from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi I'm Peter Burris analyst at wiki bond welcome to another wiki bond the cube digital community event this one sponsored by HP and focusing on hybrid storage like all of our digital community events this one will feature about 25 minutes of video followed by a crowd chat which will be your opportunity to ask your questions share your experiences and push forward the community's thinking on the important issues facing business today so what are we talking about today again hybrid storage let's get going so what is hybrid storage in a lot of shops most people have associated the cloud with public cloud but as we gain experience with the challenges associated with transforming to digital business in which we use data as a singular value producing asset increasingly IT professionals are starting to realize this important relationship between data storage and cloud services and in many respects that's really what we're trying to master today is a better understanding of how the business is going to use data to affect significant changes in how it behaves in the marketplace and it's that question of behavior that question of action that question of location that is pushing business to think differently about how its cloud architectures are going to work we're going to keep data proximate to where it's created to where it's going to be used to where it's going to be able to generate value which demands that we have storage resources in place close to that data proximate to that activity near that value producing activity and that the cloud services will have to follow in many respects that's what we're talking about when we talk about hybrid cloud today we're talking about the increasing recognition that we're going to move cloud services to the data default and not move the data into the cloud public cloud specifically so it's this ongoing understanding as we gain experience with this powerful set of technologies that data architecture is going to be increasingly distributed that storage therefore will be increasingly distributed and that cloud services will flow to where the data is required utilizing storage technologies that can best serve that set of workload so it's a more complex world that demands new levels of simplicity ease of use and optimization so that's where we're going to start our conversation so these crucial questions of how data storage and cloud are going to come together to create hybrid architectures was the basis for a great cubed conversation between silicon angle wiki bonds david Volante and HPE sun dip aurora let's hear what they had to say talk about let's talk about the break down those three things cost efficiency ease of use and resource optimization let's start with cost efficiency so obviously there's TCO there's also the way in which I consume the people I presume are looking for a different pricing model is that are you hearing that yeah absolutely so as part of the cost of of running their business and being able to operate like a cloud everybody is looking at a variety of different procurement and utilization models one of the ways HPE provides utilization model that can map to their cloud journey a public cloud journey is through Greenlake the ability to use and consume data on-demand consume compute on demand across the entire portfolio of products HPE has essentially is what a Greenlake journey looks like and let's go into ease-of-use so what do you mean by that I mean people look they think cloud they think swipe the credit card and start you know deploying machines what do you mean by easy for us ease of use translates back to how do you map to a simpler operating and support model for us the support model is the is the key for customers to be able to to realize the benefits of going to that cloud to get to a simpler support model we use AI ops and for us a offs means using a product called info site info site is a product that is uses deep learning and machine learning algorithms to look at a wide net of call home data from physical resources out there and then be able to take that data and make it actionable and the action behind that is predictiveness the prescriptive nosov creating automated support tickets enclosing automated support tickets without anybody ever having to pick up a phone and call IT support that info site model now is being expanded across the board to all HP products it started with nimble now info site is available on three part it's available on synergy and a recent announcement said it's also available on pro alliance and we expect that info set becomes the glue the automation a I do that goes across the entire portfolio of HP products so this is a great example of applying AI to data so it's like call home taking to a whole new level isn't it yeah it absolutely is and in fact what it does is it uses the call home data that we've had for a long time with products like 3par which essentially was amazing data but not being auctioned on in an automated fashion it takes that data and creates an automation tasks around it and many times that automation task leads to much simpler support experience all right third item you mentioned was resource optimization let's let's drill down into that I infer from that there's there are performance implications is maybe governance compliance you know physical placement can you elaborate that's in color yes I think it's all of the above that he just talked about it's definitely about applying the right performance level to the right set of applications we call this application of air storage the ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed which in turn means we know where it needs to reside one of the things that HP is doing in the storage domain is creating a common storage fabric with the cloud we call that the fabric for the cloud the idea there is that we have a single layer between the on-premises and off premises resources that allows us to move data as needed depending on the application needs and depending on the user needs so this crucial new factors that have to be incorporated through everyone's thinking of cost efficiency ease of use and resource optimization it's going to place new types of stress on the storage hierarchy it's gonna require new technologies to better support digital transformation David Flor an analyst here in wiki bon has been a leading thinker of the relationship between the storage hierarchy and workloads and digital thinking for quite some time I had a great conversation with David not too long ago let's hear what he had to say about this new storage hierarchy and the new technologies they're gonna make possible these changes have you've been looking at this notion of modern storage architectures for 10 years now and you've been relatively prescient in understanding what's going to happen you were one of the first guys to predict well in advance of everybody else that the crossover between flash and HDD was gonna happen sooner rather than later so I'm not gonna spend a lot of time quizzing you what do you see as a modern storage architecture let's just let it rip ok well let's start with one simple observation the days of stand-alone systems for data have gone we're in a software-defined world and you want to be able to run those data architectures anywhere where you the data is and that means in your data center where you've is created or in the cloud or in a public cloud or at the edge you want to be able to be flexible enough to be able to do all of the data services where the best place is and that means everything has to be software German Software Defined is the first proposition of a modern day in a storage so so the second thing is that there are different types of technology you have the very fastest storage which is in the in in the DRAM itself you have env dim which is the next one down from that expensive but a lot cheaper than the dim and then you have different sorts of flash you have the high-performance flash and you have the 3d flash you know as many layers as you can which is much cheaper flash and then at the bottom you have HD DS and an even tape as storage devices so how the key question is how do you manage that sort of environment well let me start because it still sounds like we still have a storage hierarchy absolutely and it still sounds like that hierarchy is defined largely in terms of access speeds yep and price point size points yes those are the two mason and and bandwidth and latency as well with it which are tied into the richer tied into those yes so what you if you're gonna have this everywhere and you need services everywhere what you have to have is an architecture which takes away all of that complexity so that you all you see from an application point of view is data and how it gets there and how it's put away and how it's stored and how it's protected that's under the covers so the first thing is you need a virtualization of that data layer the physical layer the virtualization of that physical yes and secondly you need that physical layer to extend to all the places that may be using this data you you don't want to be constrained to this data set lives here you want to be able to say ok I want to move this piece of programming to the data as quickly as I can that's much much faster than moving the data to the to the processing so I want to be able to know where all the data is for this particular dataset or file or whatever it is where they all are how they connect together what the latency is between everything I want to understand that architecture and I want a virtualized view of that across that whole the nodes that make up my hybrid cloud so let me be clear here so so we are going to use a software-defined infrastructure that allows us to place the physical devices that have the right cost performance characteristics where they need to be based on the physical realities of latency of you know power availability hardening etc on the network and the network but we want to mask that complexity from the application the application developer an application administrator yes and Software Defined helps do that but doesn't completely do it No well you you want services which say exactly so their service is on top of all that apps that are that are recognizable by the developer by the you know the business person by the administrator as they think about how they use data towards those outcomes not use a storage or use a device but use the data to reach application outcomes that's absolutely right then that's what I call the data plane which is a series of services