>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Everybody, this is Dave Vellante of the Cube. Welcome back. The continuous coverage that we're running here of the IBM Think Digital 2020 Experience. I'm with Radica Gunnar, who is a longtime Cube alum. She's the vice president for Data and AI. Expert labs and learning Radica. Always a pleasure. I wish we were seeing each other face to face in San Francisco. But, you know, we have to make the best. >>Always a pleasure to be with you, Dave. >>So, listen, um, we last saw each other in Miami Attain IBM data event. You hear a lot of firsts in the industry. You hear about Cloud? First, you hear about data. First hear about AI first. I'm really interested in how you see AI first coming customers. They want to operationalize ai. They want to be data first. They see cloud, you know, is basic infrastructure to get there, but ultimately they want insights out of data. And that's where AI comes in. What's your point of view on this? >>I think any client that's really trying to establish how to be able to develop a AI factory in their organization so that they're embedding AI across the most pervasive problems that they have in their order. They need to be able to start first with the data. That's why we have the AI ladder, where we really think the foundation is about how clients organized there to collect their data, organize their data, analyze it, infuse it in the most important applications and, of course, use that whole capability to be able to modernize what they're doing. So we all know to be able to have good ai, you need a good foundational information, architecture and the US A lot of the first steps we have with our clients is really starting with data doing an analysis of where are you with the data maturity? Once you have that, it becomes easier to start applying AI and then to scale AI across the business. >>So unpack that a little bit and talk about some of the critical factors and the ingredients that are really necessary to be successful. What are you seeing with customers? >>Well, to be successful with, a lot of these AI projects have mentioned. It starts with the data, and when we come to those kind of characteristics, you would often think that the most important thing is the technology. It's not that is a myth. It's not the reality. What we found is some of the most important things start with really understanding and having a sponsor who understands the importance of the AI capabilities that you're trying to be able to drive through business. So do you have the right hunger and curiosity of across your organization from top to bottom to really embark on a lot of these AI project? So that's cultural element. I would say that you have to be able to have that in beds within it, like the skills capabilities that you need to be able to have, not just by having the right data scientists or the right data engineers, but by having every person who is going to be able to touch these new applications and to use these new applications, understand how AI is going to impact them, and then it's really about the process. You know, I always talk about AI is not a thing. It's an ingredient that makes everything else better, and that means that you have to be able to change your processes. Those same applications that had Dev ops process is to be able to put it in production. Need to really consider what it means to have something that's ever changing, like AI as part of that which is also really critical. So I think about it as it is a foundation in the data, the cultural changes that you need to have from top to bottom of the organization, which includes the skills and then the process components that need to be able to change. >>Do you really talking about like Dev ops for AI data ops, I think is a term that's gonna gaining popularity of you guys have applied some of that in internally. Is that right? >>Yeah, it's about the operations of the AI life cycle in, and how you can automate as much of that is possible by AI. They're as much as possible, and that's where a lot of our investments in the Data and AI space are going into. How do you use AI for AI to be able to automate that whole AI life site that you need to be able to have in it? Absolutely >>So I've been talking a lot of C. XO CEO CEOs. We've held some C so and CEO roundtables with our data partner ET are. And one of the things that's that's clear is they're accelerating certain things as a result of code 19. There's certainly much more receptive to cloud. Of course, the first thing you heard from them was a pivot to work from home infrastructure. Many folks weren't ready, so okay, but the other thing that they've said is even in some hard hit industries, we've essentially shut down all spending, with the exception of very, very critical things, including, interestingly, our digital transformation. And so they're still on that journey. They realized the strategic imperative. Uh, and they don't want to lose out. In fact, they want to come out of this stronger AI is a critical part of that. So I'm wondering what you've seen specifically with respect to the pandemic and customers, how they're approaching ai, whether or not you see it accelerating or sort of on the same track. What are you seeing out there with clients? >>You know, this is where, um in pandemics In areas where, you know, we face a lot of uncertainty. I am so proud to be an IBM. Er, um, we actually put out offer when the pandemic started in a March timeframe. Teoh Many of our organizations and communities out there to be able to use our AI technologies to be able to help citizens really understand how Kobe 19 was gonna affect them. What are the symptoms? Where can I get tested? Will there be school tomorrow? We've helped hundreds of organizations, and not only in the public sector in the healthcare sector, across every sector be able to use AI capabilities. Like what we have with Watson assistant to be able to understand how code in 19 is impacting their constituents. As I mentioned, we have hundreds of them. So one example was Children's health care of Atlanta, where they wanted to be able to create an assistant to be able to help parents really understand what symptoms are and how to handle diagnosis is so. We have been leveraging a lot of AI technologies, especially right