For The Extraction Point, we've been briefed on all the important stories and events in the world of emerging information. Now, it's time to extract the data and turn it into action. Live from the SiliconANGLE Studios in the heart of Silicon Valley, this is Extraction Point with John Furrier. Hi, I'm John Furrier. Welcome to my show, The Extraction Point. I'm here with John-Luc Chatelaine. Or how would you say that in France? John-Luc Chatelaine. Okay, well, welcome to The Extraction Point. In my show, we're going to extract out the data and find the signal from the noise. We appreciate you coming into our home here in Palo Alto, California, the Cube, where we discuss all the activities going on in the marketplace, the product angle, the tech angles. But let's talk about you and your new role at DDN. You are the executive vice president of strategy and technology at Data Direct Networks. Congratulations. That's new news. Thank you. I just came from HP where you were CTO of a big division over there, previously an entrepreneur. So tell us, John-Luc, you're not new to DDN. I'm not. Tell us what's the big move and the operating role at DDN. Well, so the big move is I'm not getting any younger, but I still have the bug of the startup. So I got to do it at least one more time. I've got a few one in me. I had a great time at HP, you know, came through the acquisition of a former startup. It's a great group of people. But again, having observed what's happening in the market, I could not pass up a fantastic opportunity around bringing a new way of doing information management and information extraction and most important insight from all the data that we have to deal with today. Not new to DDN. I've been a longtime friend of the founders. They're personal friends and I've been on the board since 2006. So I was able to see the growth of the company. And when the time was right to go help them to go to the next step, I signed up. So we've been talking at Silicon Angle about storage being sexy. We coined that term last year, but now we're seeing at the recent O'Reilly event at Strata. We were doing live broadcasting. Data is at the center of all the big conversations around tech and the emerging opportunities. Last year was M&A with three parts of the world and Compellent. But storage is changing. The conversation is about big data. And there's a lot of market forces around that. DDN is not a new company to the world. They are like a growing startup. You guys certainly are well beyond the, I would call, in the garage phase. You have hundreds of millions of dollars in revenue privately held by the founders. But you're moving into a space that's like a startup. So talk about what's going on with DDN. And then talk about how that relates to what you've seen in the market over the past seven to eight years that you've been involved in. So DDN is at the stage where they've established their brand. They've established their technology. They're very well known in their market space. And they're really recognized by the customer as one of the foremost supplying that space. In fact, little known fact, but they are the largest privately held storage company in the world. And now what we need to become is the largest information company in the world. Still privately held. So what's really interesting is that we are entering a new world where information is essentially going to be the currency of the global enterprise. I very often joke that one day we'll see a megabyte of information being quoted in Singapore. But really it is a currency of the two days enterprise. And what's great is that DDN's assets are well positioned to really leverage the different inflection points that are happening. So let me give you the three high level, really interesting trend and moments in time that maybe something we haven't seen since the client server era. It's completely changing out there. We hear a lot about the information explosion. So back in early 2000 there was the buzz on every single analyst or everything. Newspaper was information explosion. I think this was a firecracker compared to the nuclear explosion that we're going to see around information. A few things have happened. We've given a voice to the machine. We've enabled them to speak to IP. We've enabled them to be mobile. So they've risen and they're very, very noisy. They have a lot to say. And today most enterprise are not ready to listen. Machine, you mean internet of things, kind of things like mobile devices to servers, pumping out requests to applications, all of the above? And more. Coke machines. Every time you put a quarter in a Coke machine there is a GPR action around that. Video cameras, you can cross the street without having some surveillance. Your meter at home, your smart meter at home is very, very verbal. So there's a lot to say to the energy company. And the infrastructure of IT today is not ready for that onslaught. I believe that what DDN has built for their normal market, the natural market of high-performance computing and rich media and cloud, are well suited to help the enterprise swallow all that information and do something with it. The second thing is... So the first is the machines, right? The first rise of the machine, they're very verbose. Highly interactive with users. Highly interactive with users. Somebody needs to listen to them. Somebody needs to listen to them because in what all the screaming noise they're doing, there's a lot of really good information. And they're all connected to the internet. They're a meter where they can be measured, right? Yeah, exactly. Now that nuclear explosion of information is also forcing a convergence. And it's a