 Jeff Kelly and myself are going to be kicking off a great event of content where we're going to follow the Big Data Trail of money, and that's kind of our theme for the week, but we have theCUBE here, our flagship studio where we broadcast the signal from the noise and interview all the execs, founders, entrepreneurs, and experts in Big Data in the room next door. But here we're going to introduce Jeff Kelly's new research report, and then we're going to follow that with a panel of venture capitalists here in Silicon Valley. We're going to talk about the trends that they're investing in, some of the dynamics in Big Data, and if you're following the Big Data industry like us, you know it's dynamic. There's companies going public, companies being bought, companies that may or may not be around in the next couple of years. More importantly, what's happening with the marketplace? Where's the growth and the key drivers? So we're super excited to share this new data with you. I'll be around for questions if you have a question at the presentation, the microphone here, but we see Big Data as really one of the most revolutionary things that has come along in the computer industry in generations, and really by itself it looks great, but when you look at how that intersects with what cloud computing's doing and the advances in infrastructure, it really enables a shift and an inflection point at the same time. You're seeing a lot of wealth creation with startups, you're seeing a lot of disruption that big companies are experiencing that are being disrupted by small startups growing. You see companies like Cloudera announcing $100 million, Horton works going public, and a slew of amazing enabling technologies. And so we see a trillion dollar market opportunity in this space when you look at the overall value being created and it really is something that's special. Someone my age has seen a couple cycles of innovation. This one is probably real special because the creativity, the entrepreneurial action, and more importantly, the big players, like IBM, like HP, and others in the industry who've built the computer industry are really participating as well. So we're super excited and we're gonna be covering it like we always do, but tonight is really about what's going on with the research, Jeff Kelly's report, follow the Big Data money trail, because as we always say, follow the money because that's where the action will be in the innovation. So with that, I'm gonna turn over to Jeff Kelly who's gonna introduce his new research. And again, after the presentation, I'll have Jeff up here for a quick fireside chat and we'll answer any questions that you have. Just raise your hand and we'll bring the mic around to you. So thanks for coming and stay with us. And if you have any needs for any break, coffee, and soda outside next door, you could get some. So thank you very much, Jeff, it's all you. John, thanks very much and thanks everybody for joining us today, appreciate it. So yeah, as John kind of laid out, we're gonna talk today about where's the money in Big Data going? Where's value being created? And how do we see this market moving forward? So we only got about 30 minutes, so let's just get right to it. Here are six questions I'm gonna try to answer tonight. And these are essentially questions that we get from the Wikibon community in one form or another. Consistently. So first, what is Wikibon's opinion of where we are in terms of Big Data market adoption, market revenue, how big is this market? And what are the enterprises actually yet who are adopting the technology doing with it? Two, what can we learn from previous markets that are more data oriented, that being the BI, it's this intelligence space and the data warehouse space? What's the same? What's different about Big Data related to those two? What about open source? That's a big wild card out there. What is our opinion about open source? How is this driving innovation? And specifically, this is very timely. There was, of course, quite a big announcement yesterday around the open data platform between companies like Pivotal and IBM and Hortonworks, et cetera. So we'll give our take on that and how we see that potentially impacting the market. Next, where's all this money that's being raised? Where's it all going? Kind of back to the main theme. Where's the money going in the Big Data space? There's quite a bit of money being raised, have been raised over the last several years, so we'll talk about where that's being invested and why you need that much money in this market. And then what about consolidation? What does this mean for the market? We've seen consolidation start, we think it's gonna expand, we'll talk about that. And finally, is this the beginning of the end of kind of Big Data? If you're talking about consolidation, does this mean this market's over, we're moving to something else? Or is there a next phase in the Big Data space? And the answer is there is another phase, and we'll talk about that. So where are we in terms of the Big Data market? So there's a couple different ways you can look at it. One, of course, market creation, revenue. We keep on with the first industry analyst firm to size the Big Data market. And basically in 2014, we have determined about $28 billion in revenue was created by the supply side in the Big Data space. And that includes all different areas from, certainly Hadoop, NoSQL, some of the technologies that get kind of bandied about when you talk about Big Data, but also the related tools and technologies, whether that's data warehousing, data integration, et cetera. And of course, professional services, which is about 40 plus percent of the market. If you look at the Hadoop and NoSQL slice in particular, you're talking about just over a billion dollars in revenue in 2014, going into 2015. So if you think about the size of that market, it's a pretty small slice of the overall market, but that's where we see a lot of the innovation happening. Now in terms of where it really gets interesting is, what are the enterprises out there doing with all this technology? And who are the enterprises that are adopting it? And what we're seeing is a pretty consistent pattern based on research we've done both quantitative and qualitative. We're seeing on one end of the spectrum, there's certainly the global 1000s, the really big enterprises out there, they are adopting Hadoop and some of the other big data approaches, there's no question about that. The other end of the spectrum are the kind of born data-driven startups. These are companies