 Welcome to Newsdesk on SiliconANGLE.TV for Tuesday, October 30th, 2012. I'm Kristen Folletti. AMD is making its move into arm-based processors, and here with his review on Big Data Week is Chief Wikibon Analyst Dave Vellante. Welcome, Dave. Morning, Kristen. AMD announced its licensing arm processors. What do you make of them? You know, it's interesting. When we covered the HP Moonshot announcement, I noticed that AMD was there, and I was asking AMD, well, why are you here? Do you have an announcement? They said, no. We're just kind of observing. We're good partners with HP. Well, now you see that AMD is really getting serious about this space, and they've got their own internal efforts called Jaguar for low-power processing, but it's clear that this is a time to market play. AMD doesn't want to be left behind in the new disruption, and so it's throwing its hat in the ring. I think this is good for the industry because it brings more players, more innovation, more capacity, and I personally think that there are segments of the data center that are really going to begin absorbing these types of processors. How significant is the low-power processor trend? I think it's significant, but it's also still a niche. I think for the mainstream data center, it's going to be a while before we see applications written that are running on these low-power processors because, you know, they still have to come up with 64-bit. Processors' transitions take a long time. The move to 64 from 32 with x86 took a very long time. So I think it's going to take some time in the mainstream. Having said that, there are a lot of new applications that are going to, I think, disrupt the traditional status quo. I was having a conversation with Amar Awadala, one of the founders of Cloud Air last week, and we talked about this trend specifically as it related to Hadoop applications. And he pointed out to me that Hadoop processors running Hadoop are way, way underutilized. They're running at around 10%. And virtualization doesn't solve the problem because when these processors are idle, they still suck up way more power than the low-power processors, arm-based processors, and things that now AMD is doing, putting in a fabric like Calzada has done and is doing with HP with Moonshot. And so these microprocessors, which come in the thousands within Iraq, are, I think, going to disrupt particularly the traditional, rather the new applications running Hadoop, where when these processors are idle, they're sucking virtually no power. They're sipping power from a straw, if you will. You just came off SiliconANGLE's Big Data Week. Tell us where you were and what it was all about. So we did two events last week with theCUBE. We were at IBM's IOD event in Las Vegas. It's IBM's big conference, their big user conference for their software group and their information management group. And then we were also at Strata and Hadoop World. Strata, O'Reilly Strata, and Cloudera's Hadoop World have merged. O'Reilly is not running it. And they're quite different shows. IBM's IOD was all about IBM, Think Big, playoff of Think. IBM essentially, as I said last week, is super glued. It's analytics business to the big data meme and is flexing its muscles and has some very impressive capabilities. Of course, they're all within this big IBM integrated portfolio. The other side of the spectrum is really Hadoop World and Strata, where you have young people and traditionalists in the industry joining together. But there's a lot of disruptions, a lot of small startups, a lot of innovation. It's very immature, but it's also very exciting. About 3,000 people gathered at Strata and Hadoop World and about probably 12,000 people at IOD. But I think it underscores, Kristen, the fact that everybody, IBM and all the startups are really focusing now on big data. The hype around big data in Hadoop seems to be at its highest in history. What do you make of the buzz? Is it real or just noise? Yes and yes. I mean, I think that there's a lot of noise out there. But when you see big data on the cover of the Harvard Business Review, I think the time has arrived and we probably hit the peak of the hype cycle. People refer to the hype cycle. This is Gartner's little tool that they use to describe markets. And I think we have hit the peak. Many people talking about it. They talk about the three Vs of big data. And I think that's getting old. I think that you're going to start seeing real applications and real business value being driven over the next 12 to 18 months. And so I think that, yes, there's a lot of hype. There's a lot of big data washing going on. But right now we're seeing a lot of tools and capabilities and platforms coming out on which I think we can start building real applications that deliver value. The big data industry is promising big solutions to big problems. Can you talk about some of them and give us a sense of how real they are? Sure. I mean, every time you have one of these over a hyped new disruptive trends like big data, there's a promise that we're going to solve all the world's problems and energy problems and world peace. But people are talking about reinventing education. They're talking about dealing with security issues. They're talking about health care issues, solving major disease problems and predicting things like the weather, et cetera. A lot of this you heard remember when the internet really started to become popular. And I think you're seeing a lot of hope, a lot of promise. Having said that, a lot of times these promises don't go fulfilled. But I think in this case, you're starting to see bits