convergence of store and compute. Sometimes I use the word moving from data shipping to function shipping. There's so much data... Can you explain that? I mean, that kind of might go over some people's heads. Shipping data... Yeah, so shipping data means moving data from point to point. Yeah, so shipping data, moving data from point to point. Which is what we've done for the past 50 years. Yeah, store to disk, move it to the servers. Move it to some infrastructure for compute. That's the old way. That's the old way and then moving back to the disk along with a new result that has been computed. I think it was fine in Dandy when we had to move a megabyte, when we have to move a gigabyte of information. But now, if in one day for a given set of video camera, I capture 10 terabyte of information, and I got to do work on it, right? Sending 10 terabyte from a store where the acquisition has happened, all the way to the compute infrastructure to extract maybe features, or people, or whatever that is, extremely costly on all points. It's costly in time. It's going to take time to move 10 terabyte of data. It's going to be costly in dollars because bandwidth is not free. So what's happening is we need to move the function, which is what we were doing in the compute part inside the store. Whether data is already there. So you're basically saying data is like bits, functions like applications, or like jobs. More relevant packaged information. The processing. So the processing is moving close to where the data is. And that's going to be very powerful because we can now start to exploit massive amount of data almost as soon as we receive it. And I forecast in real time very soon in the next 5 to 10 years. And this at a very, very large scale. So that's the second point. The first point was the nuclear explosion of machine generated data and video. Second point, conversions, store and compute. And the third point, which is really what's going to make a difference for the line of business, is a desire to extract result out of the data that you get. Extract insight and transfer that insight into reason. That's analytics. That's true value of analytics. And analytics is what changes the business. And if you can do it quickly enough, you can take decision in real time on changing some aspect of your business and either capturing new customers or better retaining the one you have today. And this is not you, a lot of people are focusing on analytics because really that has the right line of business value. It's not some geeky IT stuff. It really transforms into new knowledge. Well we're here at the home of CloudEra. It's our office we share with CloudEra, which commercials us at Hadoop, which is the big data kind of open source packaging software. And we were talking prior to coming on the video here about a value chain where data, and you mentioned is the currency of the future enterprise. Can you share with the folks out there the value chain that you mentioned? Sure. You had mentioned something around... Data information, insight result. So data, information, insight results. And context being which section there? And context is really what gives meaning to data to transform it into information. You could do a PhD thesis on trying to define what information is, but I think the best way to put it in a synthetic manner is that information is data and context. And then from that data and context, you want to extract the insight. You want to get a glimpse of what's happening. And that data, by the way, is extremely rich and extremely varied. It's not just your classic old-school transactional data, but it's also all the new and structured data and social data that's coming out. And so you want to extract that insight, and then from that insight, you want to really produce new results in your business. So the three key trends were machine-generated data. And video. And video, which is from surveillance to end-arm, mobile, et cetera. And intelligence, which is the notion of compute built into the storage for application-like functionality, not just storing data to disk somewhere and parking it somewhere, but actually acting on the data, more action-oriented data. And third is the analytics for the insight. Right, exactly. Okay, cool. So we've explored that, and we're trying to find some proof points. And one company in particular that we've had on the Cube is coming called ClickFox. And the CEO, Marco Pacelli, has been talking to a lot of big customers, and he's been telling us that a lot of the enterprises aren't there yet, and he has an analytical package that goes in and provides insight into all this massive data. And his primary customers are like mobile carriers and or device horizons and sprints of the world. So he's going in saying, look, if you don't understand your data, you can't re-engineer your processes. So he's kind of bumping up to these big clients, and no one's really adopted this yet. So what's your opinion on where we are in that value chain for the customers out there? Are they in the just trying to figure it out stage? Is there any specific examples you guys have that can share with us on who are the forward leaders in this area? Who's using the data for results? Right, so I'm familiar with ClickFox. In fact, some of their VCs are Atlanta, my hometown, although my accent is a little west of Atlanta. Atlanta. It's a rocking company, by the way. They've got a great product. I mean, they're the ones doing a lot of cutting edge work on this insight. They are. We are very early. That's about online. We are at the beginning of people are trying to understand their data and they're wrapping their head around it. In