 The Cube at IBM Impact 2014 is brought to you by headline sponsor IBM. Here are your hosts, John Furrier and Paul Gillan. Okay, welcome back everyone. We live in Las Vegas for IBM Impact. This is SiliconANGLE's The Cube, our flagship program. We go out to the events and extract the ceiling from the noise. I'm John Furrier, the founder of SiliconANGLE. I'm John Michael, it's Paul Gillan of SiliconANGLE. The next guest is Bob Pettigiano, who's the SVP of Information Analytics at IBM. Welcome to The Cube. Thank you very much, John. You did that pretty fast. You practiced that a few times. I memorized it. Well, we did, I mean, we went to 630 yesterday. So we love going wall-to-wall. We hear from the executive, from the customers. Good. And we get the perspectives, and of course we got the crowd chat out there for the crowd source commentary. So we'll probably get some questions from the crowd. I'll see the, you know, we walk into IBM. It's like walking into Nordstroms, right? You see the racks. I want that suit. It looks good. It hangs together. It matches. You know, things are really looking great right now. Things are hanging together. Cloud, mobile. Data. Existing story looks good. So I got to ask you the reality of can we get that? How do we get that, right? It's about big data and analytics. Obviously, screaming hot topic, power systems. The Google was here yesterday. A lot of relevant stuff. So just share with the folks out there. Summarize kind of the big picture of what's going on with the information group and the analytics in particular. Sure, John. So thank you. I really appreciate you having me here. So first off, you know, the company transformation is like every other company's transformation. We see massive amounts of opportunity in data, cloud, and then building systems of engagement. And the latter, it's engagement for our clients as well as for our employees. You know, every enterprise has to make sure that all of their employees are engaged in the strategy engaged in delivering exceptional client outcomes. And so I often say, you know, data is the what. Cloud is the how. And systems of engagement are the why. Now, I've been in the data space for a long time. This is my 27th year in the information management space. I started as a co-op in the research organization working on relational database systems. And, you know, 10 years ago, we were having- Is that DB2? It was DB2, with the cursors to DB2. You know, 10 years ago, if we were having this discussion, the data topic would be around what kind of cost of data compute? How do I lower the cost of computing for all the information I need to manage? But now, data is all about top line growth. It's about identifying really critical business insights in data that companies have and that have collected for a long time, how to correlate that information with other sources of information that they have cross application domains, cross lines of business domains, inside and outside the company. And how to be able to divine critical insights both historically, but also directly in the business moment. I want to ask you about the data. Let's drill down the data. Because back then, certainly, innovations have come. You guys have been involved in that. Certainly in the database side, you've seen, you know, innovations, new innovations come to the market. But the data was a known animal. You kind of knew what you were dealing with. And, you know, there's been cubes, there's been structured databases, all that stuff's been happening, right? But now, it's so wild west on the data, it's high velocity, it's diverse, there's data fusion issues, there's all kinds of kind of foreign data, dirty data. So now with unstructured, you now have the connected web, the social business component, driving a lot of new inbound data, where data is not just as known animals, more, a lot of unknown data. What are your- Uncertain data. Uncertain data, so what are your customers telling you? What are the top things that they come to you for and saying, hey, here's my problem and help me solve that? Sure. Well, look, on the topic of the unstructured data, that's always been about 80% of the information out there has been unstructured. It's been natural language. It's been documents and things that aren't always necessarily tightly correlated to outcomes in business cases. So that's one thing that we focused on for many years in the enterprise content management space is helping our clients define ways to use content as a competitive business advantage in managing the process around that unstructured information, but now correlating it more tightly with structured information. Now, in the overall space, the amount of information being created each day is about two and a half exabytes. And this is still growing at a geometric, if not exponential, curve rate. So we're going to see lots of information continuing to pour in. And as people need to define information, they need to use more and more sophisticated analytics. They need to automate the use of those analytics, meaning pushing them directly into the applications. An application was just a codification of a business procedure or process. Now what we have to do is we have to push analytics into that procedure or business process so that you can use advanced