure on all these teams, and that's just one use case. You're seeing it in IT, you're seeing it happen in marketing and sales, how people are doing business. This is going to be very, very key for these companies. The data will be, ultimately, the key. You guys are doing a great job. I do want to get to the news, and I want to get the plug in for Sumo Logic. I want to say congratulations to you guys. A press release went out today from Sumo Logic. You guys are offering free cloud-based data analytics to support work from home and online classroom environments. That's great news. Can you just share and give a plug for that, PSA? >> Sure! We basically have a lot of customers who, just like us, are now starting to work from home. As soon as this began, we got inbound demands saying, "Oh, could you get, do you have an application for this, "do you have some analytics for that, "things that support our work from home." We thought hey, why don't we just make this as a package, and actually build out-of-the-box solutions that can support people who have common working from home technologies that they used to use for 10% of their workforce, and now work for 100% of their workforce. Let's package those, let's push those out. Let's support educational institutions who are now struggling. I have two kids in here who are learning. Everything is online, right? We had to get another computer for them and all this stuff. They're younger, they're in fourth grade. They are doing this, I can see personally how the schools are struggling, how they're trying to learn this whole new model. They need to have their systems be reliable and resilient, and this is not just elementaries, but middle school, high schools, colleges have all expanded their on-premise teaching. So we said, "Okay. Let's do something to help the community "with what we do best." Which is, we can help them make sure that the things that they do, that they need to do for this remote workforce, remote learning, whatever it might be, is efficient, working, and secure. We packaged several bundles of these solutions and offered those for free for a while, so that both our customers, and non-customers, and educational institutions have something they can go and reach for when they are struggling to keep their systems up and running. >> Yeah, it's also a mindset change, too. They want comfort. They want to have a partner. I think that's great that you guys are doing for the community. Can you just give some color commentary on how this all went down? Did you guys have a huddle in your room, said, "Hey, this is a part of our business. "We could really package this up "and really push it out and help people." Is that how it all came together? Can you share some inside commentary on how this all went down and what happened? >> Yeah. Basically, we had a discussion, literally, I think, the first or the second day when we all were sent home. We got on our online meeting and sat down, and essentially learned about this inbound demand from our customers, and what they were looking to do. We were like, "Okay, why don't we, "why don't we just offer this? "Why don't we package it?" It was a cross-functional team that just sat there. It was a no-brainer. Nobody was agonizing over doing this for free or anything like that. We were just sitting there thinking, "What can we do? "Right now is the time for us to all "pull each other up and help each other. "It'll all sort itself out afterwards." >> You know, during the bubonic plague, Shakespeare wrote Macbeth during that time. You guys are being creative during this time, as the coronavirus, so props to you guys at Sumo Logic. Congratulations, and thanks for taking the time. Can you give some parting thoughts on it, for the folks who are working at home? Just some motivational inspiration from you guys? What's going to come next for you guys? >> Sure. And thank you for having me on this video. I would say that we have been making slow transition towards remote workforce as it is. In a lot of places around the world, it's not that easy to make it to an office. Traffic is getting worse, big centers are getting populated, real estate is getting more expensive, all of this stuff. I think, actually, this is an opportunity for enterprises, for companies, and for people to figure out how this is done. We can actually practice now. We're forced to practice. It might actually have positive impact on all industries. We are going to probably figure out how to travel less, probably figure out how to actually do this more effectively, the cost of doing business is going to go down, ability to actually find new jobs might broaden, because you might be able to actually find jobs at companies who never thought they could do this remotely, and now are willing to hire remote workforces and people. I think this is going to be all good for us in the end. Right now it feels painful, and everybody's scared, and all that stuff, but I think long term, both the transformation into digitally serving our customers and the transformation towards remote workforce is going to be good for business. >> Yeah. It takes a community, and we really appreciate the effort you guys make, making that free for people, the classrooms. Remember, Isaac Newton discovered gravity and calculus while sheltering in place. A lot of interesting, new things are going to happen. I appreciate it. >> Bruno: Absolutely. >> Bruno, thank you for taking the time and sharing your insights from your place, sheltering. I made a visit into the studio to get this interview and a variety of other interviews we're doing digitally here. Thanks for sharing. Appreciate your time. >> Thank you. Appreciate you as well. >> I'm John Furrier with theCUBE here. CUBE conversation with Bruno from Sumo Logic sharing his perspective on the COVID-19. The impact, the disruption and path to the future out of this, and the new normal that is going to change our lives. Thanks for watching.

>> Announcer: From San Francisco, it's the Cube, covering PagerDuty Summit 2019. Brought to you by PagerDuty. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at PagerDuty Summit in downtown San Francisco. It's about a thousand people, fourth year of the show, third year of the Cube this year, happy to be back. Ironically, (laughs) a couple weeks ago we were at Sumo Logic Illuminate down the road by the airport, and we're excited to have somebody from Sumo here to talk about how do these platforms work together. So, returning again is Jonathan Rende SVP products at PagerDuty and joining us is Bruno Kurtic. He is the founding VP of product strategy for Sumo Logic. Bruno great to see you, Jonathan welcome back. >> Thanks, for having us. >> All right so Bruno we were just at your show, now you got to take a little bit easier, probably quite not as many responsibilities. We'll talk a little bit about your relationship between the two companies cause from the outside looking in, looks like there's some redundancies, it looks like two platforms, it looks like where's my single pane of glass but in fact there's a real synergistic opportunity to work together. >> Good question, so they are two platforms but it's entirely synergistic. You know between the two technologies, PagerDuty and Sumo Logics, we sort of helped our customers who run mission critical products and services that serve their customers in fact, number one, get information from their systems and applications to understand what's happening in them and then leverage our two platforms to resolve those issues, make sure those applications are running, that their customers are happy, that they're delivering the services that they are there to deliver to them. >> And I know Jon you got a long list of great companies that you guys work for and you said it's a really key part of the company strategy. >> Yeah the ecosystem that we work with with one of our favorite partners, Sumo Logic, we use Sumo, we're a big customer of Sumo Logic as well and it's really important all of the telemetry, all the machine information that's coming in. Again the part that we play as in that is how do we orchestrate people to get work done when things go south? And how do we get the right people on and give them some information about what they're doing to help triage what they're doing. >> Right. >> So the two work together really, really well. >> So what are the themes at both keynotes? Ramin's keynote as well as Jennifer's is data. And the fact that you guys both have a giant proprietary data set about machine activity and people activity from running these businesses. I was teasing on Twitter an overnight sensation ten years in the making that you can leverage to deliver more value. So, as we look forward, data's been important but, right, now all the hot topic is machine learning and artificial intelligence. How are you now taking this next gen technology and applying it to these giant datasets to offer kind of proprietary insight to your customers. I'll start with you, Bruno. >> Sure, so there's a massive amount of data, right? It's growing at a rate of Moore's Law so there's more data than any human could cope with. And so our task at Sumo's is figuring out what is that data trying to communicate to you? So we spend a lot of effort on machine learning, pattern detection, advanced analytics, to help our customers sort through that massive amount of data to understand whether their services are available, whether they're performing, whether they're secure, whether they are compliant, and we boil that up into a set of insights that we then feed downstream or upstream in this case to PagerDuty to help those people who are responsible for those services do the work to make sure they're restored and working well. >> And I guess to compliment what Bruno is saying, one of the things that we're doing is we're also ingesting a lot data, a lot of machine data from monitoring products and from service desk products, other kind of sources of data because that also informs who needs to get engaged when a system goes down? And then what do they need to do in order to fix it? And so it's all context it's all data and how we can help narrow that down. We had a really interesting statistic this was earlier this year where we were looking at per responder how is this growth of interruptions and alerts, how is that trending? And now compared to just a couple of years ago it's about three times the amount of noise that's coming at them now per responder than three years ago. So, clearly the people on the end of this are getting overwhelmed if we don't do something intelligently (laughs) to make sense of it for them. >> Right. That's interesting cause it's really a lot false positives, (stammers) I don't know if that's the right characterization but certainly too much to prioritize and an overwhelming amount of data for a person to try to filter, so you're really trying to add that intelligence on the front end so hopefully the right problems are getting surfaced and not just this broad (laughs) base of false positives, or minor positives maybe. >> Yeah, it's funny you say false positives because one of the concepts that we have is there are you know, alerts and incidents that need to be managed, but then there are un-actionable alerts and incidents. Things that really shouldn't be bothering you. So you have to walk that fine line between what do you act on that you should take action on and what are the things you shouldn't take action on and kind of ignore? And so we use machine learning to do a lot of that work and filter out the bad noise and bring the important information in. >> Yeah, I wonder if you have any thoughts, Bruno, on how much of that filtration needs to happen (laughs) to kind of quiet down this tsunami that's coming over the transom. >> Well on our terms it's, you know, every one of our customers send us billions of records per day, literally billions. >> Jeff: Billions of records per day? >> Billions of records and so figuring out what matters amongst those billions of records is a hard job. There's a lot of false positives, false negatives that need to be sorted through, before it even gets handed up to the upstream technologies like PagerDuty, right? So, we spend a lot of time doing outlier detection, doing predictive analytics, doing sort of pattern detection, machine learning type of techniques to make sure that the stuff that gets bubbled up has as few false positives and as few false negatives as possible so that the insights that intelligent actions that need to be taken are most appropriate and can be prioritized and handled by a small team of people who own those actions. >> Right, it's funny you say billions and billions. I have a digitalization challenge, I keep throwing out to people and there's yet to be, I've yet to get a great response which has shown me a billion, a billion piece dataset in a visualization that I as a person can look at and comprehend what the heck is going on. Beyond something as simple as you know, half of them on this side and half of them on this side. I mean we're not wired for that way. We're not wired to be able to take in billions of data points. It's just not, it's just not going to happen. >> Just for that context we actually, we analyze a quadrillion records a day. So talk about billions and then you know many more orders of magnitude than that, it's, those are numbers that are hard to comprehend, right? We don't think in those numbers, right? It's really hard to humans to grasp. >> So, so how do we keep up? I mean, how do we keep up? I mean it's kind of a bigger problem, but you know as much as anybody kind of exponential growth of this data. We're barely getting into IOT and industrial IOT and sensors on everything at the house and on our clothes and our shoes. You scared about keeping up? Can we keep up? What do you, you know, kind of, how do you see this crazy trajectory on the data? We have to kind of gate it somehow? >> So from my perspective there is no sense in being scared of it, right? A digital business generates data, we all got data that can't run. So the task is to capture it, analyze it, to understand it and serve up intelligence from it, right? So our task is to keep pace with that growth and build resilient scalable systems with the analytics that are required to understand it, right and so you know we can't shy away from it, so whether we like it or not. >> Here it comes (laughs). >> It's not an easy task but we can't walk away. >> Right right, and then the other just crazy increasing complexity. No, thank you. (laughing) Is on your guy's side, really is the variety. I mean we used to talk about the old big data big three you know variety, and veracity and velocity. You know the interconnectivity of all these systems is also the thing that's growing so exponentially and so when something does break the ability to find what broke amongst this huge potential is really a hard and growing problem. >> Yeah, it is and that's why it's sitting in the middle of an ecosystem of a lot of different products that will give and send off to telemetry that we have to look at. It is really important. You know, it's almost as if the information that we're always looking for on the PagerDuty platform, it has to be items that really are actionable by a person which, you know, if you look at the information that is flowing into Sumo Logic, it's even in some ways very broad. And so it's a wider funnel, we have a narrower funnel of kind of information but they're both very complimentary at each other cause one is that humans need to act on in the moments and the other one is how do I analyze in a broader sense? >> Right. >> Even a bigger range of information so both are so critical as a part of that whole ecosystem. As I was saying, we personally use Sumo Logic as a big part of how do we actually triage actual incidents? We built tons of libraries in the Sumo Logic product so we can make sense of even a broader set of information flowing in from all of our logs in some of those critical moments. So yeah, it's great synergy. >> Good, good, well I'm glad you guys are working on this big data problem cause it's a big hairy one. >> Jon: And it just keeps getting bigger. >> And the customers only benefit right? >> Yeah. >> Well Bruno, Jonathan, thanks again for taking a few minutes. Congratulation on the collaboration. It looks like it's working pretty well. (mumbling in agreement) He's Bruno, he's Jonathan, I'm Jeff and you're watching the Cube. We're at PagerDuty Summit downtown San Francisco. Thanks for watching, we'll see you next time. (rhythmic synth music)

>> from Burlingame, California It's the Cube covering Suma logic Illuminate 2019. Brought to You by Sumer Logic >> Hey, welcome back, everybody. Jeffrey here with the Cube were at the higher Regency San Francisco Airport at Suma Logic, Illuminate, 2019 were here last year for our first time. It's a 30 year the show. It's probably 809 100 people around. 1000 packed house just had the finish. The keynote. And we're really excited to have our first guest of the day. Who's been here since the very beginning is Bruno Critic, the founding VP of product and strategy for Suma Logic, you know, great to see you. Likewise. Thank you. So I did a little homework and you're actually on the cube aws reinvent, I think 2013. Wow. How far has the cloud journey progressed? Since efforts? I think it was our first year at reinvented as well. >> That's the second year agreement, >> right? So what? What an adventure. You guys made a good bet six years ago. Seems to be paying off pretty well. >> It really has been re kind of slipped out that the cloud is gonna be a real thing. Put all of our bats into it and have been executing ever since. And I think we were right. They think it is no longer a question. Is this cloud thing gonna be re alarm enterprise gonna adopt it? It's just how quickly and how much. >> Right? Right. But we've seen kind of this continual evolution, right? Was this jump into public cloud? Everybody jumped in with both feet, and now they're pulling back a little bit. But now really seen this growth of the hybrid cloud Big announcement here with Antos and Google Cloud Platform and in containers. And, you know, the rise of doctor and the rise of kubernetes. So I don't know, a CZ. You look a kind of the evolution. A lot of positive things kind of being added to the ecosystem that have helped you guys in your core mission. >> That's right. Look, you know, five years ago, which is such a short time, But yet instead of the speed of the technology adoption and change, you know it's in It's in millennia. What's happened over the last few years is technology stocks have changed dramatically. We've gone from okay, we can host some v ems in the cloud and put some databases in the cloud. So we're now building micro service's architecture, leveraging new technologies like Kubernetes like Serverless Technologies and all the stuff And, you know, some one of the fastest growing technologies that's being adopted by some village custom base, actually the fastest kubernetes and also the fastest customer segment growing customer segments. ImmuLogic is multi clog customers, basically that sort of desire by enterprise to build choice into their offerings. Being able to have leverage over the providers is really coming to fruition right now, >> right? But the multi cloud almost it makes a lot of sense, right, because we're over and over. You want to put your workload in the environment that supposed appropriate for the workload. It kind of. It kind of flipped the bid. It was no longer. Here's your infrastructure. What kind of APs can you build on it? Now here's my app. Where should it run that maybe on Prem it may be in a public cloud. It may be in a data center, so it's kind of logical that we've come into this this hybrid cloud world that said, Now you've got a whole another layer of complexity that that's been added on. And that's really been a big part of the rise of kubernetes. >> That's right. And so, as you're adopting service's that are not equal, right, you have to create a layer that insulate you from those. Service is if you look a tw r continues intelligence report that we just announced today. You will also see that how customers and enterprise are adopting cloud service is is they're essentially adopting the basic and core compute storage network, and database service is there's a long, long tail of service that are very infrequently adopted. And that is because enterprise they're looking for a way to not get to lock Tintin into anyone. Service provider kubernetes Give them Give them that layer of insulation with in thoughts and other technologies like that, you are now able to seamlessly manage all those workloads rather there on your on premise in AWS in G C. P. In azure or anywhere else, >> right? So there's so much we can unpack. You're one of the things I want to touch on which you talked about six years ago, but it's even more thing appropriate. Today is kind of this scale this exponential growth of data on this exponential scale of complexity. And we, as people, has been written about by a lot of smart people, and I, we have a real hard time. Is humans with exponential growth. Everything's linear. Tow us. So as you look at this exponential growth and now we're trying to get insights. Now we've got a I ot and this machine a machine data, which is a whole another multiple orders of magnitude. You can't work in that world with a single painted glass with somebody looking at a dashboard that's trying to find a yellow light that's earned it. I'm going to go read. You don't have analytics. Your hose. >> That's right. This is no longer world of Ding dong lights, right? You can just like to say, Okay, red, green, yellow. The as sort of companies go digital right? Which is driving this growth in data, you know? Ultimately, that data is governed by Moore's law. Moore's law says machines are gonna be able to do twice as much every 18 to 24 months. Well, that guess what? They're gonna tell you what they're doing twice as much. Every 18 to 24 months, and that is an exponential growth rate, right? The challenge that is, budgets don't grow at that rate, either, right? So budgets are not exponentially growing. So how do you cope with the onslaught of this data? And if you're running a digital service, right, if you're serving your customers digital generating revenue through digital means, which is just about every industry. At this point in time, you must get that data because if you don't get the data, you can't run your business. This data is useful not just in operations and security. It's useful for general business abuse, useful in marketing and product management in sales and their complexity. And the analytics required to actually make sense of that data and serve it to the right constituency in the business is really hard. And that has been whatever we have been trying to solve, including this economics of machine. Dad and me talked about it today. Keynote. We're trying t bend the cost curve >> Moore's law >> yet delivered analytics that the enterprise can leverage to really not just operate an application but run their business >> right. So let's talk about this concept of observe ability. You've written box about it. When you talk to people about observe ability, what should they be thinking about? How are you defining it? Why is it important? >> It's great question, So observe ability right now is being defined as a technique right. The simplest way to think about it is people think, observe a witty I need to have these three data sets and I have observed ability. And then you have to ask yourself a question. First of all, what is Observe ability and why does it matter? I think there's a a big misconception in the market how people adopt this is that they think, observe abilities the end. But it isn't observe. Ability is the means of achieving a goal. And what we like to talk about is what is the goal? Observe, observe ability right now. Observe abilities talked about strictly in the devil up space, right? Basically, how am I going to get obs Erv City into an application? And it's maybe runtime how it's running, whether it's up and performance. The challenge with that is that is a pigeon pigeon hole view off, observe ability, observe ability. If you think about it, we talk about objectives during observe ability. Operability tau sa two ns Sorry could be up time in performance. Well, guess what a different group like security observe. Ability is not getting breached. Understanding your compliance posture. Making sure that you are compliant with with regular to re rules and things like that observe ability to a business person to a product manager who's who owns a P N. L. On some product is how are my users using this product powers my application being adopted where users having trouble. What are they and where's the user experience? Poor right? So all of this data is multifaceted and multi useful as multi uses and observing Tow us. Is his objectives driven? If you don't know what your object it is, observe. Ability is just a tool. >> I love that, you know, because it falls under this thing We talked about off the two, which is, you know, there's data, right, and then there's information in the data and then, but it is a useful information because it has to be applied to something that's right in and of itself. It has no value, and what you're talking about really is getting the right data to the right person at the right time, which kind of stumbled into another area, which is how do you drive innovation in an organization? In one of the simple concepts is democratization. Get more people more than data more than tools to manipulate the data. Then piano manager is gonna make a different decision based on different visibility than Security Person or the Dev Ops person. So how is how is that evolving? Where do you see it going? Where was it in the past? And you know, I think he made it interesting or remain made. Interesting thing in the keynote where you guys let your software be available to everyone. And there was a lot of people talking about giving Maur. People Maur access to the tools and more of the data so that they can start to drive this innovation >> abuse of an example of one of the one of the sort of aspects of when we talk about continued continues intelligence. What do we mean? So this concept of agile development didn't evolve because people somehow thought, Hey, why don't we just try to push court production all the time? Break stuff all the time. What's the What's the reason why that came about? It did not come about because somehow somebody decided so better. Software development model It's because cos try to innovate faster, so they they wanted Toa accelerate. How they deliver digital product and service is to their customers. And what's facilitates that delivery cycle is the feedback loop. They get out of their data. They push code early. They observed the data. They understand what it's telling them about how their customers are using their products, and service is what products are working with or not. And they're quickly baking that feedback back into their development cycles into the business business cycles. To make better Prada effectively, it evolved as a as a tool to differentiate and out innovate the competition. And that's to a large degree one of the ways that you deliver the right inside to the right group to improve your business right. And so this is applicable across all use cases in order pot. All departments are on the company, but that's just one example of how you think of this continuous innovation, continuous data from to use analytics and don't >> spend two years doing an M r d and another two years doing a P R d and then another to your shift >> When you when you actually ship it. Half of the assumptions that you made two years ago already all the main along, right? So now you've gotta go. You've wasted half of your development time, and you've only released half of the value that you could have other, >> right? Right. And your assumptions are not gonna be correct, right? You just don't know until you get that >> you think over time, like two years of kubernetes with a single digits percentage adoption technology and soon was customer base. Now it's 1/3 right? Right? Which means no things have changed. If I had made an assumption as of two years ago on communities, I would have no way wouldn't have done this announcement, >> right? Right. >> But we did it in an interactive mode and re benefit from that continuous information continues intelligence that we do in our own >> right, right? We fed Joe and the boys on lots of times so that it's a pretty interesting how fast that came and how it really kind of over took. Doctor has informed they contain it. Even the doctor, according to reporters. Still getting a Tana Tana traction >> and it's >> working in conjunction with communities. Communities allows you to manage those containers right, And Dr Containers are always part of the ecosystem. And so it's, you know, you know, it's like the management layer and the actual container layer, >> right? So as you look forward to give you the last word, you know, as we're really kind of getting into the SIA Teague World and five G's coming just around around the corner, which is gonna have a giant impact on an industrial I ity and this machine a machine communications, what are some of your priorities? What are you looking, you know, kind of a little bit down the road and keeping an eye on >> interesting question. You know, we used to think about I ot as is the new domain. We should think about I or tea. And maybe we need to build a solution for right. It turns out our biggest customers, customers and the way that I have personally reframed my thinking about Iris is the following Computational capacity is ubiquitous. Now, what used to be a modern application 345 years ago was something that your access to your laptop or three or mobile app, and maybe you're a smart watch Now the computation that you interface with runs in your doorbell, you know, in a light switch in your light bulbs and how's it runs everywhere runs in your shoe because when you're around, it talks to your phone to tell you how many steps you've taken, all the stuff right? Essentially, enterprises building application to serve their customers are simply pushing computation farther and farther into our being, like everywhere. There's now I, P Networks, CP use memory and all of those distributed computers are now running the applications that are serving us in our lives, right? And to me, that's what I ot is. It's just an extension off what the digital service is our and we interface with does, and it so happens that when you push computation farther and farther into our lives, you get more and more computers participating. You get more data, and many of our largest customers are essentially ingesting their full stack of iron devices to serve their customers >> right crazy future and you know, it just kind of this continual Adam ization to of computer store and memory. Well, Bruno, hopefully it will not be six years before we see you again. Congrats on the conference. And thanks for taking a few minutes. Absolutely. All right. He's Bruno. I'm Jeff. You're watching the Cube where? It's suma logic illuminate at the Hyatt Regency seven square port. Thanks for watching. We'll see you next time.