now, to be able to help, um, not just citizens and other organizations in the public and healthcare sector, but even in the consumer sector, really understand how they can use AI to be able to engage with their constituents a lot more closely. That's one of the areas where we have done quite a bit of work, and we're seeing AI actually being used at a much more rapid rate than ever >>before. Well, I'm excited about this because, you know, we were talking about the recovery, What there's a recovery look like is it v shaped? Nobody really expects that anymore. But maybe a U shaped. But the big concern people have, you know, this w shape recovery. And I'm hopeful that machine intelligence and data can be used to just help us really understand the risks. Uh, and then also getting out good quality information. I think it's critical. Different parts of the country in the world are gonna open at different rates. We're gonna learn from those experiences, and we need to do this in near real time. I mean, things change. Certainly there for a while they were changing daily. They kind of still are. You know, maybe we're on a slower. Maybe it's three or four times a week now, but that pace of change is critical and, you know, machine machines and the only way to keep up with that wonder if you could comment. >>Well, machines are the only way to keep, and not only that, but you want to be able to have the most up to date relevant information that's able to be communicated to the masses and ways that they can actually consume that data. And that's one of the things that AI and one of the assistant technologies that we have right now are able to do. You can continually update and train them such that they can continually engage with that end consumer and that end user and be able to give them the answers they want. And you're absolutely right, Dave. In this world, the answers change every single day and that kind of workload, um, and and the man you can't leave that alone to human laborers. Even human human labors need an assistant to be able to help them answer, because it's hard for them to keep up with what the latest information is. So using AI to be able to do that, it's absolutely critical, >>and I want to stress that I said machines you can't do without machines. And I believe that, but machines or a tool for humans to ultimately make the decisions in a crisis like this because, you see, I mean, I know we have a global audience, but here in the United States, you got you have 50 different governors making decisions about when and how certainly the federal government putting down guidelines. But the governor of Georgia is going to come back differently than the governor of New York, Different from the governor of California. They're gonna make different decisions, and they need data. And AI and Machine intelligence will inform that ultimately their public policy is going to be dictated by a combination of things which obviously includes, you know, machine intelligence. >>Absolutely. I think we're seeing that, by the way, I think many of those governors have made different decisions at different points, and therefore their constituents need to really have a place to be able to understand that as well. >>You know, you're right. I mean, the citizens ultimately have to make the decision while the governor said sick, safe to go out. You know, I'm gonna do some of my own research and you know, just like if you're if you're investing in the stock market, you got to do your own research. It's your health and you have to decide. And to the extent that firms like IBM can provide that data, I think it's critical. Where does the cloud fit in all this? I mentioned the cloud before. I mean, it seems to be critical infrastructure to get information that will talk about >>all of the capabilities that we have. They run on the IBM cloud, and I think this is where you know, when you have data that needs to be secured and needs to be trusted. And you need these AI capabilities. A lot of the solutions that I talked about, the hundreds of implementations that we have done over the past just six weeks. If you kind of take a look at 6 to 8 weeks, all of that on the IBM Public cloud, and so cloud is the thing that facilitates that it facilitates it in a way where it is secure. It is trusted, and it has the AI capabilities that augmented >>critical. There's learning in your title. Where do people go toe? Learn more How can you help them learn about AI And I think it started or keep going? >>Well, you know, we think about a lot of these technologies as it isn't just about the technology. It is about the expertise and the methodologies that we bring to bear. You know, when you talk about data and AI, you want to be able to blend the technology with expertise. Which is why are my title is expert labs that come directly from the labs and we take our learnings through thousands of different clients that we have interacted with, working with the technologies in the lab, understanding those outcomes and use cases and helping our clients be successful with their data and AI projects. So we that's what we do That's our mission. Love doing that every day. >>Well, I think this is important, because I mean, ah company, an organization the size of IBM, a lot of different parts of that organization. So I would I would advise our audience the challenge IBM and say, Okay, you've got that expertise. How are you applying that expertise internally? I mean, I've talked into public Sorry about how you know the data. Science is being applied within IBM. How that's then being brought out to the customers. So you've actually you've got a Petri dish inside this massive organization and it sounds like, you know, through the, you know, the expert labs. And so the Learning Center's you're sort of more than willing to and aggressively actually sharing that with clients. >>Yeah, I think it's important for us to not only eat our own dog food, so you're right. Interpol, The CDO Office Depot office we absolutely use our own technology is to be able to drive the insights we need for our large organization and through the learnings that we have, not only from ourselves but from other clients. We should help clients, our clients and our communities and organizations progress their use of their data and their