predictive modeling to set what outcomes I might want as I see variations in the data pattern for an individual or for a market or for a product. So I want to be able to take advantage of analytics in the course of the business process. It's not just what's sitting on the glass in an analyst desktop. That's also important though. So who's responsible for solving that problem? Is it IBM? Is it SAP? Is it... It's well, yeah. Look, SAP has a very minimal view of that enterprise data. They have things that are related to the ERP systems and they don't have nearly any depth in the data warehousing and data mark space. And our offerings are generally about a hundred times better price performance than what they're touting in the marketplace with things like HANA. So I don't think they have the point of view in that space to really be a company's real evangelist and advocate to how to extract information across the entire data state. But I think what you're pointing out is, there are various enterprise suppliers that can give a good insight into that information. But then there's also roles that are emerging in an enterprise we serve about what to do with that data. And we're seeing the importance of new roles like chief data officers or chief analytic officers that are helping organizations understand this cross channel problem that have been created because organizations typically have grown up in application silos. And now I have to integrate that information, make semantic sense out of how common fields might be utilized different ways and they have rules for consumption. And how can I apply advanced analytics across those rules of consumption? So the process of integration, governance, really come into play. And then lastly, organizations have to be very concerned about data privacy and security. So if you're going to be aggregating big data and pulling together information from a lot of very sensitive client information, putting together a new customer composite, my goodness. I mean, that's radioactive if not handled properly. And that can directly affect adversely brand equity if it's not managed correctly. So applying data security and governance into that process is something we do very well. Is that a product? I mean, will there be forthcoming products in that area or is it something you're using, your existing line, DB2 line, Formix line and such to solve that? No, we've taken those capabilities and we have appropriately evolved them to serve the needs of the broader estate of capabilities that are necessary to manage big data. So, you know, typically the analytic system of record was the data warehouse. But now we're introducing capabilities on Hadoop and MapReduce that augment the utility at data warehouse to wrap its arms around instruction information. We're seeing real-time analytic zones like InfoSphere Streams come into play. We're seeing new special-purpose data marks around things like our blue acceleration, in-memory columnar database come into play. The data privacy and governance has to incorporate all of that, including the operational data stores. So we've introduced in product form capabilities like the data privacy suite for Hadoop. And here at the conference, we introduced new capabilities in the form of data services that supply data masking and data privacy that are integrated directly into the blue mix environment so that information-centric developers can ensure that information they manage that's highly sensitive can be managed with the right kind of security. You know, we cover a lot of startups and, you know, I live in Silicon Valley and the Wigibon team falls in Boston area, so you kind of have a balance of a little Kool-Aid injection on one hand and... Some great innovation going on there. But, you know, when you look at the startups, they differ from the scale that IBM has to deal with. I mean, we were talking yesterday about surveillance data coming off cameras and then being integrated in real time into other databases, just massive scale complexity. Oh, so I want to drill down on the comment you said, what is the data? How is the cloud? Why is the engagement? Someone on our crowd chat just chimed in and said, aren't business outcomes the why? Which I can, you know, I want you to address that. I can see how you can separate them out. It's a good point. And then you said 80% of the unstructured data is not always tied to the outcomes in some cases. Then you said data is pushed into the application. So those are really awesome trends. So how does a customer, one, tie the data to the outcomes? And two, how do they tie the data into the application? Is it blue mix? Is it too complicated to talk on the cube? Or what? That's actually about seven questions. So I hope that I remember. Well, let me start with the person who took the time to ask a question about the cloud. It's always about business outcomes. So you're rightfully pointing out that it really is about the business outcome. But the system of engagement in many ways is what your clients are expecting. And it's that bond of trust and intimacy that they're creating with your app, with your set of applications or websites. And it's how they're interacting with your brand. So the what is that, how are you capitalizing on the fact that that client is