 Live from the Mandalay Convention Center in Las Vegas, Nevada, it's The Cube at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We're live inside The Cube, here in Las Vegas, Rybium Insights. This is The Cube where we go out to the events and they strike the signal from the noise. I'm John Furrier, co-founder of SiliconANGEL. I'm Joe McCose, Dave Vellante, and Wikibondata are also co-founder of SiliconANGEL, meeting my partner in crime here. Two days of wall-to-wall coverage. Our next guest is Nancy Hensley, director of strategy marketing database and data warehouse at IBM. Welcome back to The Cube. Thank you. Good to be here. So data warehousing and data fusion and all this stuff, data works, databases of service. Changing the game. Give us the update. What's the talk of the town here? What's going on in the hallways, in the sessions? What's the buzz? Well there's, I mean data warehousing has gone through such disruptions in the last decade. It's nothing like it used to be. I mean gone are the days when we would, getting a data warehouse, I'm not going to date myself, meant 130 boxes were delivered to your dock and then the swarm of consultants would come and six months later you'd have a data warehouse, right? And the latency wasn't all that great back then. Yeah, yeah. So that's really not the case anymore because the business just isn't going to tolerate that. So we've had lots of changes. It's not just one thing anymore. Data warehouse is a collection of capabilities that we call, whether you call it a geological data warehouse or whether you call it the big data platform, it's more than just one thing. So it is inclusive of Hadoop and streaming technology and in memory and all the things that you would do to modernize your data warehouse. I mean that wasn't really that long ago. The cloud, we're going to talk about the cloud. But that situation that you described. It wasn't. It wasn't that long ago. No, do I look that old? No, no, no. Four or five years ago you were describing actually the state of data warehousing and there are some situations where it's probably similar to that, although the spending patterns are shifting, right? There's more investment actually going into an analytic architecture more than ever before. I saw a statistic, something like a 71% increase in spend. And that's because the ROI is so much better. For every dollar you spend, you get $13 back. So if you're leveraging analytics, you're going to get that return on your investment. No doubt about it. You just have to be ready to do it. And that's the challenge a lot of our clients face today is being ready. So where are those investments going? They're not going into the 18 boxes and a zillion consultants and faster chips. Well, they're still going into data warehouse appliances. If you look at the growth of the data warehouse market, which is still a growing market, the majority of that investment is still going into a data warehouse appliance. So we're still introducing new products there. We just introduced one this week at Insight. New version of pure data for analytics that entities a system we call Mako. And the focus there was not just- As in the shark? As in shark, exactly. And the focus there was not just continuing the theme of simplicity and performance, which is what Nathisa was built on, but also secure as well. Because obviously that makes the news every day as well. It could be secure. What about sort of the traditional data warehouse world and all this new analytic stuff that seems to be coming together? When we talk to clients about the tools that they're using for their big data initiatives, they talk about data integration. And they talk about the existing data warehouse. Hadoop's in there, down lower on the list, and NoSQL's in there, kind of down lower, but those are the top two, by far. And I don't know any warehouse customers today that don't have some Hadoop capabilities within their architecture as well. So whether you're using it as an investigative technology, as a landing zone, as a storage for your cold data, I think you're going to have several reasons to leverage Hadoop alongside the data warehouse. And then the cloud. Now we're extending into the cloud. And that's really because while we were able to simplify some with things like in memory, which was a lot more easier to use with load and go and data warehouse appliances, there's still a requirement to even go faster. I mean, we're here in client saying that they need to get warehouses up for in minutes. Like in minutes, new requirements in minutes. Not hours, not days, minutes. How's the security changing the game a little bit? Because people want, in parallel to the agileness, speed, they also want to minimize incidents and breaches. So that's a challenge. How do you guys balance that? Well, there's some workloads that are never going to go into the cloud, right? And we hear clients say that all the time. So that's why the hybrid capabilities are going to be really important, so that you have the ability to put the right workload in the right place. So we've talked about this thing, we're calling the fluid data layer, right? And the idea of the fluid data layer is just that, that you want to have the ability to have your data and your analytics flow like water, right? So that they're much more accessible, that they're much more portable within the enterprise. If I develop something on my traditional data warehouse, I may want to move it to the cloud, right? I may want to make it a mobile, something that leverages