>> From around the globe, It's theCUBE. With digital coverage of IBM Think 2021, brought to you by IBM. >> Hi, welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier host of the CUBE. We got a great guest here. Brian Bouchard is the co-founder president and CEO of Alacrinet. Brian great to see you remoting in all the way from Puerto Rico to Palo Alto. >> That's right. >> Great to see you. >> Thanks for First of all, thanks John, for having me. I really appreciate the opportunity. >> Yeah, great to see you. Thanks for coming on. First of all, before we get into what you guys do and and how this all ties in to Think. What do you guys do at Alacrinet? Why the name? A it's good you're at the top of the list and alphabetically, but tell us the, the, the the secret behind the name and what you guys do. >> So, first of all Alacrinet is based on the root word alacrity which means a prompt and willing, a prompt a joyous prompt to, excuse me, to achieve a common goal. So we ultimately are a network of individuals with the traits of alacrity. So Alacrinet. So that's our name. >> Great. So what's your relationship with IBM and how you guys have been able to leverage the partnership program in the marketplace? Take us through the relationship. >> So, well, first of all Alacrinet is a platinum IBM business partner and it was awarded recently the 2020 IBM North American partner of the year award. And we were selected amongst 1600 other business partners across North America. We've been actually a consulting, an IT consulting company for almost 20 years now. And we were founded in 2002 in Palo Alto and we have focused specifically on cyber security since 2013. And then as part, go ahead. >> What are some of the things that you guys are working on? Because obviously, you know, the business is hot right now. Everyone's kind of looking at COVID saying we're going to double down on the most critical projects and no time for leisurely activities when it comes to IT. And cloud scale projects, you know mission critical stuff's happening what are you guys working on? >> So we're, we're focused on cybersecurity, our security services really compliment IBM's suite of security solutions and cover the full spectrum from our research and penetration testing, which helps identify vulnerabilities before a breach occurs. And we also have managed security services which helps prevent, detect and remediate attacks in real time. And then finally, we also have a security staffing division and a software resell division, which kind of rounds out the full amount of offerings that we have to provide protection for our clients. >> What are some of the biggest challenges you guys have as a business, and how's IBM helping you address those? >> Well, as you know, John, we all know the importance of cybersecurity in today's world, right? So it's increasing in both demand and importance and it's not expected to wane anytime soon. Cyber attacks are on the rise and there's no there's no expected end in sight to this. And in fact, just this week on 60 minutes, Jay Powell, the chairman of the federal reserve board he noted that cyber attacks were the number one threat to the stability of the US economy. Also this week, a public school in Buffalo New York was hacked with ransomware and the school you know, this, the school district is just contemplating you know, paying the ransom to the hackers. So there's literally thousands of these attacks happening every day, whether it's in local school district or a state government, or an enterprise even if you don't hear about them, they're happening In adding to the complexity that the cyber attackers pose is the complexity of the actual cybersecurity tools themselves. There isn't a single solution provider or a single technology, that can ensure a company's security. Our customers need to work with many different companies and disconnected tools and processes to build an individual strategy that can adequately protect their organizations. >> You know, I love this conversation whenever I talk to practitioners on cybersecurity, you know that first of all, they're super smart, usually cyber punks and they also have some kinds of eclectic backgrounds, but more importantly is that there's different approaches in terms of what you hear. Do you, do you put more if you add more firefighters, so to speak to put out the fires and solve the problems? Or do you spend your time preventing the fires from happening in the first place? You know, and you know, the buildings are burning down don't make fire fire, don't make wood make fire resistance, you know, more of a priority. So there's less fires needing firefighters So it's that balance. You throw more firefighters at the problem or do you make the supply or the material the business fireproof, what's your take on that? >> Yeah, well, it kind of works both ways. I mean, we've seen customers want it. They really want choice. They want to, in some cases they want to be the firefighter. And in some cases they want the firefighter to come in and solve their problems. So, the common problem set that we're seeing with our that our customers encounter is that they struggle one, with too many disparate tools. And then they also have too much data being collected by all these disparate tools. And then they have a lack of talent in their environment to manage their environments. So what we've done at Alacrinet is we've taken our cybersecurity practice and we've really specifically tailored our offerings to address these core challenges. So first, to address the too many disparate tools problem, we've been recommending that our clients look at security platforms like the IBM Cloud Pak for security the IBM Cloud Pak for security is built on a security platform that allows interoperability across various security tools using open standards. So our customers have been responding extremely positively to this approach and look at it as a way to future-proof their investments and begin taking advantage of interoperability with, and, tools integration. >> How about where you see your business going with this because, you know, there's not a shortage of need or demand How are you guys flexing with the market? What's the strategy? Are you going to use technology enablement? You're going to more human driven. Brian, how do you see your business unfolding? >> Well, actually really good. We're doing very well. I mean, obviously we made the, the top the business partner for IBM in 2020. They have some significant growth and a lot of interest. I think we really attack the market in a, in a with a good strategy which was to help defragment the market if you will. There's a lot of point solutions and a lot of point vendors that various, you know, they they spent specialized in one piece of the whole problem. And what we've decided to do is find them the highest priority list, every CSO and CIO has a tick list. So that how that, you know, first thing we need we need a SIM, we need an EDR, we need a managed service. We need, what's the third solution that we're doing? So we, we need some new talent in-house. So we actually have added that as well. So we added a security staffing division to help that piece of it as well. So to give you an idea of the cybersecurity market size it was valued at 150 billion in 2019 and that is expected to grow to 300 billion by 2027. And Alacrinet is well-positioned to consolidate the many fragmented aspects of the security marketplace and offer our customers more integrated and easier to manage solutions. And we will continue to help our customers select the best suite of solutions to address all types of cybersecurity, cybersecurity threats. >> You know, it's it's such a really important point you're making because you know, the tools just have piled up in the tool shed. I call it like that. It's like, it's like you don't even know what's in there anymore. And then you've got to support them. Then the world's changed. You get cloud native, the service areas increasing and then the CSOs are also challenged. Do I, how many CLAWs do I build on? Do I optimize my development teams for AWS or Azure? I mean, now that's kind of a factor. So, you have all this tooling going on they're building their own stuff they're building their own core competency. And yet the CSO still needs to be like maintaining kind of like a relevance list. That's almost like a a stock market for the for the products. You're providing that it sounds like you're providing that kind of service as well, right? >> Yeah, well, we, we distill all of the products that are out there. There's thousands of cybersecurity products out there in the marketplace and we kind of do all that distillation for the customer. We find using, you know, using a combination of things. We use Forrester and Gartner and all the market analysts to shortlist our proposed solutions that we offer customers. But then we also use our experience. And so since 2013, we've been deploying these solutions across organizations and corporations across America and we've, we've gained a large body of experience and we can take that experience and knowledge to our customers and help them, you know, make make some good decisions. So they don't have to, you know, make them go through the pitfalls that many companies do when selecting these types of solutions. >> Well congratulations, you've got a great business and you know, that's just a basic search making things easier for the CSO, more so they can be safe and secure in their environment. It's funny, you know, cyber warfare, you know the private companies have to fight their own battles got to build their own armies. Certainly the government's not helping them. And then they're confused even with how to handle all this stuff. So they need, they need your service. I'm just curious as this continues to unfold and you start to see much more of a holistic view, what's the IBM angle in here? How, why are you such a big partner of theirs? Is it because their customers are working with you they're bringing you into business? Is it because you have an affinity towards some of their products? What's the connection with IBM? >> All of the above. (chuckles) So I think it probably started with our affinity to IBM QRadar product. And we have, we have a lot of expertise in that and that solution. So that's, that's where it started. And then I think IBM's leadership in this space has been remarkable, really. So like what's happening now with the