which enable that to happen and and driven by the application required so we've looked at this and some of the services include you know and and compression deduplication the backup restore security data protection so that's kind of that's kind of the services that now the enterprise by or needs to think about so that those services can be applied with you know by policy yes wherever they're required based on the utilization of the data correct where it's kind of where the event takes place and then you still have at the bottom of that you have the different types of devices you still have you still want of hamsters Mickey you still want hard disk they're not disappearing but if you're gonna use hard disks then you want to use it in the right way if you're using a hard disk you know you want to give it large box you to have it going sequentially in and out all the time so the storage administration and the day the physical schema and everything else is still important in all this but it's less important less the centerpiece of the buying decision correct increasingly it's how well does this stuff prove support the services that the business is using to achieve their outcomes and you want to use course the lowest cost that you can and there will be many different options over more more options open but but the automation of that is absolutely key and that automation from a vendor point of view one of the key things they have to do is to be able to learn from the usage by their customers across as broad a number of customers as they can learn what works what doesn't work learn so that they can put automation into their own software their own software services well sounds like we're talking four things we got we got software-defined still have a storage hierarchy defined by cost and performance but with mainly semiconductor stuff we've got great data services that are relevant to the business and automation that masks the complexity from the artificial AI there is also also made many things fantastic so David's thinking on the new storage hierarchy and how it's going to relate to new classes of workload is a baseline for a lot of the changes happening in the industry today but we still have to turn technology into services that deliver higher levels of value once again let's go back to Dave volantes conversation with Sun dip Arora and here what Sun dip has to say about some of the new digital services some of the new data services they're gonna be essential to supporting these new hybrid storage capabilities we have and what it does it it gives us the opportunity now not just you look at column data from storage but then also look at call home data from the compute side and then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure you also set out a vision of this this orchestration yeah lair can you talk more about that are we talking about across all clouds whether it's on pram or at the edge or in the public cloud yeah we are we're talking about making it as simple as possible where the customers are not necessarily picking and choosing it allows them to have a strategy that allows them to go across the data center whether it's a public cloud building their own private infrastructure or running on a traditional on-premises sand structure so this vision for us cloud fabric vision for us allows for customers to do that and what about software-defined storage yeah where does that fit into this whole equation yeah I'm glad you mentioned that because that was a third tenant of what HP truly brings to our customers software-defined is is something that allows us to maximize the utilization of the existing resources that our customers have so what we've done is we've partnered with a great deal of really strong software-defined vendors such as comm world cohesive accumulo de terre I know we work very closely with the likes of veeam Zotoh and and the goal there is to do to provide our customers with a whole range of options to drive building a software-defined infrastructure build off the Apollo series of products Apollo servers or storage products for us are extremely dense storage products that allow for both cost and resource optimization so Sunday I made some fantastic points about how new storage technologies are going to be turned into usable services that digital businesses will require as they conceived of their overall hybrid storage approach here's an opportunity hear a little bit more about what HPE thinks about some of these crucial areas let's hear what they have to say in this Chuck talk short take I'm gonna introduce you to HPE primary storage if you want the agility of the public cloud but need the resiliency and speed of high-end storage for mission-critical applications this force is a trade-off of agility for resiliency high-end storage is fast and reliable but falls short on agility and simplicity what if you could have it all what if you could have both agility and resiliency for your mission-critical apps introducing the world's most intelligent storage for mission-critical apps HP primary it delivers an on-demand experience so storage is instantly available Apple wear resiliency backed with a hundred percent availability guarantee predictive acceleration so apps aren't fast some of the time but fast all the time with embedded AI let me tell you more about HPE primarily was engineered to drive unique value in high-end storage there are four areas we focus on global intelligence powered with the most advanced AI for infrastructure info site an all active architecture with multiple nodes for higher resiliency and limitless parallelization a service centric OS that eliminates the risk and simplifies management and timeless storage with a new ownership experience that keeps getting better to learn more go to hp.com slash storage slash prime era so that's been a great series of conversations about hybrid storage and I want to thank Sun dip Arora of HPE David floor of wiki bonds to look at angle jim kanby lists of wiki bonds to look and angle and my colleague David Volante for helping out on the interview side I'm Peter Burris and this has been another wiki bond the cube digital community event sponsored by HPE now stay tuned for our Crouch at which will be your opportunity to ask your questions share your experiences and push for the community's thinking on hybrid storage once again thank you very much for watching let's crouch at

(upbeat pop music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CubeConversation. >> Hello, everyone welcome to this CUBE Conversation here in Palo Alto, California, I'm John Furrier, co-host of theCUBE. We are here forth Inhi Cho Suh General Manager of IBM Watson, Customer Engagement, Former Cube alumni, I think she's been on dozens of times. Great to see you again. Welcome to our Palo Alto Studios. >> Yeah, great being here, John. >> So, we haven't chatted in awhile. IBM thing just happened, a little bit of a rainy event, here in February. Interesting change over since we last talked, but first give an update on what you're up to these days, what group are you leading, what's new? >> Okay, well first of all, I'm here based in California, which I'm excited about, and I lead our Watson West office, which is our Watson headquarters, here on the west coast, in downtown San Francisco, and we hosted our Think Conference, and at Think I lead with, in IBM, what we call our Watson Customer Engagement Business Unit, which is really the business applications, of how we apply Watson and other disruptive tech to a line of business audiences, both SAS and on premise software, so really excited about the areas of applying AI and machine learning as well as Blockchain to things like supply chain, and logistics, to order management, to next generation of retail. A lot of new, exciting areas. >> Yeah, we've had many conversations over the years from big data to as your career spanned across IBM, and you have a much more horizontal view of things, now. You're horizontally scalable, as we say in the cloud world. What's your observation of the trends these days? Because there's a lot waves. Actually, the waves that you guys announced, was the IBM, Watson NE ware and the cloud private ware. Marvin and I had an amazing conversation that video went viral. This is now getting a big tailwind for IBM. What's your thoughts in general about the overall ecosystem, because you're here in Silicon Valley, you've seen the big waves, you've got another big data world, cloud is here, multi cloud. What's your thoughts on the big mega-trends? >> Yeah, that's a good question. I think the first chapter of cloud, everyone ran to public cloud. When you look at it through the lens of enterprise, though, the hot topic right now in the second chapter is really about not just public cloud, but multi-cloud, hybrid cloud. Meaning, whether it's a private, public, it's about thinking about the applications and the nature of the applications and regardless of where the data sits, what are the implications of actually getting work done? Through, kind of, new container services, new ways of microservices in the development, of how APIs are integrated, and so, the hot topic right now is definitely hybrid cloud, multi cloud. And the work we've done to certify, what we call, IBM cloud private really enables us to not just take any business application to any cloud in our cloud, as well, but actually to enable Watson and Watson based applications also across multi cloud environments. >> So, chapter two, Jenny mentioned that in her key notes, I want to dig into that because we've been talking a lot about multi cloud architecture, and one of the big debates has been, in the industry, oh, don't pick a soul cloud. I've been writing a bunch of content about that at this DOD jedi deal with Amazon and Oracle, fighting for it out there, but that's also happening at the enterprise, but the reality is, everyone has multiple clouds. If you've got a sales force or if you've got this and that and the other thing, you probably have multiple clouds, so it's not so much soul cloud vs. as it is, workloads having a cloud for the right job and that seems to be validated at IBM Think, in talking to the top technical people and in the industry. They all say, pick the right cloud for the job. And we've heard that before in Big Data. Pick the right tool for the job. So, given that, workloads seem to be driving the demand for cloud. Since you're on the app side, how are you seeing that? Because the world's flipped. It used to be infrastructure and software enable the app's capabilities. Now the workloads have infrastructure