 Okay, welcome back to IBM's Information on Demand Live in Las Vegas. This is theCUBE's SiliconANGLE and Wikibon's flagship program. We go out to the events, extract the student from the noise, talk to the thought leaders, get all the data, share that with you. You can go to siliconangle.com or wikibon.org to get all the footage. And if you want to participate with us, we're rolling out our new innovative crowd-activated innovation application called CrowdChat. Go to crowdchat.net slash IBM IOD. Just log in with your Twitter handle or your LinkedIn and participate and share your voice. It's going to be on the record transcript of theCUBE Conversations. I'm John Furrier with SiliconANGLE. I'm joined by my co-host. Hi, everybody. I'm Dave Vellante of Wikibon.org. Thanks for watching. Anshul Bambri is here. She's the vice president of Big Data and Analytics at IBM. Many-time CUBE guest. Anshul, welcome back. Good to see you again. Thank you. So we were both down at New York City last week for the Hadoop world. Really amazing to see how that industry has evolved. I mean, you guys, I've said a number of times today, and I said this to you before. You've super glued your big data or your analytics business to the big data meme and really created a new category. I don't know if that was by design or not, but it certainly happened. Certainly by design. Well, congratulations then, because I think that, again, even a year and a half ago, those two terms, Big Data and Analytics were sort of separate. Now it's really considered as one, right? Yeah, yeah. I think because initially as people or businesses started getting really flooded with big data, right? Dealing with the large volumes, dealing with structured, semi-structured, unstructured data. They were looking at that, how do you store and manage this data in a cost-effective manner? But if you're just only storing this data, that's useless. And now obviously it's, people realize that they need, and there is insights from this data that has to be gleaned, and there's technology that is available to do that. So customers are moving very quickly to that it's not just about cost savings in terms of handling this data, but getting insights from it. So Big Data and Analytics is becoming, it's becoming synonymous. You know what's interesting to me, Anjula, is just following this business, it's like there's a zillion different nails out there, and everybody has a hammer, and they're hitting the nail with their unique hammer, but it's like IBM has a lot of different hammers. So I wonder if you could talk about that a little bit. You've got a very diverse portfolio. You don't try to force one particular solution on the client. It's sort of in its depends sort of answer. We can talk about that a little bit. Yeah, sure. So in the context of Big Data, when we look at just, let's start with transactional data, right? That continues to be the number one source where there's very valuable insights to be gleaned from it. So the volumes are growing, that we have retailers that are handling now 2.5 million transactions per hour, right? Telco industry handling 10 billion call data, detailed records every day. So when you look at that level, that volume of transactions, obviously you need engines that can handle that, that can process, analyze, and gain insights from it that you can do ad hoc analytics on this, run queries and get information out of this at the same speed at which this data is getting generated. So we announced the blue acceleration, right? Which is our in-memory column store, which gives you the power to handle these kinds of volumes and be able to really query and get value out of this very quickly. So, but now when you look at, you go beyond the structured data or beyond transactional data, there is semi-structured unstructured data, that's where, which is still data at rest is where we have big insights which leverages Apache Hadoop open source, but we've built lots of capabilities on top of that where we get, we give the customers the best of open source, plus at the same time, the ability to analyze this data. So, we have text analytics capabilities, we provide machine learning algorithms, we have provided integration with that customers can do predictive modeling on this data using SPSS, using open source languages like R, and in terms of visualization, they can visualize this data using Cognos, they can visualize this data using MicroStrategy. So, we are giving customers, like you said, it's not just, you know, there's one hammer and they have to use that for every nail. The other aspect has been around real time, and we heard that a lot at Strata, right? In the, like, I've been going to Strata since the beginning, and those, that time, even though we were talking about real time, but nobody else was. No, it's true, nobody was talking about it. Nobody was talking. Back in the Hadoop World days, it was just one big, you know, batch job. Yeah. In real time, it's now the hotbed of the conversation, you know, you're talking about Storm, these new technologies coming out with, and with yarn is done, it's been interesting. Yeah. You've seen the same thing? Yeah, so, and of course, you know, we have a very mature technology in that space, you know, in first phase streams for real time analytics has been around for a long time. It was, you know, developed initially for the U.S. government, and so we've been, you know, in the space for more than anybody else, and we have deployments in the telco space where, you know, these tens of billions of call detail records