>> Narrator: Live from Berlin, Germany, it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. (techno music) Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my cohost Peter Burris. We have two guests for this segment. We have Matt Watts, he is the director and data strategist and director of technology at NetApp, and Kenneth Cukier, a senior editor at The Economist, and author of the best-selling book Big Data, and author of a soon to be best-selling book on AI. Welcome. Thank you. Thank you much for coming on the show. Pleasure to be here. So, this is the, we keep hearing NetApp saying this is the day of the data visionary. I'd love to hear both of you talk about what a data visionary is, and why companies, why this is a necessary role in today's companies. Okay, so I think if you look at the generations that we've been through in the late nineties, early 2000's, it was all about infrastructure with a little bit of application and some data associated to it. And then as we kind of rolled forward to the next decade the infrastructure discussion became less. It became more about the applications and increasingly more about the data. And if we look at the current decade that we're in right now, the infrastructure discussions have become less, and less, and less. We're still talking about applications, but the focus is on data. And what we haven't seen so much of during that time is the roles changing. We still have a lot of infrastructure people doing infrastructure roles, a lot of application people doing application roles. But the real value in this explosion of data that we're seeing is in the data. And it's time now that companies really look to put data visionaries, people like that in place to understand how do we exploit it, how do we use it, what should we gather, what could we do with the information that we do gather. And so I think the timing is just right now for people to be really considering that. Yeah, I would build on what Matt just said. That, functionally in the business and the enterprise we have the user of data, and we have the professional who collected the data. And sometimes we had a statistician who would analyze it. But pass it along to the user who is an executive, who is an MBA, who is the person who thinks with data and is going to present it to the board or to make a decision based on it. But that person isn't a specialist on data. That person probably doesn't, maybe doesn't even know math. And the person is thinking about the broader issues related to the company. The strategic imperatives. Maybe he speaks some languages, maybe he's a very good salesperson. There's no one in the middle, at least up until now, who can actually play that role of taking the data from the level of the bits and the bytes and in the weeds and the level of the infrastructure, and teasing out the value, and then translating it into the business strategy that can actually move the company along. Now, sometimes those people are going to actually move up the hierarchy themselves and become the executive. But they need not. Right now, there's so much data that's untapped you can still have this function of a person who bridges the world of being in the weeds with the infrastructure and with the data itself, and the larger broader executives suite that need to actually use that data. We've never had that function before, but we need to have it now. So, let me test you guys. Test something in you guys. So what I like to say is, we're at the middle of a significant break in the history of computing. The first 50 years or so it was known process, unknown technology. And so we threw all our time and attention at understanding the technology. >> Matt: Yeah. We knew accounting, we knew HR, we even knew supply-chain, because case law allowed us to decide where a title was when. [Matt] Yep. But today, we're unknown process, known technology. It's going to look like the cloud. Now, the details are always got to be worked out, but increasingly we are, we don't know the process. And so we're on a road map of discovery that is provided by data. Do you guys agree with that? So I would agree, but I'd make a nuance which is I think that's a very nice way of conceptualizing, and I don't disagree. But I would actually say that at the frontier the technology is still unknown as well. The algorithms are changing, the use cases, which you're pointing out, the processes are still, are now unknown, and I think that's a really important way to think about it, because suddenly a lot of possibility opens up when you admit that the processes are unknown because it's not going to look like the way it looked in the past. But I think for most people the technology's unknown because the frontier is changing so quickly. What we're doing with image recognition and voice recognition today is so different than it was just three years ago. Deep learning and reinforcement learning. Well it's going to require armies of people to understand that. Well, tell me about it. This is the full-- Is it? For the most, yes it's a full employment act for data scientists today, and I don't see that changing for a generation. So, everyone says oh what are we going to teach our kids? Well teach them math, teach them stats, teach them some coding. There's going to be a huge need. All you have to do is look at the society. Look at the world and think about what share of it is actually done well, optimized for outcomes that we all agree with. I would say it's probably between, it's in single percents. Probably between 1% and 5% of the world is optimized. One small example: medical science. We collect a lot of data in medicine. Do we use it? No. It's the biggest scandal going on in the world. If patients and citizens really understood the degree to which medical science is still trial and error based on the gumption of the human mind of a doctor and a nurse rather than the data that they actually already collect but don't reuse. There would be Congressional hearings everyday. People, there would be revolutions in the street because, here it is the duty of care of medical practitioners is simply not being upheld. Yeah, I'd take exception to that. Just, not to spend too much time on this, but at the end of the day, the fundamental role of the doctor is to reduce the uncertainty and the fear and the consequences of the patient. >> Kenneth: By any means necessary and they are not doing that. Hold on. You're absolutely right that the process of diagnosing and the process of treatment from a technical standpoint would be better. But there's still the human aspect of actually taking care of somebody. Yeah, I think that's true, and think there is something of the hand of the healer, but I think we're practicing a form of medicine that looks closer to black magic than it does today to science. Bring me the data scientist. >> Peter: Alright. And I think an interesting kind of parallel to that is when you jump on a plane, how often do you think the pilot actually lands that plane? He doesn't. No. Thank you. So, you still need somebody there. Yeah. But still need somebody as the oversight, as that kind of to make a judgment on. So I'm going to unify your story, my father was a cardiologist who was also a flight surgeon in the Air Force in the U.S., and was one of the few people that was empowered by the airline pilots association to determine whether or not someone was fit to fly. >> Matt: Right. And so my dad used to say that he is more worried about the health of a bus driver than he is of an airline pilot. That's great. So, in other words we've been gah-zumped by someone who's father was both a doctor and a pilot. You can't do better than that. So it turns out that we do want Sully on the Hudson, when things go awry. But in most cases I think we need this blend of the data on one side and the human on the other. The idea that the data just because we're going to go in the world of artificial intelligence machine learning is going to mean jobs will be eradicated left and right. I think that's a simplification. I think that the nuance that's much more real is that we're going to live in a hybrid world in which we're going to have human beings using data in much more impressive ways than they've ever done it before. So, talk about that. I mean I think you have made this compelling case that we have this huge need for data and this explosion of data plus the human judgment that is needed to either diagnose an illness or whether or not someone is fit to fly a plane. So then where are we going in terms of this data visionary and in terms of say more of a need for AI? Yeah. Well if you take a look at medicine, what we would have is, the diagnosis would probably be done say for a pathology exam by the algorithm. But then, the health care coach, the doctor will intervene and will have to both interpret this for, first of what it means, translate it to the patient, and then discuss with the patient the trade-offs in terms of their lifestyle choices. For some people, surgery is the right answer. For others, you might not want to do that. And, it's always different with all of the patients in terms of their age, in terms of whether they have children or not, whether they want the potential of complications. It's never so obvious. Just as we do that, or we will do that in medicine, we're going to do that in business as well. Because we're going to take data that we never had about decisions should we go into this market or that market. Should we take a risk and gamble with this product a little bit further, even though we're not having a lot of sales because the profit margins are so good on it. There's no algorithm that can tell you that. And in fact you really want the intellectual ambition and the thirst for risk taking of the human being that defies the data with an instinct that I think it's the right thing to do. And even if we're going to have failures with that, and we will, we'll have out-performance. And that's what we want as well. Because society advances by individual passions, not by whatever the spreadsheet says. Okay. Well there is this issue of agency right? So at the end of the day a human being can get fired, a machine cannot. A machine, in the U.S. anyway, software is covered under the legal strictures of copywriting. Which means it's a speech act. So, what do you do in circumstances where you need to point a finger at something for making a stupid mistake. You keep coming back to the human being. So there is going to be an interesting interplay over the next few years of how this is going to play out. So how is this working, or what's the impact on NetApp as you work with your customers on this stuff? So I think you've got the AI, ML, that's kind of one kind of discussion. And that can lead you into all sorts of rat holes or other discussions around well how do we make decisions, how do we trust it to make decisions, there's a whole aspect that you have to discuss around that. I think if you just bring it back to businesses in general, all the businesses that we look at are looking at new ways of creating new opportunities, new business models, and they're all collecting data. I mean we know the story about General Electric. Used to sell jet engines and now it's much more about what can we do with the data that we collect from the jet engines. So that's finding a new business model. And then you vote with a human role in that as well, is well is there a business model there? We can gather all of this information. We can collect it, we can refine it, we can sort it, but is there actually a new business model there? And I think it's those kind of things that are inspiring us as a company to say well we could uncover something incredible here. If we could unlock that data, we could make sure it's where it needs to be when it needs to be there. You have the resources to bring to bed to be able to extract value from it, you might find a new business model. And I think that's the aspect that I think is of real interest to us going forward, and kind of inspires a lot of what we're doing. Great. Kenneth, Matt, thank you so much for coming on the show. It was a really fun conversation. Thank you. Thank you for having us. We will have more from NetApp Insight just after this. (techno music)

(bright music) >> In 2009, Hal Varian, Google's Chief Economist said that statisticians would be the sexiest job in the coming decade. The modern big data movement really took off later in the following year, after the second Hadoop World, which was hosted by Cloudera, in New York city. Jeff Hama Bachar, famously declared to me and John Furrie, in "theCUBE," that the best minds of his generation were trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now what actually transpired over the next 10 years was only a small handful of companies could really master the complexities of big data and attract the data science talent, really necessary to realize massive returns. As well, back then, cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade, and as the years passed, more and more data got moved to the cloud, and the number of data sources absolutely exploded, experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace, things like data lakes, data hubs, new open source projects, new tools, which piled on even more complexity. And as we reported, we believe what's needed is a complete bit flip and how we approach data architectures. Our next guest is Zhamak Dehgani, who is the Director of Emerging Technologies at ThoughtWorks. Zhamak is a software engineer, architect, thought leader and advisor, to some of the world's most prominent enterprises. She's in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures, favoring a decentralized over monolithic structure, and elevating domain knowledge as a primary criterion, and how we organize so-called big data teams and platforms. Zhamak, welcome to the cube, it's a pleasure to have you on the program. >> Hi David, it's wonderful to be here. >> Okay. So you're pretty outspoken about the need for a paradigm shift, in how we manage our data, and our platforms at scale. Why do you feel we need such a radical change? What's your thoughts there? >> Well, I think if you just look back over the last decades, you gave us a summary of what happened since 2010. But even if we got it before then, what we have done over the last few decades is basically repeating, and as you mentioned, incrementally improving how we manage data, based on certain assumptions around, as you mentioned, centralization. Data has to be in one place so we can get value from it. But if you look at the parallel movement of our industry in general, since the birth of internet, we are actually moving towards decentralization. If we think today, like if in this move data side, if we said, the only way web would work, the only way we get access to various applications on the web or pages is to centralize it, we would laugh at that idea, but for some reason, we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data, that they're not just within one part of organization. They're not just within even bounds of organizations. They're beyond the bounds of organization, and then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in globally in place, then how the architecture and technology and organizational structure incentives need to move, to embrace that complexity. And to me, that requires a paradigm shift. A full stack from how we organize our organizations, how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> Okay, so let's unpack that a little bit. I mean, you've spoken about and written today's big architecture, and you've basically just mentioned that it's flawed. So I want to bring up, I love your diagrams, you have a simple diagram, guys if you could bring up figure one. So on the left here, we're adjusting data from the operational systems, and other enterprise data sets. And of course, external data, we cleanse it, you've got to do the quality thing, and then serve them up to the business. So what's wrong with that picture that we just described, and give granted it's a simplified form. >> Yeah. Quite a few things. So, and I would flip the question maybe back to you or the audience. If we said that there are so many sources of the data and actually data comes from systems and from teams that are very diverse in terms of domains, right? Domain. If you just think about, I don't know, retail, the E-Commerce versus auto management, versus customer. These are very diverse domains. The data comes from many different diverse domains, and then we expect to put them under the control of a centralized team, a centralized system. And I know that centralization probably, if you zoom out is centralized, if you zoom in it's compartmentalized based on functions, and we can talk about that. And we assume that the centralized model, will be getting that data, making sense of it, cleansing and transforming it, then to satisfy a need of very diverse set of consumers without really understanding the domains because the teams responsible for it are not close to the source of the data. So there is a bit of a cognitive gap and domain understanding gap, without really understanding how the data is going to be used. I've talked to numerous, when we came to this, I came up with the idea. I talked to a lot of data teams globally, just to see, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations, that they actually didn't know, after they built these pipelines and put the data in, whether the data warehouse tables or linked, they didn't know how the data was being used. But yet they're responsible for making the data available for this diverse set of use cases. So essentially system and monolithic system, often is a bottleneck. So what you find is that a lot of the teams are struggling with satisfying the needs of the consumers, are struggling with really understanding the data, the domain knowledge is lost, there is a loss of understanding and kind of it in that transformation, often we end up training machine learning models on data, that is not really representative of the reality of the business, and then we put them to production and they don't work because the semantic and the syntax of the data gets lost within that translation. So, and we are struggling with finding people to manage a centralized system because still the technology's fairly, in my opinion, fairly low level and exposes the users of those technology sets and let's say they warehouse a lot of complexity. So in summary, I think it's a bottleneck, it's not going to satisfy the pace of change or pace of innovation, and the availability of sources. It's disconnected and fragmented, even though there's centralized, it's disconnected and fragmented from where the data comes from and where the data gets used, and is managed by a team of hyper specialized people, they're struggling to understand the actual value of the data, the actual format of the data. So it's not going to get us where our aspirations, our ambitions need to be. >> Yeah, so the big data platform is essentially, I think you call it context agnostic. And so as data becomes more important in our lives, you've got all these new data sources injected into the system, experimentation as we said, the cloud becomes much, much easier. So one of the blockers that you've cited and you just mentioned it, is you've got these hyper specialized roles, the data engineer, the quality engineer, data scientist. And it's a losery. I mean, it's like an illusion. These guys, they seemingly they're independent, and can scale independently, but I think you've made the point that in fact, they can't. That a change in a data source has an effect across the entire data life cycle, entire data pipeline. So maybe you could add some some color to why that's problematic for some of the organizations that you work with, and maybe give some examples. >> Yeah, absolutely. So in fact initially, the hypothesis around data mesh came from a series of requests that we received from our both large scale and progressive clients, and progressive in terms of their investment in data architecture. So these were clients that were larger scale, they had diverse and rich set of domain, some of them were big technology, tech companies, some of them were big retail companies, big healthcare companies. So they had that diversity of the data and a number of the sources of the domains. They had invested for quite a few years in generations, of they had multi-generations of PROPRICER data warehouses on prem that were moving to cloud. They had moved through the various revisions of the Hadoop clusters, and they were moving to that to cloud, and then the challenges that they were facing were simply... If I want to just simplify it in one phrase, they we're not getting value from the data that they were collecting. They were continuously struggling to shift the culture because there was so much friction between all of these three phases of both consumption of the data, then transformation and making it available. Consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So it's bottom line is that you're collecting all this data, there is delay, there is lack of trust in the data itself, because the data is not representative of the reality, it's gone through the transformation, but people that didn't understand really what the data was got delayed. And so there's no trust, it's hard to get to the data. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure, but it's still struggling. So we often, our solutions, like we are... Technologies, we will often point out to technology. So we go. Okay, this version of some proprietary data warehouse we're using is not the right thing. We should go to the cloud and that certainly will solve our problem, right? Or warehouse wasn't a good one, let's make a data Lake version. So instead of extracting and then transforming and loading into the database, and that transformation is that heavy process because you fundamentally made an assumption using warehouses that if I transform this data into this multidimensional perfectly designed schema, that then everybody can draw on whatever query they want, that's going to solve everybody's problem. But in reality, it doesn't because you are delayed and there is no universal model that serves everybody's need, everybody needs are diverse. Data scientists necessarily don't like the perfectly modeled data, they're for both signals and the noise. So then we've just gone from ATLs to let's say now to Lake, which is... Okay, let's move the transformation to the last mile. Let's just get load the data into the object stores and sort of semi-structured files and get the data scientists use it, but they still struggling because of the problems that we mentioned. So then what is the solution? What is the solution? Well, next generation data platform. Let's put it on the cloud. And we saw clients that actually had gone through a year or multiple years of migration to the cloud but it was great, 18 months, I've seen nine months migrations of the warehouse versus two year migrations of various data sources to the cloud. But ultimately the result is the same, unsatisfied, frustrated data users, data providers with lack of ability to innovate quickly on relevant data and have an experience that they deserve to have, have a delightful experience of discovering and exploring data that they trust. And all of that was still amiss. So something else more fundamentally needed to change than just the technology. >> So the linchpin to your scenario is this notion of context. And you pointed out, you made the other observation that "Look we've made our operational systems context aware but our data platforms are not." And like CRM system sales guys are very comfortable with what's in the CRMs system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders if you will, of data products or data services, they are now first-class citizens in the data flow, and they're injecting by design domain knowledge into the system. So I want to put up another one of your charts guys, bring up the figure two there. It talks about convergence. She showed data distributed, domain driven architecture, the self-serve platform design, and this notion of product thinking. So maybe you could explain why this approach is so desirable in your view. >> Sure. The motivation and inspirations for that approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to microservices with monolithic systems. One of the things systems where the bottleneck, the changes we needed to make was always on vertical now to how the architecture was centralized. And we found a nice niche. And I'm not saying this is a perfect way of decoupling your monolith, but it's a way that currently where we are in our journey to become data driven, it is a nice place to be, which is distribution or a decomposition of your system as well as organization. I think whenever we talk about systems, we've got to talk about people and teams that are responsible for managing those systems. So the decomposition of the systems and the teams, and the data around domains. Because that's how today we are decoupling our business, right? We are decoupling our businesses around domains, and that's a good thing. And what does that do really for us? What it does is it localizes change to the bounded context of that business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization, and that particular team, so removes the friction that often we have for both managing the change, and both serving data or capability. So if the first principle of data meshes, let's decouple this world of analytical data the same to mirror. The same way we have decoupled our systems and teams, and business. Why data is any different. And the moment you do that, so the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silos of disconnected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain context. And the second principle is, well, we have to expect a certain level of quality and accountability, and responsibility for the teams that provide the data. So let's bring products thinking and treating data as a product, to the data that these teams now share, and let's put accountability around it. We need a new set of incentives and metrics for domain teams to share the data, we need to have a new set of kind of quality metrics that define what it means for the data to be a product, and we can go through that conversation perhaps later. So then the second principle is, okay, the teams now that are responsible, the domain teams responsible for their analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product, and bring product thinking to that. And then the next question you get asked off at work by CIO or CTO is the people who build the infrastructure and spend the money. They say, well, "It's actually quite complex to manage big data, now where we want everybody, every independent team to manage the full stack of storage and computation and pipelines and access control and all of that." Well, we've solved that problem in operational world. And that requires really a new level of platform thinking to provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data, and I think that requires re-imagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away a ton of complexity that unnecessarily people get exposed to. And that's the third principle of creating self-serve infrastructure to allow autonomous teams to build their domains. But then the last pillar, the last fundamental pillar is okay, once he distributed a problem into smaller problems that you found yourself with another set of problems, which is how I'm going to connect this data. The insights happens and emerges from the interconnection of the data domains, right? It's just not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a federated model. And based on a computational model. Like once we have this powerful self-serve platform, we can computationally automate a lot of covenants decisions and security decisions, and policy decisions, that applies to this fabric of mesh, not just a single domain or not in a centralized. So really, as you mentioned, the most important component of the data mesh is distribution of ownership and distribution of architecture in data, the rest of them is to solve all the problems that come with that. >> So, very powerful. And guys, we actually have a picture of what Zhamak just described. Bring up figure three, if you would. So I mean, essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure which you kind of show here in this figure, data infrastructure as a platform down below. And you know why I love about this, Zhamak, is, to me it underscores the data is not the new oil. Because I can put oil in my car, I can put it in my house but I can't put the same code in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, sticking problem, which is that the governance which is now not a command and control, it's federated governance. So maybe you could add some thoughts on that. >> Sure, absolutely. It's one of those, I think I keep referring to data mesh as a paradigm shift, and it's not just to make it sound grand and like kind of grand and exciting or important, it's really because I want to point out, we need to question every moment when we make a decision around, how we're going to design security, or governance or modeling of the data. We need to reflect and go back and say, "Am I applying some of my cognitive biases around how I have worked for the last 40 years?" I've seen it work? Or "Do I do I really need to question?" And do need to question the way we have applied governance. I think at the end of the day, the role of the data governance and the objective remains the same. I mean, we all want quality data accessible to a diverse set of users and its users now know have different personas, like data persona, data analysts, data scientists, data application user. These are very diverse personas. So at the end of the day, we want quality data accessible to them, trustworthy in an easy consumable way. However, how we get there looks very different in as you mentioned that the governance model in the old world has been very command and control, very centralized. They were responsible for quality, they were responsible for certification of the data, applying and making sure the data complies with all sorts of regulations, make sure data gets discovered and made available. In the world of data mesh, really the job of the data governance as a function becomes finding the equilibrium between what decisions need to be made and enforced globally, and what decisions need to be made locally so that we can have an interoperable mesh of data sets that can move fast and can change fast. It's really about, instead of kind of putting those systems in a straight jacket of being constantly and don't change, embrace change, and continuous change of landscape because that's just the reality we can't escape. So the role of governance really, the modern governance model I called federated and computational. And by that I mean, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understands that domain really well, but also wears that hats of the product owner. It's an important role that has to have a representation in the governance. So it's a federation of domains coming together. Plus the SMEs, and people have Subject Matter Experts who understand the regulations in that environment, who understands the data security concerns. But instead of trying to enforce and do this as a central team, they make decisions as what needs to be standardized. What needs to be enforced. And let's push that into that computationally and in an automated fashion into the platform itself, For example. Instead of trying to be part of the data quality pipeline and inject ourselves as people in that process, let's actually as a group, define what constitutes quality. How do we measure quality? And then let's automate that, and let's codify that into the platform, so that every day the products will have a CICD pipeline, and as part of that pipeline, law's quality metrics gets validated, and every day to product needs to publish those SLOs or Service Level Objectives, or whatever we choose as a measure of quality, maybe it's the integrity of the data, or the delay in the data, the liveliness of the data, whatever are the decisions that you're making. Let's codify that. So it's really the objectives of the governance team trying to satisfies the same, but how they do it, it's very, very different. And I wrote a new article recently, trying to explain the logical architecture that would emerge from applying these principles, and I put a kind of a light table to compare and contrast how we do governance today, versus how we'll do it differently, to just give people a flavor of what does it mean to embrace decentralization, and what does it mean to embrace change, and continuous change. So hopefully that could be helpful. >> Yes. There's so many questions I have. But the point you make it too on data quality, sometimes I feel like quality is the end game, Where the end game should be how fast you can go from idea to monetization with a data service. What happens again? And you've sort of addressed this, but what happens to the underlying infrastructure? I mean, spinning up EC2s and S3 buckets, and MyPytorches and TensorFlows. That lives in the business, and who's responding for that? >> Yeah, that's why I'm glad you're asking this question, David, because I truly believe we need to reimagine that world. I think there are many pieces that we can use as utilities are foundational pieces, but I can see for myself at five to seven year road map building this new tooling. I think in terms of the ownership, the question around ownership, that would remain with the platform team, but I don't perhaps a domain agnostic technology focused team, right? That there are providing a set of products themselves, but the users of those products are data product developers, right? Data domain teams that now have really high expectations, in terms of low friction, in terms of a lead time to create a new data products. So we need a new set of tooling and I think the language needs to shift from I need a storage bucket, or I need a storage account, to I need a cluster to run my spark jobs. Too, here's the declaration of my data products. This is where the data file will come from, this is a data that I want to serve, these are the policies that I need to apply in terms of perhaps encryption or access control, go make it happen platform, go provision everything that I need, so that as a data product developer, all I can focus on is the data itself. Representation of semantic and representation of the syntax, and make sure that data meets the quality that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a reimagination. And there will be a data platform team. The data platform teams that we set up for our clients, in fact themselves have a fair bit of complexity internally, they divide into multiple teams, multiple planes. So there would be a plane, as in a group of capabilities that satisfied that data product developer experience. There would be a set of capabilities that deal with those nitty gritty underlying utilities, I call them (indistinct) utilities because to me, the level of abstraction of the platform needs to go higher than where it is. So what we call platform today are a set of utilities we'll be continuing to using. We'll be continuing to using object storage, we will continue to using relational databases and so on. So there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that enable the mesh level functionality, for example, be able to correlate and connect and query data from multiple nodes, that's a mesh level capability, to be able to discover and explore the mesh of data products, that's the mesh of capability. So it would be a set of teams as part of platform. So we use a strong, again, products thinking embedded in a product and ownership embedded into that to satisfy the experience of this now business oriented domain data teams. So we have a lot of work to do. >> I could go on, unfortunately, we're out of time, but I guess, first of all, I want to tell people there's two pieces that you've put out so far. One is how to move beyond a Monolithic Data Lake to a distributed data mesh. You guys should read that in the "Data Mesh Principles and Logical Architecture," is kind of part two. I guess my last question in the very limited time we have is are organizations ready for this? >> I think how the desire is there. I've been overwhelmed with the number of large and medium and small and private and public, and governments and federal organizations that reached out to us globally. I mean, this is a global movement and I'm humbled by the response of the industry. I think, the desire is there, the pains are real, people acknowledge that something needs to change here. So that's the first step. I think awareness is spreading, organizations are more and more becoming aware, in fact, many technology providers are reaching to us asking what shall we do because our clients are asking us, people are already asking, we need the data mesh and we need the tooling to support it. So that awareness is there in terms of the first step of being ready. However, the ingredients of a successful transformation requires top-down and bottom-up support. So it requires support from chief data analytics officers, all above, the most successful clients that we have with data mesh are the ones that, the CEOs have made a statement that, "We'd want to change the experience of every single customer using data, and we're going to commit to this." So the investment and support exists from top to all layers, the engineers are excited, the maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients of transformations that come together. Are we really ready for it? I think the pioneers, perhaps, the innovators if you think about that innovation curve of adopters, probably pioneers and innovators and lead adopters are making moves towards it, and hopefully as the technology becomes more available, organizations that are less engineering oriented, they don't have the capability in-house today, but they can buy it, they would come next. Maybe those are not the ones who are quite ready for it because the technology is not readily available and requires internal investments to make. >> I think you're right on. I think the leaders are going to lean in hard and they're going to show us the path over the next several years. And I think that the end of this decade is going to be defined a lot differently than the beginning. Zhamak, thanks so much for coming to "theCUBE" and participating in the program. >> Thank you for hosting me, David. >> Pleasure having you. >> It's been wonderful. >> All right, keep it right there everybody, we'll be back right after this short break. (slow music)