as code, made with cloud, they're driving the requirements. This is a change over. >> It is a big change and part of, I would say, when people first ran to the cloud, and a lot of the public cloud services were digital SaaS services, where people were wanting to stitch multiple applications across clouds, and that became a challenge, so in this next iteration, that I'm seeing is, really, a couple things. One is, data gravity. So, where does the data actually reside, for the workload that's actually happening? Whether it's the transactions, whether it's customer information, whether it's product information, that's one piece. The second piece is a lot more analytics, right? And the spectrum of analytics running from traditional warehouse capabilities, to more, let's say, larger scale big data projects to full blown advanced algorithms and AI applications, is, people are saying, look, not only do I want to stitch these applications across multiple clouds; I also want to make sure I can actually tap into the data to apply new types of analytics and derive new services and new values out of relationships, understanding of how products are consumed, and so forth. So, for us, when we think about it is, we want to be able to enable that fluid understanding of data across the clouds, as well as protect and be thoughtful about the data privacy rights around it, compliance around GDPR, as well as how we think about the security aspects as well, for the enterprise. >> That is a great point. I think I want to drill down on the data piece, your background on data obviously is going to be key in your job now obviously, it's pretty obvious with Watson, but David Floyd, a wiki bonds research analyst, just posted a taxonomy of hybrid cloud research report that laid out the different kinds of cloud you could have. There's edge clouds, there's all kinds of things from public to edge, so when you look at that, you're thinking, okay, the data plain is the critical nature of the cloud. Now, depending on which cloud architecture for the use case, the workload, whatever, the data plain seems to be this magical opportunity. AI is going to have a big part of that. Can you just talk about how you guys see that evolving? Because, obviously, AI is a killer part of your strategy. This data piece is inter-operating across the clouds. >> Yes. >> Data management governs you're smiling, cause there's a killer answer coming. >> Totally. This is such a great set up. Actually, Ginni even said it in her keynote at Think, which was, you can't have an AI strategy without an information architecture strategy, which is an IA strategy, and information architecture is all about what you said: it's data preparation; understanding the foundation of it, making sure you've got the right governance structure, the integration of it, and then actually how you apply the more advanced analytics on top. So, information architecture and thinking about the data aspects in all kinds of data. Majority of the data actually sits behind, what I would say, the traditional public firewall. So, it sits behind the firewalls of our enterprise clients, like 80 plus percent of it, and then, many of the clients, we actually recently did a study, with about 5,000 senior executives, across many, many thousands of organizations, and 85% of them want to apply AI to improve their customer service, to improve the way they engage their clients and their products and services, so this is a huge opportunity right now for pretty much every organization to think through; kind of their data strategy. Their information architecture strategy, as part of their overall AI strategy. >> So, a question a got on twitter comes up a lot, and, also on my notes here, I wanted to ask you is, how can companies increase transparency trust and mitigate bias in AI? Because this comes up a lot and that's the questions that come in from the community is, Hey, I got my site, my apps running in Germany. I've got users over there, I'm global. I have to manage compliance, I got all this governess now, I'm over my shoulders, kind of a pain in the butt, but also I don't want to have the software be skewed on bias and other things, and then, I also get this whole Facebook dynamic going on, where it's like, I don't trust people holding my data. This is a big, huge issue. >> It is enormous. >> You guys are in the middle of it, what's your thoughts, what's the update, what's the dynamic and what's the solution? >> So, this is a big topic. I think we could do a whole episode just on this topic alone. So, trust and developing trust and transparency in AI should be a fundamental requirement across many, many different types of institutions. So, first of all, the responsibility doesn't sit only with the technology vendors; it's a shared responsibility across government institutions, the consumers, as well as the business leaders, in terms of how they're thinking about it. The more important piece, though, is when you think about the population that's available, that really understands AI, and they're actually coding and developing on it, is that we have to think about the diverse population that's participating in the governance of it, because you don't want just one tribe or one group that's coding and developing the algorithms, or deciding the decision models. >> Like the nerds or the geeks; they're a social aspect, society aspect as well, right? Social science. >> Exactly. I actually just did a recent conversational series with Northwestern Kellogg's business school, around the importance of developing trust and transparency, not only in the algorithms themselves, but the methodology of how you think about culture and value and ethics come into play through different lens, depending on the country you live in, as you kind of referenced, depending on your different values and religious backgrounds. It may because of different institutional and/or policy positions, depending on the nature, and so there has to be a general awareness of this that's thoughtful. Now, why I'm so excited about the work we're doing at IBM is we've actually launched a couple new initiatives. One is, what we call, AI OpenScale, which is really a platform and an opportunity to have the ability to begin to apply AI, see how AI operations and models function in production. We have methodologies in terms of engaging understanding fairness, so there's a 360 degree fairness kit, which is actually available in the open source world, there's a set of tools to understand and train people on recognizing bias, so even just definitions of, what do you mean by bias? It could be things like, group think, it could be, you're just self selecting on certain data sets to reinforce your hypotheses, it could be unconscious levels and it's not just traditionally socially oriented, types of bias. >> It could be data bias, too. It could be data bias, right? >> Totally. Machine generated biases in IOT world, also. >> So, contextual and behavioral biases kind of kick into play here. >> Yeah, but it starts with transparency trust. It also starts with thoughtful governance, it starts with understanding in your position on policy around data privacy, and those things are things that should be educational conversations across the entire industry. >> How far along are we on the progress bar there? I mean, it seems like it's early and we seem to be talking for awhile, but it seems even more early than most people think. Still a lot more work. Your thoughts on where the progress bar is on this whole mash up of tech and social issues around bias and data? Where are we? >> We're really at the early stages, and part of the reason we're at the early stages is I think people have, so far, really applied AI in very simple task oriented applications. The more, what we call, broad AI, meaning multi task work flow applications are starting, and we're also starting seeing in the enterprise. Now, in the enterprise world, you can still have bias, so, for example, when you talked about data bias, one of the simple examples I use is, think about loan approvals. If one of the criteria may be based on gender, you may have a sensitivity around the lack of women owned business leaders, and that could be a scoring algorithm that says, hey, maybe it's a higher risk when in fact, it's not necessarily a higher risk, it's just that the sampling is off, right. So, that would be a detection to say, hey maybe you have sensitivity around that data set, because you actually have an insufficient amount of data. So, part of data detection and understanding biases; where you have sampling of data that's incorrect, where your segmentation could be rethought, where it may just require an additional supervision or like decision making criteria as part of your governance process. >> This is actually a great area for young people to get involved, whether at their universities or curriculum, this kind of seems to be, whether it's political science and/or data science kind of coming together, you kind of have a mash. What's your advice to people watching that might be either in high school, college, or rethinking their career, because this seems to be hot area. >> It is a hot area. I would recommend it for every student at every age, quite frankly and we're at such an early stage that it's not too late to join and you're not too young nor are you too old to actually get in the industry, so that's point one. This is a great time for everyone to get involved. The second piece is, I would just start with online courses that are available, as well as participate in communities and companies like IBM, where we actually make available on a number of our web based applications, that you can actually do some online training and courses to understand the services that we have, to begin to understand the taxonomy and the language, so a very simple set, would be like, learn the language of AI first, and then, as you're learning coding, if you're more technically inclined, there's just a myriad of classes available. >> Final question, before I move on to the topic around inclusion and diversity, machine learning is impacting all verticals. I was just in an interview, talking with Don En-ju-bin-ski, she's got a company where it's neuroscience and machine learning coming together. Machine learning's being impacted all over. We mentioned basic data bias, and machine learning can help there. Machine learning meets blank every vertical, every market, is being impacted machine learning, which will trigger some of the things you're seeing on the app side. Your thoughts, looking at where you've come from in your career