 It's The Cube. Here is your host, Jeff Crick. Hi, Jeff Crick here with The Cube. We're on the ground in Santa Clara just across the street from Intel headquarters at EMC's Silicon Valley location. And we're at the ACG Silicon Valley discussion about big data, making big money. And we're really excited to, we sat through a nice panel discussion hosted by John Furrier. We got a couple of panelists out for a few minutes to share with you what you missed and you weren't here. So first off, we have Steve Jones, Global Vice President, Big Data Cap Gemini. Welcome. Well, thanks very much for having me. So you were out on the front lines. You're actually out with customers trying to implement big data. Well, yeah, we actually have to take all this new shiny technology and solve an actual business problem. And I think that's where we talk about the big money is obviously Silicon Valley, you've got the valuations and all those things. But really for us it's about, okay, I have to make decisions better in my business. We could talk a phrase, insight at the point of action. How do I make a better decision? How do I leverage all the information within my organization to actually be able to perform more effectively? Because going forwards, that's going to be the difference between success and failure of a lot of traditional businesses. So there was a whole lot of topics that came up on the panel. One of the ones was before you relied primarily on your own data that you collected and now you're using other sources of data and trying to use the data in a larger context. Is that where you see that happening? What are some of the immediate benefits of looking beyond just your own silo data? Well, I think on the data it was about, one of the key things was the phrase that was used is the difference between explicit data and implicit data. So we've got all this explicit transactional data, the existing systems, you entered a transaction, something happened. Now we've got all these implicit data, this sensor data, this information about people or information about machines that we weren't expecting. We're pulling in weather data, we're pulling in geo data from outside. So a great example in terms of just a simple example when you think about being able to do monitoring of a device, monitoring of something as big as an aircraft engine. By able to do that means you can make sure it's in service more often. It doesn't come out of service as often, you're able to maintain it more effectively. You're actually able to change your business model that you don't buy as an airline, an engine anymore. You buy air miles that keeps your plane in the air. Your plane is service based on the air miles and that's all to do with that information capture. So I've used that example a hundred times where GE now sells basically air miles based on the engine. Is that happening? That's been happening for quite a while and one of the things about this whole sort of big data stuff is we love to talk in Silicon Valley about the social media, the shiny, shiny front end of it. But really think about it like an iceberg. That's just a bit that's above the surface that sort of bubbles up. Underneath that in the so the industrial internet, if I'm an oil company and I can optimize my field by 10, 15% I can optimize the equipment so I never have any downtime. You're looking at 20, 30, 40 million dollars a day. Not sort of talking about money cases where you think oh well this is a return on investment in five, ten years. This is if I can get this in a week earlier I don't care how much it costs. So that's really for us is these aren't just, there's a lot of real operational use cases today of people taking big data, taking new technologies like Hadoop, the stuff we're doing with cloud era pivotal at a bunch of different clients. Take an example from sort of the aviation industry. If I can as an airline pull together all of my information, pull together all of my operation information. Look at how I replan my airline more effectively and more actively. Replan my staff. I can reduce the number of cancelled flights by five to 10%. That's straight money in the bank, right? It's not theory. It's absolute straight money in the bank. I think you're absolutely right. I think out here in the valley we talk a little bit about the companies that are delivering the technology. We talk about the Edges, New York and San Francisco and I think we ignore the flyover ground as it's often referred to. But as Bill Schmarzo, the dean of big data from EMC spends a lot of his time in flyover country, in the country. There's really a lot more going on in traditional businesses that most people I don't think appreciate. Yeah, absolutely. And not just the businesses in terms of the governments. I mean, one of the ones we were talking about was if I'm a government organization, I've got these huge legacy old systems most of the time and old-style batch jobs pushing information between. And to be honest, there's fraud, there's issues, there's information quality issues. I mean, when I first moved to the US, they completely got my name wrong. The age is to fix it. Really, even those governments are moving towards this sort of single data substrate, having a data lake underneath. So instead of sinking information between systems, I go to a place to get the information. That's a real fundamental change in how these organizations