mobile engagement. And that seamless integration is really, really key. So for example, what we built with the integration between cloud and a dash DB, which we announced this week. Right, right, so that's a perfect example. I like the notion of the fluid data layer, what I'd like as a customer, I want my data transport to cut across my businesses so that everybody's using the same, sort of, you know, the old version of the truth, but I'd like that. But you get the level of portability, right? So your data's flowing like water, right? And your analytics are highly portable. So are we closer to the single version of the truth? I always thought that big data was going to mess us up. I don't know, do we ever get the single version of the truth? I think we just got repositories that helped us leverage the truth. Narrowly I think we got it. I don't know if we ever got to the single version. Well, the one that we all agreed was the single version. We got there, CFOs could get there. Right, right. Well, you have to have a trusted repository, but it may not be a single thing, right? It's going to be several repositories. We were talking about this, with Mark, this concept of, you guys don't use this term, but he said he was comfortable with it, this concept of data lake. Yes. What do you think about that term? That's not an IBM, it's not, you're not inundating us with data lake marketing, which is great. Right. But at the same time, a lot of customers are using that term. I'm sure you can. They are. I think our focus now with the three services we announced this week, DataWorks and Dash, our focus is more around making data available. So how do we make that so much easier, so much more consumable? And obviously the cloud is the best way to do that. So that it's seamless. These are the users of a big feature. Talk more about that. What is the, what do they want that's easier? Just reduce this. They don't want to have to know, right? I mean, I'll give you a perfect example. So we have cloud, which is a fantastic database. And it in itself is built on that fluidity that you could have data anywhere and it's, it doesn't matter, right? And so you can get up there and you could rapidly develop some great mobile applications. And they will be incredibly successful, whether it's a game or an application. Now you can actually take it one step further. Cause as you're all the, as these applications become successful, you're getting all this great data, right? From these new applications. And now what you can do is you can literally shred that data from your JSON data stores in a couple of clicks into dash DB and then use whatever tool you want to do some analysis. So you can leverage it one step further without ever moving it. Seamless integration. You don't care what's going on. The data works is helping you move that data. You don't really care about anything. You just the fact that you have the ability now to do analytics on data. You didn't have that ability to do before two clicks. So let's talk some more about the cloud. You've mentioned it a couple of times. What's the, what's the role? What's the importance? Is it to spin up POCs really fast? Is it to say, no, this is where I'm going to run my analytics? Talk about that. I think cloud with data warehousing is pretty interesting, right? I think it's on everyone's agenda to look into because it's all around speed. And I also think it's also about self-service. When you think about how you need to meet the needs of the business, sometimes you just have to let the business have accessible data to mess around with, to do some discovery work. And it's difficult to do that in a data warehouse to provision it quickly enough. There's some sort of short-term analytics that they may want to do, some what-if analysis. Maybe there's some trend in fashion that they want to take advantage of. It's very difficult in a traditional data warehouse to provision something quickly and give them quick access. Whereas I can have my client provision a cloud in minutes. They can do what they need to do and then it's almost highly disposable. And if we find something we want to put in production, we can pull it back on the infrastructure or put in production on cloud. And I'll get my same security model? Absolutely, absolutely. So there's, like I said, there's customers that, based on the data and the compliance and the rules and regulations that they have, some workloads are just not going to go, right? Why IBM? Yet. Why IBM? Because we're not just putting things in the cloud. We're looking to actually change that experience. Which is different than just saying, hey, I now have a data warehouse in a cloud. We're actually thinking through, we talked about the fluid data layer. What's really going to make a difference for an organization? And it's about making that data accessible, making the analytics portable. It's not just about the ability to connect things from your cloud to your hybrid infrastructure. It's about making it extremely simple and very, very fast. So simple to set up and have access and very fast in terms of the performance of the analytics themselves. So DashDB's got in memory, columnar capability. And that's the difference. And having that full suite of capabilities from the ability to acquire data, which is where most people end up spending their time to the portability of the analytics from your hybrid infrastructure to the cloud. And then all the capabilities that we have on the cloud today. And then also connecting up some of the things like we've