at IBM to now, just the evolution of what machine learning has enabled, your thoughts on the impact of machine learning. >> Oh, it's exciting and I'll give you a real simple example, so one of the great things my own team actually did was apply machine learning to, everyone loves the holiday shopping period, right? Between Thanksgiving to New Years, so we actually develop, what we call, Watson Order Optimizer and one of my favorite brands is REI, so the recreational equipment incorporated company, they actually applied our Watson Order Optimizer to optimize in real time. The best place, let's say you want to order a kayak or a T-shirt or a hiking boot, but the best way to create the algorithms to ship from different stores, and shipping from stores, for most retailers, is a high cost variable, because you don't know what the inventory positions are, you don't necessarily know the movement of traffic into that store, you may not even know what the price promotions are, so what was exciting about putting machine learning algorithms to this was, we could actually curate things like shipping and tax information, inventory positions of products in stores, pricing, a movement of goods as part of that calculation. So, this is like a set of business rules that are automatically developed, using Watson, in a way that would be almost impossible for any human to actually come up with all of the possible business roles, right? Because this is such a complex situation, and then you're trying to do it at the peak time, which is, like Black Friday, Cyber Monday Weekend, so we were able to actually apply Watson Machine Learning to create the business roles for when it should be shipped from a warehouse or a particular store. In order to meet the customer requirement, which is the fulfillment of that brand experienced, or the product experienced, so my view is, there are so many different places across the industry, that we could actually apply machine learning to, and my team is really excited about what we've been doing, especially in the next generation of supply chain. >> And it's also causing students to be really attracted to computer science, both men and women. My daughter, who is a senior at Berkeley, is interested in it, so you're starting to see the impact of machine learning is hitting all main stream, which is a good segue to my next question, we've been very passionate, I know it's one of your passions is inclusion and diversity or diversity and inclusion, there's always debates: D before I or I before D? Some say inclusion and diversity or diversity and inclusion. It's all the same thing, there's just a lot of effort going on to bring the tech industry up to par with the reality of the world, and so you have a study out. I've got a copy here. Talk about this study: Women in Leadership and the Priority Paradox. Talk about the study; what was behind it and what were some of the findings? >> Sure, and I'm excited that your daughter, that's a senior in college, is going to be another woman that's entering the workforce, and especially being in tech, so the priority paradox is that we actually looked at over 2,300 organizations, these are some of the top institutions around the world, that are curating and attracting the best talent and skills. Now, when you look at that population, we were surprised to find out that you would think by 2019-2018 that only 18% of those organizations actually had women in senior leadership positions, and what I categorize as senior leading positions, are in the see-swee, as vice presidents, maybe senior executives or senior managers; director level folks. So, that's one piece, which is, wow, given the size and the state where we are in the industry, only 18%: we could do better. Now, why do we believe that? The second piece is, you want the full population of the human capacity to think and creatively solve. Some of the world's biggest complex problems; you don't want a small population of the world trying to do this, so, the second piece of the paradox, which was the most surprising, is that 79% of these companies actually said that formalizing or prioritizing gender, fostering that kind of inclusive culture, was not a business priority, and that they had a harder time actually mapping that gap. Now, in the study, what we actually discovered though, was those companies, that did make it a priority, actually had first mover advantage, and making it a priority is quite simple. It's about understanding how to create that inclusive culture, to allow different perspectives and different experiences to be allowed in the co-creation and development. >> So, first mover advantage, in terms of what? >> Performance, actual business performance, so even though 80% of the organizations that we interviewed actually said that they've not made it a business priority, the 20% that did, we actually saw higher performance in their outcomes, in terms of business performance. >> So, this is actually a business benefit, too. I think your point is, the first mover advantage is saying, those companies that actually brought in the leadership to create that different perspective, had higher performance. >> Absolutely. >> We've talked about this before; one of the things I always say is that, tech is now mainstream, and it's 18% of the target audience of tech isn't the market, it's 50/50 or 51. Some say 51% women/men, so who's building the products for half the audience? So, again, this doesn't make any sense, so this is a good statistic. >> It is, and if you think about the students that are actually graduating out of graduate school, recently, there's actually more women graduating out of grad school than men. When you think about that population that's now entering the workforce, and what's actually happening through the pipeline, I think there's got to be thoughtful focus and programmatic improvements across the industry, around how to develop talent and make sure that different companies and organizations can move. Like you said, problem solve for creating new products that actually serve the world, not just serve certain populations, but also do it in a way that's thoughtful about, kind of, the makeup. >> And the mainstream and prep of tech obviously makes it more attractive, I mean, you're seeing a lot more women thinking about machines, like my daughter, the question is, how do they come in and not lose their footing, mentor-ship? So, what are the priorities that you see the industry needs to do? What are some of the imperatives to keep the pipeline and keep all the mentoring, obviously mentoring is hot, we see the networking built. >> Yeah, mentoring is huge. >> What's your thoughts on the best practices that you've been involved in? >> Some of the best practices we've actually done a number with an IBM, we've done a program called, Tech Re-Entry, so women that have decided to come back into the tech workforce, we actually have a 12 week internship program to do that. Another is a big initiative that we have around P-TECH, which is the next generation of workers aren't just going to have a formal college and or PHD masters type degrees. The next generation, which we're calling, is not necessarily a white collar, blue collar, what we're calling it is, new collar, meaning these are students that are able to combine their equivalent of a high school degree and early college education in one to be kind of, if you think about it, next generation of technical vocational schools, right? That quickly enter the workforce, are able to do jobs in terms of web development, in terms of cloud management, cloud services, it could be next generation of-- >> It's a huge skill gap opportunity, this is a big opportunity for people. >> It is, and we're seeing great adoption. We've seen it on a number of states across the US, this is an effort that we partner with, the states and the governors of each state, because public education has got to be done in a systematic way that you can actually sustain it for many, many years and this is something that we were excited about championing in the state of New York first. >> The ReEntry program and other things, I always tell myself, the technology is so new now you could level up a lot faster than, there's not that linear school kind of mentality, you don't need eight years to learn something. You could literally learn something pretty quickly these days because the gap between you and someone else is so short now, because it's all new skills. >> It's true, it's true. We talk about digital disruption through the lens of businesses, but there's a huge digital disruption through the lens of what you're talking about, which is our individual development and talent, and the ability to learn through so many different channels that's available now, and the focus around micro degrees, micro skills, micro certifications, there's so many ways for everyone to get involved, but I really do encourage everyone across every industry to have some knowledge and basis and understanding of tech, because tech will redefine how services and products are delivered across every category. >> And that's not male or female: that's just everyone. Again, back to technology for good, we can solve technology problems, You guys have been doing it at IBM, solve technology problems, but now the people problem is about getting people empowered, all gender, races, et cetera, the people getting the skills, getting employed, working for clouds, this is an opportunity. >> This is a huge opportunity. I think this is an exciting time. We feel like we're entering this next phase of, what I call, chapter two of cloud, this is chapter two of digital reinvention, of the enterprise, digital reinvention of the individual, actually, and it's an opportunity for every country, every population group to get involved, in so many new and creative ways, and we're at the early foundation stages in terms of both AI development, as well as new capabilities like Blockchain. So, it's an exciting time for everybody. >> Well, that's a whole nother topic. We'll have to bring you back, Inhi. Great to see you, in fact, welcome to Palo Alto. First time in our studio. Let's co-host something together, me and you. We'll do a series: John and Inhi. >> I would love that. That would be fun. I'm excited to be here. >> You can drop by our studio anytime now that you live in Palo Alto, we're neighbors. Inhi Cho Suh here, general manager IBM Watson, customer engagement, friend of theCUBE, here inside our studios, Palo Alto. I'm John Furrier, thanks for watching. (upbeat music)

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.