approach information. So for us, in terms of looking at manufacturers, telcos, retailers, suddenly they're looking at information as being their competitive advantage. And I think the difference between here in Silicon Valley where the valuation is this and all of these pieces is we're talking about companies where at the end of the day, if they don't make a profit, they get killed on Wall Street. At the end of the day, if they don't make a profit, they go out of business. If you're a retailer and you can be 2% to 3% more efficient, that means your prices can be 2% to 3% cheaper, that means you can drive your competition out of business. If you're an airline who can be 1% to 2% more efficient on your flight routing and your engine usage, that's a billion dollars in the bank. So it really is a case that this new shift towards big data, towards analytics, driving company performance, is going to be the difference in success and failure. And at the end of the day, the thing I talked about in the panel is, we talk about here in the valley, we talk about the billions of cloud errors, value at $4.4 billion. Pivotals, a multi-billion dollar company, we work with those guys, but we then take their technology. We take cloud errors technology, we go and work together on a telco. That telco is talking about delivering $20, $30 million on their own. And that's a company that makes several billion dollars just from one project. Then you look at some of the stuff we're doing at Pivotal where we're looking at pieces from like a healthcare perspective, and you're talking about literally saving lives. I mean, literally saving lives as a result of the technology. So for me, I think the real shift here in terms of those is, the valuations here are in the billions, but in those flyover states, in Europe, in Asia, we did a survey recently with it, actually this week you can with the EMC where we saw, actually it was interesting, Brazil, China and the US were the three really executing well on big data. Traditional continental Europe, actually doing really quite badly, not seeing the advantage, not seeing the stuff and it's almost like the fact from me as a European, I look at it and go, well, we invented the worldwide web, right? And we missed the dot-com boom. We missed the internet age. It looks like we're building up to miss the insight-driven age as well. So it really is gonna be that difference. And I think seeing people like Brazil and China really seeing this as the way they're gonna out-compete traditional organizations shows you how serious this is. Because you talked about kind of the ugly internet or the dirty internet. It's not about getting the latte recommendation when you walk by your local Starbucks. This is much, much bigger, right? Much deeper, much more profound, but like you said, kind of under the waterline. Well, exactly, I call it, you know, it's the dull world. It's the dull world of profit, right? It's the dull world of profit, right? These are dull companies that make a profit. They have revenue, they make a profit. If they can make more profit, they become more successful. They overtake their competition and it expands. It's basic economics. There's no bubble in that. So I think, you know, when people talk about the bubble of big data is there is no bubble in being 2% more efficient as a manufacturer. There is no bubble in being 2% more efficient as an airline. There is no bubble in an oil company getting an extra 20% out of a field. That is basic economics of traditional businesses. And it's the data which is driving that next generation effectiveness. It almost feels like kind of the big ERP sweep that happened 20 years ago in terms of the really significant efficiency improvements in core industry, which is why this isn't the bubble that maybe the dot-com bubble was or some of the others. This really transformational with real business. Exactly. It's enterprise. I mean, there's other bubbles here in the valley. We can talk about social and things like that, which may be more bubbly. But when you're selling to enterprises, I mean, it's about ERP. ERP standardized process. Every process is the same way. Now it's about, actually, I need to drive differentiation. I need to drive insight. So we've seen the wave of ERP. We then saw the sort of the dot-com. We saw a huge software development wave kick off. And although there was a bubble in nature of it, you know, a lot of fundamental companies have fundamentally changed. They've been much more digital organizations. And a lot of them are still here, right? And a lot of them are still here. And a lot of the companies that transfer, the traditional businesses, transformed to be electronic supply chains are just the norm these days. Then we saw the SAS wave came in, which again was a sort of standardization like ERP for the next generation. And now what we're seeing is this next generation of software development. This next generation insight is, I'm building on my standard processes. I'm building on my cloud deployment. But now I'm looking at, right, what are those parts? What are those decisions that if I make better, I win. Yeah, pretty fundamental. Well, Steve, thanks for stopping by. Absolutely. Exciting times, absolutely huge opportunities for companies like Capgemini. Steve Jones, big data from Capgemini. We are on the ground in Silicon Valley. I'm Jeff Frick, you're watching theCUBE.