>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> Welcome back to Midtown, New York. We are at Machine Learning Everywhere: Build Your Ladder to AI being put on by IBM here in late February in the Big Apple. Along with Dave Vellante, I'm John Walls. We're now joined by Dinesh Nirmal, who is the Vice President of Analytics Development and Site Executive at the IBM Silicon Valley lab, soon. Dinesh, good to see you, this morning, sir. >> Thank you, John. >> Fresh from California. You look great. >> Thanks. >> Alright, you've talked about this, and it's really your world: data, the new normal. Explain that. When you say it's the new normal, what exactly... How is it transforming, and what are people having to adjust to in terms of the new normal. >> So, if you look at data, I would say each and every one of us has become a living data set. Our age, our race, our salary. What our likes or dislikes, every business is collecting every second. I mean, every time you use your phone, that data is transmitted somewhere, stored somewhere. And, airlines for example, is looking, you know, what do you like? Do you like an aisle seat? Do you like to get home early? You know, all those data. >> All of the above, right? >> And petabytes and zettabytes of data is being generated. So now, businesses' challenge is that how do you take that data and make insights out of it to serve you as a better customer. That's where I've come to, but the biggest challenge is that, how do you deal with this tremendous amount of data? That is the challenge. And creating sites out of it. >> That's interesting. I mean, that means the definition of identity is really... For decades it's been the same, and what you just described is a whole new persona, identity of an individual. >> And now, you take the data, and you add some compliance or provisioning like GDPR on top of it, all of a sudden how do-- >> John: What is GDPR? For those who might not be familiar with it. >> Dinesh: That's the regulatory term that's used by EU to make sure that, >> In the EU. >> If me as a customer come to an enterprise, say, I don't want any of my data stored, it's up to you to go delete that data completely, right? That's the term that's being used. And that goes into effect in May. How do you make sure that that data gets completely deleted by that time the customer has... How do you get that consent from the customer to go do all those... So there's a whole lot of challenges, as data multiplies, how do you deal with the data, how do you create insights to the data, how do you create consent on the data, how do you be compliant on that data, how do you create the policies that's needed to generate that data? All those things need to be... Those are the challenges that enterprises face. >> You bring up GDPR, which, for those who are not familiar with it, actually went into effect last year but the fines go into effect this year, and the fines are onerous, like 4% of turnover, I mean it's just hideous, and the question I have for you is, this is really scary for companies because they've been trying to catch up to the big data world, and so they're just throwing big data projects all over the place, which is collecting data, oftentimes private information, and now the EU is coming down and saying, "Hey you have to be able to, if requested, delete that." A lot of times they don't even know where it is, so big challenge. Are you guys, can you help? >> Yeah, I mean, today if you look at it, the data exists all over the place. I mean, whether it's in your relational database or in your Hadoop, unstructured data, whereas you know, optics store, it exists everywhere. And you have to have a way to say where the data is and the customer has to consent given to go, for you to look at the data, for you to delete the data, all those things. We have tools that we have built and we have been in the business for a very long time for example our governance catalog where you can see all the data sources, the policies that's associated with it, the compliance, all those things. So for you to look through that catalog, and you can see which data is GDPR compliant, which data is not, which data you can delete, which data you cannot. >> We were just talking in the open, Dave made the point that many companies, you need all-stars, not just somebody who has a specialty in one particular area, but maybe somebody who's in a particular regiment and they've got to wear about five different hats. So how do you democratize data to the point that you can make these all-stars? Across all kinds of different business units or different focuses within a company, because all of a sudden people have access to great reams of information. I've never had to worry about this before. But now, you've got to spread that wealth out and make everybody valuable. >> Right, really good question. Like I said, the data is existing everywhere, and most enterprises don't want to move the data. Because it's a tremendous effort to move from an existing place to another one and make sure the applications work and all those things. We are building a data virtualization layer, a federation layer, whereby which if you are, let's say you're a business unit. You want to get access to that data. Now you can use that federational data virtualization layer without moving data, to go and grab that small piece of data, if you're a data scientist, let's say, you want only a very small piece of data that exists in your enterprise. You can go after, without moving the data, just pick that data, do your work, and build a model, for example, based on that data. So that data virtualization layer really helps because it's basically an SQL statement, if I were to simplify it. That can go after the data that exists, whether it's at relational or non-relational place, and then bring it back, have your work done, and then put that data back into work. >> I don't want to be a