done with cloud it. Just making it very fluid. Explain the cloud in peace, because that's something that was an acquisition. People were like, that's a good purchase. They were well respected. Bunch of geeks from MIT. Good guys. We've been following them since they broke out when they launched on SiliconANGLE. We just started SiliconANGLE. And they were just, you know, they were like, what's going on with you guys? This is before it was fashionable to be databases of service. So it's basically in the front end of the NoSQL. But obviously coming into IBM, what has that done for IBM? And what have you guys done with these guys? Well, I mean, I think everybody will agree that there's this massive growth of mobile applications. This is, are the way we engage with our clients, almost every business has changed the boundaries of engagement to be very mobile focused. So this is, you walk around, everybody around here has a phone in their hand or a tablet or a fablet or whatever it is that they have. This is the way people want to engage with most organizations. So, CloudIn has been a great acquisition for us because they get it. They allow you to have very rapid development of these mobile applications. And we have this great distributed capability so you can have your data in different places like whether it's on Amazon or Azure, and it doesn't matter. It's so simple, it's so rapid and fast. And now you can take it one step further and have the warehouse. You'll see it like on clouded.com this week. You'll see it says hello warehouse. If you click, you can move that data to a warehouse. They're gassy. But it's one piece of the puzzle on the overall customer solution. So, where would a customer use Cloud and where would they stay on-premise and what's the mix and match? It's really going to be a preference for that customer. But when you think about our corporate strategy, data, Cloud, engage, right? So, we have the data piece. We've had the data piece for years. We continue to build. And we've refreshed almost our entire data management portfolio this year. So, you look at what we've done with DB2 and the capabilities we've built in to be able to do operational analytics to do analytics right in your transactional database now without affecting the performance of the transaction. So, that's enabling real-time insight like never before. That's been refreshed. Our data warehouse appliances with Mako and now making that more secure and faster, that's been refreshed. The announcement of our Cloud data warehouse. So, that's the data piece, right? And the Cloud, as you can see, we're moving quickly into the Cloud, faster than ever before. And then the engagement part, this is where Cloud and helps us out. So, one of the things that we were chatting with Jeff shown us and others is this social data. We love the word engagement. So, that's what we live with our CrowdChat product and the things we're building there. And so, it's very valuable, the active social data. And he chose, so was mentioning that in our TED and IBM talk, which was active data. Active data, it's first party data. It's actually relevant in real time in the moment. We're hearing immersive experiences. These are the things we're hearing at this conference. This is cutting edge engagement data. So, the data's at the front end, it affects customers and they're connected. And their ability to leverage it. Yeah, so what is social data? Break that down for us. What is, I mean, because, and Jeff shown us nailed it. I mean, people are internet of things, they're connected. So, they're just another data source into the system. But they're humans. So, okay, that's data. They're streaming their life and then tweeting in an observation space. Oh my gosh, it can be everything. I mean, I don't know if you guys wear the wearables, right? So, I have two, because I am a data geek. Not one. You're A.B. testing your health? Yeah, got my Fitbit versus my Garmin. But you're right, it is all about, I think we're quickly moving more to the conversations around the internet of things because everything is being so much more instrumented. And then the ability to capture that data on the edge, which, I don't know if you know that, but our Informix capabilities, which is a highly embeddable database, is Informix very embeddable, right? So Informix is playing a big role on the edge in terms of being embedded in these devices and we're going to see lots of change coming in the next few months. In fact, we just had a press release with HP around their ARM technology having Informix now. So, I think the ability to capture all of that data is just going to explode in terms of what we can do with it. And social data, you know, you're talking about Facebook and Twitter and the ability to actually have a deeper understanding of your client's likes, dislikes, what they're doing, like never before and how you use that data to drive more targeted campaigns. But that feeds also into the benefit that we were hearing earlier in day one, which is machine learning is this fabric underneath it. So you've got the infrastructure on the data warehouse side, the data layers, you've got cloud and you've got data works, you've got all this new middleware, if you will, like a better description, software, feeding in with an active data so you can actually make better software. That seems to be the right model, do you agree? Yeah, and that kind of goes back to the data warehouse architecture now has become this collection of things that's inclusive of all this capability, right? And