>>Hi from Las Vegas, extracting the signal from the noise. It's the cube covering IBM insight 2015 brought to you by IBM. Now your host, Dave Vellante and Paul Gillin. >>Welcome back to IBM insight everybody. This is the cube. The cube goes out to the events. We extract the signal from the noise. This is I think our fourth year at IBM insight, IBM's big, big data show. IBM doesn't use that term, they call it analytics and it's been done a tremendous job of taking this giant portfolio and then building a leading the leading actually analytics business in the industry. Harriet Fryman is here, she's the vice president of marketing at IBM analytics. Harriet, welcome to the cube. Good to see you. Thanks for having me back. Yes, so the show here is big, I think bigger than anyone, you know, we've been to a lot of great energy. The solutions expo is tremendous. The, the keynote this morning were packed the general session, so you must be thrilled. >>Yeah, it's fantastic audience. And we just came off our advanced analytics keynote this afternoon. We were talking about the advances in Watson analytics. So the smart data discovery tool as well as our new release of Cognos. >>So Watson analytics is just permeating all parts of the business in the healthcare business, the cloud business, the analytics business. Talk about the impact that that little sort of experimental program with jeopardy has had on the company as a whole. >>Yeah, it's really delivering on the promise of, we talk about around the cognitive business and where Watson analytics comes in. It's really looking to bring that smart data discovery to an individual on their, um, on their PC to get instant insights into data. Whereas before they're really, um, could get access to the data, but how do they find the causation between data points versus just take a look at sales data, finance data. So Watson analytics really allows them to have that natural language question and um, have the processing behind the scenes find the interesting stuff in the data. >>Big idea is a, is it a marketing executive? You've got to love the, the fact that you can actually produce such a capability, you know, it's not like a little point product that's a platform that can touch every part of your business that can change lives. What are your, can you comment on that as again, from a marketing perspective, >>it's always fun in marketing to have a great portfolio to be able to market and something that really makes a difference to people's business. So with the, with Watson analytics and with what we're doing with Cognos around our business intelligence, it's great to market. Um, what has always been promised, I think in the BI market for many years, which is self service analytics for all. So, uh, as we're marketing both the capabilities around Watson as well as the capabilities and Cognos, it's kind of a delight to say, you know, what we were talking about give insight to everybody to make better decisions. It's really coming to fruition. >>If IBM has grown its analytics business largely through acquisition, I think you'd have 25 acquisitions. You've got a of different great brands, SPSS, core metrics, Cognos and the like is Watson, they're going to evolve and do a kind of a simulation point for all of those? >>Well, yeah. What we look at is, um, as we talked about the cognitive business and Watson really been the cognitive computing engines of, of that business. We're looking at how our analytics business really expands a company's business companies, company's ability to really understand what the data is, turning them, learn from experience of working with the data and put that into practice. So we can do that with dashboards, with reports as well, which is help people understand there's insight to be gained from data. There's value to be gained from data. And so you can apply it through being a learning company with or without having a cognitive system itself. It's, I'm going to take data, I'm going to apply analytics to understand patterns and I'm going to apply that to my business. And then I'm going to learn from the feedback loop and just keep learning, learning, learning. And that's what a cognitive business is about. >>So the BI business historically, you know, it's been interesting to watch. I mean I remember when it was called, you know, decision support, right? And, and it's put on a lot of promises, 360 degree view of the business, you know, predictive analytics and it didn't live up to those promises. And then you have this whole Hadoop movement come in and they're going to live up to those promises and then you realize, wow, they actually can't live up to those promises without the traditional data sets. And are those two worlds coming together? Is that the way that we should be thinking about this to actually fulfill on those promises? The last 15 to 20 years? >>Yeah, I think we always had the chicken and the egg, right? You can't have great analytics without great data. And what's the use of great data and as you have great analytics, so you really need both together. And then the promise has always been a great three 60 degree view of customer actually requires being able to get your arms around the data itself, reconcile it, make sense of it. And then it requires great analytics and a way to deliver it to the people who can use it in their business, be they in call center and service and sales. So the promise has always been there is the fact that we need to put it all together. We need to put together the data, as you said, Hadoop and relational data altogether inside and outside the firewall. We need able to make sense of it. So bring those entities together, do master data management, make the data, make sense as you pull it together and then have a great way for people to understand it. Consumer apply it in their business. >>So Cognos was obviously huge acquisition don't, Paul wasn't