 Okay, we're back, this is Dave Vellante and we're live from IBM's IOD Information on Demand event in Las Vegas. We're at the, this is day two. It feels like we've been going for a lot longer than that, but this is a great event, it's a transformed event, really morphing from information management, kind of governance and that whole world to business analytics, big data, think big, are really the themes of this event. IBM really showcasing its portfolio of products and visions and executive visions and customers and it is quite the event. I'm here with my co-host, Jeff Kelly. Thanks, Dave, and we're here with our next guest, Nancy Copp-Hensley, she is the senior program director of Pure Data and Atizah Product Marketing. Welcome to theCUBE. Thank you. So, I welcome Nancy, so I met Nancy sort of indirectly. I was doing a breaking analysis on IBM's Pure Data announcement and I saw this great content asset where Nancy had said, look out, Larry, here we come. So the gloves are off. I gave him fair warning. That's right, you're very kind in that regard. I thought so. I mean, that's a really professional thing to do. Well, it's IBM. Exactly, send me courtesy. So you're okay, so you're not afraid to call out the competition a little bit, it's tongue in cheek, it's fun, but talk about Pure Data. It really is your entrance into this converged block of infrastructure for a specific application purpose. Talk about what it is and then we'll get into it. Okay, so Pure Data Systems, which we announced October 9th, is really an extension of the Pure Systems family, which we announced earlier this year. The systems we announced earlier this year really help customers run their business, the Flex system and the application system, but what we're really aiming to do was to help customers deal with the data. So everybody's talking about big data, everybody's talking about how they deal with it, and the cost associated with getting the insight from the data, how do we simplify that? And if you look at all the statistics around the opportunity of big data, the one that sticks out for me the most is one from our recent institute study on the value of big data, and it just jumped off the page. It said 47% of customers are in the planning phase. So how do we get them from planning to actually gaining some value in the data? And a lot of this would be able to accelerate the value. And the Pure Data Systems family really is built on the tenants of the integrated expertise, simplified experience, and expertly integrated so that customers can have that much faster time to value. Yeah, so we've talked to our customers too, and I wonder if you could confirm this, deny it, whatever, they're saying their two biggest problems are how they deal with bringing structured and unstructured data together. And number two, how to actually get value out of it. And so they're a noodle in that, they're looking for vendors and consultants, they're talking internally. Are you seeing that as well? Absolutely, and I think customers are really struggling with what is big data, right? And some customers are looking at it as this huge thing, and really to me, big data is all data, right? It's the data that we didn't have the technology to consume in systems prior to this, and now it's just opening up opportunities, like Steve Mills said this morning, it's not necessarily about the new math, it's about the new data sources that can enhance the math we've already got. Better targeted campaigns, better information on fraud, finding it faster, and for those customers who want to take advantage of it and are trying to do that shift, they're really looking at this transition from more of a monolithic one-size-fits-all architecture where a lot of our traditional data warehouse came from to what we're calling the big data platform today. IBM calls it, it's got million names in the marketplace by various vendors, but we call it the big data platform. And the big data platform really is our way of helping customers achieve smarter analytics, which is taking advantage of the big data opportunities, so they get that competitive edge. And it's a much different looking system than systems of the past. Unlike our competitors who do believe one-size-fits-all, we are coming from the philosophy that you really need to optimize your systems to get the right performance, get the right agility, right? Because as we were trying to build these complex systems, the need for these analytics started to get stronger and we lost sight of agility, and that's key. So being able to roll out a system that supports a certain capability, whether it's unstructured data or structured data, and maintain a level of performance that you keep the business happy, that's where we need to go. Do you think we've seen the end, are we nearing the end of sort of roll your own in the whole data warehouse space? I do. In data warehouse, it's been nearing an end for some time. I think because the pressure of delivery is so tremendous, most customers now realize that roll your own is just overrated. They need to be focusing on delivering business value. Ownership is highly overrated when it comes to I own this architecture and nobody wants to run to a CMO who's under a tremendous amount of pressure to say, look, we integrated a system, he doesn't care. He's under pressure from his executives to actually produce some insight faster than his