IBM Cloud Pak for security you know, building up a security platform to allow all these point solutions to work together. That's the roadmap we want to put our customers on because we believe that's the that's the future for this, this, this marketplace. >> Yeah. And the vision of hybrid cloud having that underpinning be with Red Hat it's a Linux kernel, model of all things >> Yeah. Super NetEase. >> Locked in >> It's portable, multiple, you can run it on Azure. IBM Cloud, AWS. It's portable. I mean, yeah, all this openness, as you probably know cyber security is really a laggard in the security in the information technology space as far as adopting open standards. And IBM is I think leading that charge and you'll be able to have a force multiplier with the open standards in this space. >> Open innovation with open source is incredible. I mean, if you, if, if if open source can embrace a common platform and build that kind of control plane and openness to allow thriving companies to just build out then you have an entire hybrid distributed architecture. >> Yeah. Well, I think companies want to use the best in breed. So when we, when we show these solutions to customers they want the best in breed. They always say, I don't, when it comes to security they don't want second best. They want the best it's out there because they're securing their crown jewels. So that makes sense. So the problem with, you know having all these different disparate solutions that are all top in their category none of them talk to each other. So we need to address that problem because without that being solved, this is just going to be more it's going to compound the complexity of the problems we solve day to day. >> Awesome. Congratulations, Brian, great story. You know entrepreneur built a great business over the years. I think the product's amazing. I think that's exactly what the market needs and just shows you what the ecosystem is all about. This is the power of the ecosystem. You know, a thousand flowers are blooming. You got a great product. IBM is helping as well. Good partnership, network effects built in and and still a lot more to do. Congratulations. >> Absolutely. >> Okay. >> Thank you very much >> Brian Bouchard >> Made my impression. I appreciate that >> Thanks for coming on theCUBE Appreciate it. I'm John Furrier with IBM thinks 2021 virtual coverage. Thanks for watching. (outro music plays)

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you >>by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john ferrier host of the Q. We've got a great guest here brian Bouchard, co founder president and ceo of Allah credit brian, great to see you um promoting it all the way from Puerto rico to Palo Alto. >>Great to >>see. First of all. Thanks for having me. I really appreciate the opportunity. >>Yeah great, Great to see you. Thanks for coming on. Um first of all, before we get into what you guys do and how this all ties in to think what do you guys do? It Alex Burnett, Why the name uh is good, you're at the top of the list and alphabetically, but tell us the secret behind the name and what you guys do. >>So first of all, a crochet is based on the root word alacrity, which means a prompt and will prompt a joyous prompt itude excuse me to achieve a common goal. So we ultimately our network of >>individuals with >>the traits of alacrity. So eloquent. So that's our name. >>Great. So what's your relation with IBM and how you guys been able to leverage the partnership program in the marketplace take us through the relationship >>so Well, first of all, L. A. Quartet is a platinum IBM business partner and was awarded recently the 2020 IBM north american Partner of the Year award. And we were selected among 1600 other business partners across North America. We've been actually a consulting an IT. consulting company for almost 20 years now and we were founded in 2002 in Palo Alto. And we have focused specifically on cybersecurity since 2013. What is >>Right, what are some of the things you guys are working on? Because obviously, you know, the business is hot right now, everyone's kind of looking at Covid saying we're gonna double down on the most critical projects and no time for leisurely activities when it comes to I T and cloud scale projects, you know, mission critical stuff is happening. What are you guys working on? >>So we're focused on cybersecurity. Our our security services really complement IBM suite of security solutions and cover the full spectrum from our research and penetration testing, which helps identify vulnerabilities before it reach occurs. And we also have managed security services which helps prevent detect and remediate attacks in real time. >>And then finally, we also have a security staffing division and a software resale division which kind of rounds out the full amount of offerings that we have to provide protection for our clients. >>What are some of the biggest challenges you guys have as a business and house IBM helping you address those? >>Well, as you know, john, we all know that the importance of cyber security in today's world, so it's increasing in both demand and importance and it's not expected to wait any time soon. Cyber attacks are on the rise and there's >>no >>Uh there's no expected end in sight to this and in fact just this week on 60 minutes, uh, the Jay Powell, the chairman of the Federal Reserve Board, he noted that cyber attacks were the number one threat to the stability of the US. economy. >>Also this week, >>a public school in Buffalo new york was hacked with ransomware >>and the school, this uh, >>the school district is just contemplating you're paying the ransom to the hackers. So there's literally thousands of these attacks happening every day, whether it's in a local school district or state government or an enterprise, even if you don't hear about them, they're happening. And adding to the complexity that the cyber Attackers pose is the complexity of the actual cybersecurity tools themselves. There isn't a single solution provider or single technology that could ensure a company security. Our customers need to work with many different companies and disconnected tools and processes to build an individual strategy that can adequately protect their organizations. >>You know, I love this conversation whenever I talked to practitioners, uh, cybersecurity, you know, first of all, they're super smart, usually cyber punks, and they also have some kind of eclectic background, but more importantly, is that there's different approaches in terms of what you hear. Do you do you put more if you add more firefighters so to speak, to put out the fires and solve the problems? Or do you spend your time preventing the fires from happening in the first place? You know, and you know, the buildings are burning down, Don't make a fire fire uh don't make would make fire resistance, you know, more of a priority. So there's less fires, not firefighters. So it's that balance. You throw more firefighters at the problem or do you make the supply or the material, the business fireproof? What's your take on that? >>Well, it kind of works >>both ways. I mean, we've seen customers want to, they really want choice. They >>wanna, in some >>cases they want to be the firefighter and in some cases they want the firefighter to come in and solve their problems. So >>the common problem set that we're seeing with our our customers encounter is that they struggle one with too many disparate tools and then they also have too much data being collected by all these disparate tools and then they have a lack of talent in their environment to manage their environment. So what we've done at Lacqua net is we've taken our cybersecurity practice and we've really uh specifically tailored our offerings to address these court challenges. So first to address the too many disparate tools problem, uh We've been recommending that our clients look at security platforms like the IBM cloud pack for security. The IBM cloud fax for security is built on a security platform that allows interoperability across various security tools using open standards. So our customers have been responding extremely positively to this approach and look at it as a way to future proof their investments >>and begin taking advantage of >>interoperability with >>hand tools integration. >>Talk about what you see your business going with with this because you know there's not a shortage of of need um demand. Um How are you guys flexing with the market? Uh What's the strategy are you going to use technology enablement? You're gonna more human driven brian how do you see your business of unfolding >>Well? Actually really good. We're doing very well. I mean obviously we've made the top business partner for IBM in 2020. Um we have some significant growth and a lot of interest I think we really attacked the market in a good strategy which was to help defragment the market if you will. There's a lot of point solutions and a lot of point vendors that you know they they spent uh specialize in one piece of the whole problem and what we've decided to do is find them the highest party list. Every see so and see IO has a tick list. So >>they have that >>you know uh first thing we need we need a sim we need a E. D. >>Are we need a >>managed service? We need um what's the third solution that we're doing? So we need some new talent in house. So we actually have the added that as well. So we added a security staffing uh division to help that piece of it as well. So to give you an idea of the cybersecurity market size, It was valued at 150 billion in 2019. And that is expected to grow to 300 billion by 2027. >>And Akron is well positioned to consolidate the many fragmented aspects of the security marketplace and offer our customers more integrated and easier to manage solutions. And we will continue to help our customers select the best suite of solutions to address all types of cyber security, cyber security threats. >>You know, it's such a really important point you're making because, you know, the tools just piled up in the tool shed, I call it like that, It's like, it's like you don't even know what's in there anymore and then you've got to support them, then the world's changed, get cloud native, the service area is increasing and then the CSOs are also challenged. Do I have any clouds? Do I build on? Do I optimize my development teams for AWS or Azure? Now, that's kind of a factor. So you have all this tooling