mentioning many of them. I think we used to tell you it's one of his favorite and I think it was rather large. It was with $5 billion acquisition, I believe. And so, and then IBM has sort of supercharged that entire business. So how has Cognos evolved and where are we today? >>Yeah. So as, as I came in through the Cognos acquisition many years ago when IBM acquired us, I really have seen it just develop and expand from the day that we, uh, we came on board with IBM. It's really expanded in a couple of ways. One is that we have expanded, um, cognitive capability to get at all types of data. So you mentioned Hadoop. So now we've, we know that in order to deliver a rich understanding of what's happening in the business called the Cognos reporting capabilities need to access all of that data. And so it does, it can access relational data, data and appliances, Hadoop data, data on the cloud. So really expanding the Corpus of data that can be put into a report and consumed by business. The second, a big investment has been, um, where BI was always thought of as an it only tool. Now I ask it for a report. They have a report backlog. Some months later they may give me a report. It's not quite what I wanted. That whole world has changed now, which is really bringing BI, we imagined into business people's hands because they want the right to be able to model data to be able to author reports, distributed, shared among their colleagues. So it's been an exciting journey as we've really taken business intelligence really to the next level. >>It's all about the, the role. What's the role of the spark, the big spark initiative that IBM announced a couple of months ago vis-a-vis all of the analytics products, the spark act as kind of a preprocessor for the, the capable of the value of those, uh, those point products add or how does spark fit in with them? >>Yeah, so, um, so with our spark investment, we announced our commitment to spark back in June and since then we're really looking at as well what we coined the term, the analytics operating system. So we see it as that foundational layer that's really going to speed up the speed of analytics as well as be able to apply algorithms to a much bigger, um, Corpus of data than you traditionally would have in a statistics tool, for example. So since then, actually today we announced that we now have 15 solutions built on spark across our analytics and our commerce portfolio. A great example is we replatformed DataWorks, which is our ability for business to do data wrangling as part of the Watson analytics work process. So we see spark is really an enabling technology for ourselves and then we've committed a significant investment back into the spark community to keep it enhancing the core fundamental capabilities of spark so that everybody in the ecosystem can take advantage of that. >>He said something just a minute ago, VI re-imagined. I want to pick up on that theme because again, the BI world used to be insights for a few and then they were very productive, very productive few, right? They had a huge impact potentially on the company. But you now hear things like we heard this morning about you know, citizens and analytics and the likes. So, and you have the, you know, the BI for Hadoop vendor does your sort of attacking the old, you got the vis guys attacking that business. As we said before, it's still critical. But so what is BI re-imagined? You know mean that means more agile. It means simpler, it means embedded into the workflow or the organization. I wonder if you could describe that in some more detail. >>Of course. So when we look at business intelligence, I totally agree with you. It's really a tool that it use to develop reports or dashboards that were then delivered to the corner office, the suite for them to understand how my sales trending, what are my financials looking like, what's my production yield sort of reporting like. And that's great. Um, but that's kind of left a, a population that was not served, which was really the, uh, the business users who wanted to find insights for themselves. And that's really where the desktop discovery tools kind of were born, which was to satisfy that need out there that was not being satisfied by BI. When we're looking at re-imagining BI, we're looking at serving that community too, which means we have re redesigned the user experience of business intelligence so that those people out in the business can author their own insights, can distributed, distribute their own insights. >>And we've taken the learnings of how we designed Watson analytics and that user experience into the BI portfolio too. So let me give you an example. So for example, um, I'm looking for data. I want to report sales by product and by region. Um, I would have had to in the past have it build a model for me of that data. Now with re-imagined BI, I can be in the business, I can simply type in sales product, region. It's got to propose the data. So I don't need to know where the data's stored. It could be in Hadoop, it could be in relational. It's going to propose what data might be the most relevant to me. I can hit hit a button that says proposed model. It's going to model it for me in a way I go. So I didn't need to be a data modeler. I didn't need to know where the data was stored. So now I'm much more empowered as a business person. I don't need to offload that data into a desktop tool, worry about data silos, fragmentation of the decision process. I've now bought to that underserved population. >>So you've said what you've described, you've got a library of models and the system chooses the right one and fits for me. Is that right? Did I, >>you actually have a light. Yeah, close. You actually have a library of data sources and then you can build different models across those data sources. So you mentioned that there's a, a, uh, a dashboard tool right over here for Hadoop over