are being processed, analyzed in real time, and, you know, these telcos are using it to predict customer churn, to prevent customer churn, gaining all kinds of insights at an extremely high, you know, very low latency. So it's good to see that, you know, other companies are recognizing the need for it and are, you know, bringing other offerings out in the space. Yeah, so as we were talking before, somebody says, oh, I want to go, you know, low latency and I want to use Spark, you say, okay, no problem, we could do that, and Streams is interesting because if I understand it, you're basically acting on the data, producing analytics prior to persisting the data. So it's all in memory. So it's all in memory, and yet at the same time, my question is, is it evolving where you now can blend that sort of real time activity with maybe some batch data and talk about how that's evolving? Yeah, yeah, absolutely. So, Streams is for, you know, whereas data is coming in, it can be processed, filtered, patterns can be seen in Streams of Data by correlating, connecting different Streams of Data, and based on certain events occurring, actions can be taken. Now, it is possible that, you know, all of this data doesn't need to be persisted, but there may be some aspects or some attributes of this data that need to be persisted. You could persist this data in a database that is used as a way to populate your warehouse. You could persist it in a Hadoop-based offering like Big Insights, where you can, you know, bring in other kinds of data and enrich the data. It's like data learns from data and a different picture emerges. Jeff Jonas's puzzle analogy, yeah. So that's very valid. And so when we look at the real time, it is about taking action in real time, but there is data that can be persisted from that in both the warehouse, as well as on something like Big Insights Hadoop. I want to throw a term at you and see what this means to you. We're actually doing some crowd chats with IBM in this topic. Data economy. I was going to ask the same question. You asked it. No, no, no, no, no, no, no. So, data economy, what does data economy mean to you? What are customers doing with the data economy? Okay, so my take on this is that there are two aspects of this. One is that the cost of storing the data and analyzing the data, processing the data has gone down substantially. But the value in this data, because you can now process, analyze petabytes of this data, you can bring in not just structured but semi-structured unstructured data. You can glean information from different types of data and a different picture emerges. So the value that is in this data has gone up substantially. Previously, a lot of this data was probably discarded without people knowing that there is useful information in this. So to the business, the value in the data has gone up. What they can do with this data in terms of making business decisions, in terms of making their customers and consumers more satisfied, giving them the right products and services, and how they can monetize that data has gone up. But the cost of storing and analyzing and processing has gone down, which I think is fantastic, right? So it's a huge win-win for businesses. It's a huge win-win for the consumers because they are getting now products and services from the businesses which they were not before. So that to me is the economy of data. So this is why, John, I think IBM's really going to kill it in this business because they've got such a huge portfolio. They've got, if you look at where IOD has evolved, data management, information management, data governance, all the stuff on privacy, security, these were all cost items before. People looked at them and I got to deal with all this data. And now there's been a bit flip. And IBM is just in this wonderful position to take advantage of it. Of course, Ginny's trying to turn the battleship and trying to get everybody aligned. But the moons and stars are aligning. And really, there's a tailwind. Yeah, we have a question on Twitter from Jim Lundy, analyst, former Gartner analyst has his own firm now. Shout out to Jim. Jim, thanks for watching, as always. I know you're a Cube alum and also avid watcher and now a loyal member of the CrowdChat community. The question is, blue acceleration helps drive more data into actionable analytics and dashboards. Can IBM drive more new deals with it? Absolutely. Yeah, of course it answers yes. Yes. And can you elaborate on that for Jim? Yeah, with blue acceleration, we have had customers that have evaluated blue and against SAP HANA and have found that what blue can provide is way ahead of what SAP HANA can provide. So we have a number of accounts where people are going with the performance, the throughput, what blue provides is very unique and it's way ahead of what anybody else has in the market. Including SAP. Including SAP. And ultimately, it's value to the business, right? And that's what we are trying to do, that how do we get our customers the right technology so that they can deal with all of this data, get their arms around it, get value from this data quickly. I mean, that's really of a sense here. So I wonder if part of Jim's question is, you guessed, is driving new deals, for sure. New product, new deals. Can it drive new footprints? Is that maybe what he's asking, right? In other words, traditional IBM accounts are doing deals. Are you able to drive new footprints? Yeah, yeah, we, you know, there are customers that, I'm not going to take any names here, but which have come to us, which are