 From New York City, it's theCUBE. Covering IBM Data Science for All. Brought to you by IBM. This is IBM's event here on the west side of Manhattan. Here on theCUBE, you're live. We'll be here all day along with Dave Vellante. I'm John Walls, for Dave had to put up with all that howling music at this hotel last night. Kept them up till all hours. Lots of fun here in the city. Crazy's out last night. Yeah, but the headphones, they worked for you. Glad to hear that. People are already dressed for Halloween, you know. Yes. In New York, you know what I mean? All year, all year. 365, yeah. We have with us now the head of data science and VP at Galvanize near Caldero, and they're good to see you, sir. Thanks for being with us. We appreciate the time. Oh, of course, my pleasure. Tell us about Galvanize. I know you're heavily involved in education in terms of the tech community, but you've got corporate clients, you've got academic clients. You cover the waterfront. I know data science is your baby. But tell us a little bit about Galvanize and your mission there. Sure, so Galvanize is the learning community for technology. We provide the training in data science, data engineering, and also modern software engineering. We recently have, we build a very large, fast growing enterprise corporate training department where we basically help companies become digital, become nimble, and also very data driven so they can actually go through this digital transformation and survive in this fourth industrial revolution. We do it across all layer of the business, from the executives to managers, to data scientists and data analysts, and kind of like transforming up skill, all current skills to be modern, to be digital, so companies can actually go through this transformation. So you had on one of those items that you talked about data driven. It seems like a no brainer, right? The more information you give me, the more analysis I can apply to it, the more I can put it into my business practice, the more money I make, the more my customers are happy. It's a layup, right? It is. What is a data driven organization? And why do you have to, or do you have to convince people that this is where they need to be today? Sometimes I need to convince them, but anyway, so let's back up a little bit. We are the myths of the fourth industrial revolution. And in order to survive in this fourth industrial revolution, companies need to become nimble, as I said, become agile, but most importantly, become data driven. So the organization can actually best respond to all the predictions that are coming from these very sophisticated machine intelligence models. If the organization immediately can best respond to all of that, companies will be able to enhance their user experience, get insight about their customers, enhance performances, et cetera. And we know that the winners in this revolution, in this era, will be companies who are very digital, that master the skills of becoming a data driven organization. And we can talk more about the transformation and what it consisted of. Do you want me to? Sure. Can I just ask you a question? This fourth wave, this is what, the cognitive machine wave, or how would you describe it? Some people call it artificial intelligence. I think artificial intelligence is like big data, kind of like buzzword. I think more appropriately, we should call it machine intelligence, industrial revolution. Okay, I got a lot of questions, but carry on. But so, hit on that, so you see that as being a major era. It's a game changer. If it will, not just a chapter, but a major game changer. Yep. Why so? So, okay, I'll jump in again. Oh, you're fine. Machines have always replaced man. Automation, right? Certainly does. To some extent. Theme. But certain machines have replaced certain human tasks. Let's say that. Correct. But for the first time in history, this fourth era, machines are replacing humans with cognitive tasks. So, and that scares a lot of people. It does. Because, you look at the United States, the median income of U.S. worker has dropped since 1999 from 55,000 down to 52,000. And a lot of people believe it's sort of the hollowing out of that factor that we just mentioned. Education, many believe, is the answer. Galvanize is an organization that plays a critical role in helping deal with that problem. So, as Mark Zuckerberg says, there is a lot of hate-love relationship with AI. People love it on one side because they're excited about all the opportunities that can come from this utilization of machine intelligence. But many people are actually afraid from it. I read a survey a few weeks ago that says that 36% of population thinks that AI will destroy humanity and will conquer the world. If, you know, that's affected what people think. If I think it's going to happen, I don't think so. I highly believe that education is one of the pillars that can address this fear for machine intelligence. And you spoke a lot about jobs. I can talk about it forever. But just, my belief is that machine can actually replace some of our responsibilities, right? And not necessarily takes and replace the entire job. Let's talk about lawyers, right? Lawyers currently spend between 40 to 60% of the time writing contracts or looking at previous cases. The machine can write a contract in two minutes or look up at millions of data points of previous cases in zero time. Why a lawyer today needs to spend 40 to 60% of the time on that? It's available hours, that's why. It is. So, I don't think the machine will replace the job of the lawyer. I think in the future, the machine will replace some of the responsibilities like auditing or writing contracts, looking at previous cases. Minial labor, if you will. Yes, but you know, for example, the machine is not that great right now with negotiation skills. So maybe in the future, the job of the lawyer will be mostly around negotiation skills rather than writing contracts and et cetera. But yeah, you're absolutely right. There is a big fear in the market right now among executives, among people in the public. I think we should educate people about what is the true implications of machine intelligence in this fourth industrial revolution and era. And education is definitely one of that. Well, and one of my favorite stories when people bring up this topic is when Gary Kasparov lost to the IBM Supercomputer Blue Gene or whatever it was called. Instead of sort of giving up what he said is he started a competition where he proved that humans and machines could beat the IBM Supercomputer. So to this day has a competition where the best chess player in the world is a combination of humans and machines. And so it's that creativity. Imagination. Imagination, right. Combinatorial effects of different technologies. That education hopefully can help people see the way. Look, I'm a big fan of neuroscience. I wish I did my PhD in neuroscience. But we are very, very far away from understanding how our brain works. Now to try to imitate the brain when we don't know how the brain works, we are very far away from being in a place where a machine can actually replicate and really best respond like a human. We don't know how our brain works yet. So we need to do a lot of research on that before we actually really write very strong, powerful machine intelligence models that can actually replace us as humans and outbeat us. Not, you know, we spoke about, we can speak about