competitors. So who cares? Roll your own, it's so yesterday. Yeah, so does that because analytics are so performance hungry, and it's one of those applications where you actually do need purpose-built infrastructure to make it work? Absolutely, and when you look at the different types of analytics, and we have a peer data system for two of the key areas of analytics, operational BI. We've been talking for years about pushing business intelligence to the masses. Well, what does that mean? Well, if we're going to send it out to the touch points that are reaching our customers, we need fast data, right? We need low latency, fast ingest, and we need to have thousands of concurrent users. That is a very different workload than somebody who's crunching through huge amounts of data, scanning lots of information with a particular focus on looking at disease management patterns or whatever might be your fraud. It's a very different workload, and realistically, while we built technology to handle mixed workloads, it became impractical for customers to try and manage all of that in one environment. So the idea that your call center rep who needs another recommendation and your data scientists who's doing the really deep work would be, use the same type of platform just doesn't, it just doesn't work. It's not realistic. I think that you can do it on a smaller scale, but as you really start to scale up operational BI, the whole focus is, I can't get the information into the right touch points in the right amount of time. It doesn't do be any good. And businesses are already going to struggle with how to match up their operational process with this information. We heard a lot about it in the general session today about the, you know, basically the heaven of being able to interact with the customer at all touch points and make them the right offer. That's where we want to go, but you can't fail from a performance perspective. What's the best way in your view for clients to connect to this new emerging Hadoop world? You know what, the best way really is to pick a starting point where you know you've got some value and it may be some of your existing data that you have in-house. So there's a lot of customers like retailers that have T-Log data that they've never been able to consume and because it wasn't something that fit into the traditional structure of a data warehouse or even social media data. And we're building accelerators today in our products that help take advantage of that. So we recently announced the Accelerator as a part of our big insights release for social media analysis. And I think that's the key part. We talked about that 47% of customers still in the planning phase. It's helping them realize the value. So pick one entry point and grow it from there. So we talk a lot about, we want to break down these data silos. So how does IBM go about doing that? You mentioned the IBM Big Data Platform. In my mind, it's a holistic platform. Absolutely. But how do you practically go about doing that? You've got the two different flavors of pure data. You've got big insights. You've got streams over here. How do you make sure they all work together or optimized performance and can share data and actually feed one another insights to optimize analytics? That's a great question. It's one thing to just say we have a platform of systems. It's another thing to say they have to be integrated, right? They have to be easy to work together. They need to communicate to make this consumable. And we have connectors between our big insight systems and DB2 and Natesa makes it easy to move data back and forth. We also at the conference announced a new product that easily helps you move data in two clicks from targeted systems that include even systems other than ourselves, other companies that have databases to move it to Natesa. So you can pick Natesa technology, the pure data system for analytics. So you can pick an easy starting point and for most customers, it's that they're struggling to maintain performance and time to value. So the pure data system for analytics on the deep analytics projects is a great starting point because they can roll it out very quickly. We can move data quickly from other systems, including our own systems and get customers up and running very quickly with a high performing system and see some performance gains there. They also might have an entry point on the Hadoop side with big insights. And if they need to move that data back into DB2 or for operational analytics system or our analytics system on the pure data system, we have connectors to do that as well. So it has to be a holistic view. And we also have what used to be known as Vivisimo now called InfoSphere Data Explorer, which allows us to access data no matter where it is, which is another important part of the platform. So I think we're probably one of the only vendors who are really thinking about how to get these systems really integrated and then helping customers at any entry point really get started to build out that platform and start to shift from a more traditional architecture to taking advantage of big data. So I wonder if you could comment on this. There's some startups out there making some noise about connectors. Adapt is one, for example, saying connectors are bad. We're good. Big ETL bad, little ETL good. Conversations about sharding and all this. Really interesting