>> Announcer: Live from New York City, it's The Cube, covering IBM data science for all. Brought to you by IBM. >> Welcome back to data science for all. This is IBM's event here on the west side of Manhattan, here on The Cube. We're live, we'll be here all day, along with Dave Vallente, I'm John Walls Poor Dave had to put up with all that howling music at this hotel last night, kept him up 'til, all hours. >> Lots of fun here in the city. >> Yeah, yeah. >> All the crazies out last night. >> Yeah, but the headphones, they worked for ya. Glad to hear that. >> People are already dressed for Halloween, you know what I mean? >> John: Yes. >> In New York, you know what I mean? >> John: All year. >> All the time. >> John: All year. >> 365. >> Yeah. We have with us now the head of data science, and the VP at Galvanize, Nir Kaldero, and Nir, good to see you, sir. Thanks for being with us. We appreciate the time. >> Well 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, and I know data science is your baby. >> Nir: Right. >> 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 built 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 layers of the business, from the executives, to managers, to data scientists, and data analysts, and kind of transform and upscale all current skills to be modern, to be digital, so companies can actually go through this transformation. >> Hit on one of those items you talked about, data driven. >> Nir: Right. >> It seems like a no-brainer, right? That the more information you give me, the more analysis I can apply to it, the more I can put it in my business practice, the more money I make, the more my customers are happy. It's a lay up, right? >> Nir: It is. >> What is a data driven organization, then? Do you have to convince people that this is where they need to be today? >> Sometimes I need to convince them, but (laughs) anyway, so let's back up a little bit. We are in the midst 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 this very sophisticated machine intelligence models. If the organization immediately can best respond to all of that, companies will be able to enhance the user experience, get insight about their customers, enhance performances, and 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 you know, we can talk more about the transformation, and what it consisted of. Do you want me to? >> John: 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 a buzz word. I think more appropriately, we should call it machine intelligence industrial revolution. >> Okay. I've got a lot of questions, but carry on. >> So hitting on that, so you see that as being a major era. >> Nir: It's a game changer. >> If you will, not just a chapter, but a major game changer. >> Nir: Yup. >> Why so? >> So, okay, I'll jump in again. Machines have always replaced man, people. >> John: The automation, right. >> Nir: To some extent. >> But certain machines have replaced certain human tasks, let's say that. >> Nir: Correct. >> But for the first time in history, this fourth era, machine's are replacing humans with cognitive tasks, and that scares a lot of people, because you look at the United States, the median income of the U.S. worker has dropped since 1999, from $55,000 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. You know, Galvanize is an organization that plays a critical role in helping deal with that problem, does it not? >> So, as Mark Zuckerberg says, there is a lot of hate love relationship with A.I. 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 actually are afraid from it. I read a survey a few weeks ago that says that 36% of the population thinks that A.I. will destroy humanity, and will conquer the world. That's a fact that's 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 talk about it forever, but just my belief is that machines can actually replace some of our responsibilities, right? Not necessarily take 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 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? >> Billable 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 replaces some of the responsibilities, like auditing, or writing contracts, or looking at previous cases. >> Menial labor, if you will. >> Yes, but you know, for example, the machine is not that great right now with negotiations skills. So maybe in the future, the job of the lawyer will be mostly around negotiation skills, rather than writing contracts, 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 those. >> Well, one of my favorite stories, when people bring up this topic, is when Gary Kasparov lost to the IBM super computer, Blue Jean, or whatever it's called. >> Nir: Yup. >> Instead of giving up, what he said is he started a competition, where he proved that humans and machines could beat the IBM super computer. So to this day has a competition where the best chess player in the world is a combination between humans and machines, and so it's that creativity. >> Nir: Imagination. >> Imagination, right, combinatorial effects of different technologies that education, hopefully, can help keep those either 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 a very strong, powerful machine intelligence model that can actually replace us as humans, and outbid us. We can speak about Jeopardy, and what's on, and we can speak about AlphaGo, it's a Google company that kind of outperformed the world champion. These are very specific tasks, right? Again, like the lawyer, the machines can write beautiful contracts with NLP, machines can look at millions and 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 when you think about a digital business, 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. >> Dave: Would you agree most are not? >> I will agree that most companies say that they are data driven, but actually they're not. I work with a lot of Fortune 500 companies on a daily basis. I meet their executives and 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 truly just ask them if they put data and decisions in 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 in building what we say healthy data culture in order to enable and become data driven. Part of it is democratization of data, right? Currently what I see if lots of organizations actually open the data just for the analyst, or the marketers, people who kind of make decisions, that need to make decisions with data, but not throughout the entire organization. I know I always say that everyone in the organization makes decisions on a daily basis, from the barista, to the CEO, right? And the entirety 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 decisions on a daily basis, and companies don't excel yet in doing it. Not every company is as 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 want 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 try at Galvanize to address this problem from executive type of education, where we actually meet with the C-level executives 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 a big transformation in 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? >> Yeah. >> Well it's hard to predict that, and I'm not a machine, or what's on it. (laughs) >> 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 will be even more, with more intelligence, with more data. >> So the reason I ask, is there was an article I saw and linked, and I haven't had time to read it, but 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, the author said, if I had to predict one that would survive, it would be Amazon, to your point, because they are so data driven. The premise, again I didn't read the whole thing, was that some new data driven, digital upstart will disrupt them. >> Yeah, and you know, companies like Amazon, and Alibaba lately, that try kind of like in a competition with Amazon about who is becoming more data driven, utilizing more machine intelligence, are the ones that invested in these capabilities many, many years ago. It's no that they started investing in it last year, or five years ago. We speak about 15 and 20 years ago. So companies who were really a pioneer, and invested very early on, will predict actually to survive in the future, and you know, very much align. >> Yeah, I'm going to touch on something. It might be a bridge too far, I don't know, but you talk about, Dave brought it up, about replacing human capital, right? Because of artificial intelligence. >> Nir: Yup. >> Is there a reluctance, perhaps, on behalf of executives to embrace that, because they are concerned about their own price? >> Nir: You should be in the room with me. (laughing) >> You provide data, but you also provide that 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, (John and Dave laugh) but the fact is well known. Leaders who will not embrace these skills, and understanding, and will help the 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 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 a change in culture and habits. So it has to start from the top. The journey has to start from executive, from educating and 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 spoke about data driven, we mainly speaks about the culture aspect here, not specifically about the technical side of it. So it has to come from the top, leaders have to incorporate it in the organization, the have to give authority and power for people, they have to put the funding at first, and then, this is how it's beautiful, that you actually see it trickles down to the organization when they have a very powerful CEO that makes a decision, and moves the organization quickly to become data driven, make executives 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 is making a decision, 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? >> Yup. >> Follow the leader. >> Yup, follow the leader. >> Thanks for being with us. >> Nir: Of course, it's my pleasure. >> Pinned this interesting love hate thing that we have going on. >> We should address that. >> Right, right. That's the next segment, how about that? >> Nir Kaldero from Galvanize joining us here live on The Cube. Back with more from New York in just a bit.