sharing more information and is interacting with your brand more frequently than ever before? And then the business outcome for both the client is having that loyalty rewarded with an aspect of something special you've done for them, understanding more about that person. And the business outcome for the company is being able to capitalize on that business moment and having maybe an enriched commercial relationship with that client or serving their household or family or community in a better way. So the outcomes are the scoreboard. It's the scoreboard really, it's the result. The outcome could be, Tim asked the question, I would say the outcome is the result. I don't want to speak for IBM, but that's my view. So that's kind of like the scoreboard, right? Customers lost in one, sales up or down, costs, so on and so forth. And was the clients need-served, right? For their company, it's about the client outcome as well. So talk about the history of, you mentioned you've been with IBM for many years, you've seen the evolution. Talk about now the application market. IBM's always been in the application business. We were joking yesterday, MIS departments, DSS, decision support, data processing is buzzwords that are now coming vogue again. But the apps now are modern, we've got Bluemix. How do you push the data into the application? Talk about the kinds of complexities that have changed from the old days to new. Is it more complex now? Less complex? Well, you know, it's an interesting paradox because the amount of data that you need to understand is more complex. But there's new tools and capabilities. The economies of computing power, the economies of storage and the economies of bandwidth are allowing us to approach these problems in new and innovative ways so that we can mask the complexity. So people use terms like in-memory and columnar databases and they say, wow, it's a three times of order magnitude faster than correlational systems. But let me tell you the real power of this. You don't have to go through all the specialized modeling. You don't have to really write finely tuned SQL. You can just sort of pour the data into these systems and be able to get blazingly fast performance out of poorly written SQL. So in addition to making them faster, we've made them more simple. The no SQL movement, not only SQL movement, is also about that. It's about, I don't have to really contemplate what model I'm going to create that's going to optimize the interaction between the application and the data. I'm going to apply the schema on when I read the data as opposed to when I write the data. Because the older systems- You're not restricted with that, with schema, not worrying about the schema, you ought to park the data somewhere. So it makes it simpler. So for the application developers, things like JSON and putting delimited tags on the outside of JSON objects makes it much more simple. But then the problem has become more complex as the application domain data that you want to include as broadening. So that's where capabilities like semantically integrating the data, understanding the value of a business glossary, they have to come into play. Because you don't want to use the wrong data. You use the term uncertain data earlier. It's very voracious. And as more and more data comes online, companies have to be concerned about, am I getting the proper signal out of all of this data? You just talked about NoSQL. You just completed the acquisition of Cloud and what is the roadmap for Cloud? And how will that dovetail with what you're doing with your other database lines? Well, like I said, when we look at what you need to do in order to really properly serve a client's needs in this era of data being the next natural resource, there are multiple zones of capability that are required, both operationally and analytically. And one of the most important zones is this really important confluence between highly flexible collaborative data systems like Cloud and Doffers and being able to handle huge scales of that information and being able to scale the system in terms of what it can handle almost overnight based on a flash success of a set of capabilities that they're enabling. So Cloud and Shroadmap has always been focused on that and one of the core things they do is to help organizations understand how they use that organization of data and information for their competitive advantage. So you're not just getting a database or a backend data service, you're getting data modeling experts. You're getting data scientists that reside inside of the Cloud and System to be able to help that company build the best available product and then be able to switch on scalability as that company's products succeed. So the roadmap is spot on. Can you give us an example of the use case scenario for Cloud and the perfect application of Cloud? Well, I mean, you heard from Corey, the Corey-O-Lite Sciences today talking about genetic data and the fact that that has scaled down from what was a billion dollars to manage down to $1,000 to sequence a gene and it's creating petabytes and zettabytes of information that need to be effectively managed. Samsung's music service, Milk, is another good example. That runs with a Cloud and set of backends and so as they introduced that set