>> SiliconANGLE Media presents... theCUBE! Covering AlibabaCloud's annual conference. Brought to you by Intel. Now, here's John Furrier... >> Hello everyone, welcome to Silicon Angle's theCUBE here on the ground, in Hangzhou, China. We're here at the Intel Booth as part of our coverage, exclusive coverage of Alibaba Cloud Conference here in the cloud city. I'm John Furrier, the co-founder of SiliconANGLE, Wikibon and theCUBE. And I'm here with CJ Bruno, who is the Corporate Vice President and General Manager of Global Accounts of the sales and marketing group at Intel. That's a mouthful but basically you run a lot of the major accounts, you bring a lot of value to Intel Supplier to these big clouds. >> I do, John. We look after our top 20 or so largest partners and customers around the world. Amazing like Alibaba, edge to cloud enterprises, deep rich engagements, just an exciting, exciting time to be in the business with these big customers. >> And there's no borders to the cloud so its not as easy as saying PC, like people might think of Intel in the old days. You guys have these major cloud providers, there's a lot of intel inside so to speak but that value is enabling a new kind of functionality. We're hearing it here at the show. >> You are. We work together with partners like Ali, in the area of such big artificial intelligence development, big data analytics and of course, the cloud. We've been working with them for over 12 years now and you can see the advancements and the services that they're providing to their customers, not only domestically, here in China but on a global stage as well. >> Its interesting, Intel, you've been working with these guys for 12 years, what a journey, from an entrepreneurial 12 guys in a dorm room, or an apartment for Jackie Ma, that he talks about all the time, to now the powerhouse. What's it like, because these guys have an interesting formula going on here. They're bringing culture and art, with science, kind of sounds like Steve Jobs, technology meets liberal arts, bringing a cultural aspect. How far have they come? Give us some insight into where they've come from and where you think they're going. >> Its amazing, Jack Ma, yesterday in his keynote, talked about this event eight years ago. 120 people, John, we're standing amongst 60,000 or so, in this event today, just eight short years later. Its amazing what they've been able to do. They're driving innovation, this is not a copy economy, it's an innovation economy. They invest, very high-degree of technical acumen. Willingness to break barriers, try things people have not. Fail fast and correct. Take risks. They're entrepreneurs at heart, they're technologists in their bloodstream and they really invest to win. >> You guys are supplying. We talked to people who talk about Photonics, Deeraj Malik, who's really going deep on these pathways around. Some of the Intel innovations, some of it's like wow, mind-blowing. The other end is just practical stuff, making it easier, faster, simpler to run things. IoT, their big use case, I mean you can't get any more sexier than looking at a city cloud that's actually running the city with traffic and all those IoT devices, so what is the big thing that you guys do for Alibaba? Talk about that journey because its not one thing, what is it? What is the magical formula? >> Sure, of course, first off we deliver, we think, world-class ingredients to their world-class cloud. And enable them to deliver amazing services to their customer, at the base level. But we really work together to solve societal problems. Look at the precision medical cloud that we announced last April together, John. Genome sequencing, solving people's cancer problems, in a matter of days, instead of months. Just one example of the real use case that we bring these technologies to bear on and have an amazing influence. We work on them with the Tenatchi Medical Imaging Competition. 3,000 entrants competing to see who can identify lung cancer quickest, and we have some winners selected, just this week. So these things are real, taking this technology, solving real life problems, and business problems, around the globe. >> And its not just the big, heaving lifting technology that moves the needle, like you were mentioning but its also the micro technologies, like FPGA, you guys have got lot of things. This is like the new Intel, so I'd love to get your thoughts, if you can just take a moment to share the journey that Intel is on right now because you gave a talk yesterday, a kind of a keynote, onstage. What is the Intel journey right now look like? >> We're transforming ourselves from a PC centric company to a company that runs the cloud and powers countless numbers, billions and billions of smart-connected devices. That's a big journey we're on. We've diversified our business significantly in a five year period, John. Driving our data-center business, our IoT business, our programmable logic business as you said, our friends from former Alterra are now two years inside Intel. Our memory business, our NSG technologies, 3D NAND Optane, driving breakthroughs in SSDs and of course new technologies that we're exploring, like drones and neuromorphic computing, making sure we never miss the next big thing. >> I've been following Intel for 30 years of my career and life, as an initial user-developer and now in the media. It's interesting, Intel has never done it alone, it's always been part of the ecosystem. You have brought a lot of goods to the party, so to speak, in technology, Moore's law and the list is endless. Now is an end to end game but you look at 5G for instance, you kind of connect the dots, put a radio frequency cloud over a city and you got to run the IoT devices like a city brain, they're showing here. You got to tie it together with programmable arrays, it's a hardware thing but now the software guys are doing it. You've got cloud native with the Linux Foundation, that's DevOps. You've got data centers that are 10 to one silicon to the edge, this is a wide opportunity, how do you guys make sense of it to customers? Because its a complex story. >> It is John, look, we're the ultimate ingredient supplier. We're bringing forward technologies in artificial intelligence, in 5G, in VR and AR, areas that are just autonomous everything. Autonomous driving in particular. These are big investment areas we're driving into that require an enormous amount to compute, storage, networking, connectivity and we're making the investments to make sure we're critical partners with our customers, in all those huge growth areas. Making us a big growth company now. >> I had a great conversation with Dr. Wong, who's the founder of Alibaba Cloud, he's on the Technology Steering Committee for Alibaba Group and yesterday they just announced a 15 billion dollar investment over three years for FinTech, across the board IoT, AI, collaborate with scientists as well as artisans. This is a big deal. >> It is John, this is exactly an example of what I mentioned earlier. These guys invest to win and they have a will to win. And they want to pioneer and they want to innovate and they put their money where their mouth is, in that announcement, its pretty exciting. >> So the cloud serves quite a market, doing really well. Your global accounts are doing well, certainly in Asia and People's Republic of China, PRC, as you guys call it, extremely well but now there's a Renaissance in cloud in general, so we're expecting to see a lot more cloud service providers, maybe not as big as Alibaba but Alibaba is going to start getting customers that become SaaS companies, that's technically a cloud service provider if you think about it, if they have an application, how do you look at that mark? >> We see what is known as the super seven in the industry, the large folks, both US based and China based but then we've identified the next 60-70 next wave CSPs that are growing vibrantly around the globe and there's a long tail of another 120 that we're interacting with. You're absolutely on point, an exploding area. Significant double-digit growth for years to come and just solving, big, big life and business problems. >> So at SiliconANGLE also silicon is in the name and Wikibon Research is really big in China, here, interesting dynamic that's happening here with the data and the software and was brought up with Dr. Wong about the IoTs, kind of a nuanced point but I want to get it out for the folks watching that you're going to start to see new compute at the edge because data is now the currency of the future. It needs to flow, it's like water but at the edge it can be expensive, low latency that table stakes that everyone wants to get to. You're going to see a lot more compute or silicon at the edge of network. Internet of things coming, your view on that? >> There's no question John, that's exactly the way we see it. The time to get the data back to the long-haul data center, is very expensive and very challenging and requires an absolute redo of the network. We're moving to compute closer and closer to the data, of course, the cloud remains a vital, vital part of that but we move that compute capability closer to where the data is sensed, you can analyze it quicker, you can make faster decisions and you can implement those decisions at the edge. >> CJ, final question for you, obviously Alibaba, big part of their growth strategy is going outside mainland China, obviously doing very well here, not to knock them there but great opportunity to go into the global marketplace, specifically North America. That's going to put more competition, competition was good but it's also going to require more growth. How are you helping Alibaba and how does your relationship at Intel expand with Alibaba? >> We work with Alibaba, not only on the technical front of course but on their go-to-market plans, on ecosystem development plans and even some business models. We do that across our entire customer and partner base, John. We're seeing this explosive growth in cloud and being able to work with our partners on all four of those fronts; technology development, ecosystem development, business model development, are obviously a benefit to both of us. >> Alibaba is going to need some help because you know its competitive, Amazon had a nice run for a while, Microsoft nibbling at the heels, Google and now Alibaba coming in. Competition is good. >> We're proud to call all those innovators our customers and we work hard everyday to earn their business. >> Final, final question, this one just popped in my head. What should folks in America know about this PRC market or China market that they may not know about? Obviously they read what they read in the paper. They see the security hacks, they see the crypto-currency temporarily on hold but blockchain certainly has a lot of promise, but it's a dynamic market here. A lot of of opportunities. What should that audience know about the China market? >> I think the first thing they should know is that if they haven't come to experience it themselves they should. The scale of the opportunity, the scale of the country is like nothing people have ever seen before. As I said, the investments they're making-to innovate, to drive an innovation economy is breakthrough. You take that scale and that investment and this is a market to be reckoned with. >> Congratulations on the 12 year run with Alibaba, and now Alibaba Cloud. Looking really, really, strong, love the culture, got to unique twist; artistry and scientific cultures coming together, looking good. >> Absolutely John, thanks for letting us tell our story. >> CJ Bruno, Group Vice President, General Manager Global Accounts for Intel. I'm John Furrier with SiliconANGLE, thanks for watching.