fact, most of what you see in analytics today, and there are some exceptions, is really very traditionally done on the transactional data that comes out of the operational system. And this has not yet delivered on its promises because it's incomplete, because it needs to get not only the transactional but also the unstructured. But we are early in the adoption cycle. We need more proof that analytics can make a difference. But there are some segments that are quite advanced. The financial services industry makes heavy use of analytics and does a pretty good job around it. The hospitality industry is also fairly advanced in using analytics to try to understand how they can better suit their product to their customer base. There is the oil and gas industry. What we are going to see is gradually as proof in the pudding starting showing up, a lot of more traditional enterprise are going to say there is really true value. And to be honest, a lot of progress has to be done on the mathematical model around extracting that insight. There's some math involved. There's some math involved. It's not easy. Kids, go get a PhD in mathematics. You probably will get a good job. In the world of analytics. A lot of the very basic block and tackle function that people have is they don't know yet what question to ask of their data. So they have to understand what moves their business. What they can tweak essentially in their business to figure out what the right question is to ask. Is that because they just never had this kind of access to data in the past? It seems to be that people are clumsy around the notion of asking questions. You ask any executive old school that's not a tech geek. They have a back of the envelope, manage their business mentality. But now as we measure everything, it should be that simple. They should know the basic questions. They never had the chance to do it before? They've done it in a very backward looking view of the world. Most analytics hasn't been predictive. Most of the analytics that we see on the Fortune 500 today are reporting analytics. They look backward. And they look backward for two reasons. One of them is pure compliance and governance. I got to make sure I got my reports on my quarters for the numbers. And the other one is to think that just looking at the past can give you a good prediction of the future. And that's no longer true because we are in a very dynamic environment where stuff that has worked last week may not work this week because of some external event. So I think they have looked at the data. People have been capturing data forever. The world of EDW is not about to disappear overnight. There are some very strong players in that. But they tend to have a backward looking view of the world rather than a forward looking. Has real time been a big part of that too? In terms of reports tend to seem to be taking a long time? They're not real time. In fact, an analogy is it's like using Tivo for your data. You're going to watch the football game three weeks after it happened because you're going to have Tivo. Well, if you create reports and you take three weeks to get the report, you're looking at what happened in your game. What good is it? Game's over. Game's over. I'm going to quote you on that one. That's good. That's a memorable sound bite. It's really not proactive today. It's very reactive. It's very time-shifted. Prediction is key. And business speed, business time is going to be key. Can I extract my information when some event happens so I can predict what I'm going to do with the next? So opportunity for folks out there like the big enterprises and folks that you guys talked to. And for quite frankly, all the storage vendors have the same opportunity is just go out and build some massive data. I can go to S3 Amazon. I can go to Fry's next door here and get two terabytes for a couple bucks. You guys sell some drives. They're not inexpensive, right? So tell us through the opportunity of what it really takes. And we were talking about some of the things we're doing in social media at a silicon angle. This is an ingestion problem. You have machines throwing off massive amounts of information. You have convergence where there's needs for all kinds of speed in real time. And obviously reporting on analytics, the three things you mentioned. I mean, how do people handle the ingestion? You can't just put two terabyte drives together and string them together to handle even just pulling in Twitter data or RSS data or never mind mobile data. So talk through that pipe. What's that ingestion? Yeah, well, a few points. Number one, Fry's should not only put the price of the drive as an asset cost, but should put a little note next to it which is this is how much it's really going to cost you when you add all the software that you need to do something with. And this one terabyte drive is $99, but your real cost is going to be $699. So this is why the storage business is still a good business. That's why the cloud storage business is working pretty well, right? And the cloud storage is helping some of that but, you know, we go back to the ingestion process and let's tie to what's really needed. You need big pipes, right?  