>>Welcome back everyone. This is our final wrap-up of the Amazon web services. Reinvent conferences is SiliconANGLE and Wiki bonds. The cube is our flagship program. We go out to the events, extract the signal from the noise. I'm John furry or the founders to look an angle. And of course I'm joining my cohost partner in crime. Dave Volante, co-founder with you bond.org. Um, really exciting event, Dave, I got to say, this is our wrap up. Let's put a bow on this show. Let's put the bumper sticker on the car and let's see what, uh, what was this document? What happened day one enterprise day to infrastructure day three ties it all together with Kinesis. Amazon is doing two things. That's very, very rare in tech history, and that is a disrupting and innovating at the same time. The magic it's the magic formula. And to me, it's really two tactical executions, one ball moving the ball yard by yard first and 10, do it again to use the football analogy, moving the chains, moving the ball down the field, kind of a running game, ground game, whatever a call it. >>And then the big yardage passing play with Kinesis, I think really brings their success of an integrated stack. And I believe they're going to be the iPhone like model for the cloud they're they're light years ahead of everybody else on public cloud. Uh, they're winning the developers. And again, we just heard from Dr. Matt would kind of reiterating what we were saying in our previous segment about the diversity of the successes. It's not a one trick pony. They got diversity from startups to large enterprises to NASA. So Dave, I mean, I mean, who is going to take on Amazon, who is going to challenge Amazon? That's the question that we want to know right now. It's not looking good right now. They're got a commanding lead in the cloud space and it'd be really interesting to watch how the Kinesis, the enterprise movement, uh, with VDI announcement and workspaces and all the enhancements in the, in the performance is going to shift the sand in the industry. And you're already seeing Cisco down 12% VMware stocks down. I mean, game-changing, the sands are shifting. What's your >>Well, I think we're seeing history in the making here, John. I mean, I think last year at reinvent com leading up to we reinvent, we realized that this event was going to be big and not just the event. The event is a metaphor for the shift that's occurring in the industry. We're talking about a trillion plus dollar marketplace that Amazon is disrupting and believe it or not, they're tiny, even though there are three or 4 billion, they're tiny, it's a trillion dollar Tam that is absolutely getting flipped on its head. And what do we mean by that? Every premise about the it business is changing. We talk about a lot. Amazon has ch has turned the data center into an API. It's a very powerful concept. I think you're right on. It's the, it's the iPhone of the enterprise. Yes. That's. They're not like hall monitors checking about every application in the app store. >>That's not the point. The point is it's a consistent environment that is controlled by Amazon, very tightly controlled and it works. You know what you're getting, and it's innovating at a, at a breakneck speed. It's antithetical to everything we know about it. So, you know, you've been asking people all week what the bumper sticker is on the show. I can't wait to go back and see some of those, but I mean, this is the trend and the trend is your friend, or it might be your enemy. So when you say who's going to be able to compete with Amazon, I think Martin of eucalyptus set a set of best historically in economics. There's always people that will rent and there's always people that will buy. And the, the old guard is Amazon calls them is not going to take this lying down, but the old guard has to replicate an Amazon's model. How does it do that? It's got to create an open entry into its system. That's equivalent in terms of simplicity and power to the Amazon API. Number one, number two is it's got to be able to demonstrate to the developer community that you can inter-operate across those platforms in a way that you can get critical mass, the same way that you can with Amazon. And that's going to be the, the massive battle that's going to take place in the cloud Wars. >>I mean, I think one of the things that's interesting is that the word lock-in was something that we were talking on day one, especially in the enterprise, that's a word that gets kicked around. And you know, my feeling has always been lock-in is not necessarily a bad thing if it's, if you can, if you can have switching costs that aren't super high locking means, switching costs are so high that you can switch. I can switch from my iPhone to Android anytime I want. But the problem is that iPhone is a better product. It's integrated with the apps and I can buy all the same apps. So that's a very key thing. And I think the switching costs here are a lot higher and I, there are Amazon >>On the record. Amazon is the mother of all lock-ins. I mean, this is a beautiful business model and here's, what's so great about it is the customers. You heard them this week say if you took AWS away from me, I would burst out into tears. So Amazon's, I think brilliant challenge here is to how do they keep innovating? They're doing that, but how do they keep lowering prices? So people don't want to leave. So that that's, that's what I see as the disruptive piece. It, >>Well, being in this business all these years were, you know, a little bit older than some of the young guns were on the cube to me lock-in is moving right? You see, um, in the old days, huge capital outlays for, uh, for equipment, you had maintenance, all this stuff was locking. Now the lock-in shifting to OPEX and agility. So what's happening is Amazon is basically commoditizing the old way of how people would spend and shifting the lock-in to the op X side of the equation. I call it the heroin addiction where, Hey, it's so low cost and the agility is the lock-in. So the functionality of agility guarantees the lock. And I think that's what Amazon's betting the ranch on is that when can go to time to market, to value quicker, that is inherently a lock-in, that's a quote, user experience to use my iPhone example. >>If I'm going to have a good experience making money as an enterprise, that's good. That's good. Lock-in right. So it's all a relative term in that the lock-in has been around. I mean, they call it differentiation, but at the end of the day, I think Amazon's got a good, good play there. But like I said, I don't think Amazon has cracked the it nut yet. I think they're going to have some it penetration. And this is top of the first ending, as we were saying, the enterprise, it nut enterprise, it is not, has not. The nut has not been cracked. What >>Do you need to see to be convinced? Well, >>I just think the stack is going to be the, the same paradigm of having an integrated staff. I just want to see different levels of services because the table stakes for the enterprise are different. There's certain compliance issues and you know, they're checking the boxes right now. This is the ground game I was referring to earlier. Amazon is going to start checking the boxes. Oh, VDI, we got workspaces, I got this. I'm going to check the boxes. Ultimately the list is just too long to win everyone. Right? So I think, you know, so it's going to be an opportunity. I think OpenStack has a great hope. I think VMware and IBM and HP are big players. And I think OpenStack needs to step up its game and have a big player, pop down a billion dollars with like IBM David Linux and saying, look at OpenStack, we're behind it. And rally the troops. And that's all >>Sorry, go back to the lock-in comments because this is critical because to me, the definition of lock-in is it's, it's, it's less economically attractive to leave than it is to stay. And that's what Amazon is doing. They're making it, making it more economically attractive to stay than they are to leave. Here's why that's so important. The more people that they pull, and this is why Carlisle and back said, you know, we can't lose to the bookseller. And you said that because they know the old guard knows that if people go to Amazon, they're not going to leave. Cause it's going to be less attractive for them to leave than it is to stay. So there's a huge battle over that trillion dollar Tam. So the key is John that OpenStack and IBM and VMware and Oracle and all the others have to make it economically attractive to not go into Amazon. And that is the battle. >>One of the things that's very clear, Dave, that's coming out of the show for me. My bumper sticker is dev ops wins. And I think what that mean by that is, is