 from San Jose in the heart of Silicon Valley. It's theCUBE, covering Big Data SV 2016. Now your host, John Furrier. Okay, welcome everybody. Hello, hello, hello everyone. So welcome to Big Data SV, Big Data Silicon Valley. This is SiliconANGLE Media's event where we've been to every single Hadoop world except for the first one, which was kind of like a meetup and we've been there ever since 2010 when Hadoop was just starting and we saw the early signals, SiliconANGLE Media started in Cloudera's office. It was our first office space. We were sharing some space with Cloudera. I think they had like 13 employees at the time and we saw immediately the impact of Hadoop and we started covering with theCUBE, which was in its first season, first year and it was really something that we saw right away, it was gonna be big and we knew it was gonna be hot. We saw the transformation, we saw the impact and we're so excited and so proud to have been covering it's our seventh year covering Hadoop world's now strata Hadoop and we do our format of our ESPN of tech anchor desk which was right here yesterday and today and tomorrow we do the interviews. So tonight we have a special presentation and thanks for coming. It's gonna be bars over there at five o'clock's now open and we're gonna have a keynote presentation by Peter Burris who's SiliconANGLE Media Wikibon's head of research who's gonna introduce with George Gilbert's research around some of the new forecast data we're gonna be releasing tonight. You'll see firsthand what it's all about and then we have two panels, we have an industry panel of experts and then we're gonna have a customer panel, the customer of the industry players talking about their transformation, their use of big data going beyond Hadoop and of course we're super excited to talk about that and then networking around and again have some fun and then we're gonna have hopefully a great conversation and thanks for coming. So I'd like to introduce Peter Burris, head of research SiliconANGLE Media and general manager of Wikibon Research. So give it a hand up for Peter Burris. Okay, good job. Thanks John. It's a great pleasure to be here. Thank you very much for coming. We are doing this precisely because the community is so important to the big data universe but also to what we wanna do is engage communities differently than most of the companies that create knowledge to be consumed by businesses to help them with their strategies and change. We have a different approach and we're very excited to use tonight as a way of representing that. So I'm gonna do three things in the course in the next 20 minutes or so and then I'm gonna get off so we can get some of the other analysts up here and ultimately and most importantly some of the customers. So the three things that I'm gonna do is try to convince you that it is virtually impossible to talk about digital business without really being serious about data, that there are a lot of ways of thinking about digital business out there and a lot of use cases and a lot of different application forms that people like to talk about but it all starts with data. That the grand vision of digital business cannot be pursued at all unless we really understand the evolving role of data and information in business. The second point I'm gonna make is that the set of technologies and tools that are gonna be most important in realizing this vision of the relationship between data and business will be many of the tools that we're talking about here and those that follow. That the big data ecosystem is gonna be front and center of the process of digitization, of how we go about thinking, rethinking the role that data has in business and the process of digitization. The third thing I'm gonna tell you is that there's a lot of investment going on and Wikibon has been locked in a significant period of research, intensive research to take a look at where the market is and where we think it's going and we're talking about some very, very large numbers over the course of the next 10 years or so. So with that, after I'm done we will hold questions then I'll step off, we'll bring up an analyst panel that I will host and be more than happy to take some questions then. All right, so let's start. Now to do this, I hope to be, I'm gonna be a little bit lightweight. I'm not gonna go a lot deep into the technologies and architecture maps, I'm gonna try to avoid that. In fact, I'm gonna ask everybody, does anybody know what this is? This is the first, this is the best picture we have of the early universe. So one of the big questions that people always ask is if we start with a big bang and everything radiated out from there why wasn't it featureless? Why wasn't it the same everywhere in all directions? This picture, which is the background rate cosmic radiation of about 400,000 years after the big bang, this picture shows the first inklings of information founded in the various structures as proto galaxies and systems started to cluster together. This is the earliest known picture of the role of information in the universe as a way of thinking about it. Now that's important because normally when we think about physics or we think about business, we tend to focus on the physical things, the thing, matter, we focus on energy, we tend not to focus on information. Now when we do that, we sometimes end up with a relatively distorted picture of how things look. So if we look at a picture of our galaxy and we spot ourselves in it, we live on a relatively uninteresting place. It's kind of this rock that sits out in a relatively uninteresting sun if we take a matter and energy view of this. We aren't particularly special, but if we take an information view of where we are, suddenly everything starts to fade into the background. We can still see the features, but something pops out. Something becomes unbelievably important. And that's us. We are massive creators of information. And there are folks who believe that life itself is best defined by how well you create information given a number of different factors. Now I want to move from the very, very big to something that's very practical in today's industry. So any of us and any company has access to all the materials that I'm showing on this slide. Any of us, we can