here for maybe if another file system, etc. Well, that's great if all your data sits there. What we've done with BIS, we said, let's make that invisible and then you can pick data from any data source and bring it together into a single report. >>We had a routine of gunner on this morning talking about, uh, talking about governance. And what you're talking about was sort of democratization of, of analytics and, and everybody having their own, uh, their own tools, ability to manipulate data, I mean that has to proceed from a solid foundation of data governance. How well prepared our clients in your experience to proceed in that direction you're talking about they have that data really well hardened and bullets. >>So there's, there's a couple of steps I believe that um, clients understand that there's need to have integration and governance over the data sets, the challenges, the kind of Maverick use of data that happens in a company. So it's both tooling and technology as well as a corporate culture of how you're going to treat the data that you have in your, in your company. So where Ritika talks about the fact that you need to have a data reservoir, you need to have data warehouse, you need to have governance over that. We also need all of that governance to go all the way through to the end consumption of data. So where we've re imagined BI is to say you need that trusted source. It may sit on a server or many servers and need to make that available to everybody to self-serve and their first call to be, I shouldn't be, can I download that data into a tool myself? Cause the minute you cut that cord, your governance is gone. Now clients are starting to understand that because they're hitting that as the data discovery tools, um, start getting hold in the business, which is there's as many copies of data as people in the organization. And so one way to tackle that is to say no, I need to bring them back into the fold on the govern data and do that in a way that doesn't compromise their self service. >>So the big data meme sort of exploded around 2012 my, at the time, my 13 year old would joke and say good morning Polara and she'd say, morning daddy hashtag big data. And so I remember in 2012 when we came to insight, it was interesting to observe, but what IBM had done with this sort of bespoke portfolio of assets is put them together. And I said at the time, super glued it to the big data meme, changed the language around analytics and business outcomes and is now dominating that business or will dominate that business was kind of my prediction and it's exactly what you did in my, my version. Um, so let's talk about your portfolio. You've got purview over, so there's information management that's BI, the predictive analytics database is, is in there as well, and data integration, is that right? So there's that. What were once sort of these bespoke toolings talk about how you bring those together and bring them to market and message them? >>Yeah, yeah. It feels like there was, um, an evolution that happened in the marketplace, which is, as you said, it was almost like it had a shopping list. I'm going to go shop for BI now. I'm going to go and shop for predictive analytics and I'm going to go shop for a database and I'm going to go shop for integration. And really that's, um, great to have capability coverage. But in order to actually get insight from data, you need to be able to be in all the types of data, wherever it resides. You need to be able to put that data into context, which requires integration, master data management, and then you need to be able to deliver that, that, um, analytics and insight capability to everybody who needs it both through a dashboard as well as embedded into applications. So we really saw the opportunity to help our clients get value was to put them together and integrate them in such a way that you actually look for what business questions you want to answer. You don't shop by capability anymore. So the great thing when we look at how we market that is we can start with the business outcome or the client value and work back from there because different types of business problems require different combinations of the capabilities. >>And, and you find, I, you know, there's an old saying it's better to have overlaps than, than gaps. Do you find that you have more overlaps than gaps or do you find that you still got big gaps that you need to fill? >>Um, I think the language, we need some more English words and we need more words in the English language because when we say I need to get it data, I need to integrate it together and I need to deliver it. You could say that about Hadoop, right? Cause it does that. You could say that about a relational database. You could say that about our business intelligence tools. So sometimes people get, it appears like there's overlap because there's only so many limited words that we have to describe what we do. But it's the use cases that will prescribe which part of the portfolio we use. >>So at the, at the strata Hadoop world show this year, there were three or four big themes that emerge. You know, one was really about the data in motion in real time. You know, we talked about spark earlier. Uh, the second was the data, the database, the file system, you know, that sort of plumbing. Um, and the third was sort of complexity. Uh, everybody sort of choking on Hadoop complexity, spark helps but sparks complex too. So it seems like you guys are trying to take all that stuff and just make it invisible. Um, start with the business outcome and say, okay, you need real time. We, you know, to service this business or crime fraud, you know, is going to require some real time nature or maybe it's micro batching and whatever technology you use. Um, is that the right way to think about it that you're trying to hide that complexity and how do you hide that complexity? >>Yeah, exactly. We