that, and we refer to the cloud being in the top of the first inning, meaning really everything else was spring training. He used the baseball metaphor in the sense that this is all that this is all activation of a paradigm shift. That is so game-changing the dev ops concept of software developers. Writing code that trickles into a fully integrated stack is really amazing, right? This really replaces the pain of provisioning hardware cost of it, cost of the infrastructure. That stuff is that that is the real value of the crowd. So if you take the dev ops concepts, which to me is already a winner and put that into the enterprise market, that's going to be cloud ops. >>So to me, I think the opportunity right now for anyone who wants to with Amazon in my opinion, is to go out there and say, look it, you got to win the software developers, look at what a Mongo DB has done. We had Elliot the co-founder on, they made it good goodness for the developer. Whoever can do that for the enterprise will win. And I don't think that there's a direct one-to-one mapping of what dev ops is. It is in the Amazon world. And what dev ops is in the enterprise. I think that's more cloud ops because the guys that are provisioning EMC drives dealing with IBM and red hat a little bit slower, I would say in terms of deployment, they used to the big slow cycles. Dev ops guys are pushing code a little bit more, you know, nimble startup, clean sheet of paper, you know, Uber, Airbnb, those younger generations, but this is a generational shift and it's happening and it's all on the software. So to me, I think dev ops speaks to, >>I wanna, I wanna, uh, keep this thread going. So, so what's the playbook to, for the old guard to compete, you're saying you gotta, uh, attract developers, but that's not enough. You need a cloud platform, right? So take, for example, VMware, VMware announces, you know, hybrid cloud infrastructure as a service it's early days, they need a cloud platform. So what else do you need to compete? You need developers. You need, >>You gotta have, you gotta have trust and security, right? So here's the thing. Developers care about success of creativity for the solutions. And what Amazon's demonstrate is the time to value is the key thing. You hear people, whether they're startups or big company get to some value, double down on success, figure out how to be agile succeed. Fast, move on with the problem right now is that developers are like deer in the headlights. They go where the action is, right? And it's always been that way. I think OpenStack to me is an opportunity or whatever platform that is. Someone's got to get a big anchor tenant in that platform needs to step up and be the galvanizing force and create some solidarity around that approach for it. That is an opportunity for VMware. I think Pat Gelsinger is probably best positioned to do that. Pivotal is a, is a genius, but I think ultimately they might be biting off more than they can chew. So I worry about, you know, their car not being fast enough right now in, in the game. So, you know, worry about pivotal there. But I think VMware probably is a better position there. So they need, they need, they need infrastructure. They need this middleware, which is database queuing notifications. A lot of that, a lot of the stuff you see Amazon doing at the top of the stack managed services. So that's streaming data and all the goodness on them, >>Developers, you got to have a cloud platform at scale, you gotta have trust and security. I would add to that. You got to do things that Amazon's not going to do. So for instance, we heard all week, Amazon doesn't want to do one-offs. They don't like to do customization, whatever they do. They want everybody to benefit from that enterprise enterprises want customization. We've talked about this, John. That's why, for instance, you, you find that some of the customers won't go into Amazon, not because the security is bad, it's just different. And Amazon's not going to change the security profile. They're not going to change the policy. So enterprise, uh, players, the old guard, so to speak must continue to do custom stuff. One-off that Amazon won't do, but here's the bet that Amazon's making Amazon's that its ecosystem will over time be able to do those one-offs for the customer and put a buffer in between the Amazon platform and the customer. So that's, that's really interesting. >>Yeah. I would also add to that, that the main differentiation where Amazon and other potential people to compete with Amazon is scale, scale matters. Scale gives leverage. Amazon has proven that, and they're trying to use that leverage now to catapult into other markets for market expansion. So that's one thing. So, so, so the, so for the enterprise in particular, one area we watch heavily, I see two major trends. I see a cloud service that's similar to Amazon. It smells like an integrated stack, but it just has different feature sets tailored for the enterprise. That's more of that's the hybrid cloud clearly hybrid cloud is a winner. Amazon is not using that term hybrid cloud. And he's a hybrid ID, which is basically a head fake. It really means hybrid cloud. So that's hybrid cloud. The second thing is I think you're going to see data centers be Amazon in a box. >>So that's why I like io.com because io.com has essentially built pods and containers and essentially is cloud in a box. And I think shipping data centers is the future. And I think what I like about IO and here's why I'm interested in double clicking on that company is that they're basically shipping data centers. You've got Goldman Sachs, big companies. So IO IO has got, got that going on. And then you've got hybrid cloud. And then the third thing that's really relevant is that you started to see the vertical integration Dave of, of services. Look at CSC, CSC bought service mesh. We had, uh, this guy Jeff on earlier with, uh, that company is doing all the user experience they're offering full end-to-end full-stack developers for essentially web apps. Okay. That is a shift to what I call the dev ops world. Those two things. You're going to see these industries where it's ISV and integrators are kind of vertically integrated. They're going to actually build their own stuff. And that's going to be the, I think the innovation on the channel side. So the channel is up for grabs. Everything's being disrupted >>Battlefield. We've got developers, we've got cloud scale, we've got trust and security. We've got customization. And I'm going to add another one, which is the ecosystem, which is essentially your, you know, in part in your channel, but got to have a strong ecosystem, want to pick up this discussion with you and getting the hook. >>So the Dave wants to of what's the bumper sticker for the show. Give me the Dave Volante bumper sticker. You. We heard everyone said a story here. Um, >>What AWS, the, the trend is your friend, >>My bumper sticker. I'm going to throw a hashtag in there. The hashtag next generation computer revolution to me, this is the next generation computer revolution, total transformative hashtag next generation computer revolution. I think Amazon's leading the charge and I think they're going to shift the sands and everyone else is going to have to adjust. And that's good for everyone, Dave and the market wins a ruin murky on Hortonworks tweeted. Hey, we'd love it. Market expansion, rising tide floats all boats. And I think that's all >>Ultimately ultimately billion dollar Tam Gianna. I'm thrilled to a >>Part of covering that with the cube. I want to thank everyone for watching. Thanks. This is the day three wrap up this acute exclusive coverage from Amazon web services. Want to thank the crew here? All the guys back at the ranch. Kristen, Nicole art Lindsay, Mark Hopkins. Andrew, we got mic. We got Alex. Good job, Jeff Fricks do, uh, everyone. Jeff Kelly. We have the analysts. Come on. We've got this show covered, Dave. I think we fished this pond out. So look for us next to HP. Discover will be there. And, uh, December the week of the 10th or 11th and 12th, we'll be doing the OpenStack summit as well. Look for that. When that gets announced, um, my maybe doing the node node summit in December, we got also the spark summit and MIT event in January. The security event would be at Berkeley. We're going to all these great events tubes out of control. We've got storage, big data now cloud, we look for a lot of research. You can see a lot of cloud coverage coming out on the research. So I looked for that over the next few months, I will get bon.org. Thank you for watching. Well, that's a wrap day three exclusive coverage. This is the cube. I'm John fryer with Dave Volante here in Las Vegas until next time take care.