little food fights going on. What's IBM's take on all this? Is it, are we evolving toward a single platform? Does the customer care? Talk about that a little bit. I think the connectors are a starting point. I don't think that we have enough usage patterns out there with customers in production that are using all of the different parts of the platform to tell us what the best solution's going to be and the connectors are a very good start. Like I said, we have that 47%, just six out of my mind of customers are still stuck in that planning phase of where do I go from here. So you can get into architectural debates, but that's really not your game, is it? I mean, IBM's always focused on the business value. Absolutely. I love IBM. The challenge is not the technology. The technology is there, right? That's not the opportunity. The opportunity is the insight. So talk a little bit about talking to the business folks versus talking to the IT, which we've had this conversation a couple of times today throughout the day and I think it's really an important part of the story because you're not selling the IT, you're not, with all your own kind of a thing of the past, you're engaging. I would imagine most of the time with line of business. Absolutely. Or both. Certainly, I mean, there has to be interplay, but in terms of the culture of understanding what analytics can do for your organization, how do you go about helping your customers kind of get that cultural mindset? We have a team of people that we actually, our go-to-market strategy matches the architecture you've seen throughout the conference. That big data platform, we have a team of people that are focused on selling all of the products in that big data platform. And they work with our GBS partners and services partners in the field to conduct customer workshops. Many workshops, we're not talking long engagements, but many workshops that help drive out the business value of what the project can bring. And this is very similar, and I keep saying this to going back 10, 15 years ago to data warehousing, we were doing workshops around why you would build a data warehouse. Now we're just kind of expanding that view of why you want to expand your capabilities beyond structured data and what that can give you. Talk about what you're seeing as far as the trends related to storage, for example. We're seeing massive amounts of data, total change in the infrastructure. You hear a lot about flash. I know I don't want to geek out too much, but I'm interested in the business value that that enables. So what are you seeing there? I think tiered storage capability is definitely important going forward. We've enabled that in our operational analytics system. That's easy tier? Well, basically what we're doing is we're looking at for patterns. It's a DB2-based system. So DB2-10 has multi-temperature capabilities, although everybody defines that very differently. Let's just say it's tiered storage. So we want to keep the hot data on something like an SSD, for example, and then that helps you manage costs, right? As well as help you manage performance. So I think that's important going forward. But we're also seeing a trend with a lot of our customers who are looking at Hadoop-based systems for archival. And you've probably seen similar trends as well, because it's a cheap storage means for them. So it'll be interesting to see that start to evolve more within the marketplace, but we're hearing this as a consistent use case. Somebody said, was it on theCUBE? Somebody said, Hadoop is dead, Hadoop is a new tape. Disk is the new tape, and Flash is the new disk. Hadoop is the new tape, yeah. Hadoop is the new tape, that's pretty funny. Well, we've been struggling with the same issues for years, right, is how do we keep the hot data hot in terms of performance, but not necessarily the cold data and make it still available for customers to query. So I think it's going to play an important role there. Storage is, I mean, it's infrastructure, it's plumbing, but there's some major dislocations going on here. Not a lot of talk here at the conference about storage. IBM has its own storage conference called Edge Now. I'd like to see the storage team be here because this is where all the action is. CIOs are here, right? The business discussion is here. So unless you want to be stuck in the basement with the plumbing and the pipes, you got to be at this event where it's happening. Well, not a lot of high level executives really want to talk too much about storage. No, but if you connect, if you can connect. They just want to know they're getting the data fast. If you can connect the business value discussion to it. I mean, I think that's true. High level executives don't want to talk about it unless you can show them how you're going to make their life better. You know? And their costs lower. Yeah, look, I'm going to cut your cost. I'm going to deliver big business outcomes that drop productivity to the bottom line. I mean, people would listen to that. That's true. Yeah, I think they would listen to it as long as you could maintain a certain level of time to value. I think above all, that's what business people are really the most concerned with, is if they know that they're going after something, some sort of trend or some sort of analytic that they need, they could care less what you put it on. As long as you can deliver