(upbeat music) >> From the SiliconANGLE Media office, in Boston Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> I'm Stu Miniman, I'm here with Bobby Patrick, the Chief Marketing Officer of UiPath and Bobby, UiPath sponsored a new survey in paper that is from the economists, it's called The Advance of Automation. Tell us a little bit about why that paper was done. >> Yeah. So Robotic Process Automation is fairly new to the market. Automation, obviously has been around a while. It's been mostly in I.T. where we've automated for the last 20 years. With RPA now, you can really begin to talk to the C level executives about, "Hey, I can really drive 10, 15 percent productivity with every employee. I can really being to think about dramatic digital transformation across my entire enterprise". And so, we approached a few outlets, The Wall Street Journal being one, the FT, and The Economist. The Economist was very interested, they obviously have studies about the impacts of the workforce around productivity. And they viewed this as a really exciting effort to engage in. We obviously sponsored it as well. But the results really were from their surveys. They had multiple professionals on it, and we couldn't be more excited about the results of the paper. >> Awesome, a lot of data in there which our audience always love. What were some of the key takeaways from the results? >> High interest in automation. But only about half solved, really broad usage of automation in their company. I think what we realize here is that automation has impacted a number of areas. Certainly hard automation, hard automation is physical robots. But soft automation, or robotic automation, actually had higher awareness in it's potential. So I was surprised about that. But I think what the most important part to me is that over 90% said they thought automation could have massive impacts on their company. Not really surprising data, I would say I some cases. But I think the way they pull it all together and summarize about it's potential. I think that's what was most impactful. >> All right, Bobby, we've been loving digging in on theCUBE for years about the future of work. There is still so much concern or fear out there. "Robots are taking my job" "I throw in this new technology." And we understand in the I.T. industry, it is very rare that a technology directly replaces people. >> Right. >> As a matter of fact, we've done events with MIT and it's people plus machines. >> Right. >> It's usually the best answer. Where does this research fit with that whole second machine age and discussing their jobs? >> Yeah, I think what's great is, two years ago, RPA was not widely known, at all. And I think at the time the narrative was AI's going to replace jobs. There was a lot of fear. But that's not what we're seeing at all. And I think the paper confirms this as well. But, this is about robots doing the work we hate. Nobody misses the work that robots do. We see in terms of the results in data is that the increase in productivity actually drives a more efficient workforce and a more satisfied workforce. Happier employees. Employee engagement. Employment productivity is what we talk about often now. And so I think that narrative has shifted very quickly. And you could argue "Well it's low unemployment economy so maybe that's why." But even in certain countries that we're in like Brazil which have a much higher unemployment. The enthusiasm there is still very high. >> All right, as I mentioned, there's a lot of data in there. Which person in the organization is driving this, where is the awareness? There's geographical cuts of it. >> Right. >> So if people what to find out more, how do they get that? >> So Economist was great. They said "Hey, we love your view of this automation first era like the cloud first era", Stu that we've all be involved in for so long. The automation first era's huge and so they said "Hey, automationfirst.economist.com would be a great URL". All the content's up there now. You can download the white papers, there's a great infographic and it's part of the Economist. So, automationfirst.economist.com. >> All right, thank you so much, Bobby. I love about it automationfirst.economist.com and really all you have to do is go to that website, click a button, you don't have to fill out a long form. >> No. >> I'm guessing some robot just populates all the stuff that you need there. >> Of course. >> All right, for Bobby Patrick, I'm Stu Miniman. Thanks for joining us as always on theCUBE. (upbeat music)

>> Announcer: From Boston, Massachusetts it's The Cube. Covering AWS re:Inforce 2019. Brought to you by Amazon Web Services and it's ecosystem partners. >> Hey, welcome back everyone. This is The Cube's live coverage of AWS re:Inforce in Boston, Massachusetts. I'm John Furrier with Dave Vallante. This is re:Inforce. This is the inaugural conference for AWS on the security and Cloud security market. A new category being formed from an events standpoint around Cloud security. Our next guest is Cube alumni guest analyst Corey Quinn, and Cloud Economist with the Duckbill Group. Good to see you again. Great to have you on. Love to have you come back, because you're out in the hallways. You're out getting all the data and bringing it back and reporting. But this event, unlike the other ones, you had great commentary and analysis on. You were mentioned onstage during the Keynote from Stephen Smith. Congratulations. >> Thank you. I'm still not quite sure who is getting fired over that one, but somehow it happened, and I didn't know it was coming. It was incredibly flattering to have that happen, but it was first "Huh, awesome, he knows who I am." Followed quickly by "Oh dear, he knows who I am." And it, at this point, I'm not quite sure what to make of that. We'll see. >> It's good news, it's good business. All press is good press as they say, but let's get down to it. Obviously, it's a security conference. This is the inaugural event. We always love to go to inaugural events because, in case there's no second event, we were there - >> Corey: Oh yes >> for one event. So, that's always the case. >> Corey: Been there since the beginning is often great bragging rights. And if there isn't a second one, well, you don't need to bring it up ever again. So, they've already announced there's another one coming to Houston next year. So that'll be entertaining. >> So a lot of people were saying to us re:Inforce security event, some skepticism, some bullish on the sector. obviously, Cloud is hot. But the commentary was, oh, no one's really going to be there. It's going to be more of an educational event. So, yeah, it's more of an educational event for sure. That they're talking about stuff that they can't have time to do and reinvent. But there's a lot of investment going on there. There are players here from the companies. McAfee, you name the big name companies here, they're sending real people. A lot of biz dev folks trying to understand how to build up the sector. A lot of technical technologists here, as well. Digging in to some of the deep conversations. Do you agree? What's your thoughts of the event? >> I'm surprised, I was expecting this to be a whole bunch of people trying to sell things to other people, who were trying to sell them things in return, and it's not. There are, there are people who are using the Cloud for interesting things walking around. And that's fantastic. One thing that's always struck me as being sort of strange, and why I guess I feel sort of spiritually aligned here if nothing else. Is cost and security are always going to be trailing functions. No company is excited to invest in those things, until immediately after they really should have been investing in those things and weren't. So with time to market, velocity are always going to be something much valuable and important to any company strategically. But, we're seeing people start to get ahead of the curve in some ways. And that's, it's refreshing and frankly surprising. >> What is the top story in your mind? Top three stories coming out of re:Inforce. From industry standpoint, or from a product standpoint, that you think need to be told or amplified, or not being told, be told? >> Well there's been the stuff that we've seen on the stage and that's terrific. And, I think that you've probably rehashed those a fair bit with other guests. For me, what I'm seeing, the story that resonates as I walk around the Expo Hall here. Is we're seeing a bunch of companies that have deep roots in data centered environments. And now they're trying to come up with stories that resonate with Cloud. And if they don't, this is a transformational moment. They're going to effectively, likely find themselves in decline. But, they're not differentiating themselves from one another particularly well. There are a few very key things that we're seeing people operate within. Such as, with the new port mirroring stuff coming out of NVPC Traffics. You're right. You have a bunch of companies that are able to consume those, or flow logs. If you want to go back in time a little bit, and spit out analysis on this. But you're not seeing a lot of differentiation around this. Or, Hey we'll take all your security events and spit out the useful things. Okay, that is valuable, and you need to be able to do that. How many vendors do you need in one company doing the exact same thing? >> You know, we had a lot of sites CSO's on here and practitioners. And one of the comments on that point is Yeah, he's like, "Look I don't need more alerts." "I need things fixed." "Don't just tell me what's going on, fix it." So the automation story is also a pretty big one. The VCP traffic mirror, I think, is going to be just great for analytics. Great for just for getting that data out. But what does it actually impact In the automation piece? And the, okay there's an alert. Pay attention to it or ignore it. Or fix it. Seems to be kind of the next level conversation. Your thoughts around that piece. >> I think that as we take a look at the space and we see companies continuing to look at things like auto remediation. Automation's terrific, until the first time it does something you didn't want it to do and takes something down. At which point no one trusts it ever again. And that becomes something hard to tend to. I also think we're starting to see a bit of a new chapter as alliance with this from AWS and it's relationship with partners. I mean historically you would look at re:Invent, and you're sitting in the Expo Hall and watching the keynote. And it feels like it's AWS Red Wedding. Where, you're trying to see who's about to get killed by a feature that just comes out. And now were seeing that they've largely left aspects of the security space alone. They've had VPC flow logs for a long time, but sorting through those yourself was always like straining raw sewage with your teeth. You had to find a partner solution or build something yourself out of open source tooling from spit and duct tape. There's never been a great tool there. And it almost feels like they're leaving that area, for example, alone. And leaving that as an area rife for partners. Now how do you partner with something like AWS? That's a hard question to answer. >> So one of the other things we've heard from practitioners is they don't want incrementalism. They're kind of sick of that. They want step functions, that do as John said, remediate. >> Corey: Yeah. So, like you say, you called it the Red Wedding at the main stage. What does a partner have to do to stay viable in this ecosystem? >> Historically, the answer to that has always been to continue innovating ahead of the bow wave of AWS's own innovation. The problem is you see that slide that they put on in every event, that everyone who doesn't work at AWS sees. That shows the geometric increase in number of feature and service releases. And we all feel this sinking sensation of not even the partner side. But, they're releasing so much that I know some of that is going to fix things for my company, but I'll never hear it. Because it's drowned in the sheer volume of what they're releasing. AWS is rapidly increasing their pace of innovation to the point where companies that are not able to at least match that are going to be in for a bad time. As they find themselves outpaced by the vendor they're partnering with. >> And you heard Liberty Mutual say their number one challenge was actually the pace of Cloud. Being able to absorb all these new features >> Yes. >> And so, you mentioned the partner ecosystem. I mean, so it's not just the partners. It's the customers as well. That bow is coming faster than they can move. >> Absolutely. I can sit here now and talk very convincingly about services that don't exist. And not get called out on them by an AWS employee who happens to be sitting here. Because no one person can have all of this in their head anymore. It's outpaced most people's ability to wrap their heads around that and contextualize it. So people specialize, people focus. And, I think, to some extent that might be an aspect of why we're seeing re:Inforce as its own conference. >> So we talked a lot of CSO's this trip. >> Yeah. >> John: A lot of one on ones. We had some interviews. Some private meetings. I'm going to read you a list of key areas that they brought up as concern. I want to get you're reaction to. >> Sure. >> You pick the ones out you think are very relevant. >> Sure. >> Speedily, very fast. Vendor lock in. Spend. >> Not concerned. Yep. Security Native. >> Yeah. >> Service provider supplier relationship. Metrics, cloud securities, different integration, identity, automation, work force talent, coding security, and the human equation. There were all kind of key areas that seemed to glob and be categorically formed. Your thoughts to those. Which ones do you think jump out as criticalities on the market? >> Sure. I think right now people talking about lock in are basically wasting their time and spinning their wheels. If you, for example, you go with two cloud providers because you don't want to be locked into one. Well now there's a rife partner ecosystem. Because translating things like IAM into another provider's environment is completely foreign. You have to build an entire new security model on top of things in order to do that effectively. That's great. In security we're seeing less of an aversion to lock in than we are in other aspects of the business. And I think that is probably the right answer. Again, I'm not partisan in this battle. If someone wants to go with a different Cloud provider than AWS, great! Awesome! Make them pick the one that makes sense for your business. I don't think that it necessarily matters. But pick one. And go all in on that. >> Well this came up to in a couple of ways. One was, the general consensus was, who doesn't like multi Cloud? If you can seamlessly move stuff between Clouds. Without having to do the modification on all this code that has to be developed. >> Who wouldn't love that? But the reality is, doesn't exist. >> Corey : Well. To your point, this came up again, is that workplace, workforce talent is on CSO said "I'm with AWS." "I have a little bit of Google. I could probably go Azure." "Maybe I bought a company with dealing some stuff over there." "But for the most part all of my talent is peaked on AWS." "Why would I want to have three separate security teams peaking on different things? When I want everyone on our stack." They're building their own stacks. Then outsourcing or using suppliers where it supports it. >> Sure. >> But the focus of building their own stacks. Their own security. Coding up was critical. And having a split competency on code bases just to make it multi, was a non starter. >> And I think multi Cloud has been a symptom. I mean, it's more than a strategy. I think it's in a large part a somewhat desperate attempt by a number of vendors who don't have their own Cloud. To say Hey, you need to have a multi Cloud strategy. But, multi Cloud has been really an outcome of multiple projects. As you say, MNA. Horses for courses. Lines of business. So my question is, I think you just answered it. Multi Cloud is more complex, less secure, and probably more costly. But is it a viable strategy for things other than lock in? >> To a point. There are stories about durability. There's business reasons. If you have a customer who does not want their data living one one particular Cloud provider. Those are strategic reasons to get away from it. And to be clear, I would love the exact same thing that you just mentioned. Where I could take what I've built and run that seamlessly on other providers. But I don't just want that to be a pile of VM's and maybe some disc. I want those to be the higher level services that take care of massive amounts of my business for me. And I want to flow those seamlessly between providers. And there's just no story around that for anything reasonable or modern. >> And history would say there won't really ever be. Without some kind of open source movement to - >> Oh yes. A more honest reading of some of the other cloud providers that are talking about multi cloud extensively translates that through a slight filter. To, we believe you should look into Multi Cloud. Because if you're going all in on a single provider there is no way in the world it's going to be us. And that's sort of a challenge. If you take a look at a number of companies out here. If someone goes all in on one provider they will not have much, if anything, to sell them of differentiated value. And that becomes the larger fixture challenge for an awful lot of companies. And I empathize with that, I really do. >> Amazon started to do a lot of channel development. Obviously their emphasis on helping people make some cash. Obviously their vendors are, ecosystems a fray. Always a fray. So sheer responsibility at one level is, well we only have one security model. We do stuff and you do stuff. So obviously it's inherently shared. So I think that's really not a surprise for me. The issue is how to get successful monetization in the ecosystem. Clearly defining lines of, rules of engagement, around where the white spaces are. And where the differentiation can occur. Your thoughts on how that plays out. >> Yeah. And that's a great question. Because I don't think you're ever going to get someone from Amazon sitting in a room. And saying Okay, if you build a tool that does this, we're never, ever, ever going to build a thing that does that. They just launched a service at re:Invent that talks to satellites in orbit. If they're going to build that, I don't, there's nothing that I will say they're never going to get involved with. Their product strategy, from the outside, feels like it's a post it note that says Yes on it. And how do you wind up successfully building and scaling a business around that? I don't have a clue. >> Eddie Jafse's on the record here in The Cube and privately with me on my reporting. Saying never say never. >> Never say never. >> We'll never say never. So that is actually an explicit >> Take him at his word on that one. >> Right. And I'm an independent consultant. Where my first language is sarcasm. So, I basically make fun of AWS in the newsletter and podcast. And that seems to go reasonably well. But, I'm never going to say that they're not going to move into self deprecation as a business model. Look at some of their service names. They're clearly starting to make inroads in that space. So, I have to keep innovating ahead of that bow wave. And for now, okay. I can't fathom trying to build a business model with a 300 person company and being able to continue to innovate at that pace. And avoid the rapid shifts as AWS explores on new offers. >> And I what I like about why, well, we were always kind of goofing on AWS. But we're fanboys as well, as you know. But what I love about AWS is that they give the opportunity for their partners. They give them plenty of head's up. It's pretty much the rules of engagement is never say never. But if they're not differentiating, that's their job. >> Corey: Yeah. >> Their job is to be better. Now one thing Amazon does say is Hey we might have a competing service, but we're always going to favor the customer. So, the partner. If a customer wants an Amazon Cloud trail. They want Cloud trail for a great example. There's been requests for that. So why wouldn't they do it? But they also recognize it's bus - people in the ecosystem that do similar things. >> Corey: Yeah. >> And they are not going to actively try to put them out of business, per se. >> Oh yeah! One company that's done fantastically well partnering with everyone is PagerDuty. And even if AWS were to announce a service that wakes you up in the middle of the night when something breaks. It's great. Awesome. How about you update your status page in a timely fashion first? Then talk about me depending on the infrastructure that you run to tell me when the infrastructure that you