going on? They're building their own stuff, they're building their own core competency. And yet the sea so still needs to be like maintaining kind of like a relevance list. That's almost like a stock market for the, for the products you're providing, that it sounds like you're providing that kind of service. >>Uh, yeah, as well. Right? We distill all of the products that are out there, there's thousands of cybersecurity products out there in the marketplace and we kind of do all that distillation for the customer we find using, you know, using a combination of things we use uh Forrester and Gartner and all the market analysts to shortlist are, are solutions that we offer customers. But then we also use our experience. And so through since 2013, we've been deploying these solutions across organizations and corporations across America and we've gained a large body of experience and we can take that experience and knowledge to our customers and help them make some good decisions. So they don't have to make them go through the pitfalls that many companies do when selecting these types of solutions. >>Well, congratulations, got a great business and uh you know, that's just a basic, starts making things easier for the sea. So more so they can be safe and secure in their environment. It's funny, you know, cyber warfare, you know the private company have to fight their own battles, going to build their own armies. Certainly the government's not helping them and they're confused even know how to handle all this stuff. So they didn't they need your service. I'm just curious as this continues to unfold and you start to see much more of a holistic view. What's the IBM angle in here? Why are you such a big partner of theirs? Is it because their customers are working with you? They're bringing you into business? Is it because you have an affinity towards some of their products? What's the connection with IBM, >>all of the above? So >>I think it probably started with our affinity to IBM P radar products and we have a we have a lot of expertise in that in that solution. Um, so >>that's that's where it >>started. And then I think I B. M. S leadership in this space has been, Yeah, >>remarkable. Really. So like what's happening now with the IBM compaq for security, building a security platform to allow all these points solutions to work together. Uh that's the road map we want to put our customers on because we believe that's the that's the future for this, this uh, this marketplace >>and the vision of hybrid cloud having that underpinning be with red hat, it's a Lennox Colonel model of >>all things you can you can run it on. Sure. I've been plowed uh aws it's portable. Yeah. All this openness, as you probably know, uh, cybersecurity is really a laggard in the security and the information technology space as far as adopting open standards and IBM is I think leading that charge and you'll be able to have a force multiplier >>uh >>with open standards in the space. >>Open innovation with open source is incredible. I mean if you if if open source can embrace a common platform and build that kind of control, playing and openness to allow thriving companies to just build out, then you have an entire hybrid distributed >>architecture. Yeah, well, I think companies want to use the best in breed. So when we, when we show these solutions to customers, they want the best in breed, they always say, I don't, when it comes to security, they don't want second best. They want the best that's out there because they're securing their crown jewels. So that makes sense. Um, so the problem is having all these different disparate solutions that are all top in their category, none of them talk to each other so we need to address that problem because without that being solved this is just going to be a more, it's going to compound the complexity of the problems we solve day to day, >>awesome, congratulations brian, great story. Um you know entrepreneur built a great business over the years um I think the products amazing, I think that's exactly what the market needs and it just shows you what the ecosystems all about. This is the power of the ecosystem. You know 1000 flowers are blooming, you got a great product. IBM is helping as well. Good partnership network effect builds in and and still a lot more to do. Congratulations. >>Absolutely. Okay thank you very much >>brian thanks >>for coming on the q appreciate it. I'm Sean Fourier with IBM thinks 2021 virtual coverage. Thanks for watching. Mhm.

(funky music) >> Hi, and welcome to this CUBE conversation from theCUBE Studios in Paulo Alto, California. I'm your host, Sonia Tagare, and today we're joined by Leigh Phillips, president and CEO of SaverLife. Leigh, welcome to theCUBE. >> Hi, thanks so much for having me. >> Absolutely. So, tell us more about SaverLife and how it works. >> So, SaverLife is a non-profit organization. We work nationally, but we're based here in San Francisco, and our mission is to help working American families to save money, and to invest in themselves and their futures. So, we do that by making it engaging, rewarding, and fun for people to start saving, and leveraging financial technology to achieve scale. >> And you were previously known as EARN, so what spurred this change in branding? >> Well, it was more than a change in branding. It was actually a big shift towards technology. So, EARN, or, now known as SaverLife, has actually been around since 2001. So, we are not new, we're not a starter, we've been helping low to moderate income working families to save money for a long time. But what we've realized in recent years is that the size of the problem is really quite significant. So, about half of American families don't have $400. So they couldn't cover a $400 expense without having to borrow the money. As EARN, we were helping a lot of families here in the Bay Area, but maybe, you know, a thousand families a year at our peak, and when you have half of America that's financially insecure, we knew that the solution that we had wasn't big enough. So, a couple of years ago, the organization decided to make a pivot, and to make a pivot towards technology. I came onboard about four and a half years ago to lead that transition, and we launched SaverLife as a product, and we reached a quarter of a million people in a couple years, and decided that the people know best, and that we would rebrand the whole organization as SaverLife. So that's kind of how that came about. >> That's awesome. >> Yeah. >> So who is SaverLife specifically targeting, and are there any specific challenges with this target group? >> So, SaverLife is specifically targeting working American families, mostly low income families, so as I mentioned, financial insecurity is a really big problem here in the US, and so we hear about that a lot in the news, about income inequality, wealth inequality, but one of the most troubling statistics came out from the Federal Reserve Bank, they found that about 42% of American families couldn't cover a $400 expense without going into debt. And that's an issue that affects lots of people in different ways. So, SaverLife is really targeting low income people who are struggling to save money, and need a little help getting started with that. So, most of our clients are women, they're all across the United States and on average make about 25 thousand dollars a year or less. >> So let's talk about the current savings crisis in America. According to Bankry, 20% of Americans don't have emergency savings, and only 18% of Americans can live off their savings for only six months. So, tell us more about this crisis, and what do you think the underlying issue is? >> Yeah, it's a great question, and there are many issues that play into that, and most of them are systemic, you know. The way that people are making money and the gap between income and expenses. So, what we see is that larger numbers of people don't have basic emergency savings, and what that means is that you can't get through a financial emergency, right? And so that can have a real downward spiral effect on your life. So imagine a scenario where you have to miss a day or two of work because your child is sick, and you don't have sick leave, like a lot of people don't. And so you miss a couple days of income. Or, you get a flat tire, or a parking ticket. Those are the types of things that can really spiral out of control, so then you lose income, then you can't pay your rent, you're at risk of eviction, and all of these other problems. So what we know is that having relatively small amount of money, so even just 250 to $750 in savings is found to reduce those risks of things like eviction, or falling behind on bills or utilities really significantly. So, we're focused on getting people to that point, so that they can get through challenges. So one of the big things that we see in our population, isn't just that wages are low, which remains a really big problem in the US right now, but that income is really inconsistent. So if you're making an hourly wage job, or maybe you work in retail, or you work in a warehouse, or something like that, and you drive for Uber, whatever the case may be, your money that's coming in, you're not getting the same amount of money in your paycheck every two weeks, right? Like many of us do. And in fact, for SaverLife clients, we're seeing these swings of income of around a thousand dollars a month, month over month. So sometimes you earn more and sometimes you earn less. So in that scenario, it's really hard to stay on track towards saving, because you don't know how much money's coming in, and then you're getting hit with all these increasing expenses at the same time. >> Right. And, can you tell us a little bit about how people can save their way to financial independence, is it viable, and how have challenges changed since the disappearance of defined-benefit retirement packages? >> Yeah, so, it is possible, but it's challenging, and, you know, I do think that we need to be aware of those kind of bigger issues, right? And to focus on helping people have more consistency in their income, and reducing some of those large expenses, whether or not in, the very obviously, the cost of housing, medical care, child care, transportation, all of these things that are really holding families back. But, you know, the good news is that people are remarkable. People are resilient, and people are remarkable. And I can share a couple of stories