that have big data kind of built into their DNA. It's just part of what they do. Probably Uber is probably the best example of that, but there are quite a few others. But the reality is there's really a big part of the middle of the market. Enterprises that are not global 1000, but just a little bit smaller than that, down to the small businesses, where they're exploring big data, they're thinking about it, but really haven't taken too many steps yet, moving forward in terms of adopting the technology. So what are those early adopters doing, at least on the enterprise side? What we consistently hear, the initial use cases for our technologies like Hadoop has been focused actually on more on the cost-saving side. So concepts you've probably heard of like data warehouse optimization, data warehouse offloading. Essentially, moving data from more expensive systems to a less expensive Hadoop platform, save some money, archive that data, and reduce spend on some of these more expensive technologies. The challenge is even with those kinds of deployments, we're seeing enterprises struggling. Hadoop in general is complex, big data is complex, there's a lot of different components involved. It's not a simple monolithic technology. So we're actually seeing even some of the pioneers on the enterprise side, working with big data, working with Hadoop that you may have seen on the stage at Hadoop World three or four years ago, they are still struggling in a lot of cases to really expand beyond those pilot projects and kind of move to more production grade deployments. They're supporting not just cost savings, but in more revenue-generating applications. And to that point, when we asked enterprises in the Wikibon community how much revenue, sorry, how much ROI they're getting back from their investment in big data technologies, we heard around 55 cents on the dollar was the average. So that's not great. And frankly, since we did that survey, that was about over a year ago, I say that number's gone down because you've seen adoption increase, struggles continue, and if they're getting 50 cents on the dollar, that's, they're doing pretty well in these early days. So there's a lot of complexity involved and we're seeing most of the use cases, as I said, focused on kind of cost savings and less on kind of that revenue generation. So that's kind of where we stand in terms of the market today. Now, what can we learn from previous markets like the business intelligence market, the data warehouse market, what's the same, what's different? So we saw in both those spaces, consolidation happened. You know, in 2007, 2008 timeframe, you saw companies like Business Objects get acquired by SAP, you saw Cognos get scooped up by IBM, Hyperion get scooped up by Oracle. And the data warehouse space, particularly in the MPP data warehouse space, you saw kind of a slew of acquisitions around the 2010, 2011 timeframe, whether it's Ntiza, Astrodata, Vertica, and some others. So, you know, will we see that in the Hadoop space? We think the answer is yes. And in fact, I think you're already starting to see it. You know, there are some differences, of course. Big data comprises a lot more different technologies than just a specific set of technologies, whether it's BI or data warehousing, but you're already starting to see some acquisitions happening, whether that's in the BI and analytics space has applied to big data, companies like Pentaho getting acquired, Revolution Analytics getting acquired by Microsoft. So, you're starting to see that happen, but you're also starting to see it in the Hadoop space specifically. So, you're seeing companies like Teradata, for example, take some pretty aggressive steps in terms of acquiring companies like Revelytics and HEDAP. So, you're already starting to see this consolidation happen, and I think you're gonna continue to see that happen. We'll talk a little bit in a minute about why that is. Another interesting development in the data warehouse space in particular was kind of the consumption model. So, the appliance model, bringing hardware and software together really became the standard way for data warehouse technology to be adopted in the enterprise, and Teradata, again, really kind of led the way on that oracle later with their engineered systems. Now, do we think that's gonna happen in the big data space? I would say not to the extent that we're gonna see a lot of quote unquote appliances, but we're definitely seeing a need for more of a platform approach, tying together these different components in a way that is more consumable for the enterprise, because right now you've got so many different components in the Hadoop ecosystem, for example, trying to bring that all together as a mainstream enterprise that's somewhat risk averse when it comes to IT investments. That's not how this market's gonna expand. There needs to be a platform approach. So, that's where we see some similarities in these different markets. But there's a wildcard here, and that's the open source play. Now, in the business intelligence and data warehouse space, you did not see open source playing a huge role back in 2007, 2008, through 2011 or so. And what's really different is that open source is driving a level of innovation in the big data space that we never saw in those markets. Now, just take a step back. Open source software generally is becoming, it's not just being accepted by the enterprise, it's increasingly becoming a requirement. So, this is a very different environment from those other two markets that we think is going to lead to some good outcomes. That being, when you see acquisitions in a space, people kind of take a step back and they think, oh, the big industry whales are gonna adopt, acquire these smaller companies, the ones that are doing the innovation, and the innovation's gonna stop. The good news, I think, in this space is because of the open source nature of big data generally, Hadoop specifically, the acquisitions are gonna happen, but the innovation's gonna continue because not only are you gonna have new technologies being developed by startups out there that are being well-capitalized by venture capitalists who we'll hear from a little bit later, but a lot of the innovation in the big data space is coming from practitioners. So, it's coming from companies like Facebook, like Netflix, who are creating these technologies internally and then open sourcing them into the community, and that's gonna lead to more startups commercializing those technologies and the cycle of innovation's gonna continue in a way that