You need to have essentially a freeway to be able to bring the information to those drives in parallel. And, you know, I'm going to do the wrong horn, but this is what we do at the end. We build giant Highway 405, right? That takes massive amount of information and puts it into large pools of storage. And I can say that no one does it better than us from a performance point of view. The second part is tied to the... I'm sure, you know, Hitachi, EMC, NetApp, all these guys will be like all, well, maybe not NetApp, but Hitachi, all these guys will challenge that. But, okay, you're biased. Of course I'm biased. I know it's sending me a picture. You guys have a reputation, building the meanest, highest, good density... Very high performance drives. And that's because most of our customers, or large part of our customers, come from the high-performance computing world. Let's drill into that later. Let's stay on the ingestion point. You need a big freeway to get the information inside the stuff. But we also need an infrastructure that can bring that compute that I talked about.  That's how you're able to handle that stuff. You can do a lot of the curation pre-processing phase by just running the pre-computer algorithm right next to the metal where you lay the bits. And you want to be able to do that in-house, but you also want to be able to do that in a cloud manner, meaning you want to be able to go to some cloud infrastructure, kind of a self-serving infrastructure. You can get that on demand. That requires some tech to do that ingestion. Let's just say, take, for example, Twitter data.  There's a lot of data. Lots of data. I mean, you're going to have to put that in some space. Lots of moving parts. It's not just about moving the data from point A to point B. It's, you know, the whole indexing part of it. So you get the hardware. You get the advances in hardware. We've seen that Moore's Law and all that great stuff at the platform level. You obviously were at HP. Your last company sold to HP. So you were seven years at HP as a CTO there. And so you're familiar with this information infrastructure. How much of the information infrastructure is software-based? And will it be more software than hardware? Where's the IP going to be? The IP is going to be in software, definitively. So the hardware must have a set of characteristics that enable that software, right? So as much as there is commit utilization of hardware, there is no commit utilization of properly building hardware, right? So hardware is not going to be disappearing. But definitively, the trend's going to be to have, you know, an infrastructure where both compute and store live together. And then the whole value thing to software, that can exploit that hardware.  Because at the end of the food chain, right, from the bit sitting on the magnetic surface, all the way to the user. At the end, there is a user. There's a guy with a UI and a workflow that he has to follow. I'm John Furrier. We're here in the extraction point in talking about big data with theCUBE and Palo Alto, SiliconANGLE.com, SiliconANGLE.tv. We're extracting all the data points out of the noise and turning it into signal. I'm here with John Luke Shetlain, Executive Vice President with DDN, Data Direct Networks. Your new operational role. You've been on the board since 2006. Let's talk about big data. Big data is a big trendy word, and most people kind of think it was a marketing term for the industry. But data you mentioned is the currency of the future for big corporations and maybe even for users. What's your definition of big data? And what's your angle on the whole data market?  Can I say that you guys, and at least then journalists, you're making a lot of buzz around that world. Can we find another word? I propose lots of data. I actually don't like big data. In fact, you know, GigaOm, a friend of mine, is there in an event called Big Data, which I just think is a bad name. Mike Olson, the CEO of Cloudera, doesn't like the name Big Data. He was on stage at Strata, saying, we should get rid of that term. Tim O'Reilly actually didn't call this conference Big Data, called it Strata, data, and he didn't talk about data in particular. So, you know, it's provocative. It provides a lot of debate, but we're not pretty high on that big data because all data is big. If you're an enterprise or a big service provider, you've got to deal with all kinds of data, big, little, fast, slow, but in the aggregate, it's all data, right? It's all to be vetted. I think the difference may be the fact that an aspect of big is how rich the data is and how many facets that it has that you didn't have before. Before, when we were limited to transaction, we kind of knew what we were dealing with, now that we're dealing with a massive amount of unstructured data and that we are kind of data called Data2.0, which is kind of a hybrid mode of all the stuff produced by social network, that probably is what defines big data, that it's big and different. It's unstructured. It's highly unstructured. And the good point of unstructured data is there's a massive amount of value, but that value is really well hidden and all the magic is how you're going to extract, you know, really that insight. Because it's not coherently pulled packaged when it comes in, right? It's like you and I were talking about this, the Jodi Foster movie contact where, you know, all this kind of data noise is kind of unstructured and there's some signal in there and you have to try to find that signal. That seems to be a big part of what Facebook and Twitter and these social networks have created this notion of activity streaming, river of information, flow. There's all these metaphors about volume coming in. Can you talk about volume and kind of what all this is meaning this whole? So the volumes are large. What's interesting, and that's, you know, this is pretty obvious, but what's interesting is that those new, you know, social type of pieces of information, they dangle a lot of handles. We can start grabbing to start going down a road where, you know, there is value, very, sometimes very narrow specific signal, but