I think that's how clients are modernizing is they have to pick their starting point within that. So it may be, I need to bring in social data. It may be that I need to pull in in memory capability just to really accelerate the analytics I have. Maybe I need a data warehouse appliance to just reduce the complexity I have in my traditional reporting and analytics and some of my deep capability and maybe that I need to be able to have unstructured capability with Hadoop and do some of the machine analytics. I mean, you have to pick your starting point start to build this together. So it's another dynamic of the old data warehouse businesses. I had to go through some super analysts to get my data and I had to give him or her a long runway and I really didn't have access to the data. I could get an output, you know, an Excel or something or a CSV and play around with the data. How has that changed? Well, that actually the cloud's probably changing that the most because the cloud is going to enable self-service like never before because all of that is, you know, is very seamless to the user. I mean, I keep joking around that. I'm actually going to film my mother creating a warehouse, my 87 year old mother creating a warehouse from cloud into dash DB. Does she know what a data warehouse is? It doesn't matter, I guess. It doesn't matter. But the point is that you will be able to empower somebody who doesn't really care about the technology and the data warehouse. The fact is that they have questions they want to ask of a data set. They don't really care where the data's coming from. It's like water. You turn on the faucet, you expect the water to flow. You expect it to be clean and trusted. It's not going to make you sick. We need to have that same expectation with data in our enterprises and that is what's going to move us forward. When we can actually have data and analytics to everyone, that's when we win. Some data's pretty polluted. What are you seeing in terms of data quality? It's flowing like water. What kind of dirty water is it? It could be dirty water. Is it clean water? Well, what about data quality, data governance? You have to have it. I mean, it doesn't go away. You can't give that up because you're offering self-service because you're creating lots of problems. But a lot of organizations are spinning up these data projects with no sort of edicts around data governance. I guess nobody likes to talk about that, right? That's the boring stuff. That's why it has to be more seamlessly integrated. That's why data works is so important. Nobody wants to talk about it. Marketing people don't like to talk about that stuff because it's ugly, but it's true. Well, even a company, like when you... Well, that's true, too. When you go to try and get it funded and you're talking about security and governance, yeah, the business says, yeah, I don't think so, right? I want my shiny new toy. I want my Watson analytics, right? That stuff gets them excited. It's the behind the scenes things they don't really care about, but it has to be there. Well, so how are they dealing with that? I mean, just as soon as you say it has to be there. You're saying IBM's dealing with that through integration? We are building a lot of that in seamlessly. So like that example I gave you when I'm moving data from my cloud and store to DashDB, that's data works working behind the scenes to keep it clean, to keep the quality up, right? And as a user, I'm not going to know that. So you want to offer self-service capabilities, but you want to still maintain control of the data and the metadata. So that's policy-driven. I mean, I can inject my definition of quality into... You want to take a lot of the standards you have for your private cloud, if you will. Your on-site infrastructure or on-premises and apply a lot of that to the cloud. You can't just give all that up. On-premises, I caught that. On-premises, yes. Yeah, it makes me an official cloud person when I say that. I was corrected the other day, it's on-premises. Sorry, now you're an official cloud person too. Not on-premises, premise is a theory. I noticed Steve Mills used the term premise too and a smarter guy like that can use premise. I can too, so. We'll tell him later. On-prem, we'll call it on-prem. On-prem, it's a short one, exactly. So it's exciting time, a lot happening. Awesome. Thanks for coming on theCUBE, really appreciate it. We think data is awesome and of course we have the crowd chat thing, the active data. And I think there's a tsunami, we didn't get a chance to talk about it, I wish we had more time. The web created digital and the digital transformation was social and the social business has really got the right angle on it. I really think you guys have that right, 100% is that. And so early on, it's going to be about data-driven, data-driven enterprises that's going to be in all aspects. The internet of everything. It really is the right vision. Social business is going to be about the data and the people and the relationships. So again, it's going to be, you've got to store it somewhere. And giving the data to the people. Yeah, I don't think storage is going anywhere soon. So storage is going to be around for a while. This is theCUBE, we are here live in the social media lounge, IBM. Really has an amazing digital experience theater here in the social lounge. theCUBE was a big special presentation and great job, it's called Insight Go. We're here in theCUBE live at IBM Insight in Las Vegas. We'll be right back after this short break.