pessimist, because I am an optimist, but it's scary times for companies. If they're a 20th century organization, they're really built around human expertise. How to make something, how to transact something, or how to serve somebody, or consult, whatever it is. The 21st century organization, data is foundational. It's at the core, and if my data is all over the place, I wasn't born data-driven, born in the cloud, all those buzzwords, how do traditional organizations catch up? What's the starting point for them? >> Most, if not all, enterprises are moving into a data-driven economy, because it's all going to be driven by data. Now it's not just data, you have to change your applications also. Because your applications are the ones that's accessing the data. One, how do you make an application adaptable to the amount of data that's coming in? How do you make accuracy? I mean, if you're building a model, having an accurate model, generating accuracy, is key. How do you make it performant, or govern and self-secure? That's another challenge. How do you make it measurable, monitor all those things? If you take three or four core tenets, that's what the 21st century's going to be about, because as we augment our humans, or developers, with AI and machine learning, it becomes more and more critical how do you bring these three or four core tenets into the data so that, as the data grows, the applications can also scale. >> Big task. If you look at the industries that have been disrupted, taxis, hotels, books, advertising. >> Dinesh: Retail. >> Retail, thank you. Maybe less now, and you haven't seen that disruption yet in banks, insurance companies, certainly parts of government, defense, you haven't seen a big disruption yet, but it's coming. If you've got the data all over the place, you said earlier that virtually every company has to be data-driven, but a lot of companies that I talk to say, "Well, our industry is kind of insulated," or "Yeah, we're going to wait and see." That seems to me to be the recipe for disaster, what are your thoughts on that? >> I think the disruption will come from three angles. One, AI. Definitely that will change the way, blockchain, another one. When you say, we haven't seen in the financial side, blockchain is going to change that. Third is quantum computing. The way we do compute is completely going to change by quantum computing. So I think the disruption is coming. Those are the three, if I have to predict into the 21st century, that will change the way we work. I mean, AI is already doing a tremendous amount of work. Now a machine can basically look at an image and say what it is, right? We have Watson for cancer oncology, we have 400 to 500,000 patients being treated by Watson. So AI is changing, not just from an enterprise perspective, but from a socio-economic perspective and a from a human perspective, so Watson is a great example for that. But yeah, disruption is happening as we speak. >> And do you agree that foundational to AI is the data? >> Oh yeah. >> And so, with your clients, like you said, you described it, they've got data all over the place, it's all in silos, not all, but much of it is in silos. How does IBM help them be a silo-buster? >> Few things, right? One, data exists everywhere. How do you make sure you get access to the data without moving the data, that's one. But if you look at the whole lifecycle, it's about ingesting the data, bringing the data, cleaning the data, because like you said, data becomes the core. Garbage in, garbage out. You cannot get good models unless the data is clean. So there's that whole process, I would say if you're a data scientist, probably 70% of your time is spent on cleaning the data, making the data ready for building a model or for a model to consume. And then once you build that model, how do you make sure that the model gets retrained on a regular basis, how do you monitor the model, how do you govern the model, so that whole aspect goes in. And then the last piece is visualizational reporting. How do you make sure, once the model or the application is built, how do you create a report that you want to generate or you want to visualize that data. The data becomes the base layer, but then there's a whole lifecycle on top of it based on that data. >> So the formula for future innovation, then, starts with data. You add in AI, I would think that cloud economics, however we define that, is also a part of that. My sense is most companies aren't ready, what's your take? >> For the cloud, or? >> I'm talking about innovation. If we agree that innovation comes from the data plus AI plus you've got to have... By cloud economics I mean it's an API economy, you've got massive scale, those kinds of, to compete. If you look at the disruptions in taxis and retail, it's got cloud economics underneath it. So most customers don't really have... They haven't yet even mastered cloud economics, yet alone the data and the AI component. So there's a big gap. >> It's a huge challenge. How do we take the data and create insights out of the data? And not just existing data, right? The data is multiplying by the second. Every second, petabytes or zettabytes of data are being generated. So you're not thinking about the data that exists within your enterprise right now, but now the data is coming from several different places. Unstructured data, structured data, semi-structured data, how do you make sense of all of that? That is the challenge the customers face, and, if you have existing data, on top of the newcoming data, how do you predict what do you want to come out of that. >> It's really a pretty tough conundrum that some companies are in, because if you're behind the curve right now, you got a lot of catching up to do. So you think that we have to be in this space, but keeping up with this space, because the change happens so quickly, is really hard, so we have to pedal twice as fast just to