you might not see in other industries when consolidation happens. Now, the open data platform, of course, was an announcement that came out recently, got a lot of attention. So, just wanted to take a step back and kind of address that in particular. And I think the open data platform is interesting and it plays in with what we see happening in the market, as I mentioned earlier, about the need for a platform approach. We have a standardized core that the enterprise feel comfortable adopting, where they don't have to worry about and they're locked in as much as they might have to in other scenarios. So, the open data platform is essentially an industry consortium that's focused on hardening that core for specifically for the enterprise and making it easy to build applications on one platform that can run on another, as long as you're part of the open data platform. Now, of course, there's been some criticism, not surprisingly in this market, about this approach. And the question that the open data platform, which includes companies like Pivotal, IBM, Hortonworks, SAS, and some practitioners as well, General Electric, Verizon, one of the questions I think they do have to answer is what can or what will the open data platform do that the Apache community cannot? Why do we need another industry group focused on building out core Hadoop? So, what you'll hear from them is that, well, there's fragmentation happening in the open source space, things like different approaches to SQL on Hadoop that if you use one, this is gonna necessarily run on another Hadoop distribution and that's preventing some enterprises from taking that step, jumping into the pool, if you will, because they don't wanna get locked in. We saw that in the traditional data management space, database space, with our friends Oracle being the poster child for that. And the concern is that we don't wanna get into that kind of environment in the big data space. So, this is a play to alleviate some of those concerns. Now, of course, there's money changing hands to be part of the organization and there's taking some criticism for that, that it's kind of antithetical to the whole open source community approach where really you don't need cash, it's all about the code. If you can bring code to the project, that's all you need to get into the club, if you will. So, I think it's gonna be really interesting to watch that and I think the real barometer of the success or not of the open data platform is if they keep true to their opening mission which is to enable enterprise adoption and then let the players in the open data platform who are all capitalists here, everybody wants to make money, they're gonna compete up the stack and I think that's a good thing generally for the market if they keep their focus on the core which is building out to do making it an accessible platform for the enterprise and making people feel comfortable to bring it into their organizations. So, where's all the money going that's being raised? So, just taking a little back of the napkin look at the market in terms of venture capital, we're seeing really almost, I would say crazy amounts of money being raised. So, just if you look at the three Hadoop vendors, the three Hadoop PurePlay vendors alone, MapR, Hortonworks, and CloudEra, together they've raised over $1.6 billion. Now, when you bring into the questions you'll see on this slide, this brings into the equation the SQL players as well, you're looking at over $2 billion and then if you look at all the other players that are up in the floor at Hadoop World this week, you're probably talking over $3 billion being raised in this market. So, the question of course is, well, why do we need this much money to enable this market? Software's supposed to be a, not as capital intensive a market as the hardware market, for instance. So, where's all this money going? Why do you need this much money to build the market? And I think there were a couple reasons for this that it's important to understand. So, one, it takes time in an open source ecosystem to build the market. Now, that's separate from the innovation. Innovation is happening fast and furious and I think there's no question about that. You've got practitioners that are innovating. You've got startups that are building new technologies. So, the innovation's happening but in terms of actually building an enterprise-grade solution platform that's going to be accepted by the mainstream enterprise. It takes time in the open source community. You've gotta come to agreement on some kind of standards. You've gotta understand the different concerns of the enterprise which is not always the sweet spot for startup companies that are focused on an exciting but kind of cutting edge technology. And then just in terms of the, it takes time to build out those distribution channels and the sales channels that the startups don't necessarily have. I mean, startups are really good at innovating. They're really good at inventing stuff. They're not always great at taking that, packaging it and delivering it to the enterprise in a way they can consume it. So, I think it takes time and that's one reason you're seeing all this capital being raised. They need time to build out those channels. Now, the other thing, of course, is that this market is absolutely not confined to the small players, the small startups. You're seeing the big whales in the industry, IBM, HP, EMC and others are being very aggressive in this market. They see the opportunity. And as a startup, you've gotta compete for mind share with those companies that have these huge marketing budgets. They're marketing machines when they get them rolling. It can be very difficult as a startup to cut through some of that noise and get noticed. So I think you're seeing the need to raise money in that respect to buy some time to build the market, but also to cut through all the noise and get your message out there. Now, when you think about where the market's going, when it comes to the big players, the question is, when this market first started to evolve, people were saying, well, this is gonna be very disruptive to the big players out there, to the IBMs of the world, the Oracles, et cetera. I would say that that's actually not the case. I think, in fact, the rich are gonna get richer. This market is going to be dominated by the big players. And that might not be what everybody wants to hear in the startup community, but ultimately, these players are definitely in it for the long haul. There's no question about that. They're spending their money on