new to IBM, right? So it's a, it's that to us. And that's happening. That's net new business. That's net new business. And that's happening with us for all our big data offerings because you know, the richness that is there in the portfolio, it's not that we have, like you were saying, Dave, it's not that we have one hammer and we are going to use it for every nail that is out there. You know, as people are looking at blue, big insights for Hadoop, streams for real time. And with all this comes the whole life cycle management and governance, right? So security, privacy, all those things don't go away. So all the stuff that was relevant for the relational data, now we are able to bring that to big data very quickly. And which is I think of huge value to customers. And as people are moving very quickly in this big data space, there's nobody else who can just bring all of these assets together from and, you know, provide an integrated platform. What use cases to Jim's point, I know you don't want to name names, but can you name, can you talk about some use cases that these customers are using with blue? Like what use cases are they solving? So, you know, I, from a use cases standpoint, it is really like, you know, people are seeing performance, which is, you know, 30, 32 times faster than what they had seen when they were not using an in-memory column store. You know, so eight to 25, 32 times performance gains is, is, you know, something that is huge and is getting more and more people attracted to this. So let's take an industry, take financial services, for example. So the big ones in financial services are risk, people want to know, you know, are they a credit risk? There's obviously marketing, serving up ads. Fraud detection, you would think is another one that in more real time, are these, you know, some things that you're working on? These would be the segments and of course, you know, retail, where again, you know, there is like I was saying, right, that the number of transactions that are being handled is growing phenomenally. I gave one example, which was around 2.5 million transactions per hour, which was unheard of before. And the information that has to be gleaned from it, which is, you know, to leverage this for demand forecasting, to leverage this for gaining insights in terms of giving the customers the right kind of coupons, to make sure that those coupons are getting, you know, are being used. So it was, you know, before the world used to be, you get the coupons in your email, then the world changed to that you get coupons after you've done the transaction. Now, where we are seeing customers is that when a customer walks in the store, that's where they get the coupons based on which aisle they are in. So it's a combination of the transactional data, the location data, right? And we are able to bring all of this together. So it's blue combined with, you know, what things like streams and big insights can do that makes the use cases even more powerful and unique. So I like this new format of the crowd chat. Normally it's a one-hour crowd chat where it's kind of like thought leaders just going a pound in the way, but this is more like Reddit, AMA, but much better. Question coming in from Grant Case is, one of the themes to you is one of the themes we've heard about in the keynote was the lack of analytical talent. What is going on to contribute more value for an organization skilling up the workforce or implementing better software tools for knowledge workers? So skills is definitely an issue that has been a challenge in the industry with, and it got pretty compound with big data and the new technologies coming in. From the standpoint of what we are doing for the data scientists, which is, you know, the people who are leveraging data to gain new insights, to explore and discover what other attributes they should be adding to their predictive models to improve the accuracy of those models. So there is a very rich set of tools which are used for exploration and discovery. So we have, which is both from, you know, Cognos has such capabilities. We have such capabilities with our data explorer. So basically tooling for the predictive on the modeling. So right now the efforts on the modeling and the predictive analytics. And descriptive analytics, right? I mean, there's a lot of, when you look at that, when there's petabytes of data before people even get to predictive, there's a lot of value to be gleaned from descriptive analytics. And being able to do it at scale at petabytes of data was difficult before. And now that's possible with excellent visualization, right? So that it's taking things to, that the analytics is becoming interactive. It's not just that, you know, you are able to do this in real time, ask the questions, get the right answers. Because the models running on petabytes of data and the results coming from that is now possible. So interactive analytics is where this is going. So another question is Jim was asking, I was wondering if IBM's going around doing blue accelerator upgrades with all its existing clients. Loan origination is a no brainer upgrade. I don't even know it's loan. Yeah, so that was the kind of follow up that I had asked is it new accounts, is it new footprint or is it just sort of extending existing? It's both, it's both. What is the characteristic of a company that is successfully, or characteristics of a company that is successfully leveraging data? Big data? Yeah, yeah. So companies are thinking about now that their existing EDW, which is their enterprise data