AI. We really firmly believe this is the only way. Not only these organizations will progress that society as a whole breast, that we feel like it's part of our mission, part of our duty to make sure that it isn't just a discussion on the technology. It is about helping our clients and the community get to the outcomes that they need to using ai. >>Well, guy, I'm glad you invoke the dog food ing because, you know, we use that terminology a lot. A lot of people marketing people stepped back and said, No, no, it's sipping our champagne. Well, to get the champagne takes a lot of work, and the grapes at the early stages don't taste that pain I have to go through. And so that's why I think it's a sort of an honest metaphor, but critical your you've been a friend of the Cube, but we've been on this data journey together for many, many years. Really appreciate you coming on back on the Cube and sharing with the think audience. Great to see you stay safe. And hopefully we'll see you face to face soon. >>All right. Thank you. >>Alright. Take care, my friend. And thank you for watching everybody. This is Dave Volante for the Cube. You're watching IBM think 2020. The digital version of think we'll be right back after this short break. >>Yeah, yeah, yeah.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Hello and I'm John Furrier. We're here in theCUBE studios at Think 2018, IBM Think 2018 in Mandalay Bay, in Las Vegas. We're extracting the signal from the noise, talking to all the executives, customers, thought leaders, inside the community of IBM and theCUBE. Our next guest is Ritika Gunnar who is the VP of Product for Watson and AI, cloud data platforms, all the goodness of the product side. Welcome to theCUBE. >> Thank you, great to be here again. >> So, we love talking to the product people because we want to know what the product strategy is. What's available, what's the hottest features. Obviously, we've been talking about, these are our words, Jenny introduced the innovation sandwich. >> Ritika: She did. >> The data's in the middle, and you have blockchain and AI on both sides of it. This is really the future. This is where they're going to see automation. This is where you're going to see efficiencies being created, inefficiencies being abstracted away. Obviously blockchain's got more of an infrastructure, futuristic piece to it. AI in play now, machine learning. You got Cloud underneath it all. How has the product morphed? What is the product today? We've heard of World of Watson in the past. You got Watson for this, you got Watson for IOT, You got Watson for this. What is the current offering? What's the product? Can you take a minute, just to explain what, semantically, it is? >> Sure. I'll start off by saying what is Watson? Watson is AI for smarter business. I want to start there. Because Watson is equal to how do we really get AI infused in our enterprise organizations and that is the core foundation of what Watson is. You heard a couple of announcements that the conference this week about what we're doing with Watson Studio, which is about providing that framework for what it means to infuse AI in our clients' applications. And you talked about machine learning. It's not just about machine learning anymore. It really is about how do we pair what machine learning is, which is about tweaking and tuning single algorithms, to what we're doing with deep learning. And that's one of the core components of what we're doing with Watson Studio is how do we make AI truly accessible. Not just machine learning but deep learning to be able to infuse those in our client environments really seamlessly and so the deep learning as a service piece of what we're doing in the studio was a big part of the announcements this week because deep learning allows our clients to really have it in a very accessible way. And there were a few things we announced with deep learning as a service. We said, look just like with predictive analytics we have capabilities that easily allow you to democratize that to knowledge workers and to business analysts by adding drag-and-drop capabilities. We can do the same thing with deep learning and deep learning capabilities. So we have taken a lot of things that have come from our research area and started putting those into the product to really bring about enterprise capabilities for deep learning but in a really de-skilled way. >> Yeah, and also to remind the folks, there's a platform involved here. Maybe you can say it's been re-platformed, I don't know. Maybe you can answer that. Has it been re-platformed or is it just the platformization of existing stuff? Because there's certainly demand. TensorFlow at Google showed that there's a demand for machine learning libraries and then deep learning behind. You got Amazon Web Services with Sagemaker, Touting. As a service model for AI, it's definitely in demand. So talk about the platform piece underneath. What is it? How does it get rendered? And then we'll come back and talk about the user consumption side. >> So it definitely is not a re-platformization. You recall what we have done with a focus initially on what we did on data science and what we did on machine learning. And the number one thing that we did was we were about supporting open-source and open frameworks. So it's not just one framework, like a TensorFlow framework, but it's about what we can do with TensorFlow, Keras, PyTorch, Caffe, and be able to use all of our builders' favorite open-source frameworks and be able to use that in a way where then we can add additional value on top of that and help them accelerate what it means to actually have that in the enterprise and what it means to actually de-skill that for the organization. So we started there. But really, if you look at where Watson has focused on the APIs and the API services, it's bringing together those capabilities of what we're doing with unstructured, pre-trained services, and then allowing clients to be able to bring together the structured and unstructured together on one platform, and adding the deep learning as a service capabilities, which is truly differentiating. >> Well, I think the important point there, just to amplify, and for the people to know is, it's not just your version of the tools for the data, you're looking at bringing data in from