 Live from the Mandalay Convention Center in Las Vegas, Nevada, it's theCUBE at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are live in Las Vegas for IBM Insight. This is theCUBE inside the social lounge for IBM's Insight Go, their social digital experience, first time this year, they have influencers. Of course theCUBE is making a special presentation here. It's our third event now, Dave Vellante having been doing the information on demand. I'm John Furrier, the co-founder of Silicon Amgen, I'm my co-host Dave Vellante, with Wikibon.org, also co-founder of Silicon Angle Media. Next guest is Helene Leon, distinguished engineer, CTO of Big Data Analytics, data governance, software on system Z for Europe. Awesome title, I don't think we can get that in the lower third, but that's awesome. Welcome to theCUBE. So, mainframe is never going away. That's supposed to be dead years ago. So, mainframe, client server, PCs, now we have social, connected mobile, internet of things, and everything in between, all tethered together. How do you make sense of this? Within IBM, all that stuff still exists. How are you guys pulling that all together? Yes, what is incredible with mainframe, it does support business of customers since 50 years this year, and we are the 50s anniversary. Happy anniversary. Yeah, that's great. And what happened over 50 years is that we adapt ourselves to the new technology while protecting customer investment. And the world, you know, the value prop of the mainframe has been protecting the customer investment since day one. That was the value prop, and we succeed with that. We succeed with, you mentioned client server, we had service oriented architecture, now we have mobile, we have analytics, and all those new technology are applied to mainframe business, mainframe application and mainframe data. We are at the inside conference where we talk a lot about modern new data, unstructured data. But the first data that customer wants to analyze today is their transactional data, which is hosted for a lot of customer in existing mainframe data. We were at Big Data NYC last week with our event and also Strata Conference, Hadoop World. And you know, Syncsor is a company out there that's doing very, very well with mainframe. The mainframe's just still out there, a lot of the largest banks, some of the biggest powerhouses still out there are the mainframe. So what is the evolution? What's next? I mean, obviously the power is there, you're seeing some distributed computing going to a whole other level. What is the update? I mean, how relevant are they? Are they extended their life? It seems to be every decade, the year of the mainframe going away never goes away. There's always a life extension, the life expectancy of a mainframe just doesn't seem to stop. So customer are taking benefit of their existing application for sure and they build new one. What has been crucial for the company is in a crisis time anyway, they have to rely on what is working. You cannot remove, you cannot get rid of all your applications that are doing the business. But you need to improve them. So for many years, IBM position and the analyst as well positioned the mainframe as a back end. So all the modern stuff were built on top of the mainframe. But the mainframe was not really modernized, only access to the mainframe were modernized. Now we are at a position where we want to modernize application in order to take benefit of analytic services, for example, or modernize the data access to be more standard. And it's exactly what we do. You know during my last 20 years I'm an IMS evangelist or my art is IMS so that's great for me. And I see customer, I'm working all the time with customer that try to improve their business, what is done by IMS application. And they realize thanks to the mobile for example that the transaction number is growing. So that's IMS as a back end. But what is important they realize as well that we can change application in order to make push notification for example in order to use those new type of information that we have like geo-localization and things like that. So purpose built becomes a big part of the mainframe's role. It's purpose built and it's a lot of power still. What do you mean by purpose built? Well as you get more modernization batch analytics for instance could be great in the mainframe. So is that you're seeing kind of use cases where certain use cases really shine with the mainframe in analytics? When it's question of real time it has to run on the mainframe. You know the mainframe is really the container of the transaction business for banks, for insurance, for industry. And what we want to do like Gardner is sponsoring is a hybrid transaction and analytics processing. So the ITAP. And what it means is that we want to take some analytics services and add them next to the transaction. And it's exactly what I'm working with in order to do maybe some better for detection at the time of the customer is connected not two minutes later, not one hour later on obsolete data. Just now, when the customer is engaged with us with the machine, let's do something good. Next best action. So bringing the analytics and the transactional data together and making decisions in near real time before the fraud occurs or maybe before the customer leaves. Exactly. So what's the software layer that is enabling you to do that? What has been done by IBM Middleware is that for example we can implement directly as PSS scoring algorithm inside of the mainframe. So the scoring model are created outside of the mainframe by analyst, by mathematician. And then we take this algorithm, we deploy it in the mainframe and then application can just call this model to get instant scoring. For risk for example. For risk, for fraud, for risk, for anything. So it's again, if it's real time it has to be very quick. And that's the best value of the mainframe is to be very efficient. So scoring is very important. Decision management with rules, business rules software are able to be implemented in collocation next to the transaction. Next to the IMS transaction, next to the kicks transaction. And that's great because it's very efficient. We also have solution to accelerate all type of queries. So queries coming from the distributed worlds, queries coming from Watson analytics for example, can be directed to mainframe data and get the information very quickly. So the mainframe is not the isolated islander. It's Eden. It's Eden. Everybody here, they don't know what is the mainframe. Because it's so invisible in the business process. Nobody has an end to end view of the complete business process. So they build very nice apps, system of engagement, modern, innovative. But they don't know that the data has been created on the mainframe. It has been copied and copied and copied and finally they use it. That's not efficient. So what I promote is the fact that we can have very nice reporting tool like Watson analytics to go directly to the source of the data. All right, well that's awesome. Mainframe is just still alive. Thanks for coming on theCUBE. Really appreciate it to share that insight. And we always say, there's always that role in the mainframe. I love that container for analytics for real time and the transactions. Great stuff, Leon inside theCUBE. Thanks for joining us. We'll be right back live here in Las Vegas right after this short break. This is theCUBE. We're extracting that signal from the noise and sharing it with you here inside the social lounge as part of Insight Go digital transformation here at IBM Insight. We'll be right back after this short break.