acquisitions. They're spending on targeted strategic acquisitions that are gonna fill functional gaps that are some of the hot new technologies. And what the big whales are really good at is packaging that, delivering that to the enterprise. They have the relationships at the sea level, and they can actually move this forward in a way that those startups can. So the question, or the idea that this is gonna completely disrupt the traditional mega vendors, I don't think that's the case. I think the traditional mega vendors are gonna make a lot of money off this. Where does that leave the startups? Well, that's a good question, and that's kind of the next point I wanted to talk about is, what is all this consolidation? What's it gonna mean for the market? What's it gonna mean for all the startups out there? And as I'm looking at this slide, I didn't mean to make those fish look so mean. It's very, everyone's really happy, it's great. So sorry about that. Don't take that to mean anything. But what does this mean for the market? So again, the idea is that it's gonna be very difficult for the startups out there on the floor this week to build long-term sustainable businesses when you have the players like IBM, when players like Pivotal EMC, like Oracle playing such a big role. The question is, will there be any independent players left in five or 10 years? I mean, I think your opinion, how many people out there think out of the startups out there that are on the floor this week, in five, 10 years from now, there's gonna be a billion-dollar company left standing that's kind of an independent player? Just one. Not too many hands went out there. My opinion is, I think there will be one, maybe two. I think probably the two companies best positioned right now to be those companies are Cloudera and Hortonworks. They got off to an early start. They have laid the foundation for this entire market, really. A few slides ago, I showed the kind of the Hadoop slice of the larger market. And it's not huge compared to the rest of the big data space, but that's where we're seeing a lot of the innovation. And they're laying kind of the foundation for this. Other companies like MapR as well. So I think there will be a couple, one, maybe two of those companies that remain five years from now that are maybe a billion-dollar company. I think that's definitely possible. But a lot of those players out there, the smaller startups, they're focused on what I might call a particular tool or a sub-segment of the larger big data stack. Now, they're gonna get acquired some of those companies, they're gonna have good exits, and a lot of them are not. I mean, part of this is just the natural evolution of a new market, right? I mean, as a venture capitalist, you place your bets, you know that only a couple are gonna pay off, and most of them aren't. That's just the way it works, and that's okay. And that's not to say that the innovation that these companies are creating is not important. So it's not to say that the tools and the technologies they're developing are not important, but this goes back to an earlier point where it's gotta fit in with the larger platform approach that mainstream enterprises require. Just talking to a lot of enterprise practitioners, they talk about they get the value of big data at a high level. But when it comes to practically implementing it, they don't wanna be in the business of cobbling together various components to enable this kind of innovation around analytics, around data-driven applications. That's not their skill set. And frankly, there just aren't the skills in the larger enterprises. Even some of the global 1,000s to do that. That's not necessarily the business they wanna be in. And the other thing is a platform approach also helps from a governance and compliance perspective. When you cobble different things together, it gets more difficult to kind of track things like data lineage and how people are using the different components within the stack. So what really is required is this platform approach and as a standalone company focusing on just one tool, it's gonna be challenging to kind of build a long-term sustainable business, in our opinion, in this kind of world where platform is going to rule. The other thing you've gotta keep in mind is not just the tools need to fit into the big data stack. Big data itself needs to fit into the larger infrastructure, the larger data management landscape within a mature enterprise. So, and then if you take even a step back from that, that needs to fit into the larger infrastructure and some of the innovation that's happening in the cloud space that John talked about earlier. Now how those come together, it's gonna require some really, some significant innovation and some significant changes in the way enterprises look at procuring IT, procuring data management technologies, procuring infrastructure. So, again, it's gonna be challenging, I think, for some of the smaller companies to kind of live on as independent entities when they're focused on a slice of the stack, which is certainly important. There's a lot more out there and a lot more that has to go into consideration when you're thinking about big data as a platform. So finally, is this the beginning of the end of the big data space? When there's acquisitions, people think, okay, well, that means that's the end of the innovation and now the big whales are going to package that up and try to sell it at mass scale and make a lot of money out of this and in some cases, the enterprise customers are gonna be happy about that, there'll be some good outcomes and in some cases there aren't. So is that the scenario that we're looking at here in the big data space is the question? I would say that's not the case for a couple of reasons and a couple of points that I've already touched on. I think what's gonna happen in the next phase of big data, a few different things. One, you're gonna see enterprises are gonna get more mature about not just the technology and bringing it into their environments but some of the other, more process-centric, more non-technology challenges associated with big data. That's mainly around data governance, compliance, ethics, more of the process and people issues and political issues. I think you're gonna start to see that maturation happen. From an innovation standpoint, as I mentioned earlier, the good news is because of the open source underpinnings of this market, I don't think the innovation is gonna stop when you start to see acquisitions. In fact, you could accelerate as the market actually