warehouse, needs to be expanded. So before, if they were only dealing with warehouses, which were handling just structured data, they are augmenting that. So this is from a technology standpoint, right? They're augmenting that and building their logical data warehouse, which takes care of not just the structured data but also semi-structured and unstructured data, bringing augmenting the warehouses with Hadoop-based offerings like Big Insights, with real-time offerings like Streams, so that from an IT standpoint, they are ready to deal with all kinds of data and be able to analyze and gain information from all kinds of data. Now from the standpoint of how do you start the big data journey, the platform that at least we provide is a plug and play. So there are different starting points for businesses. They may have started with warehouses. They bring in a polystructured store with Big Insights slash Hadoop. They are building social profiles from social and public data, which was not being done before, matching that with the enterprise data, which may be in CRM systems, master data management systems inside the enterprise, and which creates quadrilands of comparisons and they are gaining more insights about the customer based on master data management, based on social profiles that they are building. So this is one big trend that we are seeing. But to take this journey, they have to take smaller bites, digest that, get value out of it, and eat it in chunks rather than try to eat the whole pie. In one chunk. So a lot of companies starting with exploration, proof of concepts, implementing certain use cases in four to six weeks, getting value, and then continuing to add more and more data sources and more and more applications. So there are those who would say those existing EDWs, many people, some people would say they should be retired. You would disagree with that piece. No, no, I think we very much need that experience and expertise. Businesses need that experience and expertise because it's not an either or. It's not that that goes away and there comes a different kind of a warehouse. It's an evolution, right? But there's a tension there, wouldn't you say? There's an organizational tension between the sort of newbies and the existing, you know, EDW crowd. I would say that maybe, you know, three years ago that was, there was a little bit of that. But there is, I mean, I talk to a lot of customers and there is, I don't see that anymore. People are, you know, they understand, they know what's happening. They are moving with the times and they know that this evolution is where the market is going, where the business is going and where the technology is going. They know they're going to be made obsolete if they don't embrace it, right? Yeah, I think, yeah. So as we get on time, I want to ask you a personal question. What's going on with you these days within IBM? Honestly, you're in a hot area. You were at just the New York last week. Tell us what's going on in your life these days. I mean, things going well. I mean, what are things you're looking at? What are you paying attention to? What's on your radar when you wake up and get to work before you get to work? What are you thinking about? What's the big picture? So obviously, you know, big data has been very fascinating, right? Lots of different kinds of applications in different industries. So working with the customers in Telco, in healthcare, banking, financial sector has been very educational, right? So a lot of learning, and that's very exciting. And what's on my radar is we are obviously now seeing that we've done a lot of work in terms of helping customers develop and their big data platform on premise. Now we are seeing more and more a trend where people want to put this on the cloud. So that's something that we have now a lot of, I mean, it's not like we haven't paid attention to the cloud, but in the coming months, you are going to see more from us where, you know, how do we build, how do we help customers build both private and public cloud offerings and, you know, where they can provide analytics as a service to different lines of business by setting up the cloud. So cloud is certainly on my mind. The software acquisition was big. It was a hole in the portfolio and that filled it. You guys are going to drive that hard. So both software and then, of course, OpenStack, right, from an infrastructure standpoint for what's happening in the open source. So we are, you know, leveraging both of those and like I said, you'll hear more about that. Well, OpenStack is key, as I see it for you guys because you have street cred when it comes to open source. I mean, what you did in Linux and you made a great business out of that. So everybody will point it, you know, whether it's Oracle or IBM and HP say, oh, they just want to sell us our stack. You've got to demonstrate that you're open and OpenStack is a great way to do that and other initiatives as well. So like I say, I think that's great. So be excited about that, yeah. Yeah, okay. All right, Joel, well, thanks very much for coming on theCUBE. It's always a pleasure to see you. Yeah, see you here. Great having you back. Thank you very much. Okay, we'll be right back live here inside theCUBE here in IBM Information on Demand. Hashtag IBM IOD, go to crowdchat.net slash IBM IOD and join the conversation where we're going to have a on the record crowd chat conversation with the folks out who aren't here on site or on site. We're here live in Las Vegas. I'm John Furrier with Dave Vaughan through right back.