anywhere the customer, your customer wants it. And that's super critical. You don't want to ignore data. You can't. You got to have access to the data that matters. >> Yeah, you know, I think one of the other critical pieces that we're talking about here is, data without AI is meaningless and AI without data is really not useful or very accurate. So, having both of them in a yin yang and then bringing them together as we're doing in the Watson Studio is extremely important. >> The other thing I want get now to the user side, the consumption side you mentioned making it easier, but one of the things we've been hearing, that's been a theme in the hallways and certainly in theCUBE here is; bad data equals bad AI. >> Bad data equals bad AI. >> It's not just about bolting a AI on, you really got to take a holistic approach and a hygiene approach to the data and understanding where the data is contextually is relevant to the application. Talk about, that means kind of nuance, but break that down. What's your reaction to that and how do you talk to customers saying, okay look you want to do AI here's the playbook. How do you explain that in a very simple way? >> Well you heard of the AI ladder, making your data ready for AI. This is a really important concept because you need to be able to have trust in the data that you have, relevancy in the data that you have, and so it is about not just the connectivity to that data, but can you start having curated and rich data that is really valuable, that's accurate that you can trust, that you can leverage. It becomes not just about the data, but about the governance and the self-service capabilities that you can have and around that data and then it is about the machine learning and the deep learning characteristics that you can put on there. But, all three of those components are absolutely essential. What we're seeing it's not even about the data that you have within the firewall of your organization, it's about what you're doing to really augment that with external data. That's another area that we're having pre-trained, enriched, data sets with what we're doing with the Wats and data kits is extremely important; industry specific data. >> Well you know my pet peeve is always I love data. I'm a data geek, I love innovation, I love data driven, but you can't have data without good human interaction. The human component is critical and certainly with seeing trends where startups like Elation that we've interviewed; are taking this social approach to data where they're looking at it like you don't need to be a data geek or data scientist. The average business person's creating the value in especially blockchain, we were just talking in theCUBE that it's the business model Innovations, it's universal property and the technology can be enabled and managed appropriately. This is where the value is. What's the human component? Is there like... You want to know who's using the data? >> Well-- >> Why are they using data? It's like do I share the data? Can you leverage other people's data? This is kind of a melting pot. >> It is. >> What's the human piece of it? >> It truly is about enabling more people access to what it means to infuse AI into their organization. When I said it's not about re-platforming, but it's about expanding. We started with the data scientists, and we're adding to that the application developer. The third piece of that is, how do you get the knowledge worker? The subject matter expert? The person who understand the actual machine, or equipment that needs to be inspected. How do you get them to start customizing models without having to know anything about the data science element? That's extremely important because I can auto-tag and auto-classify stuff and use AI to get them started, but there is that human element of not needing to be a data scientist, but still having input into that AI and that's a very beautiful thing. >> You know it's interesting is in the security industry you've seen groups; birds of a feather flock together, where they share hats and it's a super important community aspect of it. Data has now, and now with AI, you get the AI ladder, but this points to AI literacy within the organizations. >> Exactly. >> So you're seeing people saying, hey we need AI literacy. Not coding per se, but how do we manage data? But it's also understanding who within your peer group is evolving. So your seeing now a whole formation of user base out there, users who want to know who their; the birds of the other feather flocking together. This is now a social gamification opportunity because they're growing together. >> There're-- >> What's your thought on that? >> There're two things there I would say. First, is we often go to the technology and as a product person I just spoke to you a lot about the technology. But, what we find in talking to our clients, is that it really is about helping them with the skills, the culture, the process transformation that needs to happen within the organization to break down the boundaries and the silos exist to truly get AI into an organization. That's the first thing. The second, is when you think about AI and what it means to actually infuse AI into an enterprise organization there's an ethics component of this. There's ethics and bias, and bias components which you need to mitigate and detect, and those are real problems and by the way IBM, especially with the work that we're doing within Watson, with the work that we're doing in research, we're taking this on front and center and it's extremely important to what we do. >> You guys used to talk about that as cognitive, but I think you're so right on. I think this is such a progressive topic, love to do a deeper dive on it, but really you nailed it. Data has to have a consensus algorithm built into it. Meaning you need to have, that's why I brought up this social dynamic, because I'm seeing people within organizations address regulatory issues, legal issues, ethical, societal issues all together and it requires a group. >> That's right. >> Not just algorithm, people to synthesize. >> Exactly. >> And that's either diversity, diverse groups from different places and experiences whether it's an expert here, user there; all coming together. This is not really talked about much. How are you guys-- >> I think