>>Hi everybody. We're back. This is Dave Volante from Wiki bond.org. We're live at strata in Santa Clara, California. This is Silicon angle TVs, continuous coverage of the strata conference. So Riley media or Raleigh media is a great partner of ours. And thanks to them for allowing us to be here. We've been going all week cause it's day three for us. I'm here with Jeff Kelly Wiki bonds that lead big data analysts. And we're here with Jack Norris. Who's the VP of marketing at Matt bar Jack. Welcome to the cube. Thank you, Dave. Thanks very much for coming on. And you know, we've been going all week. You guys are a great sponsor of ours. Thank you for the support. We really appreciate it. How's the show going for you? >>Great. A lot of attention, a lot of focus, a lot of discussion about Hadoop and big data. >>Yeah. So you guys getting a lot of traffic. I mean, it says I hear this 2,500 people here up from 1400 last year. So that's >>Yeah, we've had like five, six people deep in the, in the booth. So I think there's a lot of, a lot of interests. There's interesting. >>You know, when we were here last year, when you looked at the, the infrastructure and the competitive landscape, there wasn't a lot going on and just a very short time, that's completely changed. And you guys have had your hand in that. So, so that's good. Competition is a good thing, right? And, and obviously customers want choice, but so we want to talk about that a little bit. We want to talk about map bar, the kind of problems you're solving. So why don't we start there? What is map are all about? And you've got your own distribution of, of, of enterprise Hadoop. You make it Hadoop enterprise ready? Let's start there. >>Okay. Yeah, I mean, we invested heavily in creating a alternative distribution one that took the best of the open source community with the best of the map, our innovations, and really it's, it's about making Hadoop more applicable, broader use cases, more mission, critical support, you know, being able to sit in and work in a lights out data center environment. >>Okay. So what was the problem that you set out to solve? Why, why do, why do we need another distribution of Hadoop? Let me ask it that way. Get nice and close to. >>So there, there are some just big issues with, with the duke. >>One of those issues, let's talk about that. There's >>Some ease of use issues. There's some deep dependability issues. There's some, some performance. So, you know, let's take those in order right now. If you look at some of the distributions, Apache Hadoop, great technology, but it requires a programmer, right? To get access to the data it's through the Hadoop API, you can't really see the data. So there's a lot of focus of, you know, what do I do once the data's in there opening that up, providing a full file based access, right? So I can look at it and treat it like enterprise storage, see the data, use my standard tools, standard commands, you know, drag and drop from a file browser. You can do that with Matt bar. You can't do that with other districts >>Talking about mountain HDFS as a NFS correct >>Example. Correct. And then, and then just the underlying storage services. The fact that it's append only instead of full random read-write, you know, causes some, some issues. So, you know, that's some of the, the ease of use features. There's a whole lot. We could discuss there. Big picture for reliability. Dependability is there's a single point of failure, multiple single points of failure within Hadoop. So you risk data loss. So people have looked at Hadoop. Traditionally is, is batch oriented. Scratchpad right. We were out to solve that, right? We want to make sure that you can use it for mission critical data, that you don't have a risk of a data loss that you've got full high availability. You've got the full data protection in terms of snapshots and mirroring that you would expect with the enterprise products. >>It gets back to when you guys were, you know, thinking about doing this. I'm not even sure you were at the company at the time, but you, your DNA was there and you're familiar with it. So you guys saw this big data movement. You saw this at duke moon and you said, okay, this is cool. It's going to be big. And it's gonna take a long time for the community to fix all these problems. We can fix them. Now let's go do that. Is that the general discussion? Yeah. >>You know, I think, I think the what's different about this. This is the first open source package. The first open source project that's created a market. If you look at the other open source, you know, Linux, my SQL, et cetera, it was really late in the life cycle of a product. Everyone knew what the features were. It was about, you know, giving an alternative choice, better Unix. Your, your, the focus is on innovation and our founders, you know, have deep enterprise background or CTO was at Google and charge of big table, understands MapReduce at scale, spent time as chief software architect at Spinnaker, which was kind of the fastest clustered Nazanin on the planet. So recognize that the underlying layers of Hadoop needed some rearchitecture and needed some deep investment and to do that effectively and do that quickly required a whole lot of focus. And we thought that was the best way to go to market. >>Talk about the early validation from customers. Obviously you guys didn't just do this in a vacuum, I presume. So you went out and talked to some customers. Yeah. >>What sorts of conversations with customers, why we're in stealth mode? We're probably the loudest stealth >>As you were nodding. And I mean, what were they telling you at the time? Yeah, please go do this. >>The, what we address weren't secrets. I there've been gyrus for open for four or five years on, on these issues. >>Yeah. But at the same time, Jack, you've got this, you got this purist community out there that says, I don't want to, I don't want to rip out HDFS. You know, I want it to be pure. What'd you, what'd you say to those guys, you just say, okay, thank you. We, we understand you're not a prospect. >>And I think, I think that, you know, duke has a huge amount of momentum. And I think a lot of that momentum is that there isn't any risks to adopting Hadoop, right? It's not like the fractured no SQL market where there's 122 different entrance, which one's going to win. Hadoop's got the ecosystem. So when you say pure, it's about the API APIs, it's about making sure that if I create a MapReduce job, it's going to run an Apache. It's going to run a map bar. It's going to run on the other distributions. That's where I think that the heat and the focus is now to do that. You also have to have innovation occurring up and down the stack that that provides choice and alternatives for. >>So when I'm talking about purists, I don't, I agree with you the whole lock-in thing, which is the elephant in the room here. People will worry about lock-in >>Pun intended. >>No, no, but good one good catch. But so, but you're basically saying, Hey, where we're no more locked in than cloud era. Right. I mean, they've got their own >>Actually. I think we're less because it's so easy to get data in and out with our NFS. That there's probably less so, >>So, and I'm gonna come back to that. But so for instance, many, when I, when I say peers, I mean some users in ISV, some guys we've had on here, we had an Abby Mehta from Triceda on the other day, for instance, he's one who said, I just don't have time to mess with that stuff and figure out all that API integration. I mean, there are people out there that just don't want to go that route. Okay. But, but you're saying I'm, I'm inferring this plenty who do right. >>And the, and by the API route, I want to make sure I understand what you're saying. You >>Talked about, Hey, it's all about the API integration. It's not >>About, it's not the, it it's about the API APIs being consistent, a hundred percent compatible. Right. So if I, you know, write a program, that's, that's going after HDFS and the HDFS API, I want to make sure that that'll run on other