jeopardy in what's on. We can speak about AlphaGo, you know, it's a Google company that kind of like outperformed the world champion. These are very specific tasks, right. Again, like the lawyer, the machine can write beautiful contracts with NLP. Machine can look at millions of, trillions of data and figure out what's the conclusion there, right. Or summarize text very fast, but not necessarily good in negotiation yet, so. So when you think about a digital business, you know, to us a digital business is a business that uses data to differentiate and serve customers and maintain customers. So when you talk about data driven, it strikes me that when everybody's saying, digital business, digital transformation, it's about a data transformation. How well they utilize data. And if you look at the bell curve of organizations, most are not. Everybody wants to be data driven. Many say they are data driven. Right. Would you agree most are not? So I will agree that most of companies says that they are data driven, but they are surely not. I work with a lot of Fortune 500 companies on a daily basis. I meet their executives and, you know, functional leaders and actually see their data and business problems that they have. Most of them do tend to say that they are data driven, but surely if you just ask them if they put data and decision at the same place, every time they have to make a decision, they don't do it. It's a habit that they don't yet have. Companies need to start investing, building what we say a healthy data culture in order to enable and become data driven. Part of it is, you know, democratization of data. Right. Currently what I see is lots of organization actually open the data just for the analysts or the marketers. People who kind of like make decisions that needs to make decisions with data, but not throughout the entire organization. You know, I always says that everyone in the organization makes decisions on a daily basis from the barista to the CEO, right? And the entire idea of becoming data driven is that data can actually help us make better decisions on a daily basis. So how about democratizing the data to everyone? So everyone from the barista to the CEO can actually make better decision on a daily basis. And companies don't excel yet in doing it. Not every company is digital as Amazon. Amazon I think is actually one of the most digital companies in the world. If you look at the digital index, not everyone is Google or Facebook. Most companies wants to be there. Most companies understand that they will not be able to survive in this era if they will not become data driven. So it's a big problem. We tried Galvanize to address this problem from executive type of education where we actually meet with the C level executive in companies and actually guide them through how to write their data strategy, how to think about prioritizing data investment to actual implementation of that. And so far we are highly successful. We were able to make big transformation in a very large important organizations. So I'm actually very proud of it. How long are these eras? Is it a century or more? This fourth industrial era. Well, it's hard to predict that. And I'm not a machine or what's on there. But certainly more than 50 years, would you say? Or maybe not, I don't know. I actually don't think so. I think it's going to be fast and we're going to move to the next one pretty soon that it will be even more, with more intelligence, with more data. So the reason I ask is there was an article I saw on LinkedIn, I have time to read it, but it was, it talked about the four horsemen, Amazon, Google, Facebook, and Apple. And it said they will all be out of business in 50 years. Now, I don't know. I think Apple probably has 50 years of cash flow in the bank, but then they said the one, if the author said if I had to predict one that would survive, it would be Amazon to your point because they're so data driven. And the premise, again, I didn't read the whole thing, was that some new data driven, digital upstart will disrupt them. Yeah, and companies like Amazon and Alibaba lately, that's kind of like in competition with Amazon about who is becoming more data driven, utilizing more machine intelligence, are the one that invested in these capabilities many, many years ago. It's not that they started invested last year or five years ago, we speak about 15 and 20 years ago. So companies who are really a pioneer and invested very early on will predict actually to survive in the future and very much aligned. Yeah, I wanted to touch on something that might be a bridge too far, I don't know, but you talk about, and Dave brought it up, about replacing human capital, right, because of artificial intelligence. Is there a reluctance perhaps on behalf of executives to embrace that, because they are concerned about their own place, because you might- You should be in the room with me. You might be okay. That you provide data, but you also provide the capability to analyze and make the best informed decision and therefore eliminate the human element of a C suite executive that maybe they're not as necessary today or tomorrow as they were two years ago. So it is absolutely true, and there is a lot of fear in the room, especially when I show them robots, they freak out typically. But the fact is well known. Leaders who will not embrace these skills and understanding and will help their organization to become agile, nimble and data-driven will not survive. They will be replaced. So on the one hand, they're afraid from it. On the other side, they see that if they will not actually do something and take an action today, they might going to be replaced in the future. Where should organizations start? Hey, I want to be data-driven. Where do I start? That's a good question. So data science, machine learning is a top-down initiative. It requires a lot of funding. It requires change in culture and habits. So it has to start from the top. It has to, the journey has to start from executive, from educating executive about what is data science, what is machine learning, how to prioritize investments in this field, how to build data-driven culture, right? When we talk about data-driven, we mainly speak about the culture aspect here, not specifically about the technical side of it. So it has to come from the top. Leaders has to encode that in the organization. They have to give authority and power for people. They have to put the funding at first. And then you see, this is how it's beautiful that you actually see it trickles down to the organization when you have a very powerful CEO that make a decision and move the organization quickly to become data-driven, make executive, look at data every time they make a decision, get them into the habit. When people look up to executives, they try to do the same. And if my boss is an example for me, someone who is looking at data every time he makes decisions, ask the right questions, know how to prioritize, set the right goals for me, this helps me and helps the organization better perform. Follow the leader, right? Follow the leader. Yeah, follow the leader. Thanks for being with us. Of course, it's my pleasure. And this interesting love-hate thing that we have going on, got a lot. And we should address that. Right, right, that's just, next segment. How about that? Nier Khadero, Galvanize, joining us here live on theCUBE, back with more from New York in just a bit.