expands in terms of enterprises adopting the technology because in some cases, it's the practitioners themselves that are innovating, creating new tools, creating new technologies and then open sourcing those for the community because increasingly, for those companies, they're not competing on that the technology itself is not what gives them the competitive advantage it's how they're applying it. So for a company like Facebook, for a company like Netflix, they're quite happy to open source some of the new innovations they're doing because they have other components, the way they use the technology, their unique data assets that they have they're using in conjunctions with those approaches, that's what gives them their differentiation and that's sort of the tool. So I do think the innovation is gonna continue thanks to kind of that open source foundation of the big data space. And finally, I think the real, what really gets really interesting is moving from, as I talked about at the opening, some of the early use cases we've seen in this space have been around cost savings. How can I save some money on my data warehouse? I've got my IT budget is flat, my data volumes are growing exponentially, something's gotta give. Well Hadoop comes in and it's good little hanging fruit. We can move some of those workloads, some of that data to a Hadoop environment, we're gonna save a lot of money, fantastic. And then that makes sense from, it's very easy from an ROI perspective to put those numbers on a spreadsheet and see how the numbers add up. But where it gets interesting is where you start talking about using all that data that now you're filling into your so-called data lake and maybe the original purpose was to offload more expensive workloads. But now you can build data-driven applications that are actually gonna focus on revenue generation. Or they might focus on cost savings and efficiency but not in an IT sense. So things like what you're seeing with GE and some of the more industrial companies focusing on predictive maintenance, those kind of use cases. Where you're actually, a very small improvement in efficiency can really impact the bottom line. So and in other cases you've got applications that are focusing on customer data, understanding your customer data better, understanding what drives them and understanding how to interact with them that's gonna provide them value by putting offers for goods and services in front of them that's gonna resonate with them and that's gonna impact revenue. So I think the next phase of this is moving more to those types of workloads. In addition, it's not just kind of the batch-oriented first generation of Hadoop. It's gonna be more and more around real time, more and more around near real time and the internet of things. I mean, I think clearly when you look at the amount of data being created by essentially objects that previously did not create data, there's so much opportunity there and so much innovation that's gonna happen in that space. I think that's where you're gonna see some of the real excitement moving from, hey, we're gonna save some money by moving some of our data warehouse workloads over to Hadoop too. What am I gonna do with all this data that's now being generated through whether it's industrial equipment, whether it's sensors on trucks, on fleets of airplanes, on wearables, whatever the case might be. I think that's where the real opportunity is gonna be and that's ultimately where the value's gonna be and you're gonna see the big data, sorry, the mega vendors who are doing some of these acquisitions, you're gonna see them competing in that space, moving up the stack to the applications, to the analytics because that's where the real money's gonna be made from a vendor standpoint and then if you look at in terms of the value you're gonna be created by the enterprise practitioners, I think that's gonna dwarf that. I mean, what I've got on screen here is some research we did, David Floyer, our CTO and I did around the industrial internet. We're looking at about a half a billion, half a trillion dollars in revenue generated through the industrial internet which is just a component of the IoT but if you look at the red bar there, over 1.2 trillion in terms of value created by the practitioners and we said this in New York at our Big Data NYC show and I think it's worth repeating here, we think the big winners in big data are gonna be the practitioners who are putting the technology to use to create new lines of business to create new streams of revenue to reinvent markets. So that's kind of how we see the market today. It's a fun market to watch, obviously there's a lot happening in this space. This week's gonna be fun to watch what's going on at the show. I encourage you all to kind of tune into theCubeSilkAngle.tv and we'll be covering all the action but thanks for your time and thanks for your attention, appreciate it. Okay, Jeff, now I get to ask the tough questions. Come on, that was great. Great presentation. How about another hand for Jeff Teller. All right. Hey, grab a seat here. All right. Before we bring the VCs out who would give a great perspective on the investment cycle, which is a great barometer in my opinion of the innovation, which even though there's some consolidation being talked about here from the research, there's not a stall on innovation at least from an anecdotal observational standpoint. But I did get some Twitter action while during your presentation and I'll start off that question and then we'll open it up to questions in the audience. I'm sure you have some. I'll just kick it off. The question came from someone on Twitter. Maybe I shouldn't say their name but where do you think the traditional enterprise is heading, IoT, newer databases reporting for big data? You kind of mentioned that on your last slide but the context of the question was interesting. It was from a recruiter who's actually dealing with Rift employees from the big whales. So the IBMs, the HPs, the EMCs, they're downsizing, it's in the news. So they're shrinking their employee base but yet they're going into new markets. So this brings up an interesting question. There's a trillion dollars of wealth creation but the big whales are also becoming leaner. Are they just too fat or are they trying to slim down? Are they not positioned for the opportunity? What's your advice to the people out there that are being Rift or in laid off, if you will, from the big companies or looking for a new career change? Well, I think from the perspective of somebody working for one of those