run is now degraded? The idea of being able to take some function like that and outsource worked well enough for them to go public. >> So where are the safe points in the ecosystem? So obviously a partner that has a strong on-prem presence that Amazon wants to get access to. >> That's a short term, or maybe even a mid term strategy. Okay. Professional services. If you're Accenture, and Ernie Young, and Deloitte, PWC, you're probably okay. Because that's not a business that Amazon really wants to be in. Now they might want to, they might want to automate as much to that as possible. But the world's going to do that anyway. But, what's your take where it's safe? >> I would also add cost optimization to that. Not from a basis of technical capability. And I think that their current tooling is disappointing. I'd argue that cost explorer and the rest of their billing situation is the asterisk next to customer obsession if we're being perfectly honest. But there's always going to be some value in an external party coming in from that space. And what form that takes is going to change. But, it is not very defensible internally to say our Cloud spend is optimized, because the vendor we're writing those large checks to tells us it is. There's always going to be a need for some third-party validation. And whether that can come through software? >> How big is that business? >> It's a great question. Right now, we're seeing that people are spending over 30 billion dollars a year on AWS and climbing. One thing we can say with a certainty in almost every case is that people's Cloud bills are not getting smaller month over month. >> Yep. >> So, it's a growing market. It's one that people feel incredibly acutely. And when you get a few drinks into people and they start complaining about various aspects of Cloud, one of the first most common points that comes up is the bill. Not that it's too high, but that it is inscrutable. >> And so, just to do a back of napkin tam, how much optimization potential is there? Is it a ten percent factor? More? >> It depends on the level of effort you're willing to invest. I mean, there's a story for almost environments where you can save 70% on your Cloud bill. All you have to do is spend 18 months of rewriting everything to use serverless primitives. Six of those months you'll be hard down across the board. And then, wait where did everyone go? Because no one's going to do that. >> Dave: You might be out of business. So it's always a question of effort spent doing optimization, versus improving features, speeding time to market and delivering something that will generate for more revenue. The theoretical upside of cost optimization is 100% of your Cloud bill. Launching the right service or product can bring in multiples of that in revenue. >> I think my theory on differentiation, Dave, is that I think Amazon is basically saying in so many words, not directly. But it's my interpretation. Hold on to the rocket ship of AWS as long as you can. And if you can get stable, hold on. If you fall off that's just your fault, right? So, what that means is, to me, move up the stack. So Amazon is clearly going to continue to grow and create scale. So the benefits to the companies create a value proposition that can extract rents out of the marketplace from value that they create on the Amazon growth. Which means, they got to lock step with Amazon on growth. And cost leap, pivot up to where there's space. And Amazon is just a steam roller that will come in. The rocket ship that's going so fast. Whatever metaphor. And so people who just say We made a deal with Amazon, we're in. And then kind of sit idle. Will probably end up getting spun off. I mean, cause it's like they fall off and Amazon will be like All right so we did that. You differentiate enough, you didn't innovate enough. But, they're going to give everyone the opportunity to take a place with the growth. So the strategy, management wise, is just constantly push the envelope. >> So that's implicit in the Amazon posture. What's explicit in Amazon's posture is build applications on our platform. And you should be okay. You know? For a while. >> Yeah. And again, I think that a lot of engineers get stuck in a trap of building something and spending all their time making their code quality as best as possible. But, that's not going to lead to a business outcome one way or another. We see stories of companies hitting success with a tire fire of an infrastructure all the time. Twitter used to display massive downtime until they were large enough to justify the time and expense of a massive rewrite. And now Twitter is effectively up all the time. Whether that's good or not is a separate argument. But, they're there. So there's always going to be time to fix things. >> Well the Twitter example is a great example. Because they built it on rails. >> Yes. >> And they put it on Amazon Cloud. It was just kind of a hack, and then all of the sudden Boom, people loved it. And then, that's to me, the benefit of Cloud. One you get the scape velocity, the investment to start Twitter was fairly low, given what the success was. And then they had to rewrite, because the scale was bursting up. That's called prototyping. >> Oh yeah. >> That's what enterprises have to do. This is the theme of, agile. Get started as a theme, just dig in. Do a hack up font. But don't get confuse that with scale. That's where the rubber meets the road. >> Right and the, Oh Cloud isn't for us because we're an exception case. There are very few companies for whom that statement is true in the modern era. And, do an honest analysis first, before deciding we're going to build our own data centers because we can do it for cheaper. If you're Dropbox, putting storage in, great. Otherwise you're going to end up in this story where Oh, well, we have 20 instances now, so we can do this cheaper in Iraq somewhere. I will bet you a house you're wrong. But okay. >> Yeah. People are telling me that. Okay final question for you. As you've wandered around and been in the sessions, been in the analyst thing. What are some slice of life commentary stories you've bumped into that you found either funny, clever, insulting, or humorous? What's out on the floor? What are some of the conversations? >> One of the best ones was a company I'm not going to name, but the story they told was fantastic. They have, they're primarily on Azure. But they also have a strong secondary presence with AWS, and that's fascinating to me. How does that work internally? It turns out their cloud of choice is Azure. And they have to mandate that with guardrails in place. Because if you give developers a choice they will all go and build on AWS instead. Which is fascinating. And there are business reasons behind why they're doing what they're doing. But that story was just very humorous. I can't confirm or deny whether it was true or not. Because it was someone with way too much to drink telling an awesome story. But the idea of having to forcibly drag your developers away from a thing in a favor of another thing? >> That's like being at a bad party. It's like Oh, the better party is over there. All my friends are over there. >> But they have a commitment to Microsoft software estate. So, that's likely why they're. >> They just deal with Microsoft. >> And I'm not saying this is necessarily the wrong approach. I just find it funny. >> Might be the right business decision, but when you ask the developers, we see that all the time, John. >> All the time. I mean I had a developer one time come to me and start, he like "Look, we thought it would be great to build on Azure. We were actually being paid. They were writing checks to incent us. And I had a revolt. Engineers were revolting. Because the reverse proxies as there was cobbled together services. And they weren't clean native services and primitives. So the engineers were revolting. So they, we had to turn down the cash from Microsoft and go back to Amazon." >> Azure is much better now, but they have to outrun that legacy shadow of at first, it wasn't great. And people try something once, "That was terrible!" Well would you like to try it again now? "Why would I do that? It was terrible!" And it takes time to overcome that knee-jerk reaction. >> Well, but to your point about the business decision. It might make business sense to do that with Microsoft. It's maybe a little bit more predictable than Amazon is as a partner. >> Oh the way to optimize your bill on another Cloud provider that isn't AWS these days is to call up your account rep and yell at them. They're willing to buy business in most cases. That's not specific to any one provider. That's most of them. It's challenging to optimize free, so we don't see the same level of expensive bill problems in most companies there as well. >> Well the good news is on Microsoft, and I was a really big critic of Azure going back a few years ago. Is that they absolutely have changed their philosophy going back, I'd say two, three years ago. In the past two years, particular 24 months, they really have been cranking. They've been pedaling as fast as they can. They're serious. There's commitment from the top. And then they tell us, so there's no doubt. They're doing it also with the Kubernetes. What they're seeing, as they're doing is phenomenal. So... >> Great developer jobs at Microsoft. >> They're in for the long game. They're not going to be a fad. No doubt about it. >> No. And we're not going to see for example the Verizon public Cloud the HP public Cloud. Both of which were turned off. The ones that we're seeing today are largely going to be to stay of the big three. Big four if we include Alibaba. And it's, I'm not worried about the long term viability of any of them. It's just finding their niche, finding their market. >> Yeah, finding their lanes. Cory. Great to have you on. Good to hear some of those stories. Thanks for the commentary. >> Thank you. >> As always great guest analyst Cube alumni, friend, analyst, Cory Quinn here in the Cube. Bringing all the top action from AWS re:Inforce. Their first inaugural security conference around Cloud security. And Cube's initiation of security coverage continues, after this break. (upbeat electronic music)

>> live from Washington D. C. It's the Cube covering aws public sector summit DC brought to you by Amazon Web services. >> Welcome back, everyone to the cubes Live >> coverage of a ws public sector summit here in Washington D. C. I'm your >> host Rebecca Night, along with my co host, John >> Furrier. We're here with Cory Quinn, Cloud Economist The Duck Billed group and a cube host at large. Welcome. Welcome to our show. A medium >> at best, most days. But we'll see what happens when ever expanding. Someday I'll be a 10 x engineer, but not today. >> Right? Right. Exactly. >> Next host. Exactly. >> There we go, >> Cloud. Stand up on the side. We need to mention that >> Yes, generally more cloud improv. But no one believes that. It's off the cuff. So we smile, we nod, we roll with Tio. Yeah, no one wants to hear me sing in any form. >> I promise. Strapping So, Cory, you have been here. You are on the ground having great conversations with people here. 18,000 people at this summit Give us give our viewers a low down on the vibe. The energy What? What do you hear? Very different >> feeling in the commercial summits you're seeing. People are focusing on different parts of the story, and one thing I find amusing is talking to people who work in the public sector. Show up in their first response is, Oh, I'm so behind and then you go to the commercial summit. You talk to people who are doing bleeding edge things, and their response is, Oh, I'm so behind and everyone thinks that they're falling behind the curve and I'm >> not sure how >> much of that is a part of people just watching a technology. Events outpace them versus the ever increasing feature velocity. If they show on slide year over year over year, consistent growth and people feel like they're being left in the dust, it's it's overwhelming. It's drinking from a fire hose. And I don't think that that gets any easier when you're talking to someone in public sector where things generally move in longer planning cycles because they definitional have to, and I'd argue should, >> but you should help them, make them feel better and say, Don't worry. The private sector feels the same way. Not just everyone >> has these problems. That's that's the poor little challenge of this is everyone believes that if you go to the one magic company, their environment is going to be wonderful. They're adopting everything. It doesn't exist. I've gone into all of the typical tech companies you would expect and talk to people. And everyone wants you for three or four drinks into them, gets very honest and starts crying. What would its higher fire their own environment is? It says a lot of conference. We're going around. Here's how we built this amazing thing as a proof of concept is what the part they don't say or for this one small, constrained application. People are trying to solve business problems, not build perfect architecture. And that's okay. >> Yeah, process. They're not. They're not businesses, their agencies. As you said, they're like, slow as molasses when it comes to moving speed. And you could even see Andy Jazzy during his fireside Shep. He's already studying, laying the groundwork. Well, >> once you're in the >> cloud, here's how you know the adoption level so you can see that it's land not landing expand like the enterprise, which is still slow. It's land, get the adoption and then expand, So the public sector clearly has a lot of red tape. I mean, no doubt about it. >> That means anyone who'd argue that point >> chairman's like 1985. It's like, you know, hot tub time machine, you know, nightmare. But Andy Jazz, he also says on differently to heavy lifting is what they want to automate away. That's the dream. That's the That's the goal. Absolute. It's hard. This is the real challenge. Is getting the public sector adopted getting the adoption, your thoughts when what you're hearing people are they jumping in? They put a toe in the water, kicking the tires. As Andy said, >> all of the above and more. I think it's a very broad spectrum and they mentioned there. I think they were 28,000 or 12,000 non profit organizations that they wind up working with as customers and they all tend to have different velocities across the board as they go down that path. I think that the idea that there's one speed or you can even draw a quick to line summary of all the public sector is a bit of a Basile explanation. I see customers are sometimes constrained by planning cycles. There's always the policies and political aspects of things where if you wind up trying to speed things up, you're talking to some people who will not have a job. If you remove the undifferentiated heavy lifting because that's been their entire career, we're going to help you cut waste out of your budget. Well, that's a hard sell to someone who is incentivized based upon the size of the budget that they control it. You wind up with misaligned incentives, and it's a strange environment. But the same thing that I'm seeing across the corporate space is also happening in public sector. We're seeing people who are relatively concerned about where they're going to hire people from what those people look like, how they're going to transform their own organizations. Digital transformations, attired term. >> And it's like you have rosy colored glasses on too much. You're gonna miss the big picture. You gotta have a little bit of skepticism. I think to me governments always had that problem where I'm just gonna give up. I'm telling different. I can't get the outcome I want, because why even try? Right? I think now, with cloud what I hear Jazzy and Amazon saying is. Hey, at least you get some clear visibility on the first position of value, so there's some hope there, right? So I think that's why I'm seeing this adoption focus, because it's like they're getting the customers. For instance, like I'm a university. I could be a professor, but my credit card down my university customer, I got a couple instances of PC to so ding and another one to the 28,000 >> exactly number of customers is always a strange >> skeptical there. But now, for the first time, you, Khun got should go to a team saying, Hey, you know all that B s about not get the job done, you can get it with clouds. So it's gettable. Now it's attainable. It's not just aspirations. >> Movers really will make the difference. In the end, with the university customer's question, the people who were in that swing >> the tide can that be a generational shift, a deb ops mindset in government? That's a big question. >> Well, they have some advantages. For example, we took a look at all the Gulf cloud announcements and the keynote yesterday, and that must have been a super easy keynote to put together because they're just using the traditional Kino slides and reinvent 2014 because it takes time to get things certified as they moved through the entire pipeline process. And there's nothing inherently wrong with that. But the services that are going into come cloud or things that are tried and tested in a lot of other environments. There's an entire community out there. There's an established body of knowledge. So a lot of the path that government is walking down has already been from a technical perspective paid for them. >> I want to riff on an idea on to make a proposal with you here in real time. You're I think what we should do is make a proposal to the U. S. Government that we basically take equity in the agencies and then take them public. >> That's not a bad idea, absolutely not about commercialized. >> The entities create a stock option program, Cory, because listen, if I'm if I'm a talent, why would I gotta work for an agency when I could make three times Mohr get public and be rich, and that's the problem with talent. You walk around the expo for here. The booths are much smaller, and I didn't understand that at first, and then it clicked for me. If you want to sell services to government, you don't buy a bigger booth. You buy a Congress person and it turns out those air less expensive. That's how acquisitions tend to work in this space. So folks walking around or not, generally going to be the customers that buy things. People walking around in many cases are the talent and looking for more talent. And it does become extremely compelling to have those people leave public sector and go into private sector. In some cases where we'll pay you three times more and added bonus most days, this is America. After all, no one's shooting at you, so that does your >> cloud. Economists were kind of joking about your title, but if you think about it, there are economics involved. It's lower cost, faster, time to value. But what we're getting at is an incentive system. So you think fiscal monetary policy of incentives. So you know, Rebecca, this this This is the challenge that the policy guys gotta figure because the mechanisms to get stuff done is by the politicians or do this or do that. We're getting at something, really, to the heart of human beings, that mission of the mission of the agency or objective they're doing for the labor of love or money? Yes, Reed, why not create an incentive system that compensate? >> You think That's incentive system for taxpayers, though, too, in the sense of >> if I can see the trillions of dollars on the >> budget, a lot of what >> governments do shouldn't necessarily be for sale. I think the idea of citizen versus customer tends to be a very wide divergence, and I generally pushback on issues to attempt, I guess, convinced those into the same thing. It's you wind up with a very striated, almost an aristocracy Socratic society. >> I don't think that tends >> to lead anywhere. Good way. Everyone is getting political today for some reason. >> Well, I >> mean fireside chat to digital >> transformations. People