that insight, they need quickly. Right. Right, so the conversation kind of, what the business side is about here. Here's how we're going to solve your business problem. Here's what we're going to do for you. They say, great. And then you've got to talk to IT about how we're going to make it happen practically. And that's where I think that conversation. Well, your buddy Larry talks a lot about flash. He talks about flash. He belongs in Finnebin. He's out on this stuff. I mean, is that, I mean, it's amazing to me to listen to the discourse here at this conference. You guys just, you got more geeks in IBM than anyone, but your marketing doesn't go there. That's a conscious decision, obviously. It absolutely is. I think, you know, he's got a big, shiny, nice, big, fast box, but unless they can help their customers derive some insight from it, it's really quite worthless. Yeah, so I mean, and I mean geek, you know, as a compliment. I've assumed on the queue, but you think it's a compliment. We prefer geek chic ourselves. Geek chic, Steve Mills is kind of an, I don't know if he's alpha geek, but he's definitely, he could geek out with the best of them. But it's impressive that your marketing, you know, just doesn't devolve into that. I mean, so often we're talking about the technology. Technology's fun. It's cool. We could definitely geek out, but it doesn't help our customers. Yeah, but you really keep it at a business value level. That's what, you know, it's made IBM. Let's separate IBM from the competition frequently. So, well, this is good. Where do you see it all going from here, Nancy? I mean, what's the, what's the, put on your, what's, break out your crystal ball. I know you can't divulge like future product plans, but maybe trend, maybe trending some of the trends you see and how that's going to manifest itself into how, you know, IBM helps its customers down the road. Well, we're definitely very focused on what we're calling the accelerators, which is a part of the big data platform and those come in different forms, right? One of them is accelerating a specific solution like we did with the digital analytics solution. Might be that our ability to integrate analytics deeper into the database like we do with Nati's analytics on the peer data for analytics system. But the whole idea is let's try and get customers to understand the value of big data faster. And then hopefully, I'm hoping the trend will be that people start realizing that big data is really all data. It's not, not just huge. It's not just coming at you fast, but it's all kinds of data sources that are now available to you and how we start to build architectures. And I'm also hoping to see that shift towards this new way forward in terms of architecture, way from the monolithic structures, more towards things like the big data platforms. We're very focused on the integration capabilities to make that happen. And I think we're probably way ahead of our competitors in terms of our thinking around that, even in the area of governance, right? As we start to become more dependent on these new data sources, like unstructured data, we do have to worry about security and compliance and all those things. We're already thinking about that. So we're not just providing a system that can give you a Hadoop distribution, for example, we're providing more value to that. And I think once customers start to do more around their exploration of big data, they'll start to see I have to make this enterprise ready. So I see that shift happening in the next year as we see more and more customers going into production, that they're starting to realize the things we realize in data warehousing after we put those things in production. I need lower latency, I need better security, I need better availability, right? I need disaster recovery. As the dependence grows on these new data sources as part of our critical decision-making, all of those things are going to come to play. So are you saying this is your to-do list or are you guys there in your community? We're already thinking about it. Absolutely. So what is on your to-do list? What are the customers telling you? I mean, for us, really, our to-do list is get them into production on being able to take advantage of these new data sources. That is, it's as simple as that. When does pure data go into production? Is it, are you in the field already? We are General Availability Friday. Oh, awesome. So we are of lots of interest. The booth over here has been non-stop packed. Get your orders in now, big backlog. Yeah, get your orders in now, it's a big backlog. They even love the new look of the system, so. The systems look great, actually. It's not just pretty, it's very functional. It really is, you know, I was noticing them the other day. You know, the, I don't know if it's the Apple mindset of, you know, design, but it's really hit the big iron world, hasn't it? These systems are beautiful. They're really well-designed and architected, you know, in a really appealing fashion. Well, congratulations, Nancy. Thank you. I'm really excited for you guys. We're thrilled to, of course, be here. Thanks for having us. And thanks for coming on the queue. Thanks for coming on the queue. Thank you. Great to be here. All right, so keep it right there. We'll be right back with our next guest. This is theCUBE. We're live from IBM IOD in Las Vegas. Keep it right there.