>> Live from Mid Town Manhattan. It's the Cube, covering BIGDATA New York City 2017. Brought to you by, SiliconANGLE Media and it's ecosystems sponsors. >> Hey welcome back everyone live here in New York City for the Cube special presentation of BIGDATA NYC. Here all week with the Cube in conjunction with Strata Data even happening around the corner. I'm John Furrier the host. James Kobielus, our next two guests Doctor Mark Ramsey, chief data officer and senior vice president of R&D at GSK, Glasgow Pharma company. And Bruno as he's the CMO at Fscale, both Cube alumni. Welcome back. >> Thank for having us. >> So Bruno I want to start with you because I think that Doctor Mark has some great use cases I want to dig into and go deep on with Jim. But Fscale, give us the update of the company. You guys doing well, what's happening? How's the, you have the vision of this data layer we talked a couple years ago. It's working so tell us, give us the update. >> A lot of things have happened since we talked last. I think you might have seen some of the news in terms of growth. Ten X growth since we started and mainly driven around the customer use cases. That's why I'm excited to hear from Mark and share his stories with the rest of the audience here. We have a presentation at Strata tomorrow with Vivens. It's a great IOT use case as well. So what we're seeing is the industry is changing in terms of how it's spying the idea platforms. In the past, people would buy idea platforms vertically. They'd buy the visualization, they'd buy the sementic and buy the best of great integration. We're now live in a world where there's a multitude of BI tools. And the data platforms are not standardized either. And so what we're kind of riding as a trend is this idea of the need for the universal semantic layer. This idea that you can have a universal set of semantics. In a dictionary or ontology. that can be shared across all types of business users and business use cases. Or across any data. That's really the trend that's driving our growth. And you'll see it today at this show with the used cases and the customers. And of course some of the announcements that we're doing. We're announcing a new offer with cloud there and tableau. And so we're really excited about again how they in space and the partner ecosystems embracing our solutions. >> And you guys really have a Switzerland kind of strategy. You're going to play neutral, play nicely with everybody. Because you're in a different, your abstraction layer is really more on the data. >> That's right. The whole value proposition is that you don't want to move your data. And you don't want to move your users away from the tools that they already know but you do want them to be able to take advantage of the data that you store. And this concept of virtualized layer and you're universal semantic layer that enables the use case to happen faster. Is a big value proposition to all of them. >> Doctor Mark Ramsey, I want to get your quick thoughts on this. I'm obviously your customer so. I mean you're not bias, you ponder pressure everyday. Competitive noise out there is high in this area and you're a chief data officer. You run R&D so you got that 20 miles stare into the future. You've got experience running data at a wide scale. I mean there's a lot of other potential solutions out there. What made it attractive for you? >> Well it feels a need that we have around really that virtualization. So we can leave the data in the format that it is on the platform. And then allow the users to use like Bruno was mentioning. Use a number of standardized tools to access that information. And it also gives us an ability to learn how folks are consuming the data. So they will use a variety of tools, they'll interact with the data. At scale gives us a great capability to really look under the cover, see how they're using the data. And if we need to physicalize some of that to make easier access in the long term. It gives us that... >> It's really an agility model kind to data. You're kind of agile. >> Yeah its kind of a way to make, you know so if you're using a dash boarding tool it allows you to interact with the data. And then as you see how folks are actually consuming the information. Then you can physicalize it and make that readily available. So it is, it gives you that agile cycles to go through. >> In your use of the solution, what have you seen in terms of usage patterns. What are your users using at scale for? Have you been surprised by how they're using it? And where do you plan to go in terms of the use cases you're addressing going forward with this technology? >> This technology allows us to give the users the ability to query the data. So for example we use standardized ontologies in several of the areas. And standardized ontologies are great because the data is in one format. However that's not necessarily how the business would like to look at the data and so it gives us an ability to make the data appear like the way the users would like to consume the information. And then we understand which parts of the model they're actually flexing and then we can make the decision to physicalize that. Cause again it's a great technology but virtualization there is a cost. Because the machines have to create the illusion of the data being a certain way. If you know it's something that's going to be used day in and day out then you can move it to a physicalized version. >> Is there a specific threshold when you were looking at the metrics of usage. When you know that particular data, particular views need to be physicalized. What is that threshold or what are those criteria? >> I think it's, normally is a combination of the number of connections that you have. So the joins of the data across the number of repositories of data. And that balanced with the volume of data so if you're dealing with thousands of rows verses billions of rows then that can lead you to make that decision faster. There isn't a defined metric that says, well we have this number of rows and this many columns and this size that it really will lead you down that path. But the nice thing is you can experiment and so it does give you that ability to sort of prototype and see, are folks consuming the data before you evoke the energy to make it physical. >> You know federated, I use the word federated but semantic virtualization layers clearly have been around for quite sometime. A lot of solution providers offer them. A lot of customers have used them for disparate use cases. One of the wraps traditionally again estimating virtualization is that it's simply sort of a stop gap between chaos on the one end. You know where you have dozens upon dozens of databases with no unified roll up. That's a stop gap on the way to full centralization or migration to a big data hub. Did you see semantic virtualization as being sort of your target architecture for your operational BI and so forth? Or do you on some level is it simply like I said a stop gap or transitional approach on the way to some more centralized environment? >> I think you're talking about kind of two different scenarios here. One is in federated I would agree, when folks attempted to use that to bring disparate data sources together to make it look like it was consolidated. And they happen to be on different platforms, that was definitely a atop gap on a journey to really addressing the problem. Thing that's a little different here is we're talking about this running on a standardized platform. So it's not platformed disparate it's on the platform the data is being accessed on the platform. It really gives us that flexibility to allow the consumer of the data to have a variety of views of the data without actually physicalizing each of them. So I don' know that it's on a journey cause we're never going to get to where we're going to make the data look as so many different ways. But it's very different than you know ten, 15 years ago. When folks were trying to solve disparate data sources using federation. >> Would it be fair to characterize what you do as agile visualization of the data on a data lake platform? Is that what it's essentially about? >> Yeah that, it certainly enables that. In our particular case we use the data lake as the foundation and then we actually curate the data into standardized ontologies and then really, the consumer access layer is where we're applying virtualization. In the creation of the environment that we have we've integrated about a dozen different technologies. So one of the things we're focused on is trying to create an ecosystem. And at scale is one of the components of that. It gives us flexibility so that we don't have to physicalize. >> Well you'd have to stand up any costs. So you have the flexibility with at scale. I get this right? You get the data and people can play with it without actually provisioning. It's like okay save some cash, but then also you double down on winners that come in. >> Things that are a winner you check the box, you physicalize it. You provide that access. >> You get crowd sourcing benefits like going on in your. >> You know exactly. >> The curation you mentioned. So the curation goes on inside of at scale. Are you using a different tool or something you hand wrote in house to do that? Essentially it's a data governance and data cleansing. >> That is, we use technology called Tamer. That is a machine learning based data curation tool, that's one of our fundamental tools for curation. So one of the things in the life sciences industry is you tend to have several data sources that are slightly aligned. But they're actually different and so machine learning is an excellent application. >> Lets get into the portfolio. Obviously as a CTO you've got to build a holistic view. You have a tool chest of tools and a platform. How do you look at the big picture? On that scale if it's been beautifully makes a lot of sense. So good for those guys. But you know big picture is, you got to have a variety of things in your arsenal. How do you architect that tool shed or your platform? Is everything a hammer, everything's a nail. You've got all of them though. All the things to build. >> You bring up a great point cause unfortunately a lot of times. We'll use your analogy, it's like a tool shed. So you don't want 12 lawnmowers right? In your tool shed right? So one of the challenges is that a lot of the folks in this ecosystem. They start with one area of focus and then they try to grow into area of focuses. Which means that suddenly everybody's starts to be a lawnmower, cause they think that's... >> They start as a hammer and turn into a lawn mower. >> Right. >> How did that happen, that's called pivoting. >> You can mow your lawn with a hammer but. So it's really that portfolio of tools that all together get the job done. So certainly there's a data acquisition component, there's the curation component. There's visualization machines learning, there's the foundational layer of the environment. So all of those things, our approach has been to select. The kind of best in class tools around that and then work together and... Bruno and the team at scale have been part of this. We've actually had partner summits of how do we bring that ecosystem together. >> Is your stuff mostly on prime, obviously a lot of pharma IP there. So you guys have the game that poll patent thing which is well documented. You don't want to open up the kimono and start the cloth until it's releasing so. You obviously got to keep things confidential. Mix of cloud, on prime, is it 100 percent on prime? Is there some versing for the cloud? Is it a private cloud, how do you guys look at the cloud piece? >> Yeah majority of what we're doing is on prime. The profile for us is that we persist the data. So it's not. In some cases when we're doing some of the more advanced analytics we burst to the cloud for additional processors. But the model of persisting the data means that it's much more economical to have on prime instance of what we're doing. But it is a combination, but the majority of what we're doing is on prime. >> So will you hold on Jim, one more question. I mean obviously everyone's knocking on your door. You know how to get in that account. They spend a lot of money. But you're pretty disciplined it sounds like you've got to a good view of you don't want people to come in and turn into someone that you don't want them to be. But you also run R&D so you got to have to understand the head room. How do you look at the head room of what you need down the road in terms of how you interface with the suppliers that knock on your door. Whether it's at scale currently working with you now. And then people just trying to get in there and sell you a hammer or a lawn mower. Whatever they have they're going to try, you know you're dealing with the vendor pressure. >> Right well a lot of that is around what problem we're trying to solve. And we drive all of that based on the use cases and the value to the business. I mean and so if we identify gaps that we need to address. Some of those are more specific to life sciences types of challenges where they're very specific types of tools that the population of partners is quite small. And other things. We're building an actual production, operational environment. We're not building a proof of concept, so security is extremely important. We're coberosa enabled end to end to out rest inflight. Which means it breaks some of the tools and so there's criteria of things that need to be in place in order to... >> So you got anything about scale big time? So not just putting a beach head together. But foundationally building out platform. Having the tools that fit general purpose and also specialty but scales a big thing right? >> And it's also we're addressing what we see is three different cohorts of consumers of the data. One is more in the guided analytics, the more traditional dashboards, reports. One is in more of computational notebooks, more of the scientific using R, Python, other languages. The third is more kind of almost at the bare middle level machine learning, tenser flow a number of tools that people directly interact. People don't necessarily fit nicely into those three cohorts so we're also seeing that, there's a blend. And that's something that we're also... >> There's a fourth cohort. >> Yeah well you know someone's using a computational notebook but they want to draw upon a dashboard graphic. And then they want to run a predefined tenser flow and pull all that together so. >> And what you just said, tied up the question I was going to ask. So it's perfect so. One of my core focuses is as a Wikibon analyst is on deep learning. On AI so in semantic data virtualization in a life sciences pharma context. You have undoubtedly a lot of image data, visual data. So in terms of curating that and enabling you know virtualized access to what extent are you using deep learning, tenser flow, convolutional neural networks to be able to surface up the visual patterns that can conceivably be searched using a variety of techniques. Is that a part of your overall implementation of at scale for your particular use cases currently? Or do you plan to go there in terms of like tenser flow? >> No I mean we're active, very active. In deep learning, artificial intelligence, machine learning. Again it depends on which problem you're trying to solve and so we again, there's a number of components that come together when you're looking at the image analytics. Verses using data to drive out certain decisions. But we're acting in all of those areas. Our ultimate goal is to transform the way that R&D is done within a pharmaceutical company. To accelerate the, right now it takes somewhere between five and 15 years to develop a new medicine. The goal is to really to do a lot more analytics to shorten that time significantly. Helps the patients, gets the medicines to market faster. >> That's your end game you've got to create an architecture that enables the data to add value. >> Right. >> The business. Doctor Mark Ramsey thanks so much for sharing the insight from your environment. Bruno you got something there to show us. What do you got there? He always brings a prop on. >> A few years ago I think I had a tattoo on my neck or something like this. But I'm happy that I brought this because you could see how big Mark's vision is. the reason why he's getting recognized by club they're on the data awards and so forth. Is because he's got a huge vision and it's a great opportunity for a lot of CTOs out there. I think the average CEO spent a 100 million dollars to deploy big data solutions over the last five years. But they're not able to consumer all the data they produce. I think in your case you consume about a 100 percent of the instructor data. And the average in this space is we're able to consume about one percent of the data. And this is essentially the analogy today that you're dealing with if you're on the enterprise. We'd spent a lot of time putting data in large systems and so forth. But the tool set that we give, that you did officers in their team is a cocktail straw lik this in order to drink out of it. >> That's a data lake actually. >> It's an actual lake. It's a Slurpee cup. Multiple Slurpees with the same straw. >> Who has the Hudson river water here? >> I can't answer that question I think I'd have to break a few things if I did. But the idea here is that it's not very satisfying. Enough the frustration business users and business units. When at scale's done is we built this, this is the straw you want. So I would kind of help CTOs contemplate this idea of the Slurpee and the cocktail straw. How much money are you spending here and how much money are you spending there. Because the speed at which you can get the insights to the business user. >> You got to get that straw you got to break it down so it's available everywhere. So I think that's a great innovation and it makes me thirsty. >> You know what, you can have it. >> Bruno thanks for coming from at scale. Doctor Mark Ramsey good to see you again great to have you come back. Again anytime love to have chief data officers on. Really a pioneering position, is the critical position in all organizations. It will be in the future and will continue being. Thanks for sharing your insights. It's the Cube, more live coverage after this short break. (tech music)

>> Announcer: Live from San Jose, California, it's The Cube. Covering Big Data, Silicon Valley, 2017. (electronic music) >> Okay, welcome back everyone, live at Silicon Valley for the big The Cube coverage, I'm John Furrier, with me Wikibon analyst George Gilbert, Bruno Aziza, who's on the CMO of AtScale, Cube alumni, and Josh Klahr VP at AtScale, welcome to the Cube. >> Welcome back. >> Thank you. >> Thanks, Brian. >> Bruno, great to see you. You look great, you're smiling as always. Business is good? >> Business is great. >> Give us the update on AtScale, what's up since we last saw you in New York? >> Well, thanks for having us, first of all. And, yeah, business is great, we- I think Last time I was here on The Cube we talked about the Hadoop Maturity Survey and at the time we'd just launched the company. And, so now you look about a year out and we've grown about 10x. We have large enterprises across just about any vertical you can think of. You know, financial services, your American Express, healthcare, think about ETNA, SIGNA, GSK, retail, Home Depot, Macy's and so forth. And, we've also done a lot of work with our partner Ecosystem, so Mork's- OEM's AtScale technology which is a great way for us to get you AtScale across the US, but also internationally. And then our customers are getting recognized for the work that they are doing with AtScale. So, last year, for instance, Yellowpages got recognized by Cloudera, on their leadership award. And Macy's got a leadership award as well. So, things are going the right trajectory, and I think we're also benefitting from the fact that the industry is changing, it's maturing on the the big data side, but also there's a right definition of what business intelligence means. This idea that you can have analytics on large-scale data without having to change your visualization tools and make that work with existing stock you have in place. And, I think that's been helping us in growing- >> How did you guys do it? I mean, you know, we've talked many times in there's some secret sauce there, but, at the time when you guys were first starting it was kind of crowded field, right? >> Bruno: Yeah. >> And all these BI tools were out there, you had front end BI tools- >> Bruno: Yep. But everyone was still separate from the whole batch back end. So, what did you guys do to break out? >> So, there's two key differentiators with AtScale. The first one is we are the only platform that does not have a visualization tool. And, so people think about this as, that's a bug, that's actually a feature. Because, most enterprises have already that stuff made with traditional BI tools. And so our ability to talk to MDX and SQL types of BI tools, without any changes is a big differentiator. And then the other piece of our technology, this idea that you can get the speed, the scale and security on large data sets without having to move the data. It's a big differentiation for our enterprise to get value out of the data. They already have in Hadoop as well as non-Hadoop systems, which we cover. >> Josh, you're the VP of products, you have the roadmaps, give us a peek into what's happening with the current product. And, where's the work areas? Where are you guys going? What's the to-do list, what's the check box, and what's the innovation coming around the corner? >> Yeah, I think, to follow up on what Bruno said about how we hit the sweet spot. I think- we made a strategic choice, which is we don't want to be in the business of trying to be Tableu or Excel or be a better front end. And there's so much diversity on the back end if you look at the ecosystem right now, whether it's Spark Sequel, or Hive, or Presto, or even new cloud based systems, the sweet spot is really how do you fit into those ecosystems and support the right level of BI on top of those applications. So, what we're looking at, from a road map perspective is how do we expand and support the back end data platforms that customers are asking about? I think we saw a big white space in BI on Hadoop in particular. And that's- I'd say, we've nailed it over the past year and a half. But, we see customers now that are asking us about Google Big Query. They're asking us about Athena. I think these server-less data platforms are really, really compelling. They're going to take a while to get adoption. So, that's a big investment area for us. And then, in terms of supporting BI front ends, we're kind of doubling down on making sure our Tableau integration is great, Power BI is I think getting really big traction. >> Well, two great products, you've got Microsoft and Tableau, leaders in that area. >> The self-service BI revolution has, I would say, has won. And the business user wants their tool of choice. Where we come in is the folks responsible for data platforms on the back end, they want some level of control and consistency and so they're trying to figure out, where do you draw the line? Where do you provide standards? Where do you provide governance, and where do you let the business lose? >> All right, so, Bruno and Josh, I want you to answer the questions, be a good quiz. So, define next generation BI platforms from a functional standpoint and then under the hood. >> Yeah, there's a few things you can look at. I think if you were at the Gartner BI conference last week you saw that there was 24 vendors in the magic quadrant and I think in general people are now realizing that this is a space that is extremely crowded and it's also sitting on technology that was built 20 years ago. Now, when you talk to enterprises like the ones we work with, like, as I named earlier, you realize that they all have multiple BI tools. So, the visualization war, if you will, kind of has been set up and almost won by Microsoft and Tableau at this point. And, the average enterprise is 15 different BI tools. So, clearly, if you're trying to innovate on the visualization side, I would say you're going to have a very hard time. So, you're dealing with that level of complexity. And then, at the back end standpoint, you're now having to deal with database from the past - that's the Teradata of this world - data sources from today - Hadoop - and data sources from the future, like Google Big Query. And, so, I think the CIO answer of what is the next gen BI platform I want is something that is enabling me to simplify this very complex world. I have lots of BI tools, lots of data, how can I standardize in the middle in order to provide security, provide scale, provide speed to my business users and, you know, that's really radically going to change the space, I think. If you're trying to sell a full stack that's integrated from the bottom all the way to visualization, I don't think that's what enterprises want anymore >> Josh, under the hood, what's the next generation- you know, key leverage for the tech, and, just the enabler. >> Yeah, so, for me the end state for the next generation GI platform is a user can log in, they can point to their data, wherever that data is, it's on Prime, it's in the cloud, it's in a relational database, it's a flat file, they can design their business model. We spend a lot of time making sure we can support the creation of business models, what are the key metrics, what are the hierarchies, what