process technology. You can superimpose that onto any environment where those public policy or whatever or national governments, the people, his issues there, processes, issues, technologies is each of one of them have their own challenge. Your thoughts on public sectors challenges opportunities. Four people process technology. >> You have to be mission driven for starters in order to get the people involved. As far as the processes go, there are inherently going to be limitations sometimes and easily observable in the form of different regulatory regimes that apply to these different workloads. And when we talk about the technology well, we're already seeing that that is becoming less of a gap over time. What used to be that o on ly we can secure a data center well enough from a physical security standpoint, there's a quote from the CIA that said on its worst day that cloud was cloud. Security was better than any on premises environment that they could build. And there's something to be said for that. Their economies of scale of like by >> the tech gaps going away. Almost zero yes. So if that OK, text, good check training fault of the people side. Absolute awareness competency processes a red tape automation opportunity. That could be. >> But this is also not to assume that the commercial world has unlock either. Where does the next generation come from? You talk to most senior cloud folks these days and most of us tend to have come up from working help desks being grumpy, you nexus in men's or you nexus movement because it's not like there's a second kind of those and we go up through a certain progression. Well, those jobs aren't there anymore. They've been automated away. The road that we walked is largely closed. Where does the next generation come from? I don't have a great answer. >> Talent question is a huge one. This is going to be the difference. Rebecca. We were riffing on this on our opening. >> It's the only one. >> Your thoughts. I mean, were you even hearing all this stuff and you've been researching this? What? Your thoughts. >> I think that we need to think more. I think tech companies need to think more broadly about where they're going to get this next generation of people, and they don't need to necessarily be people who have studied CS in school. Although, of course we need those people too. >> But the people with the bright, the creative, the expansive world views who are thinking about these problems and can learn >> the tech, I mean the tough guy, you know why >> block change you into a nice CEO and everyone gets >> rich, but I think when Jessie was saying today during his fireside, in the sense of we need to make sure that we're building tools, that >> you don't need to be a machine learning expert to deploy, you know we need to make simpler, more intuitive tools, and then that's really important here. >> Amazon does well in that environment about incentives. >> I think that >> one thing that the public sector offers that you don't often see in the venture start of world or corporate America or corporate anywhere, for that matter, is the ability to move beyond next quarter, planning the ability to look at long term projects like What >> does >> it take to wind up causing significant change across the world? Where is it take to build international space Station? You're not gonna be able to ship those things 180 days, no matter how efficiently you build things. And I think that the incentives and as you build them, have to start aligning with that. Otherwise you wind up with government trying to compete on compensation with the private sector. I don't think that works. I think you may have an opportunity to structure alignments around sentence in a very different life. >> It's an open item on the compensation. Until they agree, we'll watch. It was ideas. We'll see what tracks. But to me, in my opinion, what I think's gonna be killer for game game one here. This of this revolution is the people that come out of the woodwork because cloud attracts attract smart people and smart people are leaning into the government with cloud. It was the other way around before the cloud people, I don't want to get involved in government, and that was a big ding on government attracting qualified people. So I think Cloud is going to attract some smart people that want to help for the purpose and mission of whatever the outcome of that political or agency or government initiative with a cyber security there. People will care about this stuff who want the social equity not so much, >> Yeah, I think that's >> going to be a wild card. I think we're going to see like a new might in migration of talented people coming into quote assist government. That's a work for government to figure out how to be better at whatever the competition is and that is going to be I think the first lever of you start to see new names emerge. This person who just changed the organization over here become a hero Dev Ops mindset being applied to new environments. >> And we've seen that to some extent with the U. S. Digital service with 18 half where you have industry leaders from the commercial side moving into public sector and working in government for a time and then matriculating back into the public sector and the private sector, I think that there winds up being a lot of opportunity for more programs like that of scaling this stuff out >> and career change and career passer tissue. And there is this more fluid iti. As you're saying, >> I think that money isn't everything. You know. There's a lot of research that shows up to a certain threshold of income. You >> don't get that much happier. I don't know if Jeff >> basis is that much happier than us. I mean, >> we live in a little more bank and say, you know, >> you see the other side of it, too, is you build all these things together where you have okay. What? >> What is it >> that moves people? What do they care about. It's not just money, and I think that the old styled the old are very strict hierarchy within organizations where things are decided by tenure. Service is a bit of a problem if you have someone who works for. The EPA has been doing a deep dive cloud work for 10 years. There's nothing specific to the EPA about what that person has mastered. They shouldn't be able to laterally transition into the FDA, for example, >> Jackson Fireside Chat, Those interesting point about the fire phone that they talked about. And this is the transfer ability of skill sets and you getting at the thing that I will notice is that with Cloud attracts this interdisciplinary skill sets so you don't have to be just a coder. You khun, note how code works and be an architect, or you could be a change agent some somewhere else in an organization. So that's >> going to >> be interesting. That's not necessarily what how governments have always been siloed right? So can can these silos can these old ways of doing things. This is the question. This is why it's fun to cover this market. >> We're already >> seeing that in the public sector were being able to write code is rapidly transitioning into a very being very similar to I can speak French. Great. That's not a career in and of itself. That's a skill sad that unlocks of different right. A different career paths forward, but it doesn't wind up saving anything. It doesn't want a preserving its own modern aristocracy path forward or >> use the building an example. I don't have to learn how to pour concrete organ, right? The blueprints. Yes. So as we start getting into these systems conversations, you're going to start to see these different skill sets involved. Huge opportunity. If >> you're in >> school today and you're studying computer science, great learned something else, too, because the intersection between that and other spaces are where the knish opportunities are. That's the skill set of the future. That's where you're going to start seeing opportunities. Do not just succeed personally, but start to change the world. >> But Cory Great. Thanks for coming on and make an appearance and sharing what you found on the hallways. Good to see you. Coop con in Europe. Thanks for holding down the fort there. >> Of course I appreciate it. It was an absolute Bonner. >> Excellent. Great. Well, thank you so much. Thank >> you. I'm Rebecca Knight for John Furrier. Stay tuned. You are watching the Cube.

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)

>> [Announcer] Live from Berlin, Germany, It's The Cube, covering NetApp Insight 2017, brought to you by NetApp. >> We are wrapping up a day of coverage at NetApp Insight on The Cube. I'm Rebecca Knight, along with My cohost, Peter Burris. So, we've had a lot of great interviews here today. We've heard from NetApp executives, customers, partners about this company's transformation, and about what it's doing now to help other companies have a similar transformation. What have been some of your impressions of where NetApp is right now, and what it's saying? >> I think it starts with the observation that NetApp realized a number of years ago that if it was just going to be a commodity storage company, it was gonna have a hard time, and so NetApp itself went through a digital transformation to try to improve its understanding of how customers really engaged with it, how it could improve its operational profile's financial footprint, and the result of that was a company that, first off, was more competitive, but also that had learned something about digital transformation, and realized the relationship between the products that they were selling, the services that they were providing, the ecosystem they had that they could tap, been working with customers, and said, what is we took this knowledge, applied it to those things, what would we end up with? And so we now have a company that is still talking about products, but very much it's also talking about what businesses could do in day to day differently to effect the type of transformation that NetApp itself has been going through, and it's a compelling story. >> And you're describing this introspection that the company did, as you said, if we can't survive with our old business model, what can we do differently, and now eating it's own dog food, but then telling other companies about its story, and how its made changes. I mean, do you think NetApp is where it should be today? Are you pleased with the progress you've seen? >> Well that's one of the great challenges in the tech industry today, is nobody's quite sure where they should be. >> [Rebecca] There are no benchmarks. >> Because nobody's sure what's going on underneath them. So many years ago, in response to a reporter's questions about IBM, they said, well what do you think? Is IBM going to be successful at moving the aircraft, turning the aircraft carrier? And I said, you don't get it. IBM's problem is not that they're trying to turn the aircraft carrier, it's that they're trying to rotate the ocean, so that they could go straight, and everybody else's position would change, and that's a lot of what's happening in the technology industry today, as the people are turning, the ocean's being rotated, and there are a couple of companies, like AWS, that seem to have their fingerprint, or their finger on some of those changes. I'm not sure NetApp has that kind of a presence in the industry, but what is clear is that the direction that NetApp has taken is generating improved financial results, a lot better customer satisfaction, and it's putting them into position to play in the next round, so to speak, of competition in this industry, and in an industry that's changing this fast, that, all by itself, is a pretty good position to be in. >> Well, you know, and you're talking about the changing industry, and then also the changing employment needs that this company has in terms of getting people in their workforce who really understand, not just that data in an asset, which is what we keep hearing today, too, but really understanding how to capture the data, tease out the right insights from the data, and then deploy a strategy based on those insights that actually will create value to the business, whether that's acquiring new customers, or saving money, or earning new lines of business, too. >> Well, for example, we had a great conversation with Sheila Fitzpatrick about GDPR, this phenomenal conversation. Sheila is in charge of privacy at NetApp, and the decision that she drove was to not just to GDPR, NetApp have to GDPR here in Europe, but to GDPR across the entire company. Now two years ago, I don't know that a NetApp person would have come onto The Cube and talked about GDPR, but that is a problem, that is a challenge that every business is facing, and bringing somebody on that has made some really consequential decisions for a company like NetApp to be able to say, here's how other businesses need to think about GDPR, think about data privacy, is a clear example of NetApp trying to establish itself as a thought leader about data, and not just a thought leader about commodity storage. So I think there's a lot of changes that NetApp's gonna go through. They still are talking about on tap, they still are talking about HCI, they're talking about all the various flash products that they have, so that's still part of their conversation, but increasingly they're positioning those products, not in terms of price performance, but in terms of applications to the business based on the practical realities of data. >> And I also think we've heard a number of executives talk about NetApp having a more consultative relationship with its clients and partners, and really learning from them, how they're doing things, and then sharing the learnings at events like NetApp Insight, here, and just really on the ground more, working in partnership with these companies, too. >> Data is a physical thing, and I think a lot of people forget that. A lot of people just look at data and say, oh it's this ephemeral thing, it's out there, and I don't much have to worry about it, but physics is an issue when you're working with data. Adam Steltzner, Dr. Adam, the gentleman from NASA, he talked about the role that data science is playing in NASA Mars exploration, talked about the need to worry about sparse data, because they have dial up speeds to send data back from a place like Mars. They're working on problems, but when you start thinking in those terms, the physical limitations, the physical realities, the physical constraints of data become very real. GDPR is not a physical constraint, but it's a legal constraint, and it might as well be physics. If a company does something, we heard, for example, that there are companies out there, based on their practices and how they were hacked, would have found themselves facing $160 billion liability. >> [Rebecca] Yeah. >> Now that may not be physics, you know, I can only move so much data back from Mars, but that is a very real legal constraint that would have put those companies out of business if GDPR governance rules had been in place. So what's happening today is companies, or enterprises are looking to work with people who understand the very physical, practical, legal, and intellectual property realities of data, and if NetApp is capable of demonstrating that, and showing how you could turn that into applications, and into infrastructure that works for the business, then that is a great partner for any enterprise. >> Well do you think that other companies get it? I mean, the sense of where we are today? You use this example of GDPR, and how it really could have sent companies out of business if those rules had been in place, and they had been hacked, or suffered some huge data breach. Do you think that NetApp is setting itself up as the thought leader, and in many ways is the thought leader? Are there companies on the same level? >> No, they're not, and certainly there are a lot of tech companies that are moving in that direction, and that they're comparable with NetApp, and working both close with NetApp, and in opposition to NetApp, at least competitively, but the reality is that most enterprises are, how best to put this? Well, what I like to say is William Gibson, the famous author who coined the term cyberspace, for example, once said, the future's here, it's just evenly distributed. So there are pockets of individuals in every company who are very cognizant of these challenges, the physical realities of data, what it means, what role data actually plays, what does it mean to actually call data an asset? What's the implications on the business of looking at data as a asset? That's in place in pockets, but it's not something that's broadly diffused within most businesses, certainly not our client base, not the Wikibon angle client base, is certainly not broadly aware of some of these challenges. A lot of things have to happen over the course of the next few years for executives, and rank and file folks to comprehend the characteristics, or the nature of these changes, start to internalize, start to act in concert with the possibilities of data, as opposed to in opposition to the impacts of data. >> And those are the people who, we had guests on today just talked about the data resisters, because there are those in companies, maybe they're just an individual in a company, but that can have a real impact on the company's strategy of moving forward, deploying its data smartly. >> Yeah, absolutely, and we also had the gentleman from The Economist who made the observation that concerns about artificial intelligence impacts employment might be a little overblown. >> [Rebecca] Right, right. >> So a lot of those data resisters might be sitting there asking the question, what will be the impact of additional data on my job? And it's a reasonable question to ask, because if your business, we also talked about physicians. A radiologist, for example, someone who looks at x-rays has historically not been a patient facing person. They would sit in the back and look at the x-rays, they would write up the results, and they would give them to the clinician, who would actually talk to the patient. I, not too long ago, saw this interesting television ad where radiologists presented themselves as being close to the patient. Why? Because radiology is one of those disciplines in medicine that's likely to be strongly impacted by AI, because AI can find those patterns better than, often, a physician can. Now the clinician may be a little less effected by AI, because the patient is a human being that needs to have their hand held. >> [Rebecca] And their life is on the line. >> Their life is on the line. The healing and treatment is about whether or not the person is able to step up and heal themselves. >> [Rebecca] Right. >> So there's going to be this kind of interesting observation over the next few years. Folks that work with other people will use data to inform. Folks that work with machines, folks that don't work with other people, are likely to find that other machines end up being really, really good at their job. >> [Rebecca] Right. >> Because of the speeds of data, at the compactness of data, human beings just cannot respond to data as fast as a machine, but machines still cannot respond to people as well as people can. >> And they don't have empathy. >> And they don't have empathy, so if I were to make a prediction, I would say that, in the future, if your job is more tied to using machines, yeah, you got a concern, but if your job is tied to working with people, your job is gonna be that much more important, and increasingly, the people that are working with machines are gonna have to find jobs that have them work with other people. >> Right, right. Well it's been a great day. It's fun to work with you. This is our first time together on The Cube. It was a great day. >> Well The Cube is a blast. >> The Cube is a blast. It's a constant party. I'm Rebecca Knight for Peter Burris, this has been NetApp Insight 2017 in Berlin. We will see you next time.