big companies in their old lines of business, I would say you've got to get your resume ready because frankly you're seeing these big companies that are going through the transition from, and it's not just big data, I think you got to take a larger, you got to step back and take a larger view and it's around, in a lot of cases the cloud is actually driving a lot of those challenges. So IBM making that transition, SAP making that transition and profit margins are smaller, they're lowering guidance on when they're going to be profitable on their cloud services. So they're in this transition phase and it's going to take a little bit of time. In terms of those big companies, I still think they are well positioned if they can navigate this transition with all the shareholder pressure they're going to get in the short term, if they can navigate this transition there's a huge opportunity for those companies. And as an employee, if I'm working for one of those companies I'm looking for opportunities on that side of the business because you're seeing companies, some of the big whales, they're laying off people on the old world side of the business but simultaneously they are hiring like crazy on the big data in cloud spaces and mobile and application development spaces. So that's where I would be looking if you're kind of one of those old line sides of the business I mean there's definitely a transition going on and it's going to be painful for some people but when you come out the other side I think there's going to be a lot of value creation. Yeah, we certainly hear from the big guys that they're retooling to go after those new opportunities. So another question came in. So if you have a question just raise your hand we have a mic but I'll just go with another one here that came in for our crowd chat. It says, where's all that money being raised going? You know, a billion dollars being raised from some companies. I thought software was supposed to be capital efficient. Why are they raising all the money? Where's it going? What's happening? So what are they spending the money on? I was touching on the presentation I think. As I said, this market is because you've got the big industry whales that are participating in this market, if you're a startup you need to raise a lot of money to fight that kind of the marketing battle number one but you need time to build the market. The market's taking time to develop. It's not happening, the market itself is not developing as fast as the innovation. It's not keeping up with the level of innovation. So there's tons of innovation out there but it's not as consumable to the enterprise right now. So that's the challenge is to turn that innovation into something that the enterprise can consume and that's gonna take a little bit of time when you throw in all the complexities of an open source ecosystem, just open source generally. So you know, there's a lot that has to happen to build the market, not necessarily the innovation that's happening at a much higher pace. Any questions out there? We have a question right here. Mike in the front, because we're recording so when I just captured the question on the live stream, thanks. Yeah, hi Jeff, can you talk a little bit about the methodology you used to derive the ROI, the 1.2 trillion of return versus 500 million if I read it correctly of investment? Yeah, so when we look at the market, we talk about, when we talk to the vendor side, they are pretty clear about that they are making significant investments in this and we think from a revenue perspective, that's a pretty traditional measure that we can look at in terms of where the value creation is going to happen. If you look at past markets and the level of innovation, the level of value that's been created compared to the revenue, I think you have to increase it when you look at this market because you've got things like the open source underpinnings, you've got things like new data sources coming online, the level of innovation that's happening. So I think you have to look at it, there's certainly some more traditional ways we can model the market, but you have to look at some of the new wild card, which is open source, which I think really shakes things up and it'll be interesting to see how this plays out because frankly, I think that could be a low number. I think there could be a lot more value generated because ultimately, once the platform is developed, once the tooling is hardened, the capabilities of an enterprise that's invested in the skills internally to develop those kind of applications, I think could be endless. So frankly, I think we underestimated that to some extent, but it'll be interesting to watch. Another question in the back there? Can you elaborate a little bit about the hardware innovation? Where's the money going? Is commodity hardware good enough? Or do you see any money going? Yeah, I think the hardware side is interesting because you're seeing companies like EMC, for example, on the storage side, trying to play in this market and applying some of their capabilities to Hadoop and the data lake, for example. So I think there's some possibility there that that could gain some traction. I think what's really interesting is what's happening with players that are focusing more on making big data, making Hadoop more cloud-like, enabling companies to very quickly spin up new clusters within their private cloud. A lot of this isn't gonna go to the public cloud right now. So you're seeing companies that are focusing, Blue Data is an example of a company that's trying to help companies make it much easier, making them turnkey to press a button, spin up a cluster to lower that barrier to entry and significantly decrease the time to insight. Yeah, I mean, I can also comment on that from just from the data that I've been seeing through our research on the silicon angle side is that the hardware stuff is really aggressive. And I said earlier on the intro that the intersection of converged infrastructure is really a hot area. So if you look at cloud converged infrastructure and big data, and then in big data, you stuff in social meat, social, mobile, and apps in big data, that's kind of where I stick that. The converged infrastructure stuff is booming. Flash, for example, is awesome in memory, silicon-based analytics is gonna be a trend we think we're gonna see a lot of. So I think you're gonna see a huge innovation at the hardware layer, where you're gonna see purpose-built boxes, that may not be a good word, but I think you're gonna see solutions where the customers really