get access to the silicon, we can get access to the rare earth metals and we can get access to all, everything that's listed on here. But there's only one company that has the information, the know-how, the insight necessary to capture over 90% of the profits in the cell phone business. Just one. The only thing that distinguishes Apple is its information. We all have access to the materials, but we don't all have access to the information. So as we think about the increasing importance of information within business, the next question is, all right, so we now have to combine matter and energy and information to think about the offers and the things that we produce for customers. But the reality is most of that information is not digitized. It's not in a form that allows for simple reconfigure of the business. Rapid evolution and responses to challenges that customers present to us or opportunities that markets make possible. So fundamentally what we're trying to do with this digitization process is we are trying to translate more of our business into information. That's what we're trying to do. What can we do to take the businesses that are today largely valued whether we understand it or not based on the know-how and the information about customers and processes and operations and the research and development. What can we do to do a better job of digitizing that information so we can reform it better, combine it differently and create new sources of value? That's what we're trying to do with digitization and that is the ultimate goal of digital business. Creating those new interfaces, sometimes substituting labor with machinery and systems that are capable of doing things even more efficiently but that's what we're trying to do. And a simple way of envisioning this is we all know what financial capital is and the role that financial capital can play within a business. It is through finance and the application of capital that we actually can do some interesting things in business. It can take capital and turn it into assets that have a productive life. It's time for us in the context of digital business to start thinking about how data is digital capital. So fundamentally as we go about the process of digitizing we are creating digital capital that we can apply to a variety of productive uses to serve our customers better. So that's the first point. Now what is the role of big data in this process? I like to use the Rumsfeld model to discuss this. Now Rumsfeld famously observed that there are no knowns and known unknowns and unknown unknowns. So if we think about kind of plotting Rumsfeld's interesting little insight it was perhaps the only thing he was right about in all the years that he was doing his thing we could kind of come up with your nice little analyst four by four square little analysis approach. Well let's plot this notion of Rumsfeld and see what we end up with. This is what we end up with. No knowns, that looks a lot like reporting. Known unknowns, well the known is that we have a model in place and we don't necessarily know what questions we're gonna ask. We wanna be able to build very large query systems that can reveal more of insights out of the information that we know about. The unknown, unknown is where things get interesting because that's really where big data is. Where we on a continuous basis are creating new models, testing those models, benchmarking those models, combining data in unexpected ways and through that process creating new insights and new understanding and new digital capital. That's what that data is. Oh by the way, if you're asking what the MBA thing is, the MBA, did it come up? I can't see it. The MBA stands for Management by Astrology which believe me is a regular feature in a lot of businesses. There is. So if we think about the role of digital business or think about the role of big data in the digitizing process, it's very, it's clear that ultimately the role of big data is to create more free information. Going back to the physics analogy, you probably know what free energy is. It's energy that's available for work. Well what we're talking about here is through big data creating vast amounts of free information, information through data that's available for work. So I hope I've drawn together these concepts of very complex business now being digitized so that we can reconfigure it faster but big data being the set of tools and technologies and methods and practices that are capable of creating that data but very importantly also establishing the clear priorities for where we should be focusing our attention. Have I done it? Fantastic. So is this real? Well of course it's real, I said it is but it actually turns out that the process of turning big data into digital shows up on the vector of innovation within the big data universe. Now George Gilbert and David Floyer and Ralph Finos and myself have spent an extended period of time thinking about how this is going to play out and when we talk to our communities and we have really interesting and new ways of engaging customers to reveal those insights or as John likes to say, to call that system from the noise. What pops out is three stages of change on the path to broader digital business. Right now we are in the path or we are in the stage of starting to utilize this new technology, not so new, 10 years but 10 years is a relatively short time in the history of business to create data lakes, pour more information in so that we can apply a lot of these new tools and a lot of the new approaches of how we can do analysis on as much data as we can possibly aggregate at a very effective price point, very efficient price point. As we discover some of the limitations associated with that and as we find new ways of extending that experience and that tool set, we're now seeing significant numbers of clients and customers starting to apply these technologies to the fundamental challenges of demand. Solving the problems of demand, solving the problems of revenue and these are profound problems. They're profound because there is no process, well-accepted established process that's universal like there is for accounting for engaging customers. Customer engagement is revealed by observing customer behavior and only big data can do that and so