get in the game. So talk about the challenge, how do you address it? How do you get somebody there to say, "Yep, here's your roadmap. "I know it's going to be hard, "but once you get there you're going to be okay, "or at least you're going to be on a level playing field." >> I look at the three D's. There's the data, there's the development of the models or the applications, and then the deployment of those models or applications into your existing enterprise infrastructure. Not only the data is changing, but that development of the models, the tools that you use to develop are also changing. If you look at just the predictive piece, I mean look from the 80's to now. You look at vanilla machine learning versus deep learning, I mean there's so many tools available. How do you bring it all together to make sense which one would you use? I think, Dave, you mentioned Hadoop was the term from a decade ago, now it's about object store and how do you make sure that data is there or JSON and all those things. Everything is changing, so how do you bring, as an enterprise, you keep up, afloat, on not only the data piece, but all the core infrastructure piece, the applications piece, the development of those models piece, and then the biggest challenge comes when you have to deploy it. Because now you have a model that you have to take and deploy in your current infrastructure, which is not easy. Because you're infusing machine learning into your legacy applications, your third-party software, software that was written in the 60's and 70's, it's not an easy task. I was at a major bank in Europe, and the CTO mentioned to me that, "Dinesh, we built our model in three weeks. "It has been 11 months, we still haven't deployed it." And that's the reality. >> There's a cultural aspect too, I think. I think it was Rob Thomas, I was reading a blog that he wrote, and he said that he was talking to a customer saying, "Thank god I'm not in the technology industry, "things change so fast I could never, "so glad I'm not a software company." And Rob's reaction was, "Uh, hang on. (laughs) "You are in the technology business, "you are a software company." And so there's that cultural mindset. And you saw it with GE, Jeffrey Immelt said, "I went to bed an industrial giant, "woke up a software company." But look at the challenges that industrial giant has had transforming, so... They need partners, they need people that have done this before, they need expertise and obviously technology, but it's people and process that always hold it up. >> I mean technology is one piece, and that's where I think companies like IBM make a huge difference. You understand enterprise. Because you bring that wealth of knowledge of working with them for decades and they understand your infrastructure, and that is a core element, like I said the last piece is the deployment piece, how do you bring that model into your existing infrastructure and make sure that it fits into that architecture. And that involves a tremendous amount of work, skills, and knowledge. >> Job security. (all laugh) >> Dinesh, thanks for being with us this morning, we appreciate that and good luck with the rest of the event, here in New York City. Back with more here on theCUBE, right after this. (calming techno music)

>> Announcer: Live from New York, it's theCUBE! Covering Machine Learning Everywhere: Build Your Ladder To AI. Brought to you by IBM. >> Well, good morning! Welcome here on theCUBE. Along with Dave Vellante, I'm John Walls. We're in Midtown New York for IBM's Machine Learning Everywhere: Build Your Ladder To AI. Great lineup of guests we have for you today, looking forward to bringing them to you, including world champion chess master Garry Kasparov a little bit later on. It's going to be fascinating. Dave, glad you're here. Dave, good to see you, sir. >> John, always a pleasure. >> How you been? >> Up from DC, you know, I was in your area last week doing some stuff with John Furrier, but I've been great. >> Stopped by the White House, drop in? >> You know, I didn't this time. No? >> No. >> Dave: My son, as you know, goes to school down there, so when I go by my hotel, I always walk by the White House, I wave. >> Just in case, right? >> No reciprocity. >> Same deal, we're in the same boat. Let's talk about what we have coming up here today. We're talking about this digital transformation that's going on within multiple industries. But you have an interesting take on it that it's a different wave, and it's a bigger wave, and it's an exciting wave right now, that digital is creating. >> Look at me, I've been around for a long time. I think we're entering a new era. You know, the great thing about theCUBE is you go to all these events, you hear the innovations, and we started theCUBE in 2010. The Big Data theme was just coming in, and it appeared, everybody was very excited. Still excited, obviously, about the data-driven concept. But we're now entering a new era. It's like every 10 years, the parlance in our industry changes. It was cloud, Big Data, SaaS, mobile, social. It just feels like, okay, we're here. We're doing that now. That's sort of a daily ritual. We used to talk about how it's early innings. It's not anymore. It's the late innings for those. I think the industry is changing. The describers of what we're entering are autonomous, pervasive, self-healing, intelligent. When you infuse artificial intelligence, I'm not crazy about that name, but when you infuse that throughout the landscape, things start to change. Data is at the center of it, but I think, John, we're going to see the parlance change. IBM, for example, uses cognitive. People use artificial intelligence. I like machine intelligence. We're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. What we're seeing is a whole new set of opportunities emerging, and if you think about it, it's based on this notion of digital services, where data is at the