of services to their client base, they went kind of surprised viral almost overnight with the success of that music service to compete with iTunes and iTunes Radio and they back-end all that customer object information in the Cloud with database. So just a couple of really important trends. Hothead Games is another one that sees massive spikes in customer usage and Cloud has just continues to scale with that, scale on the new data usage patterns. So those guys really did a great job and it was my pleasure to welcome them to the IBM team. I see soft layers. I had Steve Mills on yesterday promoting that more links than Amazon, which is great. We'd love to get that implied competitive fire going but I want you to take a perspective now and share the audience out there, your perspective over the years and to the startups really because we're seeing a lot of the blue mix trying to quarter developers and bring value there. Talk about what's needed in the enterprise. It's different. The consumer app certainly, we've seen the home runs, the WhatsApp and all this great stuff going on. Square was here on stage. So the consumer's got everyone's attention but as that shifts now to the enterprise, we've seen that consumerization shift. So it's not always clear what it takes to win in the enterprise. Well, so talk to those developers. Sure, thank you. So the enterprise has a great set of advantages because they have a lot of very, very important critical data assets that they can manage in new ways to understand their clients better, understand their products better and then use that same innovation that you hear about every day with the startups and put that to work for the companies that they're serving in new applications that are engaging and that are information rich. The other thing that I think is one of the most important elements and I think we'll see the space of big data move very rapidly to fast data. And what I mean by fast data is really seizing that business moment. What can I do in the application of highly advanced predictive analytics in the context of the business transaction so that you understand that at the very moment, how to best serve that client, how to take advantage of an opportunity or how to avoid a threat or a risk. And so that application of all of those data assets not as a historical record for the enterprise but as in the in-flight of business transactions. How about the governance side? That's pretty complex too, right? I mean managing the data audits and things of that nature. We're making that very simple for clients and for enterprises with what we've done in the information integration and governance suite and what we've done with the privacy suite for Hadoop. So I think companies can learn in terms of what we can help them implement as best practices. And the fact that we do this on heterogeneous data. So we'll do the data privacy, security, data masking, data obfuscation, not just on IBM's data, but we'll do it on top of Oracle. We'll do it on top of Microsoft. We do it on other people's database. So it's one solution you can use across your entire enterprise. We're seeing this emergence of the chief data officer concept, growing number of enterprises creating that role. Do you see this as a long-term play? Well, we have chief data officers 10 years from now or is this a sort of a flash in the pan that will be absorbed in the US? I don't think it's a flash in the pan. I think it's something that we're going to continue to see ramp up, you know, some of the analysts project that by the year 2016 we'll see 50% of the Fortune 500 having chief data officers. You look it in the fields of financial services and insurance in highly regulated and governed organizations. That really is a very critical role because it's not just about, you know, how to properly govern the data. It's how to properly govern the usage of that data for the outcome that you're trying to derive. Well, IBM would take any active role in encouraging this position, encouraging your customers to proliferate this position? Well, we're taking an active role in making sure that chief data officers really have an agenda and a roadmap of things that they need to understand in order to perform their duties to the expectations of the shareholders and customers they serve. So we've built best practices and we've taken our assets and actually shared them openly in a MOOC-like environment, very similar, you know, to what's happening in spaces like Wikibon, really democratizing this notion of how to incorporate a larger community in understanding all the implications of what's happening in the marketplace. So the Big Data University has a great set of assets for chief data officers to begin to understand what the responsibilities are, what tools are available, what are the trials and tribulations of others that have gone before them, and utilize patterns of applications of these tools to help perform this new role. You brought up the Wikibon thing, which is, you know, potentially open community-based. What's your take on the whole crowdsourcing movement? Obviously in retail, people looking at you guys about social business, big part of IBM strategy. Crowdsourcing data is interesting now. The users are now connected to the internet, so you can instrument your