don't care as long as it is super fast. And the data is so massive, whether you're a financial services company or whatever, low-latency data transfer will be a key area. So from our standpoint, that's kind of a new area that we see coming out of the pure standalone server kind of market. So I think that converged infrastructure or hyper-converged or whatever the buzzword is these days is a key trend. Question over there? Hi, John. On the practitioner side, the value creation, do you have a view of use cases in that practitioner area beyond cheap storage? Yeah, so I think the first place people are looking beyond kind of that, the offloading of more expensive workloads. I mean the most obvious thing is customer data. And getting that complete view of your customer is top of mind for many companies because this is not a new challenge. People have been trying to get at this for years. And the EDW market and the BI market has been touting this get a 360 degree view of your customer for a long time now. But in reality, those markets really didn't live up to that promise. So I think that's one area where you're starting to see applications being built because people understand that that ultimately is how you're going to drive revenues. You understand your customer, you've got to personalize the experience. And this is obviously more important in consumer facing enterprises, retail for example. But you've got to provide your customers a level of personalization that they've come to expect from using applications like Facebook or LinkedIn for example. So I think that's one area, customer analytics, understanding the life cycle of a customer and understanding what should I offer that customer next to upsell that customer. So that's one area. The other thing is around, I think I alluded to this was around predictive maintenance in the industrial space. So for companies that are not necessarily as consumer focused but are more manufacturing focused or industrial in nature, the idea of improving efficiencies by fixing a problem in a piece of industrial equipment before it shuts the piece of equipment down, for example, can save you a lot of money. So if you think about an aircraft engine, doing routine maintenance on that when before the engine has a major failure. It's also good for safety by the way. You don't want to be on that plane when that happens. So that's another one predictive maintenance you're going to see. I think a lot of innovation in that. GE is one company, but other Siemens, United Technologies, all these companies are focused on collecting all that data coming off their machines, their equipment and figuring out how they can use that to improve service levels for their customers. The other trend we see on the practitioner side that's hot right now is obviously insight side of the equation, which is where you could just see data-driven insight could come from any place in the organization. People use the Moneyball example all the time for years now but literally it could come from anyone in the organization that the data-driven insight where there's innovation specifically could come from either the visualization side and our programmer or anyone within the organization. But the data is the lever for the innovation so that could come from anyone in the organization. I think that that's the practitioner trend that we see is the ease of use and the visualization is hot right now but that's leading towards that the data-driven value is gonna come from a lot of places. We have time for like two more questions. Yeah, what is your take on this ridiculous open data platform? Why private tools suddenly open up their computer stack and do you consider Cloud Foundry is a success or failure? Cloud Foundry? Or was it what you say, Cloud Foundry, okay. I have an opinion on that. Yeah, I think John has a couple of thoughts on that but from Cloud Foundry's perspective, I think it's pretty astonishing the level of growth they've had in just less than a year and I think that is the model. I think if you ask the team at Pivotal that in many ways is the model for the open data platform. Look, I think the open data platform, if it again, if it focuses on the end result which is enabling enterprise adoption and if it delivers value to customers, I think it's a fine and worthwhile organization. But that said, there's certainly, I think it needs to prove itself and it has to show that it's gonna be able to do something that the Apache community can do. We'll see if that, it means to be seen if that's gonna happen. It's kinda early. Yeah, it was just announced, we'll see. I mean, you have to hold them to their promises but it's not gonna take time to see how that plays out. I have an opinion on this. I mean, I've always been a naysayer of industry consortium so I kind of agree with Mike Olson on this point. He kind of wrote a blog post like Cloud Era on that but I think in the interviews that we had on theCUBE and just, I've been proven wrong. OpenStack came out on the cloud side. I was kind of like, oh, that's cool and then it became kind of like this marketing program and then what happened was the organization actually more often actually became more code driven and actually has done some good work. Cloud Foundry was kind of skeptical at the beginning because they were coupling together a bunch of open source concepts and projects but they delivered and then this one seems to follow that same trajectory. To me, I think we're in a new era and it's the time to see because the open source is a first class citizen now so what's happening is the movement and the shaping of these projects is gonna come down to the value and I think IBM and the big players that are involved in open data platform are serious and they're going after this market and they have a lot to lose. Like I said, they are trimming their workforces down they're trying to be leaner. So I think it's a serious move and I think we just have to watch it but again, I was skeptical of OpenStack and Cloud Foundry and I kind of had to follow my sword on that one because they delivered. They delivered revenue and value and some might not like it but if the markets grow to be a trillion it has to provide value. So I think we're in a new era now where you're gonna see the transparency there and it's gonna be a choice game. So as long as there's no lock-in spec for interoperability, I mean. Yeah, I mean, just to add to that. I think it's a good thing. If they address the pain points that enterprises are currently facing in terms of adopting and do things like things around