center. That's something that I want to poke at with the folks at IBM and our guests today. How are people going to build new companies? You're certainly seeing it with the likes of Uber, Airbnb, Waze. It's built on these existing cloud and security, off-the-shelf, if you will, horizontal technologies. How are new companies going to be built, what industries are going to be disruptive? Hint, every industry. But really, the key is, how will existing companies keep pace? That's what I really want to understand. >> You said, every industry's going to be disrupted, which is certainly, I think, an exciting prospect in some respects, but a little scary to some, too, right? Because they think, "No, we're fat and happy "and things are going well right now in our space, "and we know our space better than anybody." Some of those leaders might be thinking that. But as you point out, digital technology has transformed to the extent now that there's nobody safe, because you just slap this application in, you put this technology in, and I'm going to change your business overnight. >> That's right. Digital means data, data is at the center of this transformation. A colleague of mine, David Moschella, has come up with this concept of the matrix, and what the matrix is is a set of horizontal technology services. Think about cloud, or SaaS, or security, or mobile, social, all the way up the stack through data services. But when you look at the companies like Airbnb and Uber and, certainly, what Google is doing, and Facebook, and others, they're building services on top of this matrix. The matrix is comprised of vertical slices by industry and horizontal slices of technology. Disruptors are cobbling together through software and data new sets of services that are disrupting industries. The key to this, John, in my view, anyway, is that, historically, within healthcare or financial services, or insurance, or manufacturing, or education, those were very siloed. But digital and data allows companies and disruptors to traverse silos like never before. Think about it. Amazon buying Whole Foods. Apple getting into healthcare and financial services. You're seeing these big giants disrupt all of these different industries, and even smaller guys, there's certainly room for startups. But it's all around the data and the digital transformation. >> You spoke about traditional companies needing to convert, right? Needing to get caught up, perhaps, or to catch up with what's going on in that space. What do you do with your workforce in that case? You've got a bunch of great, hardworking people, embedded legacy. You feel good about where you are. And now you're coming to that workforce and saying, "Here's a new hat." >> I think that's a great question. I think the concern that one would have for traditional companies is, data is not foundational for most companies. It's not at their core. The vast majority of companies, the core are the people. You hear it all the time. "The people are our greatest asset." That, I hate to say it, but it's somewhat changing. If you look at the top five companies by market cap, their greatest asset is their data, and the people are surrounding that data. They're very, very important because they know how to leverage that data. But if you look at most traditional companies, people are at their core. Data is kind of, "Oh, we got this bolt-on," or it's in a bunch of different silos. The big question is, how do they close that gap? You're absolutely right. The key is skillsets, and the skills have to be, you know, we talk about five-tool baseball players. You're a baseball fan, as am I. Well, you need multi-tool players, those that understand not only the domain of whether it's marketing or sales or operational expertise or finance, but they also require digital expertise. They know, for example, if you're a marketing professional, they know how to do hypertargeting. They know how to leverage social. They know how to do SEO, all these digital skills, and they know how to get information that's relevant and messaging out into the marketplace and permeate that. And so, we're entering, again, this whole new world that's highly scalable, highly intelligent, pervasive, autonomous. We're going to talk about that today with a lot of their guests, with a lot of our guests, that really are kind of futurists and have thought through, I think, the changes that are coming. >> You can't have a DH anymore, right, that's what you're saying? You need a guy that can play the field. >> Not only play the field, not only a utility player, but somebody who's a utility player, but great. Best of breed at all these different skillsets. >> Machine learning, we haven't talked much about that, and another term, right, that certainly has different definitions, but certainly real specific applications to what's going on today. We'll talk a lot about ML today. Your thoughts about that, and how that squares into the artificial intelligence picture, and what we're doing with all those machines out there that are churning 24/7. >> Yeah, so, real quick, I know we're tight on time here. Artificial intelligence to me is the umbrella. Machine learning is the application of math and algorithms to solve a particular problem or answer a particular question. And then there's deep learning, which is highly focused neural networks that go deeper and deeper and deeper, and become auto-didactic, self-learning, in a manner. Those are just the very quick and rudimentary description. Machine learning to me is the starting point, and that's really where organizations really want to start to learn and begin to close the gap. >> A lot of ground to cover, and we're going to do that for you right here on theCUBE as we continue our coverage of Machine Learning Everywhere: Your Ladder To AI, coming up here, IBM hosting us in Midtown, New York. Back with more here on theCUBE in just a bit. (fast electronic music)