(upbeat electronic music) >> Announcer: Live, from Berlin, Germany it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host Peter Burris. We are joined by Deepak Visweswaraiah. He is the senior vice president for data fabric manageability at NetApp. Thanks so much for coming on the show, Deepak. Thank you. So let's talk about the data fabric, and why modern IT needs it to do what it needs to do. For acceleration. I think anyone attending the conference, I thought the keynote that happened yesterday Kenneth Corky from Economist actually talked about how data actually is growing. And then how much of that is becoming more and more important to companies. Not only just from an ability to be able to actually handle data, but how they make their decisions based on the amount of data that they have today. The fact that we have that technology, and we have the mindset to be able to actually handle that data, I think gives that unique power to customers who actually have that data. And within their capacity. So, if you look at it in terms of the amount of data growing and what companies are trying to do with that, the fact is that data is not all in one place, it's not all in one format, it's not all just sitting in some place. Right, in terms of the fact that we call it, you know, data being diverse, data being dynamic and then what have you. So, this data, for any CIO, if you talk to an IT organization and ask them in terms of do you even really know where all your data lives, they probably, you know, 80% of the time they don't know where it is all. And they do not know who is accessing what data. Do they actually really have the access or the right people accessing the right data? And then what have you. So, being able to look at all of this data in different silos that is there, to be able to have visibility across these, to be able to actually handle the diversity of that data, whether it is structured, unstructured, comes from, you know, the edges of the network, whether it is streaming, and different types of, you know, media for that matter, whether it is streaming, video, audios, what have you. With that kind of diversity in the data, and the fact that it lives in multiple places, how do you handle all of that in a seamless fashion? Having a ability to view all of that and making decisions on leveraging the value of that data. So, number one, is really to be able to handle that diversity. What you need is a data fabric that can actually see multiple end points and kind of bring that together in one way and one form with one view for a customer. That's the number one thing, if you will. The second thing is in terms of being able to take this data and do something that's valuable in terms of their decision making. How do I decide to do something with it? I think one of the examples you might have seen today for example, is that, we have 36 billion data points coming from our own customer base, that we bring back to NetApp, and help our customers to understand in the universe of the storage end points with all the data collected, we can actually tell them what may proactively tell them, what maybe going wrong what can actually they do better. And then how can they do this. This is really what that decision making capability is to be able to analyze. It's about being able to provide that data, for analytics to happen. And that analytics may happen whether it happens in the cloud, whether it happens where the data is, it shouldn't really matter, and it's our responsibility to provide or serve that data in the most optimized way to the applications that are analyzing that data. And that analysis actually helps make significant amount of decisions that the customers are actually looking to. The third is, with all of this that is underlying infrastructure that provides the capability to handle this large amount of data, not only, and also that diversity that I talked about. How do you provide that capability for our customers, to be able to go from today's infrastructure in their data center, to be able to have and handle a hybrid way of doing things in terms of their infrastructure that they use within their data center, whether they might actually have infrastructure in the cloud, and leveraging the cloud economics to be able to do what they do best, and, or have service providers and call locators, in terms of having infrastructure that may be. Ability to be able to seamlessly look all of that providing that technology to be able to modernize their data center or in the cloud seamlessly. To be able to handle that with our technology is really the primary purpose of data fabric. And then that's what I believe we provide to our customers. So, people talk about data as an asset. And folks talk about what you need to ensure the data becomes an asset. When we talk about materials we talk about inventory we talk about supply chain, which says there's a linear progression, one of the things that I find fascinating about the term fabric even though there's a technical connotation to it, is it does suggest that in fact what businesses need to do is literally weave a data tapestry that supports what the business is going to do. Because you cannot tell with any certainty it's certainly not a linear progression, but data is going to be connected in a lot of different ways >> Deepak: Yeah To achieve the goals of the business. Tell us a little bit about the processes the underlying technologies and how that informs the way businesses are starting to think about how data does connect? >> Deepak: Can you repeat the last part? How data connects, how businesses are connecting data from multiple sources? And turning it into a real tapestry for the business. Yeah, so as you said, data comes in from various different sources for that matter, in terms of we use mobile devices so much more in the modern era, you actually have data coming in from these kind of sources, or for example in terms of let's say IoT, in terms of sensors, that are all over the place in terms of how that data actually comes along. Now, let's say, in terms of if there is a customer or if there is an organization that is looking at this kind of data that is coming from multiple different sources all coming in to play the one thing is just the sheer magnitude of the data. What typically we have seen is that there is infrastructure at the edge, even if you take the example of internet of things. You try and process the data at the edge as much as you can, and bring back only what is aggregated and what is required back to you know, your data center or a cloud infrastructure or what have you. At the same time, just that data is not good enough because you have to connect that data with the internal data that you have about-- Okay, who is this data coming from and what kind of data, what is that meta-data that connects my customers to the data that is coming in? I can give you a couple of examples in terms of let's say there is an organization that provides weather data to farmers in the corners of a country that is densely populated, but you really can never get into with a data center infrastructure to those kind of remote areas. There are at the edge, where you have these sensors in terms of being able to sample the weather data. And sample also the data of the ground in itself, it terms of being able to, the ultimate goals is to be able to help the farmer in terms of when is the right time to be able to water his field. When is the right time to be able to sow the seeds. When is the right time for him to really cut the crops, when is the most optimized time. So, when this data actually comes back from each of these locations, it's all about being able to understand where this data is coming from, from the location, and being able to connect that to the weather data that is actually coming from the satellites and relating that and collating that to be able to determine and tell a farmer on his mobile device, to be able to say okay, here is the right time, and if you don't actually cut the crops in the next week, you may actually lose the window because of the weather patterns that they see and what have you. That's an example of what I could talk about as far as how do you connect that data that is coming in from various sources. And as a great example, I think, was at the keynote yesterday about a Stanford professor talking about the race track, it's really about that race track and not just about any race track that where the cars are actually making those laps, to be able to understand and predict correctly in terms of when to make that pit stop in a race. You really need the data from that particular race track because it has characteristics that have an impact on the wear and tear of the tires. For example. That's really all about being able to correlate that data. So it's having the understanding of the greater context but the specific context too. >> Deepak: Absolutely, absolutely. Great. You also talked about you talked about the technology that's necessary, but you also mentioned the right mindset. Can you unpack that a little bit for our viewers? The mindset I talked about earlier, was really more in terms of can we actually if you think some time before, we couldn't have attacked some of the problems that we can afford to today. It's really having the mindset of being able to from the data I can do things that I could never do before. We could solve, we can solve things in the nature of being able to being able to impact lives if you will. One of our customers leads a Mercy technology. Has built a out care platform, that provides that has a number of healthcare providers coming together. Where they were actually able to make a significant impact where they could actually determine 40% of the patients coming into their facilities, really were prevented from coming back into with a sepsis kind of diagnosis. Before then, they reduce that sepsis happening in 40% of the time. Which is a significant, significant impact, if you will, for the human. Just having that mindset in terms of you have all the data and you can actually change the world with that data, and you can actually find solutions to problems that you could never have before because you have the technology and you have that data. Which was never there before. So you can actually make those kinds of improvements. It's all about extracting those insights. >> Deepak: Absolutely. Thank you so much for coming on the show, Deepak. It was a pleasure having you Thank you for having me. Thank you very much. I'm Rebecca Knight, for Peter Burris, we will have more from NetApp Insight in just a little bit. (dramatic electronic music)

>> Narrator: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here is your host, Dave Vellante. >> Hi, everybody. This is Dave Vellante. Welcome to a special presentation, Conquering Big Data. This is part one: Data as Capital, and this is sponsored by Oracle. With me is Paul Sonderegger, a big data strategist from Oracle. Paul, it's good to see you in theCUBE again. >> It's good to be here. >> Okay, so we were talking earlier. This whole thing for us at SiliconANGLE Media started around 2010 when we started to pay attention to the dupe trend, and data is the new source of competitive advantage, data is the new oil, and in six or seven short years, we've come quite a long way. Everybody says that they want to be data-driven. Where are we today from your perspective? >> I think the cover article of the Economist just a couple of weeks ago captured it pretty well where it said the data is the world's most valuable resource, and part of the evidence for that is that the top five most valuable listed firms or publicly listed firms worldwide are all data-heavy technology companies, so we're at the point now where the effect of accumulating data, stocks of data capital is obvious and using it is obvious but nonetheless, we are still at the beginning of the changes that the rise of data capital are going to bring. >> As I said, most executives would say they want their companies to be data-driven. Many actually say, "Oh yes, our company is data-driven," but when you start to peel the onion, do you agree that most companies aren't really as data-centric as they may claim to be? >> A lot of companies, they just struggle with the philosophy of what data is and what effect it has on the way they compete. Don't get me wrong. All executives understand that more data helps you make better decisions. That's evergreen. That's a good idea. But a lot of companies fail to appreciate that data. Contrary to popular wisdom, is not abundant. There's a lot of it but it consists of countless unique observations, and so really, the way that executives need to think about data is that it is scarce. Data really consists of observations of things that are going on in the world, and if you are not there when those activities happen, when these events take place, your opportunity to capture those observations is lost. It doesn't come back. >> Okay, so let's get into this. You've written about and talked about the three principles of data capital, so let's start there and go through them. Principle one is data comes from activity. Okay. I guess that sounds obvious but what does it mean? >> This is the issue that we were just talking about. This is the first principle of data capital, that data comes from activity and a lot of executives will say, "Yes, obviously. "We put in this big ERP application back in the '90s, "and it captured all of this data about our own processes, "so then we reported on it "so we can see what's going on." All of that is true but what a lot of executives miss is that they're in competition for data. So the data that ERP apps and CRM apps and all of these enterprise applications produce, those are all data from the company's own activities but what's happening now is the digitization and datafication of activities outside the company, activities that customers carry on. It could be in everyday consumer life, it could be in B2B environments as well, it could be the movement of trucks, the movement of inventory done through supply chains run by partners. Executives have to get the habit of looking out at the world and seeing the data that is not there yet, information coming from these activities that is lost. It's either captured on paper or it's not captured at all, and putting sensors and mobile apps into those activities before their rivals do because when an activity happens, if you are not part of it, your opportunity to capture its data is lost. It doesn't come back. >> So data, raw data is abundant but the data that is actually valuable to organizations you're saying is scarce and takes a lot of refinement to use the oil analogy. >> Think about it this way. Remember Sir Edmund Halley, the guy who predicted the comet? >> Dave: Right. >> Sir Edmund Halley predicted when you will die. This is actually one of his signal achievements a lot of people have forgotten about. Halley was the first one to work out mortality tables, what is expected, what is life expectancy. The reason that that could be valuable is that he showed that life insurance policies that the British government was offering were mispriced depending on how old you were and how much longer you expected to live. The data that he used to make those calculations was not his. It came from Breslau. It came from another city, and it came from a particular church, which had kept really rigorous records during that time. Before the priests of Breslau said, "Hey, you could use this data," Halley had no ability to make this prediction. He had no ability to identify the mispricing of life insurance policies. That data, those observations was a scarce resource concentrated in another city that he needed in order to figure all this out. We have exactly the same situation now. Exactly the same situation now where companies taking observations of activities that they conduct with their partners, activities that they conduct with their customers build up into these concentrations of observations that are unique, they're proprietary, and they are the necessary fuel for creating new digital products and services. >> And many of those observations come from data outside of the organization. Okay, let's look at the second principle. Data makes more data. What are you talking about here? Are you talking about metadata? Can you explain? >> Sure. Providing data to people so they can make better decisions is always a good thing. It has been a good thing for a long time. It will continue to be a good thing. But the real money is in algorithms. The real money is in using these stocks of data capital to feed algorithms for two reasons. One is that algorithms can take decisions beyond human scale either in a more situations per unit time or simply faster than human beings can. The second reason it's important is because algorithms produce data about their own performance, which can be fed back into the model to improve their future performance. This is true of dynamic pricing algorithms, which capture data about what change did this price switch have on conversion rates, for example. It applies in fraud detection. We have customers who are banks who look at how many legitimate transactions did our current fraud detection algorithm wrongly flagged because they get complaints about it, how many fraudulent transactions did our current algorithm actually missed because investigations get kicked off through other processes. Those observations about the performance of the algorithm go back into the model improving its future performance. This applies to algorithms for inventory detection and fleet movement. So the second principle is the data tends to make more data, and this virtuous cycle with algorithms creates a competitive advantage that is very, very hard to catch. >> And I'm hearing you have to act on that data and continue to iterate. It's not obviously a one-shot static deal. We kind of all know that but it's this constant improvement that's going to give you that competitive edge. >> That's really the key, and this is at the very heart of machine learning, so all the talk about AI and all the talk about machine learning, one of the tactics of machine learning algorithms is that they learn from their own behaviors and improve their behaviors over time, so really, this particular kind of competitive advantage is baked in to the practice of machine learning and AI. >> Okay, great. Now your third principle is that platforms tend to win. You've written that this is where the real money is, so what do you mean by platforms? Are you talking about platforms versus products? What do you mean? >> Here, we're talking about platforms not as technologists often think about it where there is a foundational technology and then you build on top. We're talking about platforms as economists see them, so through the eyes of an economist, a platform is an intermediary that serves a two-sided market, and usually it makes it