governance, things around taking more of a platform approach so you don't have to stitch these things together. Things like avoiding that vendor lock-in question whether the real or imagined it's an issue. If they do those things and the other thing I think is important to watch as that organization grows, ODP grows is enterprise practitioners joining the organization not just the vendor side. If you start to see that, those two things they focus on the real pain points of the enterprise and you actually see enterprises getting involved in the organization. To me that would be a sign that they're keeping the eye on the ball. To me the red flag on that it's the quick asset test for me is if it's a marketing program then it's probably gonna fail. I mean, we've seen that in other generations. So again, OpenStack had that moment if you're not familiar with OpenStack was kind of a marketing program and then it morphed very quickly where the code itself and the benchmarks were all out in the open and it worked. It still worked, so. It's successful. If you look at a private past market only Netflix in terms of Netflix there are lots of past applications on AWS is successful. There are very few past applications are successful. If you look at a Docker, that cloud they went to Docker only. They are very focused on niche. And if you look at the cloud of bees they are only focused on Jenkins. So there is no very successful past starter right now. What do you think? I would agree with that. I think past is the battleground. I think to me it's a commoditization and scale issue. I think Amazon's been super successful because they control the past. They're really good. Amazon is an amazing platform and I think they're a key disruptor in the enterprise and they're not even enterprise clouds. So Amazon is the gold standard in my opinion of cloud. But they're not as strong on the enterprise but they're winning. They're winning in the government side. So I think everyone in the industry in my opinion that I've talked to has said either to me privately or publicly Amazon is really awesome. But Amazon's past has got some progeryness to it. But if it works do people care? To me that's again coming down to the interoperability piece. Can I get it out? Can I move it around? It has to move around. That's another question. So we've got one last question. Great questions. Hey Jeff, what needs to happen for enterprise adoption to speed up, to accelerate? And are we looking at a long road or a short road? Well I think the appetite for the enterprise is strong. So which is one reason why I think you're gonna see some consolidation because I don't think that this fractured startup ecosystem can meet that demand. But what it's gonna take in my opinion is this market has always been very driven around partnerships and integration. Because as I said big data, not a single thing. Hadoop, sometimes people equate that. Hadoop is big data, but it's just one part of it. And then even if you look within Hadoop there's a lot of different moving parts. So I think one thing that's gonna have to happen is there has to be, and this goes to the point of what ODP is trying to do is develop some standards and not just standards but harden that into a package essentially that the enterprise, the slightly more risk averse enterprise is beyond those that have kind of taken those first steps, the global 1000. Well they feel comfortable bringing into their organization knowing that they don't necessarily have the internal skills to cobble together like some of the early adopters did. So that's one thing. The other big challenge I think that's holding people back is the security and governance question. I think it doesn't get talked about enough. I think what I've heard from a lot of practitioners, early adopters is they built out, adopted Hadoop, they have great ideas for some new applications, built out these prototypes, somewhat successful and ready to roll these out into production and then you get the compliance department involved, you get legal involved and like well you can't do that. You guys didn't think about, you know, your mingling data that was never meant to live together before, you know, you've got, you know, in the U.S. it's very siloed in terms of regulations, you know, it's whether it's hip and health care so our brains are actually in finance and some others. Or in Europe you tend to have more data protection, data security and governance compliance roles that kind of span industries, but in either case, you know, when you get to that point where you want to start doing some interesting things with big data, if you hadn't thought about the governance implications as you were building out the application, that can stop it, you know, dead in its tracks. So I think the other thing that has to happen is there has to be better awareness on the enterprise side about these issues. You know, you're seeing things like, you know, the vendor community trying to talk about it a little bit more with the Data Governance Initiative launched a few weeks ago. But I think the enterprise has to have a bigger conversation around governance and security and what this means now, that you can do all these things that big data enables. Not everything it enables is legal and even when it's not necessarily illegal or breaking some rule, there are ethical considerations whether maybe you don't, just because big data allows you to do something doesn't necessarily mean you should do it. So I think those are two areas that I think would need to be addressed before we start to see adoption really speed up. These are great questions and one of the things that we're seeing is there's demand on the enterprise side and we're covering this on siliconangle.com, wikibon.org and theCUBE. So, you know, we're documenting and these are the questions. There's demand on the enterprise side, there's some uncertainties, but people are retooling because of mobile and the advantages in the economics and the business values there. So the platform is a services of battleground, Amazon's making their way into the enterprise. All this stuff is forcing all this amazing change. I think the opportunities for the startups will be there. I personally think there's a billion dollar startups out there. Cloudera's technically a billion dollar valuation already, Jeff. And so, you know, I think it's exciting. I think a lot will die, but I hope that, you know, most of them get acquired, but there's a ton of opportunity. Let's give Jeff Kelly a great round of applause. We're gonna have to bring the VCs up now.