with you about that. So, at SaverLife we encourage people to save with prizes and cash rewards, right? So we make it really easy for people to get started. We also have a really supportive online community, so this is an issue that affects half of us, right? It's not something that people should be ashamed of. This is a really big and endemic issue here in the US. So we don't judge people, you know, it's all about starting small and starting today. So what we do at SaverLife is encourage people to save what they can when they can, and then we use behavioral economics to design programmatic interventions, so features on the website, that encourage people to save. So you can save five bucks a week if that's what works for you, and then you have the chance to win prizes. We also do a tax time quest, so that's happening right now. So, tax season is one of the times when people will get a larger infusion of cash, right? Particularly low income people, who maybe are qualified for tax credits and other benefits. So, what we do is encourage people to save a portion of that refund. So we ask people to start thinking about it before they get the refund, right? That's really clear, cause once the money is in, it's usually already spent. So we start talking to people in December, why don't you pledge to save your refund? You can win prizes just for pledging. And what we've found is that getting people to think about and commit to savings resulted, last year, in 80% of those people actually putting money into savings, and saving on average 16 hundred dollars from their tax refunds. >> Sonia: Wow. That's incredible. I love how you're incentivizing this whole savings thing, because, like, that essentially just makes people want to do it more. >> Leigh: Yeah. >> So, how should people bucket their savings? Should they have an emergency fund, a college fund, a retirement fund, how should they do that? >> So what we find at SaverLife is, or what we promote, is the idea that your money should really align with your values. And what's important to you, and what you want to achieve for yourself and for your family. So we don't tell people what to save for, and we don't tell them what to spend their money on, right? So, the biggest thing that people save for with the program is emergencies. So, really having that financial cushion, so, your car breaks down, or whatever the case may be, you can take care of it without going into debt, right? 'Cause that's the cycle that we want to avoid. But then we also see people really staying on track to save for big goals. And unsurprisingly, those are still the kind of goals that we talk about a lot in this country. So, an education, for yourself or for your children, and home ownership. Those remain, kind of the most popular things that people are focused on. >> So when it comes to prioritizing how you should save, like especially for someone who's just coming off that one paycheck away from the street, kind of space, how would you recommend prioritizing your savings? >> Leigh: So, we focus on building a savings habit. That's kind of the number one thing that we want people to really think about. So, putting money away as consistently as you can. It's really the behavior change that we're looking to see. And that's why we encourage people to make those small, incremental steps. But we also know that life has a lot of ups and downs, right? Particularly for people who are, as you say, living paycheck to paycheck. And so, what we see in our data is that families are often making two deposits in one withdrawal. So they're putting money away, and then they're using that money when they need it to get through emergencies. So that's kind of the first thing that we really look to do is, once you have that savings habit, and we know it's hard, you know, to do that, especially if you're not making a lot of money at this moment. But that's really, whatever you can save to get into that habit of putting it away. >> And do you think people are more at risk of being one paycheck away from being on the street, or one big bill away from being on the street? >> Leigh: Yeah, absolutely, many people are, you know? And especially here in the Bay Area, right? When life is extremely expensive, the cost of housing is out of control, and those other expenses that people have to deal with. And if you layer on top of that, that inconsistency in people's income, not making a regular amount of money, we're putting a lot of people in a very, very perilous situation. >> Sonia: Right. So let's talk about financial empowerment. You were leading the office of financial empowerment in the city and county of San Francisco. So, tell us more about financial empowerment and why it's important for people to have it. So, you know, I started out working for the city over there for about 11 years, before there was a thing called financial empowerment. And we started working on a range of programs. I worked for the San Francisco treasurer, and what we're really looking to do is use the influence of the city, and the municipal government to try to make a more fair and equitable financial system for people in San Francisco. So we started with programs like Bank On San Francisco, which was access to banking for everybody. So the idea that everyone should be able to have a safe and affordable place to keep their money, and to save their money. So that was a program we worked on there. And then we went on to launch the country's first universal children's savings program. So today, every single kindergartner, actually, today, every single elementary school student in San Francisco has a savings account open for them by the city and county, to encourage families to save early and often, for college. So when we think about financial empowerment, and how local government plays a role, we're really looking at a couple of things. So, do you have the ability to have a safe place to keep your money, and deposit your paycheck, pay your bills, in a way that's affordable, that doesn't have high fees, and is transparent, so that's the first thing. Do you have access to financial education and coaching if you need it? So the city now has quite a robust individual financial coaching and counseling program that they run. Are you able to save and invest in your future? So, save for college, save for home ownership, save for those big things, be a small business owner. And then the fourth thing is, are your assets protected? So are we protecting you from predatory practices that can deplete your wealth? >> And why did you decide to go from the city, from a public organization to a more private organization, like SaverLife? >> Leigh: You know, it was a interesting story. So we had worked with SaverLife when it was known as EARN, at the city. So the organization was actually really closely partnered with us, so I knew them and I knew their work. So there was a couple of reasons. I became really intrigued by this idea that being here in Silicon Valley, we really should start putting the types of technology that are so transformative, really putting that to work for everybody, right? And I had been an advisor, on an advisory board to for-profit fintech starter. And I thought, "Oh, if we could take that type of tech, "and use it to help low income people "build wealth in the US, "that could be really transformative." So that was the first reason. The second reason was really thinking about the scope of this problem, and when you work for the local government, you see that trajectory, that, you know, the traffic ticket that turned into a lost drivers license that turned into a lost job, that turned into an eviction, right? Like, you see those types of issues play out, over and over in people's lives. So the idea that half of America doesn't have four or five hundred bucks, and we could actually do something about that, was really impactful to me. And then the third reason was, you know, I loved working for the San Francisco treasurer, who is amazing, but I kind of felt, as a woman, that I wanted to lead an organization in my own right. And that I had challenged myself that, I had a personal goal that if the opportunity came up, to be that leader that I was going to challenge myself to take it. And so when the opportunity came up, I just went for it. >> And what challenges did you face to become the CEO? >> I think, you know, a lot of the challenges first were within myself, you know? Like, there's a lot that goes into being a non-profit CEO, you know? You have, obviously, you're working on some of the biggest problems that are out there, and you're doing it with so few resources, you know? And so, is that kind of, you know that saying about Ginger Rogers doing everything that Fred Astaire did but backwards and in heels, it's kind of like that, right? You're trying to solve really, really, really big problems that are deeply entrenched, like half of America doesn't have $400. There's a lot of reasons for that, right? And then you're trying to do it by cobbling together philanthropic resources to make that happen. So, I think that was a challenge, like would it be a success? And then at the time, this organization was making in the midst of this massive transformation, you know? So going from seeing clients one on one in the office, to launching and building a scalable tech platform. And I don't have a tech background, you know? I can sometimes use my phone, you know? Like, that's, it's not my thing. But I was able to understand the potential. And so that was what really drew me there to challenge myself to be like, okay, well, there's a lot of people around here that have managed to figure this out, maybe I can figure it out, too. >> Sonia: Yeah, absolutely. So when we talk about people being unbanked, can you tell us more about what unbanked means and what it means for today? >> Leigh: Yeah, so when we talk about access to banking, and mainstream financial services, we usually separate that into two buckets, right? So you have unbanked, which means, people who have no formal relationship with a bank or credit union. So, you don't have a checking account, you don't have a savings account, you're going to a check cashing place, you're paying a fee, quite high fee, to turn your paycheck or whatever into cash, you're paying your bills with money orders, you know, that kind of thing. Then there's a larger category of people that are called underbanked. And so, those are people who may have that checking