are the measures, it may sound like I'm talking about OLAP. You know, that's what our history is steeped in. >> Well, faster data is coming, that's- streaming and data is coming together. >> So, I should be able to just point at those data sets and turn around and be able to analyze it immediately. On the back end that means we need to have pretty robust modeling capabilities. So that you can define those complex metrics, so you can functionally do what are traditional business analytics, period over period comparisons, rolling averages, navigate up and down business hierarchies. The optimizations should be built in. It shouldn't be the responsibility of the designer to figure out, do I need to create indeces, do I need to create aggregates, do I need to create summarization? That should all be handled for you automatically. Shouldn't think about data movement. And so that's really what we've built in from an AtScale perspective on the back end. Point to data, we're smart about creating optimal data structure so you get fast performance. And then, you should be able to connect whatever BI tool you want. You should be able to connect Excel, we can talk the MDX Query language. We can talk Sequel, we can talk Dax, whatever language you want to talk. >> So, take the syntax out of the hands of the user. >> Yeah. >> Yeah. >> And getting in the weeds on that stuff. Make it easier for them- >> Exactly. >> And the key word I think, for the future of BI is open, right? We've been buying tools over the last- >> What do you mean by that, explain. >> Open means that you can choose whatever BI tool you want, and you can choose whatever data you want. And, as a business user there's no real compromise. But, because you're getting an open platform it doesn't mean that you have to trade off complexity. I think some of the stuff that Josh was talking about, period analysis, the type of multidimensional analysis that you need, calendar analysis, historical data, that's still going to be needed, but you're going to need to provide this in a world where the business, user, and IT organization expects that the tools they buy are going to be open to the rest of the ecosystem, and that's new, I think. >> George, you want to get a question in, edgewise? Come on. (group laughs) >> You know, I've been sort of a single-issue candidate, I guess, this week on machine learning and how it's sort of touching all the different sectors. And, I'm wondering, are you- how do you see yourselves as part of a broader pipeline of different users adding different types of value to data? >> I think maybe on the machine learning topic there is a few different ways to look at it. The first is we do use machine learning in our own product. I talked about this concept of auto-optimization. One of the things that AtScale does is it looks at end-user query patterns. And we look at those query patterns and try to figure out how can we be smart about anticipating the next thing they're going to ask so we can pre-index, or pre-materialize that data? So, there's machine learning in the context of making AtScale a better product. >> Reusing things that are already done, that's been the whole machine-learning- >> Yes. >> Demos, we saw Google Next with the video editing and the video recognition stuff, that's been- >> Exactly. >> Huge part of it. >> You've got users giving you signals, take that information and be smart with it. I think, in terms of the customer work flow - Comcast, for example, a customer of ours - we are in a data discovery phase, there's a data science group that looks at all of their set top box data, and they're trying to discover programming patterns. Who uses the Yankees' network for example? And where they use AtScale is what I would call a descriptive element, where they're trying to figure out what are the key measures and trends, and what are the attributes that contribute to that. And then they'll go in and they'll use machine learning tools on top of that same data set to come up with predictive algorithms. >> So, just to be clear there, they're hypotehsizing about, like, say, either the pattern of users that might be- have an affinity for a certain channel or channels, or they're looking for pathways. >> Yes. And I'd say our role in that right now is a descriptive role. We're supporting the descriptive element of that analytics life cycle. I think over time our customers are going to push us to build in more of our own capabilities, when it comes to, okay, I discovered something descriptive, can you come up with a model that helps me predict it the next time around? Honestly, right now people want BI. People want very traditional BI on the next generation data platform. >> Just, continuing on that theme, leaving machine learning aside, I guess, as I understand it, when we talked about the old school vendors, Care Data, when they wanted to support data scientists they grafted on some machine learning, like a parallel version of our- in the core Teradata engine. They also bought Astro Data, which was, you know, for a different audience. So, I guess, my question is, will we see from you, ultimately, a separate product line to support a new class of users? Or, are you thinking about new functionality that gets integrated into the core product. I think it's more of the latter. So, the way that we view it- and this is really looking at, like I said, what people are asking for today is, kind of, the basic, traditional BI. What we're building is essentially a business model. So, when someone uses AtScale, they're designing and they're telling us, they're asserting, these are the things I'm interested in measuring, and these are the attributes that I think might contribute to it. And, so that puts us in a pretty good position to start using, whether it's Spark on the back end, or built in machine learning algorithms on the Hadoop cluster, let's start using our knowledge of that business model to help make predictions on behalf of the customer. So, just a follow-up, and this really leaves out the machine learning part, which is, it sounds like, we went- in terms of big data we we first to archive it- supported more data retension than could do affordably with the data warehouse. Then we did the ETL offload, now we're doing more and more of the visualization, the ad-hoc stuff. >> That's exactly right. So, what- in a couple years time, what remains in the classic data warehouse, and what's in the Hadoop category? >> Well, so there is, I think what you're describing is the pure evolution, of, you know, any technology where you start with the infrastructure, you know, we've been in this for over ten years, now, you've got cloud. They are going APO and then going into the data science workbench. >> That's not official yet. >> I think we read about this, or at least they filed. But I think the direction is showing- now people are relying on the platform, the Hadoop platform, in order to build applications on top of it. And, so, I think, just like Josh is saying, the mainstream application on top of the database - and I think this is true for non-Hadoop systems as well - is always going to be analytics. Of course, data science is something that provides a lot of value, but it typically provides a lot of value to a few set of people that will then scale it out to the rest of their organization. I think if you now project out to what does this mean for the CIO and their environment, I don't think any of these platforms, Teradata or Hadoop, or Google, or Amazon or any of those, I don't think do 100% replace. And, I think that's where it becomes interesting, because you're now having to deal with a hetergeneous environment, where the business user is up, they're using Excel, they're using they're standard net application, they might be using the result of machine learning models, but they're also having to deal with the heterogeneous environment at the data level. Hadoop on Prime, Hadoop in the cloud, non-Hadoop in the cloud and non-Hadoop on Prime. And, of course that's a market that I think is very interesting for us as a simplification platform for that world. >> I think you guys are really thinking about it in a new way, and I think that's kind of a great, modern approach, let the freedom- and by the way, quick question on the Microsoft tool and Tableau, what percentage share do you think they are of the market? 50? Because you mentioned those are the two top ones. >> Are they? >> Yeah, I mentioned them, because if you look at the magic quadrant, clearly Microsoft, Power BI and Tableau have really shot up all the way to the right. >> Because it's easy to use, and it's easy to work with data. >> I think so, I think- look, from a functionality standpoint, you see Tableau's done a very good job on the visualization side. I think, from a business standpoint, and a business model execution, and I can talk from my days at Microsoft, it's a very great distribution model to get thousands and thousands of users to use power BI. Now, the guys that we didn't talk about on the last magic quadrant. People who are like Google Data Studio, or Amazon Quicksite, and I think that will change the ecosystem as well. Which, again, is great news for AtScale. >> More muscle coming in. >> That's right. >> For you guys, just more rising tide floats all boats. >> That's right. >> So, you guys are powering it. >> That's right. >> Modern BI would be safe to say? >> That's the idea. The idea is that the visualization is basically commoditized at this point. And what business users want and what enterprise leaders want is the ability to provide freedom and openness to their business users and never have to compromise security, speed and also the complexity of those models, which is what we- we're in the business of. >> Get people working, get people productive faster. >> In whatever tool they want. >> All right, Bruno. Thanks so much. Thanks for coming on. AtScale. Modern BI here in The Cube. Breaking it down. This is The Cube covering bid data SV strata Hadoop. Back with more coverage after this short break. (electronic music)

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and Alkira. I looked at many a company and Alkira seemed the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

(upbeat music) >> Welcome everyone to this CUBE coverage of AWS re:Invent 2021. We have a lot going on at this year's re:Invent with over 100 guests on the program, and I'm excited to welcome two of those guests here with me right now. We are joined by Ralph Munsen, the Chief Information Officer at Warner Music Group and Atif Khan, the CTO of Alkira and founder of Alkira as well. Gentlemen, welcome to the program. >> Thank you so much, Lisa. So glad to be here with you. >> Good to be here. >> Yeah. Good old fashioned Zoom is become our best friend in the last 22 months or so I'm losing count. Atif, I'd like to start with you. I know Alkira has been on the key before, but it's been a while and you guys are a relatively young company. Give the audience an overview of Alkira and what it is that you deliver. >> Absolutely, Lisa. So we started back in may of 2018, and the Cloud networking space, multicloud networking. And we came out of stealth mode back in April of 2020, and launched the company. In fact, one of our first events coming out of stealth mode was a Cuban interview back in April of 2020. So here at Telecare, what we are doing is we are building a Cloud platform, which allows customers to build a common network across multiple Clouds with built-in network and security services, with the policy and management layer on top full end to end visibility and governance capabilities. And all of this is delivered as a service and consumed as a service as well. And I'm very glad to be here with Ralph, who is from Warner Music Group and is one of our marquee customers. So I'll let Ralph introduce himself, and tell us a bit more about Alkira and WMTS Cloud journey. >> That sounds great. Ralph, why don't you start by giving the audience? I'm sure everyone knows Warner Music Group, but in case there's anyone out there that might not. Give us a little bit of a background. >> Yeah, so the Warner Music Group has been around since 1950 and 1940 even it had its roots at Hollywood and out of Warner Brothers Pictures, Today, say global company in 79 countries we operated. If the 100 employees and we have two major divisions, we have our era recorded music division, which has the labels people commonly turn to Atlantic records, Warner brothers records, and so forth. And then we have our publishing division, which is more a chapel, which is where our songwriters live. And of course we have some singer songwriters that are on both sides of our business. But now currently people may know our artists. We have ed Sheeran, Bruno Mars, Coldplay, Cardi B, Blake Shelton and I could go on and on. But exciting, great year, we're having one of our best years ever. And I'm so glad to be here and partnering with an Alkira. >> Excellent. I love all of those artists that you mentioned. Fantastic. So let's talk a little bit now Ralph about the backstory. Talk to me about the IT infrastructure at Warner Music Group, what you had there and some of the challenges that you had that you came to Alkira to solve. >> Yeah, well initially when I took over about five years ago now, we were very much a data center based business with traditional networking and IT functions. Additionally with our foreign affiliates, IT was sort of decentralized in the sense that a lot of the networking and data center components were left to regions. And so while we operated globally, we didn't really operate globally, at Warner among our affiliates. So one of the challenges was how do we get out of the data center? Cloud was new. One of the big things that were coming with big data, which is absolutely right for moving, going straight to the Cloud, especially if you don't have anything on-prem and how do we rationalize all of these different locations and conduct all the M&A work we've been doing? So it was quite a challenge, really. At the end, we wanted to have one view of the network, and now Alkira. I looked at many of companies and I'm curious in the best to provide that to us. So. >> Well, talk to me a little bit more about why Alkira, because as Atif was saying, they're very young. What came out of stealth mode during the pandemic Warner Music Group, being around since the 40s and 50s, the legacy institution, a great brand. What made you take a risk on such an early stage startup? >> Quite frankly, there was nothing in the space (chuckles) at the time you loved, there were companies that had components of it, of what Alkira does, which is basically network orchestration allowing us to use existing components. And nobody has the whole package, especially incorporating security. So, we figured why not take, take a chance? There's no, it won't hurt you no harm. And if anything is successful, it will give us a great ability to manage our network, much more efficiently taking things that took days down to hours and being able to do it much more efficiently with much fewer staff, as opposed to hiring a lot more because when you orchestrate all the components that are underneath, obviously it requires more bodies, more resources. >> Right. That efficiency and cost optimization is key there. Atif I have to ask you, talk to me about, this is only a few years ago, the gap in the market that you and your brothers saw a few years ago, when you founded the company, because as Rob was saying, there was nobody else in the market at the time that could do what you're doing. >> Yeah, absolutely. So Lisa, as you know, myself and Amir, we were also a part of the founding team of Viptela, which was the SD-WAN Company. So back in the day when we did SD-WAN, the requirement was to connect sites together. So if you go back like 5, 10, 5, 7, 10 years ago, networking was done to connect sites together, which could be remote sites, data centers, sites to data centers, all of that together. But fast forward, a few more years with the adoption of Cloud, requirements changed from the networking perspective. So now your network is not just connecting sites together, but most of the traffic now is from sites or users, which could be sitting anywhere. If you look at, what's going on? in the pandemic people are working from all across the globe. They are not just sitting in campuses or sites. So traffic patterns are from sites or users mostly to the Cloud or SaaS applications. So now networks also need to evolve and they need to be built inside the Cloud rather than from outside or connecting into the Cloud. So Cloud access is one capability, but building a network inside the Cloud becomes a requirement. And secondly, now it's not just only about connectivity because security becomes even more important because your security perimeter is changing as well. So securing all these Cloud networks becomes very, very complicated. And now as Ralph can tell you, majority of the enterprises have a multicloud strategy and each Cloud is done differently. So the moment you bring in multiple Clouds, multiple regions across the globe, it becomes so complicated for enterprises to build and manage. They need something, or a platform which makes it easy, gives them one way of doing networking, building a common network across whether you're connecting multiple Clouds or Clouds to your on-prem locations or Clouds to internet or sites to internet. So that's where we saw this gap and we decided to build Alkira to tackle this problem. >> Got it. So Rob, let's talk now about what you've implemented as a team was saying we live in this, in this work from anywhere hybrid multicloud world. Talk to us about Warner, what you implemented and maybe a little bit about your multicloud strategy, if you've got one. >> Ralph: Yeah. So over the last five years, Warner has migrated entirely into Cloud. And to this point before it's multicloud, we're mainly in AWS, but we do have some pleasure and some Google Cloud. And with that, I was telling Atif and Amir. It was interesting and they built a Cloud on site. They totally forgot about the networking aspect. So (laughs), you have ease of use for services and servers inside (indistinct) cloud, but networking is not really present, not to mention when it was built out, it wasn't made to go to competing Clouds. So most companies are facing this problem. How do you treat these environments as a single holistic environment? How do you turn things up, turn things down? How do you secure it, When every single one is different habits, selling unique ways of doing things? So that really was, how we ended up looking for an out Alkira, because I just kept looking at the costs and the profit print grow and grow and grow. And the complexity to a (indistinct) before is growing exponential. One change in one thing would lead to two changes to another. If you add another Cloud or you add another point on the network, you've got exponential growth and complexity, complexity, you have to deal with. So one stop shop. (chuckles) >> One stop shop and reducing that complexity. Talk to me about reducing complexity, and what you're accomplishing there. Especially, in the last year and a half as things have been so dynamic, shall we say? (chuckles) >> Yeah, well, I will say this. It was turnkey for the most part. It took a matter of months as opposed to years, because out of the box, there was a lot of integrations with the major network of players. So as of right now, you can buy firewalls, routing, VPC, things like this, they all exist, but they're not orchestrated together. Right? And then you have policies and security, again not orchestrating a different set of tools. So it really only took us two to three months to get it up and running, I acts, I just had a conversation (chuckles) with them when we were going to finish. So I think we'll be finishing this up completely in January and sometime. So, I was pretty sure. >> LISA: That's fantastic. So really, >> Yeah. >> Sorry Relaph fast time to market there with getting things implemented. Talk to me about from a business outcome perspective, you are CIO, what are some of the outcomes? That this technology is enabling you to deliver back to the business? >> Yeah, it really, the number 1, 2 big ones come to mind. One being able to provide them a secure enterprise. I know when there is the change it's made uniforms for our network without, some of older something's being forgotten about. So that's number one, security is big. You can imagine a company like more ever marquee brands, all brands, any company of marquee brands are targets today. That's number one. Number two is our time to market for eminent. So when we buy a company the time it takes us to get them to be completely part of Warner and therefore start realizing the business case and benefits sort of reasonably bought. Bought the company to begin with. So, we're buying a lot more and we're turning them up and turning those business cases up faster. But usually those cases would say things like six months to a year to integrate with us, and then we can unlock the set of benefits. Now it's more like, two to three months and you start to be able to lock the benefits sooner. And of course, those are different than a case by case basis, but that's. >> Sure, but significantly faster there, you're looking at a two to three X multiplier there, as you talked about. >> Ralph: right. >> Now, you mentioned multicloud Ralph. So here we are at re:Invent. I imagine part of your AWS as part of your Cloud infrastructure and they're a technology partner of ALkira's. >> Ralph: Correct. Yeah. So AWS is actually our biggest Cloud provider of the three, and yeah (laugh) they're their partner without cure. So Good. >> And Atif then you, Alkira's technology partner of AWS, correct? >> Yeas. Alkira is a technology partner of AWS, we are also available on AWS marketplace. So customers can consume, AlKira's platform from AWS marketplace as well. >> But given the fact that so many businesses in every industry are multicloud, I assume that you work with all the Cloud vendors. Atif Yeah? >> Absolutely. So our platform runs inside of the Cloud and runs in AWS is a Cloud as well. And from there it connects to multiple Clouds. So if customers need to connect to Azure or AWS from there or Oracle Cloud or any other Cloud, for that matter, they can connect from our platform and our platform is it scales horizontally. So as customers needs scale, it scales as well. And one of the key advantages is, it's consumed as a service. So there's no software to download or hardware to run for or to acquire for any of the customers. It's a software solution and it's consumed as a service. >> Got it. Ralph one on one more question for you before we wrap things up here, want to get your recommendations for IT Executives, CEOs, who might be in a similar situation to you, whether or not they are with a legacy organization, what are some of your recommendations that you say you need to be looking at a, B and C? >> Yeah, I would primarily say really need to be looking at some of these newer technologies that can help speed up, people, especially in this case to transition to the Cloud and that planning ahead of time, especially goal-setting, I find to be it's any of these places, providers is absolutely Paramount, because you can, if you don't make your own (indistinct) take that step forward and you can end up with shelter. So I make sure that it's very important that when you commit to that, you commit fully, you plan it out and you make sure you actually use it to get the benefits. One of my tech key is software. So. (chuckles) (Lisa Laughing) I'm a bit of it so. >> Well, you've been there and It costs a lot of money and it doesn't do any good. It doesn't move the business forward. And in this day and age, there is a competitor right behind the rear view mirror who might be smaller, more nimble, and more agile, who can take your place easily. >> Absolutely. >> If the organization isn't willing to take the risks and commit, as you said, Atif last question over for you, where are the customers go to learn more? I know you are at re:Invent your booth 1628, but what do you recommend folks go attendees of the event, as well as just other prospects to go to learn more about what you guys are delivering for companies like Warner Music Group. >> So if you're at re:Invent, please stop by our booth. And one of our Cloud specialists will give you a demo as well. So it's a very quick demo and you'll see, how we are reinventing networking for the Cloud narrow. You can also go to our website and you'll find a lot of information on our website. You can request a demo there as well. So look forward to seeing most of you at our booth and those who are not attending in person, please go visit our website. >> Lisa: Reinventing Networking. I like your play on words. They are Atif very appropriate. Gentlemen, thank you for joining me today talking about Alkira, Warner Music Group, what you guys are doing together and how this new early stage technology is really quite transformative. We appreciate your insights. >> Thank you. >> Thank you so much. >> For Ralph Munsen and Atif Khan, I'm Lisa Martin, and you're watching theCUBE's continuous coverage of AWS re:Invent 2021. Thanks for watching. (soft techno music)