the second phase is to start to apply these tools and tie it back into the revenue systems so that we can start to digitize engagement. Now as we do that, we'll gain more experience and be able to tie that further back into the operational systems with technologies that allow us to tune the way the systems behave in a quite profound way, namely through self-tuning and that's happening now we're seeing the first phases of that technology become available. These different phases are starting to drive significant new growth. Now the big data business was around $20 billion, just a little bit more than $20 billion in 2014. We expect it's gonna top $92 billion in 2026, growing at about 14, 15% per year for an extended period of time. The driver of this is not rip and replace but accreting capabilities and functions on top of existing technologies. We don't see data lake technology, for example, going away. We see it being refined and improved and added to a broader mix of approaches for driving new value through software that's capable of engaging customers differently, updating operations and increasing the productivity of the assets that are in place, et cetera. So over the next number of years we see the various new phases kicking in and continuing to add growth. When we think about the mix of hardware, software and services, well today it tends to be service-oriented but over the next few years and around 2020, 2021 our expectation is that we will see services be eclipsed by software as more of the experience in the industry finds its way into code. Now this is a big assumption because one of the biggest challenges that we've heard about here over the past couple of days is that the model for conceiving of how developers enter into the space and create new types of value through software is not yet obvious. Lot of work yet to do. Lot of understanding of how to simplify the administration of the underlying tool set while at the same time presenting a set of targets that developers can use to attack problems with more common tool sets. Lot of work left to do on that. So the reality is we're not gonna see the services business go away or get crushed at all but we are gonna see it increasingly see the expertise it's generated as we take on these new problems of demand, these new problems of revenue and drive that into software as the fundamental underlying infrastructure matures and we see a better target. We think Spark's gonna be a big part of that. So one of the questions that we always get is is this a winner take all market? Is it a market that features what are known as network externalities or network efficiencies so that today's winners or today's leaders necessarily end up being tomorrow's winners? And the answer is probably not, probably not. We think that this is gonna be a maelstrom of innovation and churn and change and that we're gonna see breakaways as people uncover new ways of doing things and as new customers find advance the state of the art thinking about how to use big data and create new digital services and capabilities within their businesses. So we don't expect that this is gonna be a marketplace where today's leaders end up being tomorrow's winners. But that doesn't mean that there aren't some interesting things happening. The most interesting thing is that the vast majority of this marketplace is still being captured by companies that we are seeing on the show floor often for the first time. No company has captured more than 10% of this market. That is an indication of an extremely young market. And because we don't expect that there are significant network externalities or network effects in this marketplace, we'll see some companies gain more market shares, new business models emerge and new breakaways and technology happen and the normal serendipity that occurs when invention and innovation come together to create significant change. But our expectation is that this is gonna be a market for an extended period of time that offers enormous opportunity for a wide array of players. Moreover, we think that this is such a powerful marketplace and features so much opportunity that we will see customers, today's customers, find ways to become tomorrow's suppliers as we find new ways of utilizing digital capital to alter partnerships, alter relationships with companies or customers and dramatically accelerate the success rates of new product introductions or engagement within the marketplace. So this is something we're gonna spend a lot of time on over the course of the next few weeks and months and years. Next, we're gonna look hard at this question of how our applications, how's an application ecosystem going to evolve? We're gonna take a look at how the next five or six years are likely to feature in the creation of an ecosystem that's capable of delivering applications. So we've got a couple people here. George Gilbert's here. George, are you out there anywhere? George is there. David Floyer, David Floyer. So when you network, find these guys, they can go into more detail on the model. They're very well-versed in it. What we're gonna do now is we're gonna transition from my yapping to an analyst panel. And we're gonna have Mike Galtieri, who's a good friend from Forrester Research. I've known Mike for a long time, super analyst. Come on up along with Tony Bear. Tony, are you here? Tony and George. And we'll spend some time going in a little bit deeper into this question of how the big data market's gonna unfold and unlock new value. And then after that panel's over, and that'll be right around 555 or so, we're gonna invite a couple of customers up. We've got Matt Olson from CenturyLink. We've got Rakesh Kant from US Bank to talk about operations and big data, the whole notion of data supply chains. And then finally, John Furrier, who himself has been spending a number of years thinking about how big data and engagement are gonna work, is gonna round out that panel to fill out the triumvirate of concerns. Operations and big data, supply chain, you know, the data in, the chain in, and then finally the demand chain, so to speak, of big data. So with that, I wanna thank you very much. Please enjoy the rest of the evening. Come by and see us in theCUBE tomorrow. And let's keep this community going. Thank you.