distributions. Right. >>And that's your promise. Yeah. Okay. All right. So now where I was going with this was th again, there are some peers to say, oh, I just don't want to mess with all that. Now let's talk about what that means to mess with all that. So comScore was a big, high profile case study for you guys. They, they were cloud era customer. They basically, in my understanding is a couple of days migrated from Cloudera to Mapbox. And the impetus was, let's talk about that. Why'd they do that >>Performance data protection, ease of use >>License fee issues. There was some license issues there as well, right? The, the, your, your maintenance pricing was more attractive. Is that true? Or >>I read more mainly about price performance and reliability, and, you know, they tested our stuff at work real well in a test environment, they put it in production environment. Didn't actually tell all their users, they had one guys debug the software for half a day because something was wrong. It finished so quickly. >>So, so it took him a couple of days to migrate and then boom, >>Boom. And they've, they handle about 30 billion objects a day. So there, you know, the use of that really high performance support for, for streaming data flows, you know, they're talking about, they're doing forecasts and insights into web behavior, and, you know, they w the earlier they can do that, the better off they are. So >>Greg, >>So talk about the implications of, of your approach in terms of the customer base. So I'm, I'm imagining that your customers are more, perhaps advanced than a lot of your typical Hadoop users who are just getting started tinkering with Hadoop. Is it fair to say, you know, your customers know what they want and they want performance and they want it now. And they're a little more advanced than perhaps some of the typical early adopters. >>We've got people to go to our website and download the free version. And some of them are just starting off and getting used to Hadoop, but we did specifically target those very experienced Hadoop users that, you know, we're kind of, you know, stubbing their toes on, on the issues. And so they're very receptive to the message of we've made it faster. We've made it more reliable, you know, we've, we've added a lot of ease of use to the, to the Hindu. >>So I found this, let me interrupt, go back to what I was saying before is I found this comment that I found online from Mike Brown comScore. Skipio I presume you mean, he said comScore's map our direct access NFS feature, which exposes a duke distributed file system data as NFS files can then be easily mounted, modified, or overwritten. So that's a data access simplification. You also said we could capitalize on the purchase of map bar with an annual maintenance charge versus a yearly cost per node. NFS allowed our enterprise systems to easily access the data in the cluster. So does that make sense to you that, that enterprise of that annual maintenance charge versus yearly cost per node? I didn't get that. >>Oh, I think he's talking about some, some organizations prefer to do a perpetual license versus a subscription model that's >>Oh, okay. So the traditional way of licensing software >>And that, that you have to do it basically reinforces the fact that we've really invested in have kind of a, a product, you know, orientation rather than just services on top of, of some opensource. >>Okay. So you go in, you license it and then yeah. Perpetual license. >>Then you can also start with the free edition that does all the performance NFS support kick the tires >>Before you buy it. Sorry. Sorry, Jeff. Sorry to interrupt. No, no problem >>At all. So another topic, a lot of interest is security making a dupe enterprise ready. One of the pillars, there is security, making sure access controls, for instance, making sure let's talk about how you guys approach that and maybe how you differentiate from some of the other vendors out there, or the other >>Full Kerberos support. We Lincoln to enterprise standards for access eldap, et cetera. We leveraged the Linux, Pam security, and we also provide volume control. So, you know, right now in Hindu in Apache to dupe other distributions, you put policies at the file level or the entire cluster. And we see many organizations having separate physical clusters because of that limitation, right? And we'd provide volume. So you can define a volume. And in that volume control, access control, administrative privileges data protection class, and, you know, in a sense kind of segregate that content. And that provides a lot of, a lot of control and a lot more, you know, security and protection and separation of data. >>That scenario, the comScore scenario, common where somebody's moving off an existing distribution onto a map are, or, or you more going, going, seeing demand from new customers that are saying, Hey, what's this big data thing I really want to get into it. How's it shake out there >>Right now? There's this huge pent up demand for these features. And we're seeing a lot of people that have run on other distributions switched to map our >>A little bit of everything. How about, can you talk a little bit about your, your channel? You go to market strategy, maybe even some of your ecosystem and partnerships in the little time. >>Sure. So EMC is a big partner of the EMC Greenplum Mr. Edition is basically a map R you can start with any of our additions and upgrade to that. Greenplum with just a licensed key that gives us worldwide service and support. It's been a great partnership. >>We hear a lot of proof of concepts out there >>For, yeah. And then it just hit the news news today about EMC's distribution, Mr. Distribution being available with UCS Cisco's ECS gear. So now that's further expanded the, the footprint that we have about. >>Okay. So you're the EMC relationship. Anything else that you can share with us? >>We have other announcements coming out and >>Then you want to pre-announce in the queue. >>Oops. Did I let that slip >>It's alive? So be careful. And so, in terms of your, your channel strategy, you guys mostly selling direct indirect combination, >>It's it? It, it's kind of an indirect model through these, these large partners with a direct assist. >>Yeah. Okay. So you guys come in and help evangelize. Yep. Excellent. All right. Do you have anything else before we gotta got a roll here? >>Yeah, I did wonder if you could talk a little bit about, you mentioned EMC Greenplum so there's a lot of talk about the data warehouse market, the MPB data warehouses, versus a Hadoop based on that relationship. I'm assuming that Matt BARR thinks well, they're certainly complimentary. Can you just touch on that? And, you know, as opposed to some who think, well, Hadoop is going to be the platform where we go, >>Well, th th there's just, I mean, if you look at the typical organization, they're just really trying to get their, excuse me, their arms around a lot of this machine generated content, this, you know, unstructured data that just growing like wildfire. So there's a lot of Paducah specific use cases that are being rolled out. They're also kind of data lakes, data, oceans, whatever you want to call it, large pools where that information is then being extracted and loaded into data warehouses for further analysis. And I think the big pivot there is if it's well understood what the issue is, you define the schema, then there's a whole host of, of data warehouse applications out there that can be deployed. But there's many things where you don't really understand that yet having to dupe where you don't need to find a schema a is a, is a big value, >>Jack, I'm sorry. We have to go run a couple of minutes behind. Thank you very much for coming on the cube. Great story. Good luck with everything. And sounds like things are really going well and market's heating up and you're in the right place at the right time. So thank you again. Thank you to Jeff. And we'll be right back everybody to the strata conference live in Santa Clara, California, right after this word from our.