it will be more. >> John: It will, you think so? >> Absolutely it will be more. >> What do you see from customers? You've done a lot of client meetings. Are they talking about this? Or they still more in the how do I stand up AI, literacy. >> They are starting to talk about it because look, imagine if you train your model on bad data. You actually have bias then in your model and that means that the accuracy of that model is not where you need it to be if your going to run it in an enterprise organization. So, being able to do things like detect it and proactively mitigate it are at the forefront and by the way this where our teams are really focusing on what we can do to further the AI practice in the enterprise and it is where we really believe that the ethics part of this is so important for that enterprise or smarter business component. >> Iterating through the quality the data's really good. Okay, so now I was talking to Rob Thomas talking about data containers. We were kind of nerding out on Kubernetes and all that good stuff. You almost imagine Kubernetes and containers making data really easy to move around and manage effectively with software, but I mentioned consensus on the understanding the quality of the data and understanding the impact of the data. When you say consensus, the first thing that jumps in my mind is blockchain, cryptocurrency. Is there a tokenization economics model in data somewhere? Because all the best stuff going on in blockchain and cryptocurrency that's technically more impactful is the changing of the economics. Changing of the technical architectures. You almost can say, hmm. >> You can actually see over a time that there is a business model that puts more value not just on the data and the data assets themselves, but on the models and the insights that are actually created from the AI assets themselves. I do believe that is a transformation just like what we're seeing in blockchain and the type of cryptocurrency that exists within there, and the kind of where the value is. We will see the same shift within data and AI. >> Well, you know, we're really interested in exploring and if you guys have any input to that we'd love to get more access to thought leaders around the relationship people and things have to data. Obviously the internet of things is one piece, but the human relationship the data. You're seeing it play out in real time. Uber had a first death this week, that was tragic. First self-driving car fatality. You're seeing Facebook really get handed huge negative press on the fact that they mismanaged the data that was optimized for advertising not user experience. You're starting to see a shift in an evolution where people are starting to recognize the role of the human and their data and other people's data. This is a big topic. >> It's a huge topic and I think we'll see a lot more from it and the weeks, and months, and years ahead on this. I think it becomes a really important point as to how we start to really innovate in and around not just the data, but the AI we apply to it and then the implications of it and what it means in terms of if the data's not right, if the algorithm's aren't right, if the biases is there. It is big implications for society and for the environment as a whole. >> I really appreciate you taking the time to speak with us. I know you're super busy. My final question's much more share some color commentary on IBM Think this week, the event, your reaction to, obviously it's massive, and also the customer conversations you've had. You've told me that your in client briefings and meetings. What are they talking about? What are they asking for? What are some of the things that are, low-hanging fruit use cases? Where's the starting point? Where are people jumping in? Can you just share any data you have on-- >> Oh I can share. That's a fully loaded question; that's like 10 questions all in one. But the Think conference has been great in terms of when you think about the problems that we're trying to solve with AI, it's not AI alone, right? It actually is integrated in with things like data, with the systems, with how we actually integrate that in terms of a hybrid way of what we're doing on premises and what we're doing in private Cloud, what we're doing in public Cloud. So, actually having a forum where we're talking about all of that together in a unified manner has actually been great feedback that I've heard from many customers, many analysts, and in general from an IBM perspective, I believe has been extremely valuable. I think the types of questions that I'm hearing and the types of inputs and conversations we're having, are one of where clients want to be able to innovate and really do things that are in Horizon three type things. What are the things they should be doing in Horizon one, Horizon two, and Horizon three when it comes to AI and when it comes to AI and how they treat their data. This is really important because-- >> What's Horizon one, two and three? >> You think about Horizon one, those are things you should be doing immediately to get immediate value in your business. Horizon two, are kind of mid-term, 18 to 24. 24 plus months out is Horizon 3. So when you think about an AI journey, what is your AI journey really look like in terms of what you should be doing in the immediate terms. Small, quick wins. >> Foundational. >> What are things that you can do kind of projects that will pan out in a year and what are the two to three year projects that we should be doing. This are the most frequent conversations that I've been having with a lot of our clients in terms of what is that AI journey we should be thinking about, what are the projects right now, how do we work with you on the projects right now on H1 and H2. What are the things we can start incubating that are longer term. And these extremely transformational in nature. It's kind of like what do we do to really automate self-driving, not just cars, but what we do for trains and we do to do really revolutionize certain industries and professions. >> How does your product roadmap to your Horizons? Can you share a little bit about the priorities on the roadmap? I know you don't want to share a lot of data, competitive information. But, can you give an antidotal or at least a trajectory of what the priorities