um, if you take the analogy of a car, everybody drives a car, but we don't necessarily have to understand how the engine works and you know, when we buy the car, we don't open up the hood and take a look and have everybody explain every single piece part and how they all work together. And that's sort of our destiny for what we're doing with insight, what we're doing with the solutions we build, which is yes, it has all those capabilities inside it, but you don't have to be technically savvy enough to understand what that is. You just need to know that it does what you want it to do for your business. So our is with data management, the hide, all the complexity of different data containers behind the scenes using big sequel or ways to access and make that transparent. Then with the analytics, we're looking to make the analytics transparent. So whether you're using an algorithm written in spark, you use an algorithm written in R, it doesn't matter. You're looking to have an algorithm apply to, to find patterns. >>But the way you would hide that complexity over the last 15 years is a big services engagement. And that's changing. Am I, am I understanding that right? I mean you're, you're changing that. You're driving more software into the platform and you're doing it with API APIs and, and, and less of an emphasis on leading with services, more of an emphasis on leading with business outcomes. And then mapping the technology to that. Is that, is that fair or is it still very heavily services led? >>Yeah, we definitely live the lead with the business outcomes. Um, as we look to support hybrid cloud environments, some of that technical complexity is, is made invisible because of the way that we use cloud. So you don't have to worry about deployment and enter production. The other thing we do with our services is we're much more focused on how are you going to apply the data that you have. How you get to apply analytics to actually change your business or services is much more in discussion of how are you going to make this impactful for your business versus the bits and bites of how do you install it, configure it and deploy it. >>But who, who is, who on the back end is going to do that dirty work. And who do you see in the companies you work with? Is there a specialized data function emerging within the CEO's organization? Is it, is it independent? Is it a set of independent of it is too important to the business or who who, who do you recommend do that backend plumbing work? >>Because we always used to talk about two populations in a client business and then it and how business and it would work together. We actually see a third leg of the stool happening, which is around the data professionals, so that's all the way from a chief data officer to achieve data scientists, data engineers, to application developers to implement those insights. So we see this third profession emerging in our clients. Now what's interesting is when they report into the it organization, they're more centered on data management, integration, governance. When they report into the business, they're much more focused on applying analytics for business outcomes, but you're absolutely right. There's this third data savvy PR profession that's really rising in importance and you see a lot more appetite in clients to get that data savviness as a population in the company. >>At this point, you don't see any pattern emerging for where that function lives in the organization. Does that so? >>Correct. We see two, two distinct patterns in it. To better manage the data in the business to better drive an outcome from analytics. >>Do you see this, is the CDO a coming role? Is that, is that a high growth function within the big corporations you work with? >>It's definitely a function that is pretty much becoming established. They're called chief data officers or chief analytics officers sitting at the table helping with the business strategy of how to apply data for a difference in. >>And is that something CIO should worry about? >>Um, I don't, I don't know if they were, I'd have to ask a CIO that question, but definitely the CIO world is shifting much more to how do I provide the it infrastructure as a service provider. And then the CDO is C D O is taking that role with the data and analytics. We'll wait to see how it falls. >>Well, one of the, one of the sort of sea level question I think was about two years ago, the garden forecast, the chief marketing officers would spend more than CEOs by 2017 on it. Are you seeing that really happen? >>We're definitely seeing that. Um, the business side, the CMOs, the VP of sales, the chief operations officers driving much more of the decisions around analytics and data. The other thing that we're seeing is, um, and I think IDC actually quoted this is the rise of the profession of data science. It's outpacing the rise of it. >>Yeah. I mean in terms of growth rate we presume interesting or Harriet really appreciate you coming on the cube. We gotta we gotta leave it there. But last question is sort of, when you think about insight 2015, think about all the, the developments that have occurred over the last say four or five years. So how would you sort of summarize where we are today? What's the bumper sticker on insight 2015 >>the bumper sticker on insight 2015 is as its name in first insights to outcomes. You talked about big data five years ago. We're really shifting from being data hoarders and worrying about what the, how much data we have and what type it is to being insight hunters, which is how can I get the insights I need to make a difference to the, >>and that's where the business value is. Harry, thanks very much for coming on the queue. It's great to see you. All right, keep right there, buddy. We'll be back with our next guest right after this. This is the cube. We're live from insight 2015 in Las Vegas. We'll be right back.