pretty good one. So I'm a big fan of all of this because not only are we getting just data, but the natural interaction of people with any information is creating metadata. The fact that you like something is pure magic for Facebook, right? It's the ultimate gesture data point, isn't it? Because you're saying I endorse this. I can tell you more. But also you're almost giving, you know, through what you like, you're almost classifying the information for Facebook, right? They could show you a stream of video where in itself you would need hours on end to be able to understand the meaning of the video, but it's four or five people that have a similar profile on their interest, like that piece of video. Suddenly you're creating metadata around that video and now the guy on the receiving end of like now has a piece of classification. When you start having the metadata, you know, you're halfway there to extracting your true insight. So the volume is about flow, size of information. There's also velocity, right? We're talking about real time in context of that. It creates a lot of noise, right? Lots of noise. You've got to get analytics, you've got to get all this together. You talked about the contact movie out there for the folks the younger audience might not remember, but there was a movie called Contact with Jody Foster in there. Share that story with us. It was a good metaphor. Yeah, it's a great metaphor. At the beginning of Contact there is an observation post somewhere in the island listening to the white noise of space and suddenly there's a gentleman who happens to have extra natural power because he's blind so he has a better hearing than somebody else that hears a pattern inside that white noise and they start reading down that pattern and they realize that pattern is really a recording that was done in 1940 or something like that of Hitler. But that doesn't matter what he was, what was interesting is this guy encoded the white noise, right? He latched on something very specific and was able to really pull true information out of this. I found that energy, you know, very akin to what an enterprise is going to have to do, or even the consumer for that matter, right? But an enterprise is going to have to do to start extracting... And this ties into your point earlier about how the social data unstructured as diverse and omnidirectional as it comes in, you can latch on to things that look narrow. This is that kind of example where it looks like a very small, maybe small piece of data, but when you unpack it, it provides huge value. That's kind of what you were getting at there. And every piece of context associated with that data, who's listening to it, when they're listening to it, what is their interest on that? This all creates metadata. That's a small point earlier about from the role of storage and compute where most analytics have been looking in the rear view mirror at reporting. That's time shifted. When you get into real time, it's really predictive. So the insight, results aspect of this new value chain is going to come from these new data sources and the ability to act on them. Act on it and act on it quickly. And therefore in the store compute, you don't want to let's take Tweet as an example, just an example. You don't want to accumulate massive amount of Tweet all day and then at 8 p.m. sent it for analysis to figure out what people are thinking about your product. You really want, as you're capturing all those tweets, you want to compute right there. You don't want to move. You know we're working on it, so don't want to give too much detail out to the audience out there. But seriously, you're working on some pretty cool tech. This is not like doing this. You could have some serious technology to get at this kind of level. What's your advice to folks out there both on the I want to expand my career in this area from a tech perspective and also from a customer perspective. How do they jump in? What would you share with them? You've been in the information infrastructure side of the business for a long time. What's your advice to the person who wants to advance their career? Either heavy duty computer science dude or some data scientists to an enterprise. How do I get started? What's the first couple steps? If you don't know what to major in I'll preach for my own church. Get a major in CS, you're fine. But you can get a major in math because you're also fine. And in fact you're going to be very complimentary. If you get a dual degree in math and computer science then they can have your job or my job. They can be good on camera. I don't have to be to get my job. Seriously, there's a lot of people out there who are, this new data scientist role is interesting, but it's more the math jock, not so much the programmer where you have converges. You make the storage and compute kind of coming together. There's kind of collision between disciplines now, right? The math geek is really going to figure out the formula that makes the magic. Generally they have no clue how to implement the formula and make it efficient. That's where the CS guys are coming in. That's why I say those two guys are good and they're fairly complimentary. If you're a business guy be very aware of your business process and educate yourself on what is the tuning point of your business. So you can translate how you can optimize your business to the math geek and to the computer science geek. So then they can do the magic behind the scene. There's a whole slew of new jobs I think are going to be created around information. The notion of information architect is going to be more popular than ever. There's also that was a role in the past