are and some guiding principals? >> I hinted at some of it, but I only talked about the Studio, right... During this discussion, but still Studio is just one of a three-pronged approach that we have in Watson. The Studio really is about laying the foundation that is equivalent for how do we get AI in our enterprises for the builders, and it's like a place where builders go to be able to create, build, deploy those models, machine learning, deep learning models and be able to do so in a de-skilled way. Well, on top of that, as you know, we've done thousands of engagements and we know the most comprehensive ways that clients are trying to use Watson and AI in their organizations. So taking our learnings from that, we're starting to harden those in applications so that clients can easily infuse that into their businesses. We have capabilities for things like Watson Assistance, which was announced this week at the conference that really helped clients with pre-existing skills like how do you have a customer care solution, but then how can you extend it to other industries like automotive, or hospitality, or retail. So, we're working not just within Watson but within broader IBM to bring solutions like that. We also have talked about compliance. Every organization has a regulatory, or compliance, or legal department that deals with either SOWs, legal documents, technical documents. How do you then start making sure that you're adhering to the types of regulations or legal requirements that you have on those documents. Compare and comply actually uses a lot of the Watson technologies to be able to do that. And scaling this out in terms of how clients are really using the AI in their business is the other point of where Watson will absolutely focus going forward. >> That's awesome, Ritika. Thank you for coming on theCUBE, sharing the awesome work and again gutting across IBM and also outside in the industry. The more data the better the potential. >> Absolutely. >> Well thanks for sharing the data. We're putting the data out there for you. theCUBE is one big data machine, we're data driven. We love doing these interviews, of course getting the experts and the product folks on theCUBE is super important to us. I'm John Furrier, more coverage for IBM Think after this short break. (upbeat music)

>> Narrator: From San Jose, in the heart of Silicon Valley, it's The Cube, covering Big Data SV 2016. Now your hosts, John Furrier and Peter Burris. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data Week, Big Data SV Strata Hadoop. This is The Cube, SiliconANGLE's flagship program. We go out to the events and extract the signals from the noise. I'm John Furrier, my co-host is Peter Burris. Our next guest is Ritika Gunnar, VP of Data and Analytics at IBM and David Richards is the CEO of WANdisco. Welcome to The Cube, welcome back. >> Thank you. >> It's a pleasure to be here. >> So, okay, IBM and WANdisco, why are you guys here? What are you guys talking about? Obviously, partnership. What's the story? >> So, you know what WANdisco does, right? Data replication, active-active replication of data. For the past twelve months, we've been realigning our products to a market that we could see rapidly evolving. So if you had asked me twelve months ago what we did, we were talking about replicating just Hadoop, but we think the market is going to be a lot more than that. I think Mike Olson famously said that this Hadoop was going to disappear and he was kind of right because the ecosystem is evolving to be a much greater stack that involves applications, cloud, completely heterogeneous storage environment, and as that happens the partnerships that we would need have to move on from just being, you know, the sort of Hadoop-specific distribution vendors to actually something that can deliver a complete solution to the marketplace. And very clearly, IBM has a massive advantage in the number of people, the services, ecosystem, infrastructure, in order to deliver a complete solution to customers, so that's really why we're here. >> If you could talk about the stack comment, because this is something that we're seeing. Mike Olson's kind of being political when he says make it invisible, but the reality is there is more to big data than Hadoop. There's a lot of other stuff going on. Call it stack, call it ecosystem. A lot of great things are growing, we just had Gaurav on from SnapLogic said, "everyone's winning." I mean, I just love that's totally true, but it's not just Hadoop. >> It's about Alldata and it's about all insight on that data. So when you think about Alldata, Alldata is a very powerful thing. If you look at what clients have been trying to do thus far, they've actually been confined to the data that may be in their operational systems. With the advent of Hadoop, they're starting to bring in some structured and unstructured data, but with the advent of IOT systems, systems of engagement, systems of records and trying to make sense of all of that, Alldata is a pretty powerful thing. When I think of Alldata, I think of three things. I think of data that is not only on premises, which is where a lot of data resides today, but data that's in the cloud, where data is being generated today and where a majority of the growth is. When I think of Alldata, I think of structured data, that is in your traditional operational systems, unstructured and semi-structured data from IOT systems et cetera, and when I think of Alldata, I think of not just data that's on premises for a lot of our clients, but actually external data. Data where we can correlate data with, for example, an acquisition that we just did within IBM with The Weather Company or augmenting with partnerships like Twitter, et cetera, to be able to extract insight from not just the data that resides within the walls of your organization, but external data as well. >> The old expression is if you want to go fast, do it alone, if you want to go deeper and broader and more comprehensive, do it as a team. >> That's right. >> That expression can be applied to data. And you look at The Weather data, you think, hmmm, that's an outlier type acquisition, but when you think about the diversity of data, that becomes a really big deal. And the question I want to ask you guys is, and Ritika, we'll start with