account relationship with a bank or a credit union, but they're still using these types of alternative services. So that could be money orders, it could be high cost predatory pay day lending, auto title lending, like these, kind of, systems that are outside of mainstream finance. And that actually affects quite a lot of people here in the US. About, I think, 7 to 8% of people are completely unbanked, but a much more significant portion are considered underbanked. And I think there are a lot of reasons for that, it's usually split about 50-50 between people who have never had an account before. So those may be people who don't think banks are for them, don't feel welcome in that environment, don't trust banks, you know, so those are some of the reasons. But then the other half of people who are unbanked is because they've had bad or negative experiences with banking, and they've made a decision that banking didn't work for them. It was too costly, often that's the reason, hidden fees, overdraft fees, those types of penalties, and just decided that, you know what, it was better for me to manage my money in a different way. >> And how has SaverLife helped these people feel more secure in their financial investments? >> Leigh: So when we first launched SaverLife, it's gone through so many, so much. So much transformation and change over the years, as we've been, really adopting some of those tech based practices around iteration, and being user driven, and really trying to deliver something that will work for people. So what we heard when we first launched, was, you know, I know that saving is something I need to do for myself and my family, I think pretty much everybody knows and understands that, but it's too hard for me right now, you know? Either I've lost my job, I've been, I've had an illness, or a family member's had an illness, a lot of real reasons why people are unable to do that. And so people would say, "But I really want to get there, "so what can you do to help me?" So, at SaverLife specifically, we work with large numbers of people, we have about a quarter of a million people who've signed up for SaverLife in the last three years, which is really cool. We went from serving ten thousand people in a decade, actually six thousand people in a decade, to 250 thousand people in three years, which is pretty cool. So that shows us that there's a big need and interest for this. So anyone that goes to saverlife.org and signs up is going to get weekly financial coaching content from a certified financial coach who specializes in helping people with lower incomes to build wealth. If you link your account to our platform, you're going to qualify to win prizes for saving your own money. So it's kind of like this no-lose lottery in a way, like, you gain 'cause you're saving, and you have the opportunity to win money, and it's completely free. So, there's a lot of real benefits that we have on the platform that are designed specifically to help people who are struggling financially. >> Well, that's awesome. Leigh, thank you so much for being on theCUBE and thank you for your insight. >> Thanks so much for having me. >> Absolutely. >> I enjoyed speaking with you. >> I'm Sonia Tagare, thank you for watching this CUBE conversation. See you next time. (funky music)

(funky music) >> Hi, and welcome to this CUBE conversation from theCUBE Studios in Paulo Alto, California. I'm your host, Sonia Tagare, and today we're joined by Leigh Phillips, president and CEO of SaverLife. Leigh, welcome to theCUBE. >> Hi, thanks so much for having me. >> Absolutely. So, tell us more about SaverLife and how it works. >> So, SaverLife is a non-profit organization. We work nationally, but we're based here in San Francisco, and our mission is to help working American families to save money, and to invest in themselves and their futures. So, we do that by making it engaging, rewarding, and fun for people to start saving, and leveraging financial technology to achieve scale. >> And you were previously known as EARN, so what spurred this change in branding? >> Well, it was more than a change in branding. It was actually a big shift towards technology. So, EARN, or, now known as SaverLife, has actually been around since 2001. So, we are not new, we're not a starter, we've been helping low to moderate income working families to save money for a long time. But what we've realized in recent years is that the size of the problem is really quite significant. So, about half of American families don't have $400. So they couldn't cover a $400 expense without having to borrow the money. As EARN, we were helping a lot of families here in the Bay Area, but maybe, you know, a thousand families a year at our peak, and when you have half of America that's financially insecure, we knew that the solution that we had wasn't big enough. So, a couple of years ago, the organization decided to make a pivot, and to make a pivot towards technology. I came onboard about four and a half years ago to lead that transition, and we launched SaverLife as a product, and we reached a quarter of a million people in a couple years, and decided that the people know best, and that we would rebrand the whole organization as SaverLife. So that's kind of how that came about. >> That's awesome. >> Yeah. >> So who is SaverLife specifically targeting, and are there any specific challenges with this target group? >> So, SaverLife is specifically targeting working American families, mostly low income families, so as I mentioned, financial insecurity is a really big problem here in the US, and so we hear about that a lot in the news, about income inequality, wealth inequality, but one of the most troubling statistics came out from the Federal Reserve Bank, they found that about 42% of American families couldn't cover a $400 expense without going into debt. And that's an issue that affects lots of people in different ways. So, SaverLife is really targeting low income people who are struggling to save money, and need a little help getting started with that. So, most of our clients are women, they're all across the United States and on average make about 25 thousand dollars a year or less. >> So let's talk about the current savings crisis in America. According to Bankry, 20% of Americans don't have emergency savings, and only 18% of Americans can live off their savings for only six months. So, tell us more about this crisis, and what do you think the underlying issue is? >> Yeah, it's a great question, and there are many issues that play into that, and most of them are systemic, you know. The way that people are making money and the gap between income and expenses. So, what we see is that larger numbers of people don't have basic emergency savings, and what that means is that you can't get through a financial emergency, right? And so that can have a real downward spiral effect on your life. So imagine a scenario where you have to miss a day or two of work because your child is sick, and you don't have sick leave, like a lot of people don't. And so you miss a couple days of income. Or, you get a flat tire, or a parking ticket. Those are the types of things that can really spiral out of control, so then you lose income, then you can't pay your rent, you're at risk of eviction, and all of these other problems. So what we know is that having relatively small amount of money, so even just 250 to $750 in savings is found to reduce those risks of things like eviction, or falling behind on bills or utilities really significantly. So, we're focused on getting people to that point, so that they can get through challenges. So one of the big things that we see in our population, isn't just that wages are low, which remains a really big problem in the US right now, but that income is really inconsistent. So if you're making an hourly wage job, or maybe you work in retail, or you work in a warehouse, or something like that, and you drive for Uber, whatever the case may be, your money that's coming in, you're not getting the same amount of money in your paycheck every two weeks, right? Like many of us do. And in fact, for SaverLife clients, we're seeing these swings of income of around a thousand dollars a month, month over month. So sometimes you earn more and sometimes you earn less. So in that scenario, it's really hard to stay on track towards saving, because you don't know how much money's coming in, and then you're getting hit with all these increasing expenses at the same time. >> Right. And, can you tell us a little bit about how people can save their way to financial independence, is it viable, and how have challenges changed since the disappearance of defined-benefit retirement packages? >> Yeah, so, it is possible, but it's challenging, and, you know, I do think that we need to be aware of those kind of bigger issues, right? And to focus on helping people have more consistency in their income, and reducing some of those large expenses, whether or not in the Bay Area obviously, the cost of housing, medical care, child care, transportation, all of these things that are really holding families back. But, you know, the good news is that people are remarkable. People are resilient, and people are remarkable. And I can share a couple of stories with you about that. So, at SaverLife we encourage people to save with prizes and cash rewards, right? So we make it really easy for people to get started. We also have a really supportive online community, so this is an issue that affects half of us, right? It's not something that people should be ashamed of. This is a really big and endemic issue here in the US. So we don't judge people, you know, it's all about starting small and starting today. So what we do at SaverLife is encourage people to save what they can when they can, and then we use behavioral economics to design programmatic interventions, so features on the website, that encourage people to save. So you can save five bucks a week if that's what works for you, and then you have the chance to win prizes. We also do a tax time quest, so that's happening right now. So, tax season is one of the times when people will get a larger infusion of cash, right? Particularly low income people, who maybe are qualified for tax credits and other benefits. So, what we do is encourage people to save a portion of that refund. So we ask people to start thinking about it before they get the refund, right? That's really clear, cause once the money is in, it's usually already spent. So we start talking to people in December, why don't you pledge to save your refund? You can win prizes just for pledging. And what we've found is that getting people to think about and commit to savings resulted, last year, in 80% of those people actually putting money into savings, and saving on average 16 hundred dollars from their tax refunds. >> Sonia: Wow. That's incredible. I love how you're incentivizing this whole savings thing, because, like, that essentially just makes people want to do it more. >> Leigh: Yeah. >> So, how should people bucket their savings? Should they have an emergency fund, a college fund, a retirement fund, how should they do that? >> So what we find at SaverLife is, or what we promote, is the idea that your money should really align with your values. And what's important to you, and what you want to achieve for yourself and for your family. So we don't tell people what to save for, and we don't tell them what to spend their money on, right? So, the biggest thing that people save for with the program is emergencies. So, really having that financial cushion, so, your car breaks down, or whatever the case may be, you can take care of it without going into debt, right? 