business 100%. Yeah, I think, you know, this notion also of the refining of that information. So I think crowdsourcing information, crowdsourcing data is going to continue to be a big movement. And I see open data initiatives happening everywhere. You know, we worked with an organization, Datacind, at IOD last year. So a shout out to Jake Poreway and the guys at Datacind. They've done a tremendous job of embracing open data initiatives and helping organizations that don't necessarily have the means and sophistication on their own staff to take advantage of all this crowdsourcing and all the available data to solve the problems that they're struggling with. So we see this crowdsourcing continuing, but we also see this important aspect of helping people refine that data and make the proper correlations without making low-quality correlations and deriving wrong outcomes. So I use the analogy, if this was the petrochemical space, I was going to say oil, right? You know, if I had a gallon of crude oil, you know, I'd have to build my own refinery in order to get a quarter of gasoline out of that. And you get leverage too, the bigger, and then with the internet scale, Steve's pointing out, you can get the refinery up and the scale is really amazing. You know, it's not, your costs don't go up as much as the value. Right, so it's a place where smaller companies and smaller organizations, maybe, you know, teams inside of bigger organizations can come. You know, IBM will have a set of services there. We'll handle the data appropriately in terms of privacy and security. But it'll also be a point of collaboration for some of these things to happen so that people can identify, well, if I'm using this data to solve this problem, what other information is necessary? And you saw me pull back the curtain a little bit on innovations like Catalyst Insight, to talk about what we understand about data and how to put that to work for novice analysts so that we can help them seek out and identify other information sources, other patterns of data that'll help them make better quality decisions more rapidly. A lot of smaller businesses believe they can't play in the big data realm, but Open Data Initiative is really an example of how they can take advantage of Open Data Available Data. So who are the primary, what's primary coalition or the constituency for Open Data? Is it government agencies, primarily, public data sources? It's, look, I think it's really anybody who wants to get involved from a community basis on. I applaud the cities that have taken the initiative to open up some of the interfaces. We've done some work with Pittsburgh, we did some work with Honolulu. All right, so cities that are being very progressive about understanding that much like these systems of engagement that we talked about for commercial enterprises, well, every community is a system of engagement. So if you're going to be a good governor or a good mayor or a good city councilman, you want to understand how you're engaging your citizens in helping improve the community and helping improve the community services. So these Open Data- I love that line. Every community is a systems of engagement. So how do companies get their communities engaged? What is your roadmap for them? What would you share to the guy that's scratching his head with the CIO for IT, social business manager, looking at crowd communities or developer communities? How do they get those communities engaged? How do they wire them together? How do they get started? Well, look, a company like IBM has over 15,000 analytics consultants that are ready to go to work for the clients that would like our help. We've done nearly 40,000 business analytics engagements. We've identified common patterns that relate to business outcome problems that our clients are looking to serve. And we've done that cross-industry and we've done that on a whole variety of different data. We've also opened that up to a variety of business partner relationships to cooperate in that environment space. So I say, bring us your tough problems and we'll collaborate on- Okay, we got to get cut to the next program, but I want to give you the final word here. Great, great segment. Thanks for taking the time sharing your perspective. We love it. Tell the folks out there why this point in time in history is so important. All these things, the confluence of trends are coming together. Why does everyone seem to be on red alert, ready alert to go do all this great rebuilding and growing? Well, look, I mean, I think it's quite clear. It is everywhere. You look data, cloud and engagement. Data being the next natural resource on really helping companies define how to enrich those systems of engagement or identify whole new business models and top line growth. So like I said, I've been at this for 27 years. This is the most exciting time and IBM is the most exciting company to partner with in order to help solve these problems. Papachiano, Senior Vice President at IBM, right in the heart of all the action, he's in the war zone. If you want to call, use the war analogy or the land of opportunity and certainly fruit is coming off the tree, big time of value and outcomes. This is theCUBE. We'll be right back with our next guest after this short break. Thanks guys.