(techy music playing) >> Hello, everyone, welcome to theCUBE Studios here in Palo Alto, California. I'm John Furrier, the cofounder of SiliconANGLE Media Inc., also cohost of theCUBE. We're here for a CUBE Conversation on Thought Leader Thursday and I'm here with Chris Cummings, who's a senior manager, advisor, big-time industry legend, but he's also the Chasm Group, right now, doer, Crossing the Chasm, famous books and it's all about the future. Formerly an exec at Netapp, been in the storage and infrastructure cloud tech business, also friends of Stanford. Season tickets together to go to the tailgates, but big Cal game coming up of course, but more importantly a big-time influence in the industry and we're going to do some drill down on what's going on with cloud computing, all the buzzword bingo going on in the industry. Also, AWS, Amazon Web Services re:Invent is coming up, do a little preview there, but really kind of share our views on what's happening in the industry, because there's a lot of noise out there. We're going to try to get the signal from the noise, thanks for watching. Chris, thanks for coming in. >> Thank you so much for having me, glad to be here. >> Great to see you, so you know, you have seen a lot of waves of innovation and right now you're working with a lot of companies trying to figure out the future. >> That's right. >> And you're seeing a lot of significant industry shifts. We talk about it on theCUBE all the time. Blockchain from decentralization all the way up to massive consolidation with hyper-convergence in the enterprise. >> Mm-hmm. >> So a lot of action, and because of the day the people out in the marketplace, whether it's a developer or a CXO, CIO, CDO, whatever enterprise leader's doing the transformations. >> Chris Collins: We got all of them. >> They're trying to essentially not go out of business. A lot of great things are happening, but at the same time a lot of pressure on the business is happening. So, let's discuss that, I mean, you are doing this for work at the Chasm Group. Talk about your role, you were formerly at Netapp, so I know you know the storage business. >> Right. >> So we're going to have a great conversation about storage and impact infrastructure, but at the Chasm Group how are you guys framing the conversation? >> Yeah, Chasm Group is really all about helping these companies process their thinking, think about if they're going to get to be a platform out in the industry. You can't just go and become a platform in the industry, you got to go knock down problem, problem, problem, solution, solution, solution. So we help them prioritize that and think about best practices for achieving that. >> You know, Dave Alante, my co-CEO, copartner, co-founder at SiliconANGLE Media and I always talk about this all the time, and the expression we use is if you don't know what check mate looks like you shouldn't be playing chess, and a lot of the IT folks and CIOs are in that mode now where the game has changed so much that sometimes they don't even know what they're playing. You know, they've been leaning on this Magic Quadrant from Gartner and all these other analyst firms and it's been kind of a slow game, a batch kind of game, now it's real time. Whatever metaphor you want to use, the game has changed so the chessboard has changed. >> Chris: Mm-hmm. >> So I got to get your take on this because you've been involved in strategy, been on product, you worked at growth companies, big companies, start-ups, and now looking at the bigger picture, what is the game? I mean, right now if you could lay out the chessboard, what are people looking at, what is the game? >> So, we deal a lot with customer conversations and that's where it all kind of begins, and I think what we found is this era of pushing product and just throwing stuff out there. It worked for a while but those days are over. These folks are so overwhelmed. The titles you mentioned, CIO, CDO, all the dev ops people, they're so overwhelmed with what's going on out there. What they want is people to come in and tell them about what's happening out there, what are their peers doing and what problems are they trying to solve in order and drive it that way. >> And there's a lot of disruption on the product side. >> Yes. >> So tech's changing, obviously the business models are changing, that's a different issue. Let's consider the tech things, you have-- >> Mm-hmm. >> A tech perspective, let's get into the tech conversation. You got cloud, you got private cloud, hybrid cloud, multi-cloud, micro-machine learning, hyper-machine learning, hyper-cloud, all these buzzwords are out there. It's buzzwords bingo. >> Chris: Right. >> But also the reality is you got Amazon Web Services absolutely crushing it, no doubt about it. I mean, I've been looking at Oracle, I've been looking at Google, I've been looking at SAP, looking at IBM, looking at Alibaba, looking at Microsoft, the game is really kind of a cloak and dagger situation going on here. >> That's right. >> A lot of things shifting on the provider side, but no doubt scale is the big issue. >> Chris: That's right. >> So how does a customer squint through all this? >> The conversations that I've had, especially with the larger enterprises, is they know that they've got to be able to adopt and utilize the public cloud capabilities, but they also want to retain that degree of control, so they want to maintain, whether it's their apps, their dev ops, some pieces of their infrastructure on prem, and as you talked about that transition it used to be okay, well we thought about cloud was equal to private cloud, then it became public cloud. Hybrid cloud, people are hanging on to hybrid cloud, sometimes for the right reasons and sometimes for the wrong reasons. Right reasons are because it's critical for their business. You look at somebody, for instance, in media and entertainment. They can't just push everything out there. They've got to retain control and really have their hands around that content because they've got to be able to distribute it, right? But then you look at some others that are hanging on for the wrong reasons, and the wrong reasons are they want to have their control and they want to have their salary and they want to have their staff, so boy, hybrid sounds like a mix that works. >> So I'm going to be having a one-on-one with Andy Jassy next week, exclusive. I do that every year as part of theCUBE. He's a great guy, good friend, become a good friend, because we've been a fan of him when no one loved Amazon. We saw the early, obviously at SiliconANGLE, now he's the king of the industry, but he's a great manager, great executive, and has done a great job on his ethos of Bezos and Amazon. Ship stuff faster, lower prices, the flywheel that Amazon uses. Everything's kind of on that-- And they own Twitch, which we stream, too, and we love. But if you could ask Andy any questions what questions would you ask him if you get to have that one-on-one? >> Yeah, well, it stems from conversations I've had with customers, which was probably once a week I would be talking to a CIO or somebody on that person's staff, and they'd slide the piece of paper across and say this is my bill. I had no idea that this was what AWS was going to drive me from a billing perspective, and I think we've seen... You know, we've had all kinds of commentary out there about ingress fees, egress fees, all of that sort of stuff. I think the question for Andy, when you look at the amount of revenue and operating margin that they are generating in that business, how are they going to start diversifying that pricing strategy so that they can keep those customers on without having them rethink their strategy in the future. >> So are you saying that when they slide that piece of paper over that the fees are higher than expected or not... Or low and happy, they're happy with the prices. >> Oh, they're-- I think they're-- I think it's the first time they've ever thought that it could be as expensive as on-premise infrastructure because they just didn't understand when they went into this how much it was going to cost to access that data over time, and when you're talking about data that is high volume and high frequency data, they are accessing it quite a bit, as opposed to just stale, cold, dead stuff that they want to put off somewhere else and not have to maintain. >> Yeah, and one of the things we're seeing that we pointed at the Wikibon team is a lot of these pricings are... The clients don't know that they're being billed for something that they may not be using, so AI or machine learning could come in potentially. So this is kind of what you're getting at. >> Exactly. >> The operational things that Amazon's doing to keep prices low for the customer, not get bill shock. >> Chris: That's right. >> Okay, so that's cool. What else would you ask him about culture or is there anything you would ask him about his plans... What else would you ask him? >> I think another big thing would be just more plans on what's going to be done around data analytics and big data. We can call it whatever we want, but they've been so good at the semi-structured or unstructured content, you know, when we think about AWS and where AWS was going with S3, but now there's a whole new phenomenon going on around this and companies are as every bit as scared about that transition as they were about the prior cloud transition, so what really are their plans there when they think about that, and for instance, things like how does GPU processing come into play versus CPU processing. There's going to be a really interesting discussion I think you're going to have with him on that front. >> Awesome, let's talk about IT. IT and information technology departments formerly known as DP, data processing, information-- All that stuff's changed, but there were still guys that were buying hardware, buying Netapp tries that you used to work for, buying EMC, doing data domain, doing a lot of stuff. These guys are essentially looking at potentially a role where-- I mean, for instance, we use Amazon. We're a big customer, happy customer. >> Chris: Mm-hmm. >> We don't have those guys. >> Chris: Right. >> So if I'm an IT guy I might be thinking shit, I could be out of a job, Amazon's doing my job, so I'm not saying that's the case but that's certainly a fear. >> Chris: Absolutely. >> But the business models have to shift from old IT to new IT. >> Chris: Mm-hmm. >> What does that game look like? What is this new IT game? Is it more, not a department view, is it more of a holistic view, and what's the sentiment around the buyers and your customers that you talk to around how do they message to the IT guys, like, look, there's higher valued jobs you could go to. >> Right. >> You mention analytics... >> That's right. >> What's the conversation? Certainly some guys won't make the transition and might not make it, but what's the narrative? >> Well, I think that's where it just starts with what segment are you talking about, so if you look at it and say just break it down between the large enterprise, the uber enterprise that we've seen for so long, mid-size and smaller, the mid-size and smaller are gone, okay. Outside of just specific industries where they really need that control, media and entertainment might be an example. That mid-size business is gone for those vendors, right? So those vendors are now having to grab on and say I'm part of that cloud phenomenon, my hyper-cloud of the future. I'm part of that phenomenon, and that becomes really the game that they have to play, but when you look at those IT shops I think they really need to figure out where are they adding value and where are they just enabling value that's being driven by cloud providers, and really that's all they are is a facilitator, and they've got to shift their energy towards where am I adding value, and that becomes more that-- >> That's differentiation, that's where differentiation is, so non-differentiated labor is the term that Wikibon analysts use. >> Oh, okay. >> That's going down, the differentiated labor is either revenue generating or something operationally more efficient, right? >> That's right, and it's all going to be revenue generating now. I mean, I used to be out there talking about things like archiving, and archiving's a great idea. It's something where I'm going to save money, okay, but I got this many projects on my list if I'm a CIO of where I can save money. I'm being under pressure about how am I going to go generate money, and that's where I think people are really shifting their eyeballs and their attention, is more towards that. >> And you got IOT coming down the pike. I mean, we're hearing is from what I hear from CIOs when we have a few in-depth conversations is look, I got to get my development team ramped up and being more cloud native, more microservice and I got to get more app development going that drives revenue for my business, more efficiency. >> Chris: Right. >> I have a digital transformation across the company in terms of hiring culture and talent. >> Chris: Mm-hmm. >> And then I got pressure to do IOT. >> Chris: Right. >> And I got security, so of those five things, IOT tends to fall out, security takes preference because of the security challenges, and then that's already putting their plate full right there. >> That's right, that's real time and those people are-- >> Those are core issues. >> Putting too much pressure on that right now and then you're thinking about IT and in the meantime, by the way, most of these places don't have the dev ops shop that's operating on a flywheel, right? So you're not... What's it, Goldman Sachs has 5,000 developers, right? That's bigger than most tech companies, so as a consequence you start thinking about well, not everybody looks like that. What the heck are they going to do in the future. They're going to have to be thinking about new ways of accessing that type of capability. >> This is where the cloud really shines in my mind. I think in the cloud, too, it's starting to fragment the conversations. People will try to pigeonhole Amazon. I see Microsoft-- I've been very critical of Microsoft in their cloud because-- First of all, I love the move that they're making. I think it's a smart move business-wise, but they bundle in 365 Office, that's not really cloud, it's just SAS, so then you start getting into the splitting of the hairs of well, SAS is not included in cloud. But come on, SAS is cloud. >> Chris: Mm-hmm. >> Well, maybe Amazon should include their ecosystem that would be a trillion dollar revenue number, so all companies don't look the same. >> That's right. >> And so from an enterprise that's a challenge. >> Chris: Mm-hmm. >> Do I got to hire developers for Asger, do I got to hire developers for Amazon, do I got to hire developers for Google. >> Chris: Mm-hmm. >> There's no stack consistency across private enterprises to cloud. >> Chris: So I have-- >> Because I'm a storage guy, I've got Netapp drives and now I've got an Amazon thing. I like Amazon, but now I got to go Asger, what the hell do I do? >> I got EMCs here and I got Nimbles there and HP and I've still got tape from IBM from five decades ago, so, John, I got a great term for you that's going to be a key one, I think, in the ability. It's called histocompatibility, and this is really about... >> Oh, here we go. Let's get nerdy with the tape glasses on. >> It's really about the ability to be able to inter-operate with all this system and some of these systems are live systems, they're current systems. Some of it's garbage that should've been thrown out a long time ago and actually recycled. So I think histocompatibility is going to be a really, really big deal. >> Well, keep the glasses on. Let's get down in the weeds here. >> Okay. >> I like the-- With the pocket protector, if you had the pocket protector we'd be in good shape. >> Yep. >> So, vendors got to compete with these buzzwords, become buzzword bingo, but there are trends that you're seeing. You've done some analysis of how the positionings and you're also a positioning guru as well. There's ways to do it and that's a challenge is for suppliers, vendors who want to serve customers. They got to rise above the noise. >> Chris: That's right. >> That's a huge problem. What are you seeing in terms of buzzword bingo-- >> Oh, my goodness. >> Because like I said, I used to work for HP in the old days and they used to have an expression, you know, don't call it what it is because that's boring and make it exciting, so the analogy they used was sushi is basically cold, dead fish. (laughing) So, sushi is a name for cold, dead fish. >> Chris: Yeah. >> So you don't call your product cold, dead fish, you call it sushi. >> Chris: Right. >> That was the analogy, so in our world-- >> Chris: That was HP-UX. >> That was HP-UX, you know, HP was very engineering. >> Yes. >> That's not-- Sushi doesn't mean anything. It's cold, dead fish, that's what it is. >> Right. >> That's what it does. >> That's right. >> So a lot of vendors can error in that they're accurate and their engineers, they call it what it is, but there's more sex appeal with some better naming. >> Totally. >> What are you seeing in terms of the fashion, if you will, in terms of the naming conventions. Which ones are standing out, what's the analysis. >> Well, I think the analysis is this, you start with your adjectives with STEM words, John, and what I mean by that is things like histocompatibility. It could start with things like agility, flexibility, manageability, simplicity, all those sorts of things, and they've got to line those terms up and go out there, but I think the thing that right now-- >> But those are boring, I saw a press release saying we're more agile, we're the most effective software platform with agility and dev ops, like what the hell does that mean? >> Yeah, I think you also have to combine it with a heavy degree of hyperbole, right? So hyperbole, an off-the-cuff statement that is so extreme that you'd never really want to be tested on it, so an easy way to do that is to add hyper in front of all that. So it's hyper-manageability, right, and so I think we're going to see a whole new class of words. There are 361 great adjectives with STEMs, but-- >> Go through the list. >> Honestly. >> Go through the list that you have. >> I mean, there's so many, John, it's... >> So hyper is an easy one, right? >> Hyper is easy, I think that's a very simple one. I think now we also see that micro is so big, right, because we're talking about microservices and that's really the big buzzword in the industry right now. So everything's going to be about micro-segmenting your apps and then allowing those apps to be manifest and consumed by an uber app, and ultimately that uber app is an ultra app, so I think ultra is going to be another term that we see heading into the spectrum as well. >> And so histocompatibility is a word you mentioned, just here in my notes. >> Yep. >> You mentioned, so histo means historical. >> Exactly. >> So it means legacy. >> Chris: That's right. >> So basically backwards compatible would be the boring kind of word. >> Chris: That's right. >> And histocompatibility means we got you covered from legacy to cloud, right. >> Uh-huh. >> Or whatever. >> You bet. >> Micro-segmentility really talks to the granularity of data-driven things, right? >> That's right, another one would be macro API ability, it's kind of a mouthful, but everyone needs an API. I think we've seen that and because they're consuming so many different pieces and trying to assemble those they've got to have something that sits above. So macro API ability, I think, is another big one, and then lastly is this notion of mobility, right. We talk about-- As you said earlier, we talked about clouds and going from-- It's not just good enough to talk about hybrid cloud now, it's about multi-cloud. Well, multi-cloud means we're thinking about how we can place these apps and the data in all kinds of different spaces, but I've got to be able to have those be mobile, so hyper-mobility becomes a key for these applications as well. >> So hyper-scale we've seen, we've seen hyper-convergence. Hyper is the most popular-- >> Chris: Absolutely. >> Adjective with STEM, right? >> Chris: It's big. >> STEM words, okay, micro makes sense because, you know, micro-targeting, micro-segmentation, microservices, it speaks to the level of detail. >> Chris: Right. >> I love that one. >> Chris: Right. >> Which ones aren't working in your mind? We see anything that's so dead on arrival... >> Sure, I think there's a few that aren't working anymore. You got your agility, you got your flexibility, you got your manageability, and you got your simplicity. Okay, I could take all four of those and toss those over there in the trash because every vendor will say that they have those capabilities for you, so how does that help you distinguish yourself from anyone else. >> So that's old hat. >> It's just gone. >> Yeah, never fight fashion, as Jeremy Burton at EMC, now at Dell Technologies, said on theCUBE. I love that, so these are popular words. This is a way to stand out and be relevant. >> That's right. >> This is the challenge for vendors. Be cool and relevant but not be offensive. >> Yeah. >> All right, so what's your take on the current landscape for things like how do companies market themselves. Let's say they get the hyper in all the naming and the STEM words down. They have something compelling. >> Chris: Right. >> Something that's differentiated, something unique, how do companies stand out above the crowd, because the current way is advertising's not working. We're seeing fake news, you're seeing the analyst firms kind of becoming more old, slower, not relevant. I mean, does the Magic Quadrant really solve that problem or are they just putting that out there? If I'm a marketer, I'm a B2B marketer. >> Yeah. >> Obviously besides working with theCUBE and our team, so obviously great benefits. Plug there, but seriously, what do you advise? >> Yeah, I think the biggest thing is, you know, you think about marketing as not only reaching your target market, but also enabling your sales force and your channel partners, and frankly, the best thing that I've found in doing that, John, is starting every single piece that we would come up with with a number. How much value are we generating, whether it's zero clicks to get this thing installed. It's 90% efficiency, and then prove it. Don't just throw it out there and say isn't that good enough, but numbers matter because they're meaningful and they stimulate the conversation, and that's ultimately what all of this is. It's a conversation about is this going to be relevant for you, so that's the thing that I start with. >> So you're say being in the conversation matters. >> Absolutely. >> Yeah. >> Absolutely. >> What's the thought leadership view, what's your vision on how a company should be looking at thought leadership. Obviously you're seeing more of a real-time-- I call it the old world was batch marketing. >> Chris: Mm-hmm. >> E-mail marketing, do the normal things, get the white papers, do those things. You know, go to events, have a booth, and then the new way is real-time. >> Chris: Mm-hmm. >> Things are happening very fast-- >> That's right. >> In the market, people are connected now. It's a global, basically, message group. >> That's right. >> Twitter, LinkedIn, Facebook and all this stuff. >> It's really an unfulfilled need that you guys are really looking to fill, which is to provide that sort of real-time piece of it, but I think vendors trip over themselves and they think about I need a 50 page vision. They don't need a 50 page vision. What they need is here are a couple of dimensions on which this industry is going to change, and then commit to them. I think the biggest problem that many vendors have is they won't commit, they hedge, as opposed to they go all in behind those and one thing we talk about at Chasm Institute is if you're going to fail, fail fast, and that really means that you commit full time behind what you're pushing. >> Yeah, and of course what the Chasm, what it's based upon, you got to get to mainstream, get to early pioneers, cross the chasm. The other paradigm that I always loved from Jeffrey Moore was inside the tornado. Get inside the tornado because if you don't get in you're going to be spun out, so you've got to kind of get in the game, if you will. >> Chris: That's right. >> Don't overthink it, and this is where the iteration mindset comes in, "agile" start-up or "agile" venture. Okay, cool, so let's take a step back and reset to end the segment here. >> Mm-hmm. >> Re:Invent's coming up, obviously that's the big show of the year. VMworld, someone was commenting on Facebook VMworld 2008 was the big moment where they're comparing Amazon now to VMworld in 2008. >> Chris: Right. >> But you know, Pat Gelsinger essentially cut a great deal with Andy Jassy on Vmware. >> Chris: Right. >> And everything's clean, everything's growing, they're kicking ass. >> Chris: Mm-hmm. >> They got a private cloud and they got the hybrid cloud with Amazon. >> Yeah, it's that VMcloud on Amazon, that really seems to be the thing that's really driving their move into the future, and I think we're going to see from both of those folks, you are going to see so much on containers. Containerization, ultra-containers, hyper-containers, whatever it may be. If you're not speaking container language, then you are yesterday's news, right? >> And Kubernetes' certainly the orchestration piece right underneath it to kind of manage it. Okay, final point, what's in store for the legacy, because you're seeing a few major trends that we're pointing out and we're watching very closely, which really I put into two buckets. I know Wikibon's a more disciplined approach, I'm more simple about that. The decentralization trend we're seeing with Blockchain, which is kind of crazy and