and pieces. I'll give an example. Take fraud detection, for example. It used to be a situation where banks and credit card companies would do things like fraud detection. It would take months and months and months to analyze the data and then try to identify the subterfusion. Today you're seeing that in real time. So you're actually starting to see some major advancements beyond just clicking on ads. Corruptions you see coming as a result of big data. Well I think the, let me talk about the technical standpoint and maybe the organizational and business perspective. So from a technical perspective, the big innovation of big data and Hadoop was really the notion that Google put forth, which is instead of bringing petabytes of data into a box through a little network pipe, leave the data where it is and bring five megabytes or 10 megabytes of code to the data and distribute it across the network. And that innovation has led to the ability to deal with massive amounts of information, which previously the only way you could deal with that was to stuff it into a God box and make that your data temple. So that technical realization really is a phenomenon that has taken off and has enabled us to really deal with all this information in new ways. It's dramatically cut the cost of storing data. We don't have to go to a big EMC or NetApp or IBM box and stick all the data in there. We can leave it where it is. We can use commodity components. So that was really profound in a sense. I think from a business and organizational standpoint, things are changing. The money tree is changing. CMOs are going to be spending more on big data than CIOs. And I think that you're seeing really the reemergence of shadow IT and lines of business really driving the agenda, as opposed to, for example, the CIO or the general council. How should CIOs think about big data? Is it a problem or an opportunity? Well, I think increasingly it's going to be viewed as an opportunity. As I just mentioned, the general council has kind of been the tail wagging the information management dog for the last five or seven years, post-NRON, post-911. There's been a lot of emphasis on security and privacy and defensible disposal and the like. I think that's clearly shifting to opportunity. And I think CIOs, as I mentioned, really should be aligning with CMOs. I think CMOs are getting the budget. So if I were a CIO, I'd be looking at a couple of things. One, I'd be looking at managing my application portfolio like an investment portfolio and really trying to invest in some of these new emerging applications, not just keeping the lights on, but trying to grow the business. And I think the second aspect of that, and it's very much related, is I would be aligning with the businesses and particularly the CMOs to really try to identify ways to monetize big data and drive revenue. I think if CIOs don't do that, they're going to be marginalized and they're going put in a place of less value, as I say, keeping the lights on, running the existing infrastructure. I think it's much more exciting to align with where the action is, the revenue producing. I think the opportunity is there for CIOs to turn IT from a cost center into a profit center. There's a premise in the big data business that data beats algorithms. In other words, the more data you have, the ability to analyze, the better your information will be, and that data will beat sophisticated modeling every time. What do you think about that? I think it's an interesting point that you're making, Kristen, and I think that generally the consensus is that you can beat algorithms every time with more data. And as an example, I gave earlier, which is fraud detection. Think about the way in which we used to do fraud detection. We would sample data from transactions, and then we would build sophisticated models to try to identify where criminals were committing fraud or stealing identities. That has completely changed. Sampling is dead. Because of Hadoop and because of this concept where you can leave the data where it is and bring the code to the data, you can now operate on the entire corpus of data. So again, no more sampling. What that does is it allows organizations in near real time to identify theft and act upon that. So that's an example where data clearly beats algorithms. Having said that, in order to create sustainable competitive advantage, personally, I believe that the algorithms will improve. As you get more data, new algorithms will be developed to exploit that data. And this is really where competitive advantage comes in. There's a big discussion in the industry. Oftentimes, Moneyball, the book Moneyball, is used as an example. Saber metrics is now used across the entire major league baseball. And differentiators come in the form of training and farm systems and minor league systems and pitching coaches and the things like that. More so, for instance, in analytics. However, that's a very rudimentary example. Certainly, there's a lot of data in baseball, but there are a lot more complicated problems that people are out solving. And personally, I believe that within financial services and energy exploration and within governments that serious value will be developed around new algorithms that are exploiting these massive amounts of data. So I think it's a virtuous cycle. The data gets better and the algorithms get better and more business value is created. Well, Dave, thank you so much for joining us today and we'll talk with you again soon. Thanks for having me, Kristen. Talk to you next time. 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