'Cause that's the cycle that we want to avoid. But then we also see people really staying on track to save for big goals. And unsurprisingly, those are still the kind of goals that we talk about a lot in this country. So, an education, for yourself or for your children, and home ownership. Those remain, kind of the most popular things that people are focused on. >> So when it comes to prioritizing how you should save, like especially for someone who's just coming off that one paycheck away from the street, kind of space, how would you recommend prioritizing your savings? >> Leigh: So, we focus on building a savings habit. That's kind of the number one thing that we want people to really think about. So, putting money away as consistently as you can. It's really the behavior change that we're looking to see. And that's why we encourage people to make those small, incremental steps. But we also know that life has a lot of ups and downs, right? Particularly for people who are, as you say, living paycheck to paycheck. And so, what we see in our data is that families are often making two deposits in one withdrawal. So they're putting money away, and then they're using that money when they need it to get through emergencies. So that's kind of the first thing that we really look to do is, once you have that savings habit, and we know it's hard, you know, to do that, especially if you're not making a lot of money at this moment. But that's really, whatever you can save to get into that habit of putting it away. >> And do you think people are more at risk of being one paycheck away from being on the street, or one big bill away from being on the street? >> Leigh: Yeah, absolutely, many people are, you know? And especially here in the Bay Area, right? When life is extremely expensive, the cost of housing is out of control, and those other expenses that people have to deal with. And if you layer on top of that, that inconsistency in people's income, not making a regular amount of money, we're putting a lot of people in a very, very perilous situation. >> Sonia: Right. So let's talk about financial empowerment. You were leading the office of financial empowerment in the city and county of San Francisco. So, tell us more about financial empowerment and why it's important for people to have it. So, you know, I started out working for the city over there for about 11 years, before there was a thing called financial empowerment. And we started working on a range of programs. I worked for the San Francisco treasurer, and what we're really looking to do is use the influence of the city, and the municipal government to try to make a more fair and equitable financial system for people in San Francisco. So we started with programs like Bank On San Francisco, which was access to banking for everybody. So the idea that everyone should be able to have a safe and affordable place to keep their money, and to save their money. So that was a program we worked on there. And then we went on to launch the country's first universal children's savings program. So today, every single kindergartner, actually, today, every single elementary school student in San Francisco has a savings account open for them by the city and county, to encourage families to save early and often, for college. So when we think about financial empowerment, and how local government plays a role, we're really looking at a couple of things. So, do you have the ability to have a safe place to keep your money, and deposit your paycheck, pay your bills, in a way that's affordable, that doesn't have high fees, and is transparent, so that's the first thing. Do you have access to financial education and coaching if you need it? So the city now has quite a robust individual financial coaching and counseling program that they run. Are you able to save and invest in your future? So, save for college, save for home ownership, save for those big things, be a small business owner. And then the fourth thing is, are your assets protected? So are we protecting you from predatory practices that can deplete your wealth? >> And why did you decide to go from the city, from a public organization to a more private organization, like SaverLife? >> Leigh: You know, it was a interesting story. So we had worked with SaverLife when it was known as EARN, at the city. So the organization was actually really closely partnered with us, so I knew them and I knew their work. So there was a couple of reasons. I became really intrigued by this idea that being here in Silicon Valley, we really should start putting the types of technology that are so transformative, really putting that to work for everybody, right? And I had been an advisor, on an advisory board to for-profit fintech starter. And I thought, "Oh, if we could take that type of tech, "and use it to help low income people "build wealth in the US, "that could be really transformative." So that was the first reason. The second reason was really thinking about the scope of this problem, and when you work for the local government, you see that trajectory, that, you know, the traffic ticket that turned into a lost drivers license that turned into a lost job, that turned into an eviction, right? Like, you see those types of issues play out, over and over in people's lives. So the idea that half of America doesn't have four or five hundred bucks, and we could actually do something about that, was really impactful to me. And then the third reason was, you know, I loved working for the San Francisco treasurer, who is amazing, but I kind of felt, as a woman, that I wanted to lead an organization in my own right. And that I had challenged myself that, I had a personal goal that if the opportunity came up, to be that leader that I was going to challenge myself to take it. And so when the opportunity came up, I just went for it. >> And what challenges did you face to become the CEO? >> I think, you know, a lot of the challenges first were within myself, you know? Like, there's a lot that goes into being a non-profit CEO, you know? You have, obviously, you're working on some of the biggest problems that are out there, and you're doing it with so few resources, you know? And so, is that kind of, you know that saying about Ginger Rogers doing everything that Fred Astaire did but backwards and in heels, it's kind of like that, right? You're trying to solve really, really, really big problems that are deeply entrenched, like half of America doesn't have $400. There's a lot of reasons for that, right? And then you're trying to do it by cobbling together philanthropic resources to make that happen. So, I think that was a challenge, like would it be a success? And then at the time, this organization was making in the midst of this massive transformation, you know? So going from seeing clients one on one in the office, to launching and building a scalable tech platform. And I don't have a tech background, you know? I can sometimes use my phone, you know? Like, that's, it's not my thing. But I was able to understand the potential. And so that was what really drew me there to challenge myself to be like, okay, well, there's a lot of people around here that have managed to figure this out, maybe I can figure it out, too. >> Sonia: Yeah, absolutely. So when we talk about people being unbanked, can you tell us more about what unbanked means and what it means for today? >> Leigh: Yeah, so when we talk about access to banking, and mainstream financial services, we usually separate that into two buckets, right? So you have unbanked, which means, people who have no formal relationship with a bank or credit union. So, you don't have a checking account, you don't have a savings account, you're going to a check cashing place, you're paying a fee, quite high fee, to turn your paycheck or whatever into cash, you're paying your bills with money orders, you know, that kind of thing. Then there's a larger category of people that are called underbanked. And so, those are people who may have that checking account relationship with a bank or a credit union, but they're still using these types of alternative services. So that could be money orders, it could be high cost predatory pay day lending, auto title lending, like these, kind of, systems that are outside of mainstream finance. And that actually affects quite a lot of people here in the US. About, I think, 7 to 8% of people are completely unbanked, but a much more significant portion are considered underbanked. And I think there are a lot of reasons for that, it's usually split about 50-50 between people who have never had an account before. So those may be people who don't think banks are for them, don't feel welcome in that environment, don't trust banks, you know, so those are some of the reasons. But then the other half of people who are unbanked is because they've had bad or negative experiences with banking, and they've made a decision that banking didn't work for them. It was too costly, often that's the reason, hidden fees, overdraft fees, those types of penalties, and just decided that, you know what, it was better for me to manage my money in a different way. >> And how has SaverLife helped these people feel more secure in their