bubbly but very infrastructure relevant, this decentralized, disrupting, non-decentralized incumbence, so that's one trend and the other one is what cloud's doing to legacy IT vendors, Oracle, you know, these traditional manufacturers like that HP and Dell and all these guys, and Netapp which is transforming. So you've got disruption on both sides, cloud and like a decentralized model, apps, what's the position, view, from your standpoint, for these legacy guys? >> It's going to be quite an interesting one. I think they have to ride the wave, and I'll steal this from Peter Levine, from Andreessen, right? He talks about the end of cloud computing, and really what that is is just basically saying everything is going to be moving to the edge and there's going to be so much more compute at the edge with IOT and you can think about autonomous vehicles as the ultimate example of that, where you're talking about more powerful computers, certainly, than this that are sitting in cars all over the place, so that's going to be a big change, and those vendors that have been selling into the core data center for so long are going to have to figure out their way of being relevant in that universe and move towards that. And like we were talking about before, commit to that. >> Yeah. >> Right, don't just hedge, but commit to it and move. >> What's interesting is that I was talking with some executives at Alibaba when I was in China for part of the Alibaba Cloud Conference and Amazon had multiple conversations with Andy Jassy and his team over the years. It's interesting, a lot of people don't understand the nuances of kind of what's going on in cloud, and what I'm seeing is it's essentially, to your point, it's a compute game. >> Chris: Yeah. >> Right, so if you look at Intel for instance, Alibaba told me on my interview, they don't view Intel as a chip company anymore, they're a compute company, right, and CJ Bruno, one of the executives there, reaffirmed that. So Intel's looking at the big picture saying the cloud's a computer. Intel Inside is a series of compute, and you mentioned that the edge, Jassy is building a set of services with his team around core compute, which has storage, so this is essentially hyper-converged cloud. >> That's right. >> This is a pretty big thing. What's the one thing that people might not understand about this. If you could kind of illuminate this trend. I mean, the old Intel now turned into the new Intel, which is a monster franchise continuing to grow. >> Mm-hmm. >> Amazon, people see the numbers, they go oh, my god, they're a leader, but they have so much more headroom. >> Chris: Right, right. >> And they've got everyone else playing catch up. >> Yeah. >> What's the real phenomenon going on here? >> I think you're going to see more of this aggregation phenomenon where one vendor can't solve this entire problem. I mean, look at most recently, in the last two weeks, Intel and AMD getting together. Who would've thought that would happen? But they're just basically admitting we got a real big piece of the equation, Intel, and then AMD can fulfill this niche because they're getting killed by NVIDIA, but you're going to see just more of these industry conglomerations getting together to try and solve the problem. >> Just to end the segment, this is a great point. NVIDIA had a niche segment, graphics, now competing head to head with Intel. >> Chris: That's right. >> So essentially what's happening is the landscape is completely changing. Once competitors no longer-- New entrants, new competitors coming in. >> Chris: Mm-hmm. >> So this is a massive shift. >> Chris: It is. >> Okay, Chris Cummings here inside theCUBE. I'm John Furrier of CUBE Conversation. There's a massive shift happening, the game has changed and it's incumbent upon start-ups, venture capital, you know, Blockchain, ICOs or whatever's going on. Look at the new chessboard, look at the game and figure it out. Of course, we'll be broadcasting live at AWS re:Invent in a couple weeks. Stay tuned, more coverage, thanks for watching. (techy music playing)

(quite music) >> I'll open that while he does that. >> Co-Host: Good, perfect. >> Man: All right, rock and roll. >> This is Robin Matlock, the CMO of VMware, and you're watching theCUBE. >> This is John Siegel of VPA Product Marketing at Dell EMC. You're watching theCUBE. >> This is Matthew Morgan, I'm the chief marketing officer at Druva and you are watching theCUBE. >> Announcer: Live from midtown Manhattan, it's theCUBE. Covering BigData New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (rippling music) >> Hello, everyone, welcome to a special CUBE live presentation here in New York City for theCUBE's coverage of BigData NYC. This is where all the action's happening in the big data world, machine learning, AI, the cloud, all kind of coming together. This is our fifth year doing BigData NYC. We've been covering the Hadoop ecosystem, Hadoop World, since 2010, it's our eighth year really at ground zero for the Hadoop, now the BigData, now the Data Market. We're doing this also in conjunction with Strata Data, which was Strata Hadoop. That's a separate event with O'Reilly Media, we are not part of that, we do our own event, our fifth year doing our own event, we bring in all the thought leaders. We bring all the influencers, meaning the entrepreneurs, the CEOs to get the real story about what's happening in the ecosystem. And of course, we do it with our analyst at Wikibon.com. I'm John Furrier with my cohost, Jim Kobielus, who's the chief analyst for our data piece. Lead analyst Jim, you know the data world's changed. We had commenting yesterday all up on YouTube.com/SiliconAngle. Day one was really set the table. And we kind of get the whiff of what's happening, we can kind of feel the trend, we got a finger on the pulse. Two things going on, two big notable stories is the world's continuing to expand around community and hybrid data and all these cool new data architectures, and the second kind of substory is the O'Reilly show has become basically a marketing. They're making millions of dollars over there. A lot of people were, last night, kind of not happy about that, and what's giving back to the community. So, again, the community theme is still resonating strong. You're starting to see that move into the corporate enterprise, which you're covering. What are you finding out, what did you hear last night, what are you hearing in the hallways? What is kind of the tea leaves that you're reading? What are some of the things you're seeing here? >> Well, all things hybrid. I mean, first of all it's building hybrid applications for hybrid cloud environments and there's various layers to that. So yesterday on theCUBE we had, for example, one layer is hybrid semantic virtualization labels are critically important for bridging workloads and microservices and data across public and private clouds. We had, from AtScale, we had Bruno Aziza and one of his customers discussing what they're doing. I'm hearing a fair amount of this venerable topic of semantic data virtualization become even more important now in the era of hybrid clouds. That's a fair amount of the scuttlebutt in the hallway and atrium talks that I participated in. Also yesterday from BMC we had Basil Faruqi talking about basically talking about automating data pipelines. There are data pipelines in hybrid environments. Very, very important for DevOps, productionizing these hybrid applications for these new multi-cloud environments. That's quite important. Hybrid data platforms of all sorts. Yesterday we had from ActIn Jeff Veis discussing their portfolio for on-prem, public cloud, putting the data in various places, and speeding up the queries and so forth. So hybrid data platforms are going increasingly streaming in real time. What I'm getting is that what I'm hearing is more and more of a layering of these hybrid environments is a critical concern for enterprises trying to put all this stuff together, and future-proof it so they can add on all the new stuff. That's coming along like cirrus clouds, without breaking interoperability, and without having to change code. Just plug and play in a massively multi-cloud environment. >> You know, and also I'm critical of a lot of things that are going on. 'Cause to your point, the reason why I'm kind of critical on the O'Reilly show and particularly the hype factor going on in some areas is two kinds of trends I'm seeing with respect to the owners of some of the companies. You have one camp that are kind of groping for solutions, and you'll see that with they're whitewashing new announcements, this is going on here. It's really kind of-- >> Jim: I think it's AI now, by the way. >> And they're AI-washing it, but you can, the tell sign is they're always kind of doing a magic trick of some type of new announcement, something's happening, you got to look underneath that, and say where is the deal for the customers? And you brought this up yesterday with Peter Burris, which is the business side of it is really the conversation now. It's not about the speeds and feeds and the cluster management, it's certainly important, and those solutions are maturing. That came up yesterday. The other thing that you brought up yesterday I thought was notable was the real emphasis on the data science side of it. And it's that it's still not easy or data science to do their job. And this is where you're seeing productivity conversations come up with data science. So, really the emphasis at the end of the day boils down to this. If you don't have any meat on the bone, you don't have a solution that rubber hits the road where you can come in and provide a tangible benefit to a company, an enterprise, then it's probably not going to work out. And we kind of had that tool conversation, you know, as people start to grow. And so as buyers out there, they got to look, and kind of squint through it saying where's the real deal? So that kind of brings up what's next? Who's winning, how do you as an analyst look at the playing field and say, that's good, that's got traction, that's winning, mm not too sure? What's your analysis, how do you tell the winners from the losers, and what's your take on this from the data science lens? >> Well, first of all you can tell the winners when they have an ample number of referenced customers who are doing interesting things. Interesting enough to get a jaded analyst to pay attention. Doing something that changes the fabric of work or life, whatever, clearly. Solution providers who can provide that are, they have all the hallmarks of a winner meaning they're making money, and they're likely to grow and so forth. But also the hallmarks of a winner are those, in many ways, who have a vision and catalyze an ecosystem around that vision of something that could be made, possibly be done before but not quite as efficiently. So you know, for example, now the way what we're seeing now in the whole AI space, deep learning, is, you know, AI means many things. The core right now, in terms of the buzzy stuff is deep learning for being able to process real time streams of video, images and so forth. And so, what we're seeing now is that the vendors who appear to be on the verge of being winners are those who use deep learning inside some new innovation that has enough, that appeals to a potential mass market. It's something you put on your, like an app or something you put on your smart phone, or it's something you buy at Walmart, install in your house. You know, the whole notion of clearly Alexa, and all that stuff. Anything that takes chatbot technology, really deep learning powers chatbots, and is able to drive a conversational UI into things that you wouldn't normally expect to talk to you and does it well in a way that people have to have that. Those are the vendors that I'm looking for, in terms of those are the ones that are going to make a ton of money selling to a mass market, and possibly, and very much once they go there, they're building out a revenue stream and a business model that they can conceivably take into other markets, especially business markets. You know, like Amazon, 20-something years ago when they got started in the consumer space as the exemplar of web retailing, who expected them 20 years later to be a powerhouse provider of business cloud services? You know, so we're looking for the Amazons of the world that can take something as silly as a conversational UI inside of a, driven by DL, inside of a consumer appliance and 20 years from now, maybe even sooner, become a business powerhouse. So that's what's new. >> Yeah, the thing that comes up that I want to get your thoughts on is that we've seen data integration become a continuing theme. The other thing about the community play here is you start to see customers align with syndicates or partnerships, and I think it's always been great to have customer traction, but, as you pointed out, as a benchmark. But now you're starting to see the partner equation, because this isn't open, decentralized, distributed internet these days. And it is looking like it's going to form differently than they way it was, than the web days and with mobile and connected devices it IoT and AI. A whole new infrastructure's developing, so you're starting to see people align with partnerships. So I think that's something that's signaling to me that the partnership is amping up. I think the people are partnering more. We've had Hortonworks on with IBM, people are partner, some people take a Switzerland approach where they partner with everyone. You had, WANdisco partners with all the cloud guys, I mean, they have unique ITP. So you have this model where you got to go out, do something, but you can't do it alone. Open source is a key part of this, so obviously that's part of the collaboration. This is a key thing. And then they're going to check off the boxes. Data integration, deep learning is a new way to kind of dig deeper. So the question I have for you is, the impact on developers, 'cause if you can connect the dots between open source, 90% of the software written will be already open source, 10% differentiated, and then the role of how people going to market with the enterprise of a partnership, you can almost connect the dots and saying it's kind of a community approach. So that leaves the question, what is the impact to developers? >> Well the impact to developers, first of all, is when you go to a community approach, and like some big players are going more community and partnership-oriented in hot new areas like if you look at some of the recent announcements in chatbots and those technologies, we have sort of a rapprochement between Microsoft and Facebook and so forth, or Microsoft and AWS. The impact for developers is that there's convergence among the companies that might have competed to the death in particular hot new areas, like you know, like I said, chatbot-enabled apps for mobile scenarios. And so it cuts short the platform wars fairly quickly, harmonizes around a common set of APIs for accessing a variety of competing offerings that really overlap functionally in many ways. For developers, it's simplification around a broader ecosystem where it's not so much competition on the underlying open source technologies, it's now competition to see who penetrates the mass market with actually valuable solutions that leverage one or more of those erstwhile competitors into some broader synthesis. You know, for example, the whole ramp up to the future of self-driving vehicles, and it's not clear who's going to dominate there. Will it be the vehicle manufacturers that are equipping their cars with all manner of computerized everything to do whatnot? Or will it be the up-and-comers? Will it be the computer companies like Apple and Microsoft and others who get real deep and invest fairly heavily in self-driving vehicle technology, and become themselves the new generation of automakers in the future? So, what we're getting is that going forward, developers want to see these big industry segments converge fairly rapidly around broader ecosystems, where it's not clear who will be the dominate player in 10 years. The developers don't really care, as long as there is consolidation around a common framework to which they can develop fairly soon. >> And open source is obviously a key role in this, and how is deep learning impacting some of the contributions that are being made, because we're starting to see the competitive advantage in collaboration on the community side is with the contributions from companies. For example, you mentioned TensorFlow multiple times yesterday from Google. I mean, that's a great contribution. If you're a young kind coming into the developer community, I mean, this is not normal. It wasn't like this before. People just weren't donating massive libraries of great stuff already pre-packaged, So all new dynamics emerging. Is that putting pressure on Amazon, is that putting pressure on AWS and others? >> It is. First of all, there is a fair amount of, I wouldn't call it first-mover advantage for TensorFlow, there've been a number of DL toolkits on the market, open source, for the last several years. But they achieved the deepest and broadest adoption most rapidly, and now they are a, TensorFlow is essentially a defacto standard in the way, that we just go back, betraying my age, 30, 40 years ago where you had two companies called SAS and SPSS that quickly established themselves as the go-to statistical modeling tools. And then they got a generation, our generation, of developers, or at least of data scientists, what became known as data scientists, to standardize around you're either going to go with SAS or SPSS if you're going to do data mining. Cut ahead to the 2010s now. The new generation of statistical modelers, it's all things DL and machine learning. And so SAS versus SPSS is ages ago, those companies are, those products still exist. But now, what are you going to get hooked on in school? What are you going to get hooked on in high school, for that matter, when you're just hobby-shopping DL? You'll probably get hooked on TensorFlow, 'cause they have the deepest and the broadest open source community where you learn this stuff. You learn the tools of the trade, you adopt that tool, and everybody else in your environment is using that tool, and you got to get up to speed. So the fact is, that broad adoption early on in a hot new area like DL, means tons. It means that essentially TensorFlow is the new Spark, where Spark, you know, once again, Spark just in the past five years came out real fast. And it's been eclipsed, as it were, on the stack of cool by TensorFlow. But it's a deepening stack of open source offerings. So the new generation of developers with data science workbenches, they just assume that there's Spark, and they're going to increasingly assume that there's TensorFlow in there. They're going to increasingly assume that there are the libraries and algorithms and models and so forth that are floating around in the open source space that they can use to bootstrap themselves fairly quickly. >> This is a real issue in the open source community which we talked, when we were in LA for the Open Source Summit, was exactly that. Is that, there are some projects that become fashionable, so for example, a cloud-native foundation, very relevant but also hot, really hot right now. A lot of people are jumping on board the cloud natives bandwagon, and rightfully so. A lot of work to be done there, and a lot of things to harvest from that growth. However, the boring blocking and tackling projects don't get all the fanfare but are still super relevant, so there's a real challenge of how do you nurture these awesome projects that we don't want to become like a nightclub where nobody goes anymore because it's not fashionable. Some of these open source projects are super important and have massive traction, but they're not as sexy, or flair-ish as some of that. >> Dl is not as sexy, or machine learning, for that matter, not as sexy as you would think if you're actually doing it, because the grunt work, John, as we know for any statistical modeling exercise, is data ingestion and preparation and so forth. That's 75% of the challenge for deep learning as well. But also for deep learning and machine learning, training the models that you build is where the rubber meets the road. You can't have a really strongly predictive DL model in terms of face recognition unless you train it against a fair amount of actual face data, whatever it is. And it takes a long time to train these models. That's what you hear constantly. I heard this constantly in the atrium talking-- >> Well that's a data challenge, is you need models that are adapting and you need real time, and I think-- >> Oh, here-- >> This points to the real new way of doing things, it's not yesterday's model. It's constantly evolving. >> Yeah, and that relates to something I read this morning or maybe it was last night, that Microsoft has made a huge investment in AI and deep learning machinery. They're doing amazing things. And one of the strategic advantages they have as a large, established solution provider with a search engine, Bing, is that from what I've been, this is something I read, I haven't talked to Microsoft in the last few hours to confirm this, that Bing is a source of training data that they're using for machine learning and I guess deep learning modeling for their own solutions or within their ecosystem. That actually makes a lot of sense. I mean, Google uses YouTube videos heavily in its deep learning for training data. So there's the whole issue of if you're a pipsqueak developer, some, you know, I'm sorry, this sounds patronizing. Some pimply-faced kid in high school who wants to get real deep on TensorFlow and start building and tuning these awesome kickass models to do face recognition, or whatever it might be. Where are you going to get your training data from? Well, there's plenty of open source database, or training databases out there you can use, but it's what everybody's using. So, there's sourcing the training data, there's labeling the training data, that's human-intensive, you need human beings to label it. There was a funny recent episode, or maybe it was a last-season episode of Silicone Valley that was all about machine learning and building and training models. It was the hot dog, not hot dog episode, it was so funny. They bamboozle a class on the show, fictionally. They bamboozle a class of college students to provide training data and to label the training data for this AI algorithm, it was hilarious. But where are you going to get the data? Where are you going to label it? >> Lot more work to do, that's basically what you're getting at. >> Jim: It's DevOps, you know, but it's grunt work. >> Well, we're going to kick off day two here. This is the SiliconeANGLE Media theCUBE, our fifth year doing our own event separate from O'Reilly media but in conjunction with their event in New York City. It's gotten much bigger here in New York City. We call it BigData NYC, that's the hashtag. Follow us on Twitter, I'm John Furrier, Jim Kobielus, we're here all day, we've got Peter Burris joining us later, head of research for Wikibon, and we've got great guests coming up, stay with us, be back with more after this short break. (rippling music)