around data warehousing. There was a data warehouse, but again it was kind of old school. And it was all about librarian kind of thing. Putting data in boxes, creating schemas and all this. But you're not in a world where you have to create schema, you have to learn to be extremely loose. So once a librarian, I would just simplify and say librarian like role to much more strategic math, technical solution architect kind of thing, right? Right, solution architect around the information. The other one is the one of information steward. The other one is don't get me wrong. We're not going to lose some of the right discipline and how to manage your information. And there is an aspect of governance that is important. So that's another business, another job that's going to create one of information steward. People that care for information on behalf of somebody, they don't own it, but they care for that information on behalf. And focus on data quality. Anyway John Luke Shetlain, the EVP at www.shetlain.com. Thanks for coming into the Cube today. Final question, I wanted to just drill on to talk about data direct networks. You mentioned there's a lab in Mountain View, California. What are some of the cool things you're working on? I mean you guys honestly have built a great sustainable business, self-funded, which is kind of the hall of fame category as a company, the sense that you've gone pretty much funded by the founders and have grown so big and have such a massive growth strategy ahead of you, I mean looking really good and the market's hot. But you've got to have some tech. You've got to have some more tech coming. We have lots of tech. Talk about what's one of the coolest things you're working on to some of the more practical things that's going to get you guys to a billion dollars in revenue. Let's go on the practical stuff. We've got to keep on doing what we're doing. We're going to do it faster, better and with more quality than anybody else on the planet. This is the block and tackling. We've got to be good at what we do, which has got to be always good at what we do. On the new stuff we do, it's massive investment in software. We're just opening, we're enlarging our office here in Mountain View, moving a few miles away. We're hiring that crazy. We've got two other labs, one in Colorado where a lot of our raid operating system is being done and then another lab in Columbia, Maryland. We're hiring everywhere. In fact, I tweeted last week I can't keep up with the job page on DDN slash career because of our growth. It's a good problem to have, trust me. But why would someone want to go work there? I'm asking that question. You've got to attract some talent. You've got Google, Facebook, you've got all these guys. We do cool stuff.  We do a lot of work on, for example, key value stores. We do a lot of work on new object file system. We do a lot of cool stuff on the cloud. We have a whole team of 10 people that it's in the process for using version 2 of a cloud builder, which is going to be very sexy. What's the most exciting thing that you see in the future coming around the corner that other people might not see? In other words, that gets you excited in terms of a trend. Is it cloud? Anyone can build a cloud? Is it more techy? Peek around the corner and share your vision with what's really exciting you. Well, as I said, what excites me is the size of the opportunity. Everywhere I look, there is not a place where I say, this is going to be a dud. This is not going to last. And we better move on. I can peek into every single one of the market we play. Whether it's governance, whether it's rich media, whether it's cloud, it just doesn't stop. Believe it or not, all growth is limited by the number of people we can put on the street looking on doors. We can sustain 30% growth for a long, long time if only we could get the people. But if I look around the corner, I'm really excited with all the virtualization work we do around virtualization compute inside the right-of-way. Not to harp about it, but I spoke about function chipping. I want to enable that function chipping. I want it to make it trivial for somebody to take the algorithm and almost transparently distribute some of that work to be done in our devices. That's a cool part. And then around the cloud the interesting opportunity of leveraging all those technologies I talked about to focus on hybrid cloud for specific industry. Because if I can through what we're building and how we're building it facilitate, reduce the TCO for users in the given industry to do their job, that's exciting. And it doesn't have to be a private cloud. It could be an industry-based cloud. It could be a private cloud for somebody in their business. But just like as you were mentioning, you can slide your credit card on Amazon and get some store and get some compute. I think we're going to be able to, if we have a credit card for a given industry we're going to be able to slide that credit card in that specialized cloud for the industry and get stuff done that we expect to be done there. What is that industry? It's an exciting time and I got to say I get intoxicated by the new opportunities that are emerging. We're in the publishing business. We have siliconangle.tv, siliconangle.com on the publishing and video side. Obviously we see the video challenges all the time and the greatness of video. I think it can help you with your video. Trying to get us some drives. We need some drives. But I honestly see business opportunities expand, we're self-funded, kind of like you guys. In the old days 10 years ago, 20 years ago Akamai was leveraged up with huge amounts of capital. I could literally