 Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Oh, 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, I'm now joined by Dennis Nirmal, who's the Vice President of Analytics Development and Site Executive at the IBM Silicon Valley Labs. Dennis, good to see you this morning, sir. Thank you, John. Fresh from California. Yeah. You look great. Thanks. All right, you've talked about this, and it's really your world, data, the new normal. I mean, explain that. What do you say is the new normal? What exactly, how is it transforming and what are people having to adjust to in terms of the new normal? Right. So, if you look at data, I mean, I would say each and every one of us has become a living data set. Our age, our race, our salary, what our likes, our dislikes, every business is collecting every second. I mean, every time you use your phone, right, that data is transmitted somewhere, stored somewhere, and airlines, for example, is looking, what do you like? I mean, do you like an ILC? Do you like to get home early? All those data is being generated. All the above, right? And petabytes and setabytes of data is being generated. So now, business's 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 come to, but the biggest challenge is that how do you deal with this tremendous amount of data? That is the challenge, and create insights out of it. Well, that's interesting. I mean, that means the definition of identity is really, I mean, for decades, it's been the same. What you just described is a whole new persona, identity of an individual. Right. Now you take the data and you add some compliance or provisioning like GDPR on top of it. All of a sudden, how do you- GDPR, for those who might not be familiar with that. Right, that's a regulatory term that's used by EU to make sure that, you know, 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 that customer has? How do you get that consent from the customer to go do all this? So there's a whole lot of challenges as data as 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? You know, how do you create the policies that's needed to generate that data? All those things needs to be, that's those are the challenges that enterprises face. I mean, you bring up GDPR, which it flows, you're not familiar with it. 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, so 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, 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, so today if you look at it, the data exists all over the place. I mean, whether it's in your relational database where you're in your Hadoop on structured data where it's in an object store, it exists everywhere. And you have to have a way to say where the data is and does the customer has the consent given to go for you to look at the data, for you to delete the data, all those things. So 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 go through that catalog and you can say which data is GDPR compliant, which data is not, which data you can delete, which data you cannot. And 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 regimen 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. It's like, 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 a good question. So 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 all those things. So 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 federation or 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 was to simplify it, that can go after the data that exists whether it's a relational or non-relational place and then bring it back, have your work done, and then put that data back into it. So I don't want to be a pessimist, because I am an optimist, but it's scary times for companies. If you look at the 20th century organization, they're really built around human expertise. How to make something, or how to transact to something, or how to serve somebody with a consultant, whatever it is. The 21st century organization, data is foundational. It's at the core, and if my data is all over the place, and 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? Right, so most, if not all, enterprise 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, right? I mean, if you're building a model, having a model generating accuracy is key. How do you make it performant or governance or secure? That's another challenge, right? So there's a, how do you make it measurable? I mean, monitor all those things. So I mean, if you take three or four core tenants, that's what the 21st century is 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 tenants into the data so that as the data grows, the applications can also scale? Big task, right? So if you look at the industries that have been disrupted, taxis, hotels, books, advertising, retail, thank you, yeah. Maybe less, 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. So 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 we're going to wait and see. That seems to me to be a recipe for disaster. What are your thoughts on that? So 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 machine can basically look at an image and say what it is, right? We have Watson for cancer oncology. I mean, we have 400 to 500,000 patients being treated by Watson. So AI is changing not just from an enterprise perspective but from a socioeconomic perspective and 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, 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 sort of be a silo buster? So a 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 life cycle, 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 visualization or 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? So that whole, the data becomes the base layer, but then there's a whole life cycle 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 you 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 things to compete. If you look at these disruptions in taxis and retail, I mean 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. Right, it's a huge challenge. I mean how do we take the data and create insight out of the data and not just existing data, right? The data is multiplying by the second. I mean every second, petabytes or set of bytes 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 on structured 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 new coming data, how do you predict what do you want to come out of that? I heard, I mean it's 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 I mean, so you would 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. Right, I mean, so if you look at, I look at as 3Ds, 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. I mean, if you look at just the predictive piece, I mean, look at from the 80s to now, I mean, you look at machine, 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? And 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. So everything is changing. So how do you bring, as an enterprise, you keep up a float 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 60s and 70s. It's not an easy task. I mean, 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. Yeah. And 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. He said, thank God I'm not in the technology industry. Things change so fast. I'm so glad I'm not a software company. And Rob's reaction was, hang on, 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 Emelt said I went to bed and 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 in process that always hold us up, right? I mean, technology is one piece and that's where I think companies like IBM make a huge difference. It's like, you understand enterprise because you bring the 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 their architecture, all those things. And that involves tremendous amount of work, skills and knowledge. Job security. Yeah. All right, 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 after this.