>> Narrator: Live from the Javits Center in New York City. It's the Cube, covering the Inforum 2017. Brought to you by Infor. >> We are wrapping up the Cube's day two coverage of conference here in New York City at Inforum. My name is Rebecca Knight, along with my cohost Dave Vellante. We're joined by Dan Barnhardt. He is the Infor Vice President of Communications. Thanks so much for joining us. >> Yes, thank you for having me. Thank you for being here two days in a row. >> It's been a lot of fun. We've had a great time. So yeah, congratulations, it's been a hugely successful conference, a lot of buzz. Recap it for us, what's been most exciting for you? >> Sure, this was our second year having a forum in New York, which is our home town. I think it was a more exciting conference than last year. We unveiled some incredible development updates, led by Coleman, our AI offering, which is an incredible announcement for us, as well as Networked CloudSuites, which takes the functionality from our GT Nexus commerce network, and bakes it into our CloudSuites, the mission critical industry CloudSuites, that we offer on the Amazon Web Services cloud. Those were really exciting developments, as well as some other announcements we made with regard to product. And then, in addition to product, we had a lot of customer momentum that we shared. Last year, we had customers like Whole Foods and Travis Perkins up here. We continued the momentum with big enterprise customers making big bets on Infor, led by Koch Industries who invested more than two billion dollars this year at Infor, and are now modernizing their human resources and their financial operations with Infor CloudSuites. Moving to the cloud HR for 130,000 employees at Koch Industries which is an incredible achievement for the product, and for cloud HR. And, that's very exciting, as well as other companies like FootLocker, which were recognized with the Innovation Award for our Progress Makers Award. They're using talent science, data science to power their employees, not to power their employees, but to drive their employees towards greater productivity and greater happiness, because they've got the right people in the right fit for FootLocker, that's very exciting. And, of course, Bank of America, our Customer of the Year, which uses our HR solutions for their workforce, which obviously is exceptionally large. >> Yes, there was a great ceremony this morning, with a lot of recognition. So, let's talk a little bit more about Coleman, this was the big product announcement, really the first product in AI for Infor. Tell us a little bit about the building blocks. >> For certain. We have a couple of AI offerings now, like predictive hotel pricing, predictive demand and assortment planning in retail, but we have been building towards Coleman and what we consider the age of networked intelligence for multiple years. Since we architected Infor CloudSuite to run mission critical ERP in the cloud, we developed the capability of having data, mission critical data that really runs a business, your manufacturing, finance, distribution core functions, in the cloud on AWS, which gives us hyper-scale compute power to crunch incredible data. So, that really became possible once we moved CloudSuite in 2014. And then in 2015, we acquired GT Nexus, which is a commerce network that unites, that brings in the 80 percent of enterprise data that lies outside the four walls, among suppliers, and logistics providers, and banks. That unified that into the CloudSuite and brought that data in, and we're able to crunch that using the compute power of AWS. And then last year at Inforum, we announced the acquisition of Predictix, which is a predictive solutions for retail. And when building those, Predictix was making such groundbreaking development in the area of machine learning that they spun off a separate group called Logicblox, just to focus on machine learning. And Inforum vested heavily, we didn't talk a lot about Logicblox, but that was going to deliver a lot of the capabilities along with Amazon's developments with Lex and Alexa to enable Coleman to come to reality. So we were able then to acquire Birst. Birst is a BI program that takes, and harmonizes, the data that comes across CloudSuite and GT Nexus in a digestible form that with the machine learning power from Logicblox can power Coleman. So now we have AI that's pervasive underneath the application, making decisions, recommending advice so that people can maximize their potential at work, not have to do more menial tasks like search and gather, which McKenzie has shown can take 20 percent of your work week just looking for the information and gathering the information to make decisions. Now, you can say Coleman get me this information, and Coleman is able to return that information to you instantly, and let you make decisions, which is very, very exciting breakthrough. >> So there's a lot there. When you and I talked prior to the show, I was kind of looking for okay, what's going to be new and different, and one of the things you said was we're really going to have a focus on innovation. So, in previous Inforums it's really been about, to me anyway, we do a lot of really hard work. We're hearing a lot about acquisitions, certainly AI and Coleman, how those acquisitions come together with your, you know, what Duncan Angove calls the layer cake, you know the wedding cake stack, the strategy stack, I call it. So do you feel like you've achieved those objectives of messaging that innovation, and what's the reaction then from the customer base? >> Without a doubt. I wouldn't characterize anything that we said last year as not innovative, we announced H&L Digital, our digital transformation arm which is doing some incredible custom projects, like for the Brooklyn Nets, essentially money balling the NBA. Look forward to seeing that in next season a little bit, and then more in the season to come. Some big projects with Travis Perkins and with some other customers, care dot com, that were mentioned. But this year we're unveiling Coleman, which takes a lot of pieces, as Duncan said sort of the wedding cake, and puts them together. This has been a development for years. And now we're able to unveil it, and we've chosen to name it Coleman in honor of Katherine Coleman Johnson, one of the ladies whose life was told in the movie Hidden Figures, and she was a pioneer African-American woman in Stem, which is an important cause for us. You know, Infor years ago when we were in New Orleans unveiled the Infor Education Alliance program so that we can invest in increasing Stem education among young people, all young people with a particular focus on minorities and women to increase the ranks of underrepresented communities in the technology industry. So this, Coleman, not only pays honor to Katherine Johnson the person, but also to her mission to increase the number of people that are choosing careers in Stem, which as we have shown is the future of work for human beings. >> So talk a little bit more about Infor's commitment to increasing number to increasing, not only Stem education, but as you said increasing the number of women and minorities who go into Stem careers. >> Certainly. We, you know Pam Murphy who is our chief operating officer, this has been an incredibly important cause to her as well as Charles Phillips our CEO. We launched the Women's Infor Network, WIN, several years ago and that's had some incredible results in helping to increase the number of women at Infor. Many years ago, I think it was Google that first released their diversity report, and it drew a lot of attention to how many women and how many minorities are in technology. And they got a lot of heat, because it was about 30, 35 percent of their workforce was female, and then as other companies started rolling out their diversity report, it was a consistent number between 30 to 35 percent, and what we identified from that was not that women are not getting the jobs, it's that there aren't as many women pursuing careers in this type of field. >> Rebecca: Pipeline. >> Yes. So in order to do that, we need to provide an environment that nurtures some of the specific needs that women have, and that we're promoting education. So we formed the WIN program to do that first task, and this year on International Women's Day in early March, we were able to show some of the results that came from that, particularly in senior positions, SVP, VP, and director level positions at Infor. Some have risen 60 percent the number of women in those roles since we launched the Women's Infor Network just a couple of years ago. And then we launched the Education Alliance Program. We partnered with institutions, like CUNY the City University of New York, the New York Urban League, and universities now across the globe, we've got them in India, in Thailand and China, in South Korea to help increase the number of people who are pursuing careers in Stem. We've also sponsored PBS series and Girls Who Code, we have a hack-athon going on here at Inforum with a bunch of young people who are building, sort of, add-on apps and widgets that go to company Infor. We're investing a lot in the growth of Stem education, and the next generation. >> And by the way, those numbers that you mentioned for Google and others at around 30, 34 percent, that's much better than the industry average. They're doing quote, unquote well and still far below the 50 percent which is what you would think, you know, based on population it would be. So mainly the average is around, or the actual number's around 17 percent in the technology business, and then the other thing I would add is Amazon, I believe, was pretty forthcoming about its compensation, you know. >> Salesforce really started it, Marc Benioff. >> And they got a lot of heat for it, but it's transparency is really the starting point, right? >> It was clear really early for companies like Salesforce, and Amazon, and Google, and Infor that this was not something that we needed to create talking points about, we were going to need to effect real change. And that was going to take investment and time, and thankfully with leadership like Charles Phillips, our CEO, and Marc Benioff were making investments to help make sure that the next generation of every human, but particularly women and minorities that are underrepresented right now in technology, have those skills that will be needed in the years to come. >> Right, you have to start with a benchmark and then know where you're moving from. >> Absolutely, just like if you're starting a project to transform your business, where do you want to go and what are the steps that are going to help you get there? >> Speaking of transforming your business, this is another big trend, is digital transformation. So now that we are at nearing the end of day two of this conference, what are you hearing from customers about this jaunting, sometimes painful process that they must endure, but really they must endure it in order to stay alive and to thrive? >> Without a doubt. A disruption is happening in every industry that we're seeing, and customers across all of the industries that Infor serves, like manufacturing, healthcare, retail, distribution, they are thinking about how do we survive in the new economy, when everything is digital, when every company needs to be a technology company. And we are working with our customers to help first modernize their systems. You can't be held back by old technology, you need to move to the cloud to get the flexibility and the agility that can adapt to changing business conditions and disruptions. No longer do you have years to adapt to things, they're happening overnight, you must have flexible solutions to do that. So, we have a lot of customers. We just had a panel with Travis Perkins, and with Pilot Flying J, who was on the Cube earlier, talking about how their, and Cook Industries our primary investor now, talking about how they're re-architecting their IT infrastructure to give them that agility so they can start thinking about what sort of projects could open up new streams of revenue. How could we, you know, do something else that we never thought of, but now we have the capability to do digitally that could be the future of our business? And it's really exciting to have all the CIOs, and SVPs of technology, VPs of technology, that are here at Inforum talking about what they're doing, and how they're imagining their business. It's really incredible to get a peek at what they're doing. >> You know, we were talking to Debbie earlier. One of the interesting things that I, my takeaway is on the digital transformation, is you know, we always say digital is data and then what we talked about was the ability to traverse industry value change, not just vertically but horizontally. Amazon buying Whole Foods is a perfect example, Amazon's a content company, Apple's getting into financial services. I wonder if you could comment on your thoughts on because you're so deep into micro-verticals, and what Debbie said was well I gave a consumer package good example to a process manufacturing company. And they were like what are you talking about, and she said look, let me connect the dots and the light bulbs went off. And they said wow, we could take that CPG example and apply it, so I wonder when we talk about digital transformation, if you see or can foresee your advantage in micro-verticals as translating across those verticals. >> Without a doubt. We talk about it as adjacent innovation. And Charles points back to an example, way back from the creation of the niche in glass, and how that led to additional businesses and industries like eyeglasses and fire preparedness, and we look at it that way for certain. We dive very deep into key industries, but when we look at them holistically across and we say oh, this is happening within the retail industry, we can identify key functionality that might change the industry of disruption, not disruption, distribution. Might disrupt the distribution industry, and we can apply the lessons learned by having that industry specialization into other industries and help them realize a potential that they weren't aware of before, because we uncovered it in one place. That's happening an awful lot with what we do with retail and assortment planning and healthcare. We run 70 percent of the large hospitals in the US, and we're learning a lot from retail and how we might help hospitals move more quickly. When you are managing life and death situations, if you are planning assortment or inventory for those key supplies within a hospital, and you can make even small adjustments that can have huge impact on patient care, so that's one of the benefits of our industry-first strategy, and the adjacent innovation that we cultivate there. >> I know we're not even finished with Inforum 2017, but we must look ahead to 2018. Talk a little bit about what your goals for next year's conference are. >> For sure. You're correct, we're not finished yet with Inforum. I know everyone here is really excited about Bruno Mars who's entertaining tonight, but we are looking forward to next year's conference as well, we're already talking about some of the innovative things that we'll announce, and the customer journeys that are beginning now, which we'd like to unveil there. We are going to be moving the conference from New York, we're going to move to Washington DC in late-September, September 24th to 27th in Washington DC, which we're very excited about to let our customers, they come back every year to learn more. We had seven thousand people attending this year, we want to give them a little bit of a variety, while still making sure that they can reach, you know, with one stop from Europe and from Asia, cause customers are traveling from all over the world, but we're very excited to see the growth that would be shared. This year, for instance, if you look at the sponsors, we had our primary SI partner Avaap was platinum partner last year. In addition to Avaap this year, we were joined by Accenture, and Deloitte, Capgemini, Grant Thorton, all of whom have built Infor practices over the last 12 months because there's so much momentum over our solutions that that is a revenue opportunity for them that they want to take advantage of. >> And the momentum is just going to keep on going next year in September. So I'll see you in September. >> Yeah, thank you very much. I appreciate you guys being here with us for the third year, second year in a row in New York. >> Indeed, thank you. I'm Rebecca Knight for Dave Vellante, we will have more from Inforum 2017 in a bit.

>> Announcer: Live from the Javits Center in New York City, it's theCUBE! Covering Inforum 2017. Brought to you by Inforum. >> Welcome to day one of theCUBE's coverage of Inforum here at the Javits Center in New York City. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. We are also joined by Jim Kobielus, who is the lead analyst for artificial intelligence at Wikibon. Thanks so much. It's exciting to be here, day one. >> Yeah, good to see you again, Rebecca. Really, our first time, we really worked a little bit at Red Hat Summit. >> Exactly, first time on the desk together. >> It's our very first time. I first met you a little while ago, and already you're an old friend. >> This is the third time we've done Inforum. The first time we did it was in New Orleans, and then Infor decided to skip a year. And then, last year, they decided to have it in the middle of July, which is kind of a strange time to have a show, but there are a lot of people here. I don't know what the number is, but it looks like several thousand, maybe as many as 4000 to 5000. I don't know what you saw. >> Rebecca: No, no, I feel like this is a big show. >> Jim: Heck, for July? For any month, actually. >> Exactly, particularly at a time where we're having a lot of rail issues, issues at LaGuardia too, so it's exciting. >> theCUBE first met Infor at the second Amazon re:Invent. I remember the folks at Amazon told us, "We really have an exciting SAS company. "It's the largest privately-held SAS company in the world." We were thinking, is that SAS? And they said, "No, no, it's a company called Infor." We said, "Who the heck is Infor?" And then we had Pam Murphy on. That's when we first were introduced to the company, and then, of course, we were invited to come to New Orleans. At the time, the questions around Infor were, who is Infor? What are they all about? And then it became, okay, we started to understand the strategy a little bit. For those of you who don't familiar with Infor, their strategy from early on was to really focus on the micro-verticals. We've talked about that a little bit. Just a quick bit of history. Charles Phillips, former president of Oracle, orchestrator of the M&A at Oracle, PeopleSoft, Siebel and many others, left, started Infor to roll up, gold-funded by Golden Gate Capital and other private equity, substantial base of Lawson Software customers, and then, many, many other acquisitions. Today, fast forward, you got a basically almost $3 billion company with a ton of debt, about $5 billion in debt, notwithstanding the Koch brothers' investment, which is almost $2.5 billion, which was to retire some of the equity that Golden Gate had, some of the owners, Charles and the three other owners took some money off the table, but the substantial amount of the investment goes into running the company. Here's what's interesting. Koch got a 2/3 stake in the company, but a 49% voting share, which implies a valuation of about, I want to say, just under four billion. Let's call it 3.7, 3.8 billion. For a $2 billion to $3 billion company, that's not a software company with 28% operating margin. That's not a huge valuation. So, we'll ask Charles Phillips about that, I mean, some of this wonky stuff in the financials, you know, we want to get through. I'm sure Infor doesn't want to talk too much about that. >> But it is true. It is, for a unicorn, for a privately-held company, this is one of them. This is up there with Uber and Airbnb, and it's a question that, why isn't it valued at more? >> My only assumption here is they went to Koch and said, "Okay, here's the deal. "We want $2 billion plus. "You only get 49%, only. "If you get 49% of the company in terms of voting rights, "we'll give you 2/3 in terms of ownership. "It's a sweetheart deal. "Of course, it's a lot of dough. "You get a board seat." Maybe two board seats, I can't remember. "And we'll pump this thing up, we'll build up the equity, "and we'll float it someday in the public markets, "and we'll all make a bunch of dough "and our shareholders will all be happy." That's the only thing I can assume, was this sort of conversation that went on. Well, again, we'll ask Charles Phillips, see if he answers that. But James, you sat in yesterday at the analyst event, you got sort of the history of the company, and the fire hose of information leading up to what was announced today, Coleman AI. What were your impressions as an analyst? >> Well, first of all, my first impression was a thought, a question. Is Infor with Coleman AI simply playing catch-up in a very, I call it a war of attrition in the ERP space. Really, it's four companies now. It's SAP, it's Microsoft, it's Oracle, and it's Infor duking it out. SAP, Microsoft and Oracle all have fairly strong AI capabilities and strategies and investments, and clearly they're infused, I was at Microsoft Build a few months ago. They're infusing those capabilities into all of their offerings. With Coleman, sounds impressive, thought it's just an early announcement, they've only begun to trickle it out to their vast suite. I want to get a sense, and probably later today we'll talk to Mr. Angove, Duncan Angove. I want to get a sense for how does, or does, Infor intend to differentiate their suite in this fiercely competitive ERP world? How will Coleman enable them to differentiate it? Right now it seems like everything they're announcing about Coleman is great in terms of digital assistance, conversational interface, everybody does this, too, now, with chatbots and so forth, in-line providing recommendations. Everybody's doing that. Essentially, everybody wants to go there. How are they going to stand apart with those capabilities, number one? Number two is just the timeline. They have this vast suite, and we just came from the keynote, where Charles and the other execs laid out in minute detail the micro-vertical applications. What is their timeline for rolling out those Coleman capabilities throughout the suite so customers can realize they have value? And is there a layered implementation? They talked about augmentation versus automation, and versus assistance. I'd like to see sort of a layer of capabilities in an architecture with a sense for how they're going to invest in each of those capabilities. For example, they talked about open source, like with TensorFlow, which is a new deep learning framework from Google Open Source. I just want to get a deep dive into where the investment funds that they're getting from Koch and others, especially from Koch, where that's going in terms of driving innovation going forward in their portfolio. I'm not cynical about it, I think they're doing some really interesting things. But I want some more meat on the bones of their strategy. >> Well, it's interesting, because I think Infor came into the show wanting to message innovation. They're not known as an innovative company. But you heard Charles Phillips up there talking, today he was talking about quantum computing, he was talking about the end of Moore's Law, he was obviously talking about AI. They named Coleman after Katherine Coleman Johnson. >> Here's my speculation. My speculation, of course, they recently completed the acquisition of Birst. Brad Peters did a really good discussion of Birst, the BI startup that's come along real fast. My sense, and I want to get confirmation, is that, possibly, Birst and Brad Peters and his team, will they drive the Coleman strategy going forward? It seems likely, 'cause Birst has some AI assets that Brad Peters brought us up to speed on yesterday. I want to get a sense for how Birst's AI and Coleman AI are going to come together into a convergence. >> But wouldn't they say that it's quote-unquote embedded, embedded AI? >> Jim: It'll be invisible, it has to be. >> You know, buried within the software suite? We saw, like you said, in gory detail the application portfolio that Infor had. I think one of the challenges the company has, it's like some of my staff meetings. Not everything is relevant to everybody. Very clearly, they have a lot of capabilities that most people aren't aware of. The question is, how much can they embed AI across those, and where are the use cases, and what's the value? And it's early days, right? >> Oh, yeah, very much. And you know, in some of those applications, probably many of them, the automation capabilities that they described for Coleman will be just as important as the human augmentation capabilities. In other words, micro-verticalize their AI in diverse ways going forward across their portfolio. In other words, one AI brush, broad brush of AI across every application probably won't make sense. The applications are quite different. >> I want to talk about the use cases, here. The selling points for these things are making the right decision all the time, more quickly. >> Jim: Productivity accelerators for knowledge workers, all that. >> And one of the other points that was made is that there are fewer arguments, because we are all looking at the same data, and we trust the data. Where do you see Birst and Coleman? Give me an example of where you can see this potentially transforming the industry? >> "We all trust data." Actually, we don't all trust data, because not all data is created the same. Birst comes into the portfolio not just to, really great visualizations and dashboarding and so forth, but they've got a well-built data management backend for data governance and so forth, to cleanse the data. 