financial investments? >> Leigh: So when we first launched SaverLife, it's gone through so many, so much. So much transformation and change over the years, as we've been, really adopting some of those tech based practices around iteration, and being user driven, and really trying to deliver something that will work for people. So what we heard when we first launched, was, you know, I know that saving is something I need to do for myself and my family, I think pretty much everybody knows and understands that, but it's too hard for me right now, you know? Either I've lost my job, I've been, I've had an illness, or a family member's had an illness, a lot of real reasons why people are unable to do that. And so people would say, "But I really want to get there, "so what can you do to help me?" So, at SaverLife specifically, we work with large numbers of people, we have about a quarter of a million people who've signed up for SaverLife in the last three years, which is really cool. We went from serving ten thousand people in a decade, actually six thousand people in a decade, to 250 thousand people in three years, which is pretty cool. So that shows us that there's a big need and interest for this. So anyone that goes to saverlife.org and signs up is going to get weekly financial coaching content from a certified financial coach who specializes in helping people with lower incomes to build wealth. If you link your account to our platform, you're going to qualify to win prizes for saving your own money. So it's kind of like this no-lose lottery in a way, like, you gain 'cause you're saving, and you have the opportunity to win money, and it's completely free. So, there's a lot of real benefits that we have on the platform that are designed specifically to help people who are struggling financially. >> Well, that's awesome. Leigh, thank you so much for being on theCUBE and thank you for your insight. >> Thanks so much for having me. >> Absolutely. >> I enjoyed speaking with you. >> I'm Sonia Tagare, thank you for watching this CUBE conversation. See you next time. (funky music)

>> from Palo Alto, California It's the Cube covering the EM Where women Transforming technology twenty nineteen. Brought to You by VM Wear >> Hi Lisa Martin with the Cube on the ground at the end. Where in Palo Alto, California, for the fourth annual Women Transforming Technology, even W. T. Squared on event that is near and dear to my heart. Excited to welcome back to the Cube pretty much. Rog Lachman, engineering leader, blocked in at the end where pretty much It's so great to have you back on the Cube. Thank you, Lisa. It's amazing to be here, and I can't believe it's been a year, a year. And so last year, when Protein was here, she launched her book. Nevertheless, she persistent love the title You just Did a session, which we'LL get to in a second, but I'd love to get your your experiences in the last year about the book launch. What's the feedback? Ben? What are some of the things that have made me feel great and surprised you at the same time? It's been fantastic. I wasn't expecting that when I started to write this book. It was more like I want to impact one woman's life. But what was interesting is I delivered around twenty twenty five talks last year. My calendar's booked for this year, but every time I go give a talk, my Lincoln goes crazy and I'm connecting with all these women and men. And it's just fantastic because they're basically resonating with everything I talk about in the book. I spoke at the Federal Reserve. Wow, I was like, This is a book on tech and they were like, No, this impacts all of us And I spoke to a group of lawyers and actually, law firms have fifty fifty when they get into law, right when they get into whenever I mean live, I'm not that familiar with it. But getting to partner is where they don't have equality or diversity, and it's resonated. So now I'm like, maybe I should just take the word check out What? You It's been impactful. And so last year was all about companies, so I did. You know, I spoke at uber I spoken Veum, where spoken nutanix it's looking a lot of these companies last year. This year is all about schools, fantastic schools of all different type, so I you know, I've done a talk at San Jose State. I went to CMU. They invited me over Carnegie Mellon. I supported the robotics team, which is all girls team. Nice. And it was fantastic because these girls high school kids were designing robots. They were driving these robots. They were coding and programming these robots and was an all girls team. And I asked them, I said, But you're excluding the men and the boys and they said no. When it's a combined boy girls team, the women end up the girls and organizing the men of the boys are actually writing the code. They're doing the drilling there, doing all that. And so the girls don't get to do any of that. And I was looking at just the competition and as watching these teams, the boy girl steams and those were all organizing. And I thought, this is exactly what happens in the workforce. You're right. Yeah. We come into the workforce, were busy organizing, coordinating and all that, and the men are driving the charge. And that's why these kids where this is at high school, Yeah, thirteen to seventeen, where this is becoming part of their cultural upbringing. Exactly. Pretty. In great. Yes, yes. And a very young age. So that was fascinating. I think that surprised me. You know, you were asking me what surprised you that surprised me. And what also surprised me was the confidence. Though these girls were doing all these things. I've never built a robot. I would love to. I haven't built a robot, and they were doing all these amazing things, and I thought, Oh, my God, >> they're like, >> confident women. But they were not. And it was because they felt that there was too much to lose. They don't want to take risks, they don't want to fail. And it was that impostor syndrome coming back so that conditioning happens way more impossible syndrome is something that I didn't even know what it wass until maybe the last five or six years suddenly even just seeing that a very terse description of anyone Oh, my goodness, it's not just me. And that's really a challenge that I think the more the more it's brought to light, the more people like yourself share stories. But also what your book is doing is it's not just like you were surprised to find out It's not just a tech. This is every industry, Yes, but his pulse syndrome is something that maybe people consider it a mental health issue and which is so taboo to talk about. But I just think it's so important to go. You're not alone. Yeah, vast majority men, women, whatever culture probably have that. Let's talk about that. Let's share stories. So that your point saying why I was surprised that these young girls had no confidence. Maybe we can help. Yes, like opening up. You know, I'm sharing it being authentic. Yeah. So I'm looking at my second book, which basically says what the *** happens in middle school? Because what happens is somewhere in middle school, girls drop out, so I don't know what it is. I think it's Instagram or Facebook or boys or sex. I don't know what it is, but something happens there. And so this year of my focus is girls and you know, young girls in schools and colleges. And I'm trying to get as much research as I can in that space to see what is going on there, because that totally surprised me. So are you kind of casting a wide net and terms like as you're. Nevertheless, she persisted. Feedback has shown you it's obviously this is a pervasive, yes issue cross industry. This is a global pandemic, yes, but it's your seeing how it's starting really early. Tell me a little bit about some of the things that we can look forward to in that book. So one thing that's important is bravery, Which reshma So Johnny, who's the CEO off girls code? She has this beautiful quote, she says. We raise our voice to be brave, and we'd raise our girls to be perfect, pretty telling. And so we want to be perfect. We won't have the perfect hair, the perfect bodies. We want a perfect partner. That never happens. But we want all that and because we want to be perfect, we don't want to take risks, and we're afraid to fail. So I want to focus on that. I want to talk to parents. I want to talk to the kids. I want to talk to teachers, even professors, and find out what exactly it is like. What is that conditioning that happens, like, why do we raise our girls to be perfect because that impacts us at every step of our lives. Not even careers. It's our lives. Exactly. It impacts us because we just can't take that risk. That's so fascinating. So you had a session here about persistent and inclusive leadership at W T squared forth and you will tell me a little bit about that session today. What were some of the things that came up that you just said? Yes, we're on the right track here. So I started off with a very depressing note, which is twenty eighty five. That's how long it's gonna take for us to see equality. But I talked about what we can do to get to twenty twenty five because I'm impatient. I don't want to wait twenty eighty five I'LL be dead by them. We know you're persistent book title. You know, my daughter will be in the seventies. I just don't want that for her. So, through my research, what I found is we need not only women to lean in. You know, we've have cheryl sound. We're talking about how women need to lean in, and it's all about the women. And the onus is on the woman the burdens on the woman. But we actually need society. Selena. We need organizations to lean in, and we need to hold them accountable. And that's where we're going to start seeing that changes doing that. So if you take the m r. I. You know, I've been with him for ten years, and I always ask myself, Why am I still here? One of the things we're trying to do is trying to take the Cirrus early this morning rail Farrell talked about like on the panel. He said, We are now Our bonuses are tied to, you know, domestic confusion, like we're way have to hire, you know, not just gender, right, Like underrepresented communities as well. We need to hire from there, and they're taking this seriously. So they're actually making this kind of mandatory in some sense, which, you know, it kind of sucks in some ways that it has to be about the story that weighing they're putting a stake in the ground and tying it to executive compensation. Yes, it's