roll out a video CDN content delivery network for very little capital. Compared to what it was. Talk about that dynamic. That's not trivial to do but now you can do it. What Amazon was for storage you can do that for content. Let me give you an example. One of the big things we hear about cloud is that it's so easy to do for startups like us you can develop on the cloud low startup cost, get validation and ship that product. The enterprise side was you put your credit card down and you can put test and ops in the cloud. But the CIOs were rejecting that because they didn't want to put their critical apps into the cloud. What you guys are talking about is with software I can put the entire function production into the cloud. That's what private cloud dream is. So where are we with that and what's your angle on that? That's really what the private cloud is is to be able to offer the same level of SLA and QOS and sometimes security although there's too much fear around public cloud security that's not justified but I understand why some industry has a lot of security. It's fairly easy to do now because you can really find out there we provide some, other people provide them some building block appliances that are already a package all that is necessary to build your cloud and you can do it almost just in time you don't have to buy some giant infrastructure on day one and wait till you have 99 users to start buying a giant infrastructure for 101 users you can start really small and as the demand pens up, like in your case demand of people watching citycon.tv you can just add new boxes whether they're software hardware appliance doesn't matter a metaphor for boxes to just grow your cloud there's a lot of stuff that But for the enterprise they want to have that same effect where how someone can put their credit card down and get Amazon S3 for example but there's a glass ceiling there with Amazon you can do it up to a certain point and then it's like wow it gets complex, configuration management automation, SLAs the list kind of goes on and on that once you hit a certain tipping point in Amazon it's over the top expensive and complex and risky but the thing with with any cloud solution especially involving storage because storage has a really high cost of TCO as I was saying the $1 you spend in storage the asset is really $8 you're spending in real money and it doesn't always scale with the capacity so there is when you are a service provider and a world of storage there's kind of a point as to which you're no longer have an economy of scale for the customers you have an economy of scale for you but it becomes really too expensive for the customers and that's a good experience point where as an enterprise you may think hey I'm going to run my own storage my own cloud basically in my walls in my data center because then my asset cost even with a TCO even if I include the TCO it's still going to be less than what I could pay for that's a good point TCO total cost of ownership is a big issue and a lot of people don't buy with TCO in mind it's always after the fact it's always after the fact it's the classic shark fin you don't know what's under the water there's an extra cost we're here with John Luke Chetlien the EVP at Data Direct Networks final parting comment just share with us the vision in your mind five to ten years not from a DDN perspective but taking about all your experience as a startup entrepreneur executive tech geek what's going to be different in our world five to ten years from now our life our technology and storage all the stuff's data is going to drive that what's going to be different I think that number one the opportunity are limitless there's more opportunity to create now than there was ever and it's all because of that information being the currency that information being as my former boss at HP used to say it's a natural resource we're going to all tap on so think of massive amount of oil flowing and we all can play with that we all can do something with that oil we all get very successful and wealthy doing that we can do the same with information so all life in the five to ten years will be completely driven by how that information is being used and what's interesting is that today we're looking for information and looking for insight but ten years from now the information will find us and the insight will find us so it's no longer the Google model god bless Google we do them every day but you got to look for something what's going to happen going forward we just had this conversation with Blacko CEO Rich Scranton computer science should be working for us like the Star Trek magic needs to come back what's going to happen is that information will find us will find us in the context that we are I expect that not that far from now you'll be landing in you'll be taking money out of an ATM machine and along with the receipt for the rubles that you took will be a map to the nearest McDonald's for example because information knows that you're a McDonald's lover and that's what you want when you land in the foreign countries go eat a McDonald's so that's the case for information find the user and that's what makes a big big difference in fact we see today even on some GPS system you buy a GPS system and as you're going to point at this little icon that you have there and there's a Starbucks there you did not look for Starbucks you were just driving along looking at a GPS hopefully the road too and you saw there was a Starbucks sign you stop by the Starbucks and have a coffee that is the beginning of information finding people that's the future


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