'Cause if you have dirty data, you can't derive high-quality decisions from the data. >> Rebecca: Excellent point, right. >> That's really my general take on where it's going. In terms of the Birst, I think the Birst acquisition will become pivotal in terms of them taking their data-driven functionality to the next level of consumability, 'cause Birst has done a really good job of making their capability consumable for the general knowledge worker audience. >> Well, a couple things. Actually, let me frame. Charles Phillips, I thought, did a good job framing the strategy. Sort of his strategy stack, if you will, starting with, at the bottom of the stack, the micro-verticals strategy, and then moving up the next layer was their decision to go all cloud, AWS Cloud. The third was the network. Infor made an acquisition of a company called GT Nexus, which is a commerce platform that has 18 years of commerce data and transaction data there. And the next layer was analytics, which is Birst, and I'll come back to that. And then the top layer is Coleman AI. The Birst piece is interesting, because we saw the ascendancy of Tableau and its land-and-expand strategy, and Christian Chabot, the CEO of Tableau, used to talk about, and they said this yesterday, the slow BI, you know, cubes, and the life cycle of actually getting an answer. By the time you get the answer, the market has changed. And that's what Tableau went after, and Tableau did very, very, well. But it turned out Tableau was largely a desktop tool. Wasn't available in the Cloud. It is now. And it had its limitations. It was basically a visualization tool. What Infor has done with Birst is they're positioning the old Cognos, which is now IBM, and the micro strategies of the world as the old guard. They're depositioning Tableau, and they didn't use that specific name, Tableau, but that's what they're talking about, Tableau and Click, as less than functional. Sort of spreadsheet plus. And they are now the rich, robust platform that both scales and has visualization, and has all the connections into the enterprise software world. So I thought it was interesting positioning. Would love to talk to some customers and see what that really looks like. But that, essentially, was the strategy stack that Charles Phillips laid out. I guess the last point I'd make as I come back to the decision to go AWS, you saw the application portfolio. Those are hardcore enterprise apps which everybody says don't live in the Cloud. Well, 55% of Infor's revenue is from the Cloud, so, clearly, it's not true. A lot of these apps are becoming cloud-enabled. >> Jim: Yeah, most of them. >> Most of them? >> Most of them are, yeah. BI, mode-predictive analytics, most AI. Machine learning is going in the Cloud. >> 'Cause Oracle's argument is, Oracle will be only one who can put those apps in the Cloud. >> 'Cause the data lives in the Cloud. It's trained on the data. >> Not all the data lives in the Cloud. >> It's like GT Nexus. That's EDI, that's rich EDI data, as they've indicated for training this new generation of neutral networks, machine learning and deep learning models continuously from fresh transaction data. You know that's where GT Nexus and e-commerce network fits into this overall strategy. It's a massive pile stream of data for mining. >> But, you know, SAP has struggled in the Cloud. SuccessFactors, obviously, is their SAS play. Most of their stuff remains on-prem. Oracle again claims they have the only end-to-end hybrid. You see Microsoft finally shipping Azure Stack, or at least claiming to soon be shipping Azure Stack. They've obviously got a strategy there with their productivity estate. But here you have Infor-- >> Don't forget IBM. They've got a very rich, high-rated portfolio. >> Well, you heard, I don't know if it was Charles, somebody took a swipe at IBM today, saying that the company's competitors have purchased all these companies, these SAS companies, and they don't have a way to really stitch them together. Well, that's not totally true. Bluemix is IBM's way. Although, that's been a heavy lift. We saw with Oracle Fusion, it took over a decade and they're still working on that. So, Infor, again, I want to talk to customers and find out, okay, how much of this claim that everything's seamless in the Cloud is actually true? I think, obviously, a large portion of the install base is still that legacy on-prem Lawson base that hasn't modernized. That's always, in my view, enforced big challenges. How do you get that base, leverage that install base to move, and then attract new customers? By all accounts, they're doing a pretty good job of it. >> I don't think what's going on, I don't think a lot of lift-and-shift is going on. Legacy Lawson customers are not moving in droves to the Cloud with their data and all that. There's not a massive lift-and-shift. It's all the new greenfield applications for these new use cases, in terms of predictive analytics. They're being born and living their entire lives in the Cloud. >> And a lot of HR, a lot of HCM, obviously, competing with Workday and Peoplesoft. That stuff's going into the Cloud. We're going to be unpacking this all day today, and tomorrow. Two days here of coverage. >> Indeed, yes indeed. >> Dave: Excited to be here. >> It's going to be a great show. Bruno Mars is performing the final day. >> Jim: Bruno Mars? >> I know, very-- >> You know a company's doing good, Infor, when they can pay for the likes of a Bruno Mars, who's still having mega hits on the radio. I wish I was staying long enough to catch that one. >> I know, indeed, indeed. Well, for Dave and Jim, I'm Rebecca Knight, and we'll be back with more from Inforum 2017 just after this. (fast techno music)

>>so erin you're a technique for a Cloudera, you're a whiz kid from Brown, you have, how many Brown people are engineers here at Cloudera >>as of monday, we have five full timers and two interns at the moment and we're trying to hire more all the time. >>Mhm. So how many interns? >>Uh two interns from Brown this this summer? A few more from other schools? Cool, >>I'm john furry with silicon angle dot com. Silicon angle dot tv. We're here in the cloud era office in my little mini studio hasn't been built out yet, It was studio, we had to break it down for a doctor, ralph kimball, not richard Kimble from uh I called him on twitter but coupon um but uh the data warehouse guru was in here um and you guys are attracting a lot of talent erin so tell us a little bit about, you know, how Claudia is making it happen and what's the big deal here, people smart here, it's mature, it's not the first time around this company, this company has some some senior execs and there's been a lot, a lot of people uh in the market who have been talking about uh you know, a lot of first time entrepreneurs doing their startups and I've been hearing for some folks in in the, in the trenches that there's been a frustration and start ups out there, that there's a lot of first time entrepreneurs and everyone wants to be the next twitter and there's some kind of companies that are straddling failure out there? And and I was having that conversation with someone just today and I said, they said, what's it like Cloudera and I said, uh, this is not the first time crew here in Cloudera. So, uh, share with the folks out there, what you're seeing for Cloudera and the management team. >>Sure. Well, one of the most attractive parts about working Cloudera for me, one of the reasons I, I really came here was have been incredibly experienced management team, Mike Charles, they've all there at the top of this Oregon, they have all done this before they founded startups, Growing startups, old startups and uh, especially in contrast with my, the place where I worked previously. Uh, the amount of experience here is just tremendous. You see them not making mistakes where I'm sure others would. >>And I mean, Mike Olson is veteran. I mean he's been, he's an adviser to start ups. I know he's been in some investors. Amer was obviously PhD candidates bolted out the startup, sold it to yahoo, worked at, yahoo, came back finish his PhD at stanford under Mendel over there in the PhD program over this, we banged in a speech. He came back entrepreneur residents, Excel partners. Now it does Cloudera. Um, when did you join the company and just take us through who you are and when you join Cloudera, I want your background. >>Sure. So I, I joined a little over a year ago is about 30 people at the time. Uh, I came from a small start up of the music online music store in new york city um uh, which doesn't really exist all that much anymore. Um but you know, I I sort of followed my other colleagues from Brown who worked here um was really sold by the management team and also by the tremendous market opportunity that that Hadoop has right now. Uh Cloudera was very much the first commercial player there um which is really a unique experience and I think you've covered this pretty well before. I think we all around here believe that uh the markets only growing. Um and we're going to see the market and the big data market in general get bigger and bigger in the next few years. >>So, so obviously computer science is all the rage and and I'm particularly proud of hangout, we've had conversations in the hallway while you're tweeting about this and that. Um, but you know, silicon angles home is here, we've had, I've had a chance to watch you and the other guys here grow from, you know, from your other office was a san mateo or san Bruno somewhere in there. Like >>uh it was originally in burlingame, then we relocate the headquarters Palo Alto and now we have a satellite up in san Francisco. >>So you guys bolted out. You know, you have a full on blow in san Francisco office. So um there was a big busting at the seams here in Palo Alto people commuting down uh even building their burning man. Uh >>Oh yeah sure >>skits here and they're constructing their their homes here, but burning man, so we're doing that in san Francisco, what's the vibe like in san Francisco, tell us what's going on >>in san Francisco, san Francisco is great. It's, I'm I live in san Francisco as do a lot of us. About half the engineering team works up there now. Um you know we're running out of space there certainly. Um and you're already, oh yeah, oh yeah, we're hiring as fast as we absolutely can. Um so definitely not space to build the burning man huts there like like there is down, down in Palo Alto but it's great up there. >>What are you working on right now for project insurance? The computer science is one of the hot topics we've been covering on silicon angle, taking more of a social angle, social media has uh you know, moves from this pr kind of, you know, check in facebook fan page to hype to kind of a real deal social marketplace where you know data, social data, gestural data, mobile data geo data data is the center of the value proposition. So you live that every day. So talk about your view on the computer science landscape around data and why it's such a big deal. >>Oh sure. Uh I think data is sort of one of those uh fundamental uh things that can be uh mind for value across every industry, there's there's no industry out there that can't benefit from better understanding what their customers are doing, what their competitors are doing etcetera. And that's sort of the the unique value proposition of, you know, stuff like Hadoop. Um truly we we see interest from every sector that exists, which is great as for what the project that I'm specifically working on right now, I primarily work on H. D. F. S, which is the Hadoop distributed file system underlies pretty much all the other um projects in the Hadoop ecosystem. Uh and I'm particularly working with uh other colleagues at Cloudera and at other companies, yahoo and facebook on high availability for H. D. F. S, which has been um in some deployments is a serious concern. Hadoop is primarily a batch processing system, so it's less of a concern than in others. Um but when you start talking about running H base, which needs to be up all the time serving live traffic than having highly available H DFS is uh necessity and we're looking forward to delivering that >>talk about the criticism that H. D. F. S has been having. Um Well, I wouldn't say criticism. I mean, it's been a great, great product that produced the HDs, a core parts of how do you guys been contributing to the standard of Apache, that's no secret to the folks out there, that cloud area leads that effort. Um but there's new companies out there kind of trying a new approach and they're saying they're doing it better, what are they saying in terms and what's really happening? So, you know, there's some argument like, oh, we can do it better. And what's the what, why are they doing it, that was just to make money do a new venture, or is that, what's your opinion on that? Yeah, >>sure. I mean, I think it's natural to to want to go after uh parts of the core Hadoop system and say, you know, Hadoop is a great ecosystem, but what if we just swapped out this part or swapped out that part, couldn't couldn't we get some some really easy gains. Um and you know, sometimes that will be true. I have confidence that that that just will not simply not be true in in the very near future. One of the great benefits about Apache, Hadoop being open source is that we have a huge worldwide network of developers working at some of the best engineering organizations in the world who are all collaborating on this stuff. Um and, you know, I firmly believe that the collaborative open source process produces the best software and that's that's what Hadoop is at its very core. >>What about the arguments are saying that, oh, I need to commercialize it differently for my installed base bolt on a little proprietary extensions? Um That's legitimate argument. TMC might take that approach or um you know, map are I was trying to trying to rewrite uh H. T. F. >>S. To me, is >>it legitimate? I mean is there fighting going on in the standards? Maybe that's a political question you might want to answer. But give me a shot. >>I mean the Hadoop uh isn't there's no open standard for Hadoop. You can't say like this is uh this is like do compatible or anything like that. But you know what you can say is like this is Apache Hadoop. Uh And so in that sense there's no there's no fighting to be had there. Um Yeah, >>so yeah. Who um struggling as a company. But you know, there's a strong head Duke D. N. A. At yahoo, certainly, I talked with the the founder of the startup. Horton works just announced today that they have a new board member. He's the guy who's the Ceo of Horton works and now on bluster, I'm sorry, cluster announced they have um rob from benchmark on the board. Uh He's the Ceo of Horton works and and one of my not criticisms but points about Horton was this guy's an engineer, never run a company before. He's no Mike Olson. Okay, so you know, Michaelson has a long experience. So this guy comes into running and he's obviously in in open source, is that good for Yahoo and open sources. He they say they're going to continue to invest in Hadoop? They clearly are are still using a lot of Hadoop certainly. Um how is that changing Apache, is that causing more um consolidation, is that causing more energy? What's your view on the whole Horton works? Think >>um you know, yahoo is uh has been and will continue to be a huge contributor. Hadoop, they uh I can't say for sure, but I feel pretty confident that they have more data under management under Hadoop than anyone else in the world and there's no question in my mind that they'll continue to invest huge amounts of both key way effort and engineering effort and uh all of the things that Hadoop needs to to advance. Um I'm sure that Horton works will continue to work very closely with with yahoo. Um And you know, we're excited to see um more and more contributors to to Hadoop um both from Horton works and from yahoo proper. >>Cool, Well, I just want to clarify for the folks out there who don't understand what this whole yahoo thing is, It was not a spin out, these were key Hadoop core guys who left the company to form a startup of which yahoo financed with benchmark capital. So, yahoo is clearly and told me and reaffirm that with me that they are clearly investing more in Hadoop internally as well. So there's more people inside, yahoo that work on Hadoop than they are in the entire Horton's work company. So that's very clear. So just to clear that up out there. Um erin. so you're you're a young gun, right? You're a young whiz like Todd madam on here, explain to the folks out there um a little bit older maybe guys in their thirties or C IOS a lot of people are doing, you know, they're kicking the tires on big data, they're hearing about real time analytics, they're hearing about benefits have never heard before. Uh Dave a lot and I on the cube talk about, you know, the transformations that are going on, you're seeing AMC getting into big data, everyone's transforming at the enterprise level and service provider. What explains the folks why Hadoop is so important. Why is that? Do if not the fastest or one of the fastest growing projects in Apache ever? Sure. Even faster than the web server project, which is one of the better, >>better bigger ones. >>Why is the dupes and explain to them what it is? Well, you know, >>it's been it's pretty well covered that there's been an explosion of data that more data is produced every every year over and over. We talk about exabytes which is a quantity of data that is so large that pretty much no one can really theoretically comprehend it. Um and more and more uh organizations want to store and process and learn from, you know, get insights from that data um in addition to just the explosion of data um you know that there is simply more data, organizations are less willing to discard data. One of the beauties of Hadoop is truly that it's so very inexpensive per terabyte to store data that you don't have to think up front about what you want to store, what you want to discard, store it all and figure out later what is the most useful bits we call that sort of schema on read. Um as opposed to, you know, figuring out the schema a priority. Um and that is a very powerful shift in dynamics of data storage in general. And I think that's very attractive to all sorts of organizations. >>Your, I'll see a Brown graduate and you have some interns from Brown to Brown um, Premier computer science program almost as good as when I went to school at Northeastern University. >>Um >>you know, the unsung heroes of computer science only kidding Brown's great program, but you know, cutting edge computer science areas known as obviously leading in a lot of the computer science areas do in general is known that you gotta be pretty savvy to be either masters level PhD to kind of play in this area? Not a lot of adoption, what I call the grassroots developers. What's your vision and how do you see the computer science, younger generation, even younger than you kind of growing up into this because those tools aren't yet developed. You still got to be, you're pretty strong from a computer science perspective and also explained to the folks who aren't necessarily at the browns of the world or getting into computer science, what about, what is that this revolution about and where is it going? What are some of the things you see happening around the corner that that might not be obvious. >>Sure there's a few questions there. Um part of it is how do people coming out of college get into this thing, It's not uh taught all that much in school, How do how do you sort of make the leap from uh the standard computer science curriculum into this sort of thing? And um you know, part of it is that really we're seeing more and more schools offering distributed computing classes or they have grids available um to to do this stuff there there is some research coming out of Brown actually and lots of other schools about Hadoop proper in the behavior of Hadoop under failure scenarios, that sort of stuff, which is very interesting. Google uh actually has classes that they teach, I believe in conjunction with the University of Washington um where they teach undergraduates and your master's level, graduate students about mass produced and distributed computing and they actually use Hadoop to do it because it is the architecture of Hadoop is modeled after um >>uh >>google's internal infrastructure. Um So you know that that's that's one way we're seeing more and more people who are just coming out of college who have distributed systems uh knowledge like this? Um Another question? the other part of the question you asked is how does um how does the ordinary developer get into this stuff? And the answer is we're working hard, you know, we and others in the hindu community are working hard on making it, making her do just much easier to consume. We released, you cover this fair bit, the ECM Express project that lets you install Hadoop with just minimal effort as close to 11 click as possible. Um and there's lots of um sort of layers built on top of Hadoop to make it more easily consumed by developers Hive uh sort of sequel like interface on top of mass produce. And Pig has its own DSL for programming against mass produce. Um so you don't have to write heart, you don't have to write straight map produced code, anything like that. Uh and it's getting easier for operators every day. >>Well, I mean, evolution was, I mean, you guys actually working on that cloud era. Um what about what about some of the abstractions? You're seeing those big the Rage is, you know, look back a year ago VM World coming up and uh little plugs looking angle dot tv will be broadcasting live and at VM World. Um you know, he has been on the Q XV m where um Spring Source was a big announcement that they made. Um, Haruka brought by Salesforce Cloud Software frameworks are big, what does that look like and how does it relate to do and the ecosystem around Hadoop where, you know, the rage is the software frameworks and networks kind of collide and you got the you got the kind of the intersection of, you know, software frameworks and networks obviously, you know, in the big players, we talk about E M C. And these guys, it's clear that they realize that software is going to be their key differentiator. So it's got to get to a framework stand, what is Hadoop and Apache talking about this kind of uh, evolution for for Hadoop. >>Sure. Well, you know, I think we're seeing very much the commoditization of hardware. Um, you just can't buy bigger and bigger computers anymore. They just don't exist. So you're going to need something that can take a lot of little computers and make it look like one big computer. And that's what Hadoop is especially good at. Um we talk about scaling out instead of scaling up, you can just buy more relatively inexpensive computers. Uh and that's great. And sort of the beauty of Hadoop, um, is that it will grow linearly as your data set as your um, your your scale, your traffic, whatever grows. Um and you don't have to have this exponential price increase of buying bigger and bigger computers, You can just buy more. Um and that that's sort of the beauty of it is a software framework that if you write against it. Um you don't have to think about the scaling anymore. It will do that for you. >>Okay. The question for you, it's gonna kind of a weird question but try to tackle it. You're at a party having a few cocktails, having a few beers with your buddies and your buddies who works at a big enterprise says man we've got all this legacy structured data systems, I need to implement some big data strategy, all this stuff. What do I do? >>Sure, sure. Um Not the question I thought you were going to ask me that you >>were a g rated program here. >>Okay. I thought you were gonna ask me, how do I explain what I do to you know people that we'll get to that next. Okay. Um Yeah, I mean I would say that the first thing to do is to implement a start, start small, implement a proof of concept, get a subset of the data that you would like to analyze, put it, put Hadoop on a few machines, four or five, something like that and start writing some hive queries, start writing some some pig scripts and I think you'll you know pretty quickly and easily see the value that you can get out of it and you can do so with the knowledge that when you do want to operate over your entire data set, you will absolutely be able to trivially scale to that size. >>Okay. So now the question that I want to ask is that you're at a party and I want to say, what do you >>do? You usually tell people in my hedge fund manager? No but seriously um I I tell people I work on distributed supercomputers. Software for distributed supercomputers and that people have some idea what distributed means and supercomputers and they figure that out. >>So final question for I know you gotta go get back to programming uh some code here. Um what's the future of Hadoop in the sense of from a developer standpoint? I was having a conversation with a developer who's a big data jockey and talking about Miss kelly gets anything and get his hands on G. O. Data, text data because the data data junkie and he says I just don't know what to build. Um What are some of the enabling apps that you may see out there and or you have just conceiving just brainstorming out there, what's possible with with data, can you envision the next five years, what are you gonna see evolve and what some of the coolest things you've seen that might that are happening right now. >>Sure. Sure. I mean I think you're going to see uh just the front ends to these things getting just easier and easier and easier to interact with and at some point you won't even know that you're interacting with a Hadoop cluster that will be the engine underneath the hood but you know, you'll you'll be uh from your perspective you'll be driving a Ferrari and by that I mean you know, standard B. I tool, standard sequel query language. Um we'll all be implemented on top of this stuff and you know from that perspective you could implement, you know, really anything you want. Um We're seeing a lot of great work coming out of just identifying trends amongst masses of data that you know, if you tried to analyze it with any other tool, you'd either have to distill it down so far that you would you would question your results or that you could only run the very simplest sort of queries over um and not really get those like powerful deep insights, those sort of correlative insights um that we're seeing people do. So I think you'll see, you'll continue to see uh great recommendations systems coming out of this stuff. You'll see um root cause analysis, you'll see great work coming out of the advertising industry um to you know to really say which ad was responsible for this purchase. Was it really the last ad they clicked on or was it the ad they saw five weeks ago they put the thought in mind that sort of correlative analysis is being empowered by big data systems like a dupe. >>Well I'm bullish on big data, I think people I think it's gonna be even bigger than I think you're gonna have some kids come out of college and say I could use big data to create a differentiation and build an airline based on one differentiation. These are cool new ways and, and uh, data we've never seen before. So Aaron, uh, thanks for coming >>on the issue >>um, your inside Palo Alto Studio and we're going to.