easier, cheaper, faster for the two sides to do business with each other. So just to use a very familiar example, credit cards are a payment platform, and they serve a two-sided market. On one side, you have merchants. On the other side, you have consumers. And of course, we as consumers, we want to carry the card more merchants will take. Merchants want to take the card more consumers have in their pocket. And so growth on one side of the market tends to encourage growth on the other side of the market. They kind of ladder up like that, and that means that platform competition tends toward a winner-take-all outcome, and so we have seen this in, say, the competition for the desktop operating system. That was a platform competition. We see it in the competition for the mobile operating system but it's also something that you see in gaming platforms, for example. More game developers want to develop for the platforms where there are more gamers. Gamers want to have the platform where there are more games. The reason that this matters now is because the digitization and datafication of more daily activities brings platform competition to industries that have never see it before. So just to use a simple example, look at farming. You can now have a drone. It will go out and take pictures of a field, and the drone will do spectrographic analysis of the images, and it's looking for green, which is a proxy for the degree of chlorophyll in the plants. It uses that information to inform the fertilizer spreader about how to tailor the fertilizer to the plants, not to the field but to the individual plants. The tractor in the middle is in competition to be the platform for digital agricultural services, and that is not how makers of large agricultural equipment typically think about competition. >> Okay, so let's move on. If data is so important, it's the new source of competitive advantage, we're talking today about data as capital, but the accounting field doesn't look at data as the same way in which they do a financial asset. You don't see companies recognizing the value of data on their balance sheets yet at the same time, you said the top five firms worldwide in terms of market value are data-oriented. So I'm sure that's much greater than the capital assets that they have on their books. So what's going on there? Should the accounting world be coming into the 21st century? Should companies wait until they do? What are your thoughts on that? >> I won't presume to give the accounting industry any advice on what they ought to but I will say that regardless of how the accounting standards look at data. The most successful data-driven companies, they already recognize that data is a true asset despite the fact that they cannot put it on the balance sheet as an asset with a certain dollar value. These firms, they already recognize that data is not just a record of what happened, it is a raw material for creating new digital products and services. In that way, it is capital like capital equipment, like financial capital, like if you do not have this input, you cannot create the service that you have in mind. And so that's why these data-heavy companies are not satisfied with the stocks of data capital they've got. These platform businesses are constantly on the lookout for new activities they can go digitize and datafy, adjacent activities that are next to the ones that they have already captured in order to further build out this stock of data capital, in order to create more raw material for new products and services. I will presume to give corporations in general advice, and the advice is that you've got to get this idea that data is not just a record of what happened, it is a raw material for new digital products and services. Digital products and services are the competitive field for providing value to your customers. >> So don't wait for the accounting industry to catch up is really your advice there. >> Not at all. >> So you said digitize, datafy, and that's leads us what you've talked in the past about data trade, the monetization question, so let's talk about monetization. How should organizations think about monetizing data? Should they be selling data? Should they be thinking about it differently? Why should they be monetizing data? >> The first thing to remember is that data trade is a decades-old practice. Credit bureaus were one of the first kinds of companies to build an entire business on the trade of data, and so they're accumulating information about consumers and then providing them to banks so the banks can more easily, quickly, effectively make lending decisions, and that increases access to credit, which is a good thing overall. It's a very, very useful thing. But what's happening now is that the data trade is massively expanding, buying and selling of data about different kinds of aspects of consumer buying and shopping behavior, for example but we're also starting to see the buying and selling of data in the world of the Internet of Things. As you may know, Oracle has a very large data marketplace, the largest online marketplace, a data marketplace of consumer shopping and browsing behavior, so we have five billion consumer profiles, 400 million business profiles, $3 trillion in transactions. One of the things to note about this whole business is that the data in our marketplace is created by a whole set of other firms. Just to give you one example, there's 15,000 websites which are the sources for online browsing behavior, those websites have no idea what value that data will provide to the companies who use it. They don't know. Instead, they are originating this data, and they are selling it on for these secondary purposes, and those secondary purposes really are discovered by the companies who buy the data and use it, and that data then goes into targeting marketing campaigns. It goes into refining product launch plans. It goes into redesigning social media publishing calendars and activities. The reason all this matters is because data consists of observations. The value from those observations only happens when it gets used. There is this curious issue. Just like Edmund Halley needed data from Breslau in order to figure out life expectancy and figure out the proper pricing of these insurance policies, we have the same issue today where data originates in one set of activities but the firms that create it may not create the greatest value from it, and so we need these data marketplaces in order to grow the overall value created from this digitization and datafication. >> Paul, are there pitfalls that people should, I'm sure there are many but maybe a couple you could point to that people need to think about when they enter this data monetization journey? >> Sure. One of the ones that comes out right away is personally identifiable information and invasions of privacy. So one of the ways to deal with that is to anonymize these records, strip out all the personally identifiable information, and then the next step that you can take is to aggregate them. So on that first piece about stripping out personally identifiable information, there are obvious pieces like name, first name, last name, and social security number, taxpayer ID number but new regulations in Europe, the General Data Protection Regulation, the GDPR has expanded the notion of personally identifiable information to any piece of data that could be uniquely tied back to a specific individual, so for example, something like an IMEI number, that unique code for your phone as it connects to the cellular network, in some cases perhaps even IP address. So this notion of personally identifiable information is expanding, so that's one thing for companies to be aware of. This notion of aggregation is an interesting one because even the GDPR says that if you aggregate a whole bunch of records together, and reidentification of those individual records is no longer possible, the GDPR doesn't even apply to those data products, so one of the things companies should be thinking about is can they create data products that provide observations about a part of the world that other firms are interested in and yet at a high enough, at a large enough level of aggregation that the issues are around personally identifiable information are all resolved. >> And this becomes really important. GDPR goes in effect next May, next May 18. >> Next May. >> So things to think about. All right. Last question before we summarize this. Metrics, even though the accounting industry isn't counting data as an asset, are there new metrics that organizations are using or should be using to quantify the value of their data? >> There are. McKinsey writes about this occasionally. They have taken just a really simple, back of the envelope calculation for looking at revenue per employee for companies in a given industry, and then calling out the radical differences in revenue per employee for firms known to be highly data-centric versus others who perhaps are older or have been in the business longer or who have greater traditional capital assets, so something even that simple can be a useful tool but I suspect that we're going to need a new family of metrics. There has been talk for a while about data productivity, about measuring that. It's often been difficult to do but we've entered into a new world now where observations about how data gets used within a company, looking at the queries going against the data management infrastructure is now not only possible but cost-effective. I suspect that we're actually going to see a new metric of data productivity that is related to traditional measures of labor productivity and capital productivity, which economists have known about for a long time, but I think we'll see a way of measuring the work done, the value-creating work done by a company's digital data infrastructure which can then be related to what's their return on invested capital as well as what is their labor productivity. I think we'll start to see a new set of metrics like that. >> And it maybe is implicit in even the McKinsey example of revenue per employee, something as simple as that. Maybe if you could isolate that and identify the input of labor and capital, maybe you can get to that. >> And then if you could isolate the input of work done by queries acting on data, then yeah, you ought to be able to establish that relationship. >> Okay, good. Let's summarize. Before I do, I just want to remind people to think about some questions. We're going to have a Q&A session right after this in the chat area right below. Okay, so we kind of introduced the notion of data capital and talked about why it's important. You mentioned the top five firms worldwide in terms of value are data-oriented companies, and then we talked about your three principles around data capital. Why don't you summarize the three for us? >> Sure. Data comes from activity, so digitize and datafy activities outside your firms before your rivals do. Data tends to make more data, so feed the data you've got into algorithms so that they can create data about their own performance creating a virtuous cycle. And then the third is platforms tend to win, and here, companies really need an active imagination to look at their industries and their business models and imagine them, either imagine their own business model reinvented as a platform, an intermediary between two side of the market where the digitization and datafication helps them create a new kind of value, or imagine another firm like that that comes to attack them. >> Okay, and then we talked about the accounting industry, how it has not begun to recognize data as value, put in a balance sheet, et cetera. You chose not to suggest that they should or should not. Rather, you chose to focus on the companies, the organizations that they should not wait for the accounting industry to catch up, that they should really dive in and begin thinking about how to digitize, you call it datafy, and that led to a conversation on monetization, and then you talked about data markets as a critical emerging, re-emerging entity and dynamic that's occurring there. Maybe some comments? >> Sure. For decades now, we've had businesses with traditional business models working as data sellers. Again, credit bureaus are a good example, market research firms are another good one, LexisNexis, Bloomberg but I think what we're going to see is a rise in data marketplaces where you've got a new kind of business model. It's an exchange. And you've got data originators providing data into the marketplace for sale, and you've got buyers on the other side, probably mostly companies but there could be nonprofits, there could be governments as well actually, and those, those are actually really exciting because exchanges like that, increases in data trade help to spread the wealth of data capital to more parties. It makes it possible for companies who need data but have not datafied the activities that they just discovered they care about go and source that data. It also helps firms who have managed to create these data capital assets but they're not sure what to do with them themselves make them available to places where they can create value. >> Excellent. Then you talked about ways to avoid some of the pitfalls, particularly those associated with personal information and the upcoming GDPR, and then we wrapped with a conversation around metrics, some simple metrics have been posed like revenue per employee, and you noted a McKinsey study that those data-oriented companies have a higher revenue per employee but then you suggested that we're going to start peeling back those metrics and looking at the contribution of labor plus capital in terms of what you call, a new metric called data productivity, so we're going to follow that and hopefully talk to you down the road and learn more about that. Paul, thanks so much for spending some time with us. I really appreciate it. >> Thank you. >> You're welcome. Okay, now as I say, think about your questions. Go down below. Paul and I will be here for a Q&A in the chat below. Thanks for watching, everybody. We'll see you next time. (light music)

>> Announcer: From the Silicon Valley Media Office in Boston, Massachusetts, it's The Cube! Now, here's your host, Dave Vellante. >> Hi, everybody, welcome to a special Silicon Angle, The Cube on the ground. We're going to be talking about data capital with Paul Sonderegger, who is a big data strategist at Oracle, and he leads Oracle's data capital initiative. Paul, thanks for coming in, welcome to The Cube. >> Thank you, Dave, it's good to be here. >> So data capital, it's a topic that's gaining a lot of momentum, people talking about data value, they've talked about that for years, but what is data capital? >> Well, what we're saying with data capital, is that data fulfills the literal economic textbook definition of capital. Capital is a produced good, as opposed to a natural resource that you have to invest to create it, and it is then an necessary input into some other good or service. So when we define data capital, we say that data capital is the recorded information necessary to produce a good or service. Which is really boring, so let me give you an example. So imagine, picture a retailer. A retailer wants to go into a new market. To do that, the retailer has to expand its inventory, it has to extend its supply chain, it has to buy property, all of these kinds of investments. If it lacks the financial capital to make all of those investments, it can't go, cannot go into that new region. By the same token, if this retailer wants to create a new dynamic pricing algorithm, or a new recommendation engine, but lacks the data to feed those algorithms, it cannot create that ability. It cannot provide that service. Data is now a kind of capital. >> And for years, data was viewed by a lot of organizations, particularly general counsel, as a liability, and then the big data meme sort of took off and all of a sudden, data becomes an asset. Are organizations viewing data as an asset? >> A lot of organizations are starting to view data as an asset, even though they can't account for it that way. So by current accounting standards, companies are not allowed to treat the money that they spend on developing information, on capturing data, as an asset. However, what you see with these online consumer services, the ones that we know, Uber, Airbnb, Netflix, Linkedin, these companies absolutely treat data as an asset. They treat it, not just as a record of what happened, but as a raw material for creating new digital products and services. >> You too, you tweeted out an article recently on Uber, and Uber lost about, what is it? 1.2 billion- >> At least. >> Over six months, at least. >> At least. >> And then the article calculated how much it was actually paid, I mean basically, the conclusion was it paid 1.2 billion for data. >> Yeah. >> It was about $1.20 per data for ride record, which actually is not a bad deal, when you think about it that way. >> Well, that's the thing, it's not a bad deal when you consider that the big picture they have in view is the global market for personal transportation, which The Economist estimates is about 10 trillion dollars annually. Well, to go after a 10 trillion dollar market, if you can build up a unique stock of data capital, of a billion records at about a billion dollars per record, that's probably a pretty good deal, yeah. >> So, money obviously is fungible, it's currency. Data is not a currency, but digital data is fungible, right, I mean, you can use data in a lot of different ways, can't you? >> No, no, it's, and this actually is a really important point, it's a really important point. Data is actually not fungible. This is part of data's curious economic identity. So data, contrary to popular wisdom, data is not abundant. Data consists of countless unique observations, and one of the issues here is that, two pieces of data are usually not fungible. You can't replace one with the other because they carry different information. They carry different semantics. So just to make it very, very concrete, one of the things that we see now, a huge use of data capital is in fraud detection. And one of our customers handles the fraud detection for person-to-person mobile payments. So say you go away for a weekend with a friend, you come back, you want to split the tab, and you just want to make a payment directly to the other person. You do this through your phone. Those transactions, that account to account transfer, gets checked for possible fraudulent activity in the moment, as it happens, and there is a scoring algorithm that sniffs those transactions and gives it a score to indicate whether or not it may be fraudulent or if it's legitimate. Well, this company, they use the information they capture about whether their algorithm captured, caught, all of the fraudulent transactions or missed some, and whether that algorithm mistakenly flagged legitimate