you, there's always a few pressure points we've seen in big data. When that pressure is relieved, you've seen growth, and one was big data analytics kind of stalled a little bit, the winds kind of shifted, eye of the storm, whatever you want to call it, then cloud comes in. Cloud is kind of enabling that to go faster. Now, a new pressure point that we're seeing is go faster with digital transformation. So Alldata kind of brings us to all digital. And I know IBM is all about digitizing everything and that's kind of the vision. So you now have the pressure of I want all digital, I need data driven at the center of it, and I've got the cloud resource, so kind of the perfect storm. What's your thoughts on that? Do you see that similar picture? And then does that put the pressure on, say, WANdisco, say hey, I need replication, so now you're under the hood? Is that kind of where this is coming together? >> Absolutely. When I think about it, it's about giving trusted data and insights to everyone within the organization, at the speed in which they need it. So when you think about that last comment of, "At the speed in which they need it," that is the pressure point of what it means to have a digitally transformed business. That means being able to make insights and decisions immediately and when we look at what our objective is from an IBM perspective, it's to be able to enable our clients to be able to generate those immediate insights, to be able to transform their business models and to be able to provide the tooling and the skills necessary, whether we have it organically, inorganically, or through partnerships, like with WANdisco to be able to do that. And so with WANdisco, we believe we really wanted to be able to activate where that data resides. When I talk about Alldata and activation of that data, WANdisco provided to us complementary capabilities to be able to activate that data where it resides with a lot of the capabilities that they're providing through their fusion. So, being able to have and enable our end-users to have that digitally infused set of reactive type of applications is absolutely something... >> It's like David, we talk about, and maybe I'm oversimplifying your value proposition, but I always look at WANdisco as kind of the five nines of data, right? You guys make stuff work, and that's the theme here this year, people just want it to work, right? They don't want to have it down, right? >> Yeah, we're seeing, certainly, an uptick in understanding about what high availability, what continuous availability means in the context of Hadoop, and I'm sure we'll be announcing some pretty big deals moving forward. But we've only just got going with IBM. I would, the market should expect a number of announcements moving forward as we get going with this, but here's the very interesting question associated with cloud. And just to give you a couple of quick examples, we are seeing an increasing number of Global 1,000 companies, Fortune 100 companies move to cloud. And that's really important. If you would have asked me 12 months ago, how is the market going to shape up, I'd have said, well, most CIO's want to move to cloud. It's already happening. So, FINRA, the major financial regulator in the United States is moving to cloud, publicly announced it. The FCA in the UK publicly announced they are moving 100% to cloud. So this creates kind of a microcosm of a problem that we solve, which is how do you move transactional data from on-premise to cloud and create a sort of hybrid environment. Because with the migration, you have to build a hybrid cloud in order to do that anyway. So, if it's just archive systems, you can package it on a disk drive and post it, right? If we're talking about transactional data, i.e, stuff that you want to use, so for example, a big travel company can't stop booking flights while they move their data into the cloud, right? They would take six months to move petabyte scale data into cloud. We solve that problem. We enable companies to move transactional data from on-premise into cloud, without any interruption to services. >> So not six months? >> No, not six months. >> Six hours? >> And you can keep on using the data while it is in transit. So we've been looking for a really simplistic problem, right, to explain this really complex algorithm that we've got that you know does this active-active replication stuff. That's it, right? It's so simple, and nobody else can do it. >> So no downtime, no disruption to their business? >> No, and you can use the cloud or you can use the on-prem applications while the data is in transit. >> So when you say all cloud, now we're on a theme, Alldata, all digital, all cloud, there's a nuance there because most, and we had Gaurav from SnapLogic talk about it, there's always going to be an on-prem component. I mean, probably not going to see 100% everyone move to the cloud, public cloud, but cloud, you mean hybrid cloud essentially, with some on-prem component. I'm sure you guys see that with Bluemix as well, that you've got some dabbling in the public cloud, but ultimately, it's one resource pool. That's essentially what you're saying. >> Yeah, exactly. >> And I think it's really important. One of the things that's very attractive e about the WANdisco solution is that it does provide that hybridness from the on-premises to cloud and that being able to activate that data where it resides, but being able to do that in a heterogeneous fashion. Architectures are very different in the cloud than they are on premises. When you look at it, your data like may be as simple as Swift object store or as S3, and you may be using elements of Hadoop in there, but the architectures are changing. So the notion of being able to handle hybrid solutions both on-premises and cloud with the heterogeneous capability in a non-invasive way that provides continuous data is something that is not easily achieved, but it's something that every enterprise needs to take into account. >> So Ritika, talk about the why the WANdisco partnership, and specifically, what are some of the conversations you have with customers? Because, obviously there's, it sounds like, the need to go faster and have some of this replication active-active and kind of, five nines if you will, of making stuff not go down or non-disruptive