transactions as fraudulent. They capture all of those false positives and false negatives, feed it back into the system, and improve the performance of the algorithm for the next go around. Here's why this matters: the data created by that algorithm about its own performance, is a proprietary asset. It is unique. And no other data with substitute for it. And in that way, it becomes the basis for a sustainable competitive advantage. >> It's a great example. So the algorithm maybe is free, you can grab an algorithm, it's how you apply it that is proprietary, and now, okay, so we've established that the data is not fungible. But digital data doesn't necessarily have high asset specificity. Do you agree with that? In other words, I can use data in different ways, if it's digital. Yeah, absolutely, as a matter of fact, this is one of the other characteristics of data. It is non-rivalrous, is what economists would call it. And this means that two parties can use the same piece of data at the same time. Which is not the case with, say, a tractor. One guy on a tractor means that none of the other people can ride that tractor. Data's not like that. So data can be put to multiple uses simultaneously. And what becomes very interesting is that different uses of data can command different prices. There's actually a project going on right now where Harvard Law School is scanning and digitizing the entire collection of US case law. Now this is The Law, the law that we all as Americans are bound to. Yet, it is locked up in a way, in just, in all of these 43,000 books. Well, Harvard and a startup called Ravel Law, they are working on scanning and digitizing this data, which can then be searched, for free, all of these, you can search this entire body of case law, for free, so you can go in and search "privacy," for example, and see all of the judgements that mention privacy over the entire history of US case law. But, if you want, for example, to analyze how different judges, current sitting judges, rule on cases related to privacy, well, that's a service that you would pay for from Ravel. The exact same data, their algorithms are working on the same body of data. You can search it for free, but the analysis that you might want on that same data, you can only get for a fee. So different uses of data can command different prices. >> So, some excellent examples there. What are the implications of all of this for competitive strategies, what should companies, how should they apply this for competitive strategies? >> Well, when we think about competitive strategy with data capital, we think in terms of three principles of data capital, is what we call them. The first one is that data comes from activity. The second one is, data tends to make more data, and the third is that platforms tend to win. So these three principles, even if we just run through them in their turn, the first one, data comes from activity, this means that, in order to capture data, your company has to be part of the activity that produces it at the time that activity happens. And the competitive strategy implication here is that, if your company is not part of that activity when it happens, your chance to capture its data is lost, forever. And so this means that interactions with customers are critical targets to digitize and datify before the competition gets in there and shuts you out. The second principle, data tends to make more data, this is what we were talking about with algorithms. Analytics are great, they're very important, analytics provide information to people so that they can make better choices, but the real action is in algorithms. And here is where you're feeding your unique stock of data capital to algorithms, that not only act on that data, but create data about their own performance, that then improve their future performance, and that data capital flywheel becomes a competitive advantage that's very hard to catch. The third principle is that platforms tend to win. So platforms are common in information-intensive industries, we see them with a credit card, for example, we see them in financial services. A credit card is a payment platform between consumers on the one side, merchants on the other. A video game console is a platform between developers on the one side and gamers on the other. The thing about platform competition is that it tends to lead toward a winner-take-all outcome. Not always, but that's how it tends to go. And with the digitization and datification of more activities, platform competition is coming for industries that have never seen it before. >> So platform beats product, but it's winner-take-all, or number two maybe breaks even, right? >> That tends to be the way it goes. >> And number three loses money, okay. The first point you were making about, you've got to be there when the transaction occurs, you've got to show up. The second one's interesting, data tends to make more data. So, and you talked about algorithms and improving and fine-tuning in that feedback loop. I would imagine customers are challenged in terms of investments, do they spend money on acquiring more data, or do they spend money on improving their algorithms, and then the answer is got to do both, but budgets are limited. How are customers dealing with that challenge? >> Well, prioritization becomes really critical here. So not all data is created equal, but it's very difficult to know which data will be more valuable in the future. However, there are ways to improve your guess. And one of the best ways is to, go after data that your competition could get as well. So this is data that comes from activities with customers. Data from activities with suppliers, with partners. Those are all places where the competition could also try to digitize and datify those activities. So companies should really look outside their own four walls. But the next part, you know, figuring out, what do you do with it? This is where companies really need to take a page out of actual science as they approach data science, and science is all about argument. It's all about experimentation, testing, and keeping the hypotheses that are proven and discarding the ones that are disproven. What this means is that companies need a data lab environment, where they can cut the time, the cost, the effort, of forming and testing new hypotheses, getting new answers to new questions from their data. >> Okay, so, data has value, you've got to prioritize. How do you actually value the data so that I can prioritize and figure out what I should be focusing on in the lab and in production? >> Yeah, well, the basic answer is to go where the money is. So there are a couple things you can do with data. One is that you can improve your operational effectiveness, and so here, you should go look at your big cost areas, and focus your limited data science and managerial resources on trying to figure out, hey, can we become more efficient in whatever your big cost driver is? If it's shipping and logistics, if it's inventory management, if it's customer acquisition, if it's marketing and advertising, so that's one way to go. The next big thing that you can do with data is try to create a new product or service, a new ... create new value in a way that generates revenue. Here, there is a little caveat, which is that, companies may also want to consider creating new capabilities, maybe enriching the customer experience, making connections across multiple channels, that they can't actually charge for, not today. But, what they get, is data that no one else has. What they get from, let's say, making an investment into, bring together the in-store shopping experience with the, with the targeted emails, with, with communication through social feeds and through Twitter. Let's say that they invest in trying to tie that data together, to get a richer picture of their consumers' behavior. They might not be able to charge for that today. But, they may get insight into the way that shopping experience works that no one else can see, which then leads to a value-added service tomorrow. And I know it all sounds very speculative, but this is basically the nature of prototyping, of new product creation. >> Well, Uber's overused as an example, but this is a good application of Uber because they, essentially they pay for driver acquisition, which doesn't scale well. >> Yeah. >> But they get data. >> That's right. >> Because they're there at the point of the transaction and the activity and they've got data that nobody else has. >> Yeah, yeah, that's exactly right, and, you know, one of the ways to think about that is that, you're like a blackjack player, counting cards, and every time you play a hand as a company, you get data, information that may help you improve your future bets. This is why Vegas kicks out card counters, because it's an advantage for the future. But what we're talking about here, in digitizing activity with customers, every time you capture data about your interaction with those customers, you gain something simply for having carried out that activity. >> And so, thinking about, back to value for a minute, I mean I can envision some kind of value flow methodology where you assess the data intensity of the activity, and then assign some kind of, I don't know, score or a value to that activity, and then you can then look at that in relation to other activities. Is that a viable approach? >> It absolutely is. What companies need here is a new way to measure how much data they've got, how much they use, and then ascribe ... value created, you know, by that data. So the, how much they've got, you know, we can think about this, we always talk in terms of gigabytes and petabytes. But really we need some finer measurements. Data is an observation about something in the real world. And so, companies should start to think about measuring their data in terms of observations, in terms of attribute-value pairs. So even thinking about the record captured per activity, that's not enough. Companies should start thinking in terms of, how many columns are in that record? How many attributes are captured in these observations we make from that activity? The next issue, you know, how much do they use? Well, now, companies need to look at, how many of these observations are being touched, are being tapped by queries? Whether they're automatically generated, whether they are generated ad hoc by some data scientist, rooting around for some new understanding. So there's a set of questions there about, what percentage of these observations we possess are we actually using in queries of some kind? And then the third piece, how much value do we create from it? This is where ... This is a tough one, and it's really an estimation. It's, most likely what we need here is a new method for attributing the, profitabilty of a particular business unit to its use of that data. And I realize this is an estimation, but this is, there's a precedent for this in brand valuation, this is the coin of the realm when you're talking about putting a value to intangible assets. >> Well, as long as you're consistently applying that methodology across your portfolio, then, then at least you've got a relative measure and you can get back to prioritization, which is a key factor here. Is there an underlying technical architecture that has to be in place to take advantage of all this data capital momentum? >> There is, there is, companies are moving toward a hybrid cloud, big data architecture. >> What does that mean? >> It means that almost all the buzzwords are used, and we're going to need new ones. No, what it means is that, companies are going to find themselves in a situation where some of their computing activities, storage, processing, application execution, analytics, some of those activities will take place in a public cloud environment, some of it will take place within their own data centers, reconfigured to act as private clouds. And there are lots of potential reasons for this. There could be, companies have to deal with, not only existing regulations, which sometimes will prevent them from putting data up into a cloud, but they are also going to have to deal with regulatory arbitrage, maybe the regulations will change, or maybe they've got agreements with partners that are embodied in service level agreements that again require them to keep the data under their own observation. Even in that case, even in that case, the business still wants to consume all of those computing resources inside the data center as if they were services. The business doesn't care where they come from. And so this is one of the things that Oracle is providing, is an architecture for Oracle public cloud, and private cloud in the data center. It is the same on both sides of the wire. And in fact, can even be purchased in the same way so that even these, this Oracle cloud at customer, these machines, they are purchased on a subscription basis, just as public cloud capabilities are. And the reason this is good is because it allows IT leaders to provide to the business, computing capabilities, storage capabilities, you know, as needed, that can be consumed as services, regardless of where they come from. >> Yeah, so you've got the data locality issue, which is speed of light problems, you don't want to move data, then you've got compliance and governance, and you're saying, that hybrid approach allows you to have the cake and eat it, too. >> Yeah. >> Essentially. Are there other sort of benefits to taking this approach? >> Well, one of the, you know, the, one of the other pieces that we should talk about here is the big data aspect, and really what that means is, that, relational, Hadoop, NoSQL, graph database, repositories, they're all going to, they're all peers. They're all peers now, and, you know, this is Oracle's perspective, and as I'm sure you know, Oracle makes a relational database, it's very popular. Yeah, we've been doing it for a while, we're pretty good at it. Oracle's perspective on the future of data management is that Hadoop, NoSQL, graph, relational, all of these methods of data management will be peers and act together in a single high-performance enterprise system. And here's why. The reason is that, as our customers digitize and datify more of their activities, more of the world, they're creating data that's born in shapes and formats that don't necessarily lend themselves to a relational representation. It's more convenient to hold them in a Hadoop file system, and it's more convenient to hold them in just a great big key value store like NoSQL. And yet, they would like to use these data sources as if they were in the same system and not really have to worry about where they are. And we see this with, we see this with telecom providers who want to combine call data records with customer, warehouse, you know, customer data in the data warehouse. We see it with financial services companies who want to do a similar thing of combining research with portfolio investments records of what their high net worth customers have invested, with transaction data from the equities markets. So we see this polyglot future, the future of all of these different data management technologies, and their applications in the analytics built on top, working together, and existing in this hybrid cloud environment. >> So that's different than the historical Oracle, at least perceived messaging, where a lot of people believe that Oracle sees its Oracle database as a hammer, and every opportunity is a nail. You're telling a completely different story now. >> Well, it turns out there are many nails. So, you know, the hammer's still a good thing, but it turns out that, you know, there are also brads and tacks and Philips and flathead screwdrivers too. And this is just one of the consequences of our customers creating more kinds of data. Images, audio, JSON, XML, you know, spectrographic images from drones that are analyzing how much green is in a photograph because that indicates the chlorophyll content. We know, we know that our customers' ability to compete is based on how they create value from data capital. And so Oracle is in the business of making the things that make data more valuable, and we want to reinvent enterprise computing as a set of services that are easier to buy and use. >> And SQL is the lowest common denominator there, because of the skill sets that are available, is that right or? >> Well, it's funny, it's not necessarily a lowest common denominator, it turns out it's just incredibly useful. (laughs) Sequel is not just a technology standard, it's actually, in a manner of speaking, it's sort of a thinking standard. SQL is based on literally hundreds of years of hard thinking about how to think straight. You can trace SQL back to predicate logic, which was one of the critical ideas in the renaissance of mathematics and logic in the 1800s. So SQL embodies this way to think about, to think logically, to think about the attributes of things and their values and to reason about them in an automated fashion. And that is not going away, that in fact is going to become more powerful, more useful. >> Business processes are wired to that way of thinking, is what you're saying. >> That's exactly right. If you want to improve your operational effectiveness as a company, you're going to have to standardize some of your procedures and automate them, and that means you're going to standardize the information component of those activities. You can automate them better. And you're going to want to ask questions about, how's it going? And SQL is incredibly useful for doing that. >> So we went way over our time, this is very interesting discussion, but I have to ask you, what is it you do at Oracle? Do you work with customers to help them understand data strategies and catalyze new thinking? What's your day-to-day like? >> Yeah, I do a lot of this, a lot of telling the story, because we're in a huge time of change. Every 20 years or so, the IT industry goes through an architectural shift, and that changes, not just the technologies used to create value from data, but it changes the very value created from data itself. It changes what you can do with information. So, I spend a lot of time explaining these ideas of data capital, and sitting down with executives at our customers, helping them understand how to look out at the world and see the data that is not there yet, and what that means for the way that they compete, and then we talk through the competitive strategies that follow from that, and the technical architecture required to execute those strategies. >> Excellent. Well, Paul, thanks very much for sharing your knowledge with our Cube audience and coming into the Silicon Angle Media Studios here at Marlborough. >> Well, it's my pleasure. Thanks for having me. >> All right, you're welcome. Okay, thanks for watching, everybody. This is The Cube, Silicon Angle Media's special on the ground production. We'll see you next time. (peppy synth music)