operations or whatever the buzzword is, but you know, what's the motivation from your standpoint? Because IBM is very customer-centric. What are some of the conversations and then how does WANdisco fit into those conversations? >> So when you look at the top three use cases that most clients use for even Hadoop environments or just what's going on in the market today, the top three use cases are you know, can I build a logical data warehouse? Can I build areas for discovery or analytical discovery? Can I build areas to be able to have data archiving? And those top three solutions in a hybrid heterogeneous environment, you need to be able to have active-active access to the data where that data resides. And therefore, we believe, from an IBM perspective, that we want to be able to provide the best of breed regardless of where that resides. And so we believe from a WANdisco perspective, that WANdisco has those capabilities that are very complementary to what we need for that broader skills and tooling ecosystem and hence why we have formed this partnership. >> Unbelievably, in the market, we're also seeing and it feels like the Hadoop market's just got going, but we're seeing migrations from distributions like Cloudera into cloud. So you know, those sort of lab environments, the small clusters that were being set up. I know this is slightly controversial, and I'll probably get darts thrown at me by Mike Olson, but we are seeing pretty large-scale migration from those sort of labs that were set up initially. And as they progress, and as it becomes mission-critical, they're going to go to companies like IBM, really, aren't they, in order to scale up their infrastructure? They're going to move the data into cloud to get hyperscale. For some of these cases that Ritika was just talking about so we are seeing a lot of those migrations. >> So basically, Hadoop, there's some silo deployments of POC's that need to be integrated in. Is that what you're referring to? I mean, why would someone do that? They would say okay, probably integration costs, probably other solutions, data. >> If you do a roll-your-own approach, where you go and get some open-source software, you've got to go and buy servers, you've got to go and train staff. We've just seen one of our customers, a big bank, two years later get servers. Two years to get servers, to get server infrastructure. That's a pretty big barrier, a practical barrier to entry. Versus, you know, I can throw something up in Bluemix in 30 minutes. >> David, you bring up a good point, and I want to just expand on that because you have a unique history. We know each other, we go way back. You were on The Cube when, I think we first started seven years ago at Hadoop World. You've seen the evolution and heck, you had your own distribution at one point. So you know, you've successfully navigated the waters of this ecosystem and you had gray IP and then you kind of found your swim lanes and you guys are doing great, but I want to get your perspective on this because you mentioned Cloudera. You've seen how it's evolving as it goes mainstream, as you know, Peter says, "The big guys are coming in and with power." I mean, IBM's got a huge spark investment and it's not just you know, lip service, they're actually donating a ton of code and actually building stuff so, you've got an evolutionary change happening within the industry. What's your take on the upstarts like Cloudera and Hortonworks and the Dishrow game? Because that now becomes an interesting dynamic because it has to integrate well. >> I think there will always be a market for the distribution of opensource software. As that sort of, that layer in the stack, you know, certainly Cloudera, Hortonworks, et cetera, are doing a pretty decent job of providing a distribution. The Hadoop marketplace, and Ritika laid this on pretty thick as well, is not Hadoop. Hadoop is a component of it, but in cloud we talk about object store technology, we talk about Swift, we talk about S3. We talk about Spark, which can be run stand-alone, you don't necessarily need Hadoop underneath it. So the marketplace is being stretched to such a point that if you were to look at the percentage of the revenue that's generated from Hadoop, it's probably less than one percent. I talked 12 months ago with you about the whale season, the whales are coming. >> Yeah, they're here. >> And they're here right now, I mean... >> (laughs) They're mating out in the water, deals are getting done. >> I'm not going to deal with that visual right now, but you're quite right. And I love the Peter Drucker quote which is, "Strategy is a commodity, execution is an art." We're now moving into the execution phase. You need a big company in order to do that. You can't be a five hundred or a thousand person... >> Is Cloudera holding onto dogma with Hadoop or do they realize that the ecosystem is building around them? >> I think they do because they're focused on the application layer, but there's a lot of competition in the application layer. There's a little company called IBM, there's a little company called Microsoft and the little company called Amazon that are kind of focused on that as well, so that's a pretty competitive environment and your ability to execute is really determined by the size of the organization to be quite frank. >> Awesome, well, so we have Hadoop Summit coming up in Dublin. We're going to be in Ireland next month for Hadoop Summit with more and more coverage there. Guys, thanks for the insight. Congratulations on the relationship and again, WANdisco, we know you guys and know what you guys have done. This seems like a prime time for you right now. And IBM, we just covered you guys at InterConnect. Great event. Love The Weather Company data, as a weather geek, but also the Apple announcement was really significant. Having Apple up on stage with IBM, I think that is really, really compelling. And that was just not a Barney deal, that was real. And the fact that Apple was on stage was a real testament to the direction you guys are going, so congratulations. This is The Cube, bringing you all the action, here live in Silicon Valley here for Big Data Week, BigData SV, and Strata Hadoop. We'll be right back with more after this short break.

>>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.