pretty bold. Yes. So organizations are leaning in, and we need more of that to happen. Yeah. So what are some of the things that you think could, based on the first *** thing you talked about the second one that you think could help some of the women that are intact that are leaving at an alarming rate for various reasons, whether it's family obligations or they just find this is not an environment that's good for me mentally. What are some of the things that you would advise of women in that particular situation? First thing is that it's to be equal partnership at home. A lot of women leave because they don't have that. They don't have that support on having that conversation or picking the right partner. And if you do pick the wrong partner, it's having that conversation. So if you have equal partnership at home, then it's both a careers that's important. So you find that a lot of women leave tech or leave any industry because they go have babies, and that happens. But it's just not even that, like once they get past that, they come backto work. It's not satisfying because they don't get exciting projects to work on that you don't get strategic projects, they don't have sponsors, which is so important toward the success, and they they're you know, people don't take a risk on them, and they don't take a risk. And so these are some of those things that I would really advice women. And, you know, my talk actually talked about that. Talked about how to get mail allies, how to get sponsors. Like what? You need to actually get people to sponsor you. Don't talk to me a little bit more about that. We talk about mentors a lot. But I did talk this morning with one of our guests about the difference between a sponsor and a mentor. I'd love you to give Sarah some of your advice on how women can find those sponsors. And actually, we activate that relationship. So mentors, uh, talk to you and sponsors talk about okay. And the way to get a sponsor is a is. You do great work. You do excellent work. Whatever you do, do it well. And the second thing is B is brag about it. Talk about it. Humble bragging, Yeah. Humble bragging talkabout it showcases demo it and do it with people who matter in organizations, people who can notice your work building that brand exactly. And you find that women are all the men toward and under sponsored. Interesting, Yes. How do you advise that they change that? There was a Harvard study on this. They found that men tend to find mentors are also sponsors. So what they do is, you know, I like you to stick pad girl singer, he says. Andy Grove was his mentor, but Andy Grove was also his sponsor in many ways, in for his career at Intel, he was a sponsor and a mental. What women tend to do is we find out like even me, like I have female spot him. Mentors were not in my organization, and they do not have the authority to advocate for me. They don't They're not sitting in an important meeting and saying, Oh, patina needs that project for team needs to get promoted. And so I'm not finding the right mentors who can also be my sponsors, or I'm not finding this one says right, and that's happens to us all the time. And so the way we have to switch this is, you know, mentors, a great let's have mentors. But let's laser focus on sponsors, and I've always said this all of last year. I'm like the key to your cell. Success is sponsorship, and I see that now. I am in an organization when my boss is my sponsor, which is amazing, because every time I go into a meeting with him, he says, This is about pretty much grew up. This is a pretty mers group. It's not me asking him. He's basically saying It's pretty nose grow, which is amazing to hear because I know he's my mentor in sponsor as well. And it's funny when I gave him a copy of my book and I signed it and I said, And he's been my sponsor to be more for like ten years I said, Thank you for being my sponsor and he looked at me. He said, Oh, I never realized it was your sponsor So that's another thing is men themselves don't know they're in this powerful position to have an impact, and they don't know that they are sponsors as well. And so we need. We need women to Fox and sponsors. I always say find sponsors. Mentorship is great, but focus of sponsors Look, I think it's an important message to get across and something I imagine we might be reading about in your next book to come. I know. Yeah, well, we'LL see. Artie, thank you so much for stopping by the Cube. It's great to talk to you and to hear some of the really interesting things that you've learned from nevertheless you persistent and excited to hear about book number two and that comes out. You got a combined studio. I'd love to thank you and thank you. I'm Lisa Martin. You're watching the queue from BM Where? At the fourth Annual Women Transforming Technology event. Thanks for watching.

(upbeat music) >> Live from Toronto, Canada, it's theCUBE. Covering Global Cloud and Blockchain Summit 2018. Brought to you by theCUBE. >> Hello everyone and welcome back to theCUBE's live coverage in Toronto for the Blockchain Cloud Summit, part of the Blockchain Futurist event happening tomorrow and Thursday here in Toronto. I'm John Furrier with Dave Vellante. We're here with Gabriel Abed who's the founder of Bitt and also the Digital Asset Fund. Great story he's been there from the beginning. President at creation in the movement that's now changing the world. Blockchain and cryptocurrency certainly. Infrastructure and token economics, changing how things are doing. And rolling out, reimagining everything from infrastructure to value exchanges. Gabriel welcome to theCUBE. >> Thank you it's great to be here. >> So we were just talking on camera, you like to go after the big changes. You're an entrepreneur, you have that fire in your belly. You've been very successful. Where are we? I mean, you've been part of the movement, we're now on the cusp of mainstream adoption, there's still work to do. >> Oh, plenty of work. Lots of infrastructure still to build, many regulators and legislators still to educate, lots of laws still to be amended and changed. And, at the end of the day, it's happening and it's happening quickly and beautifully right now. The entire industry is changing. >> One of the things that you've done, you've taken on some big projects and you've made change happen. Regulation is one of the hottest topics we're hearing certainly in the United States, it affects innovation and there's so much entrepreneurial activity happening right now. There's so many entrepreneurs, alpha entrepreneurs really want to do great things, and regulation is just a blocker. It's an antibody for innovation. And you've busted through that. And it's probably going to continue. The old guard is either going to be replaced or adapting to the technology. You've done that, and a lot of people want to do what you've done. What's the secret? What's the secret of your success? How have you taken on these big, incumbent positions and taken them over >> But you're not running from regulators, you're embracing them. >> No, no, I think regulators are important to a responsible and sophisticated market. When my partner and I started Bitt in 2013, 2014, we immediately realized that if we wanted to build a product for the monetary authorities around the world, we needed to have the buy-in from the regulators. So from day one we were regulator-friendly. And it's not to say that we don't believe in a decentralized future, I'm as big of an advocate for decentralization and the freedom of information as anyone else, but I'm also a big believer in if you're a product for a market in the traditional world you have to involve the regulators in order to ensure that product does its job, keeps the consumers safe, and ensures that the economy around it doesn't collapse. So regulators are critical in this field. >> Talk about what you guys have done. Take a minute to explain the project you did, how it worked out, the tenacity, but also, what was the outcome? What were you trying to do in the project and where is it right now? >> It depends on the project you're referring to >> Maybe start at the beginning >> The Caribbean >> Let's start at the beginning. >> Yeah, yeah. >> Okay, so, Gabriel Abed, born, raised, educated in Barbados, around the age of 19, I decided I was going to take my computer science education a bit further. I went to Canada, I did a Bachelor of IT, where I majored in network security. In Ontario, the University of Ontario. And, unlike the rest of of my peers, who usually stay in Canada, I decided to go back to my little nation with the education that I had just received. And I took that education home, and started one of the world's first blockchain companies, but at the time I didn't understand blockchain per se, I understood it as a commodity, as a cool investment, I didn't understand the true nature behind the protocol itself. It was only until 2013 that my partner and I ran one of the larger mining operations in the world, that we realized a commodity was actually a protocol. A network tool. A system that you could build on top of. So in 2014, we actually created one of the world's first blockchain assets, on Bitcoin's blockchain. And that a representation of a digital dollar for a central bank. And the notion behind Bitt.com in 2014 was, let's compete with cash, because it's inefficient, it's costly, and it slows down the movement of society. So what we wanted to do is create a digital version of that, that would save economies hundreds of millions of dollars. Cash is expensive to to create, that linen, plastic, paper money, it's easily forged, it can be counterfeited, it's hard to transport, it has an expense to transport, it has an expense to count, it has an expense to secure, and then it has overheads around the entire ecosystem of accountability. Whereas, a blockchain-based digital dollar eliminates all of those efficiencies, and increases the ability for a monetary authority to trace, track, and have a better form of anti-money laundering, counter-terrorism financing and a better overview of their entire society. So that all, we took that notion, went to the central bank of Barbados, who at the time was being led by Dr. DeLisle Worrell, and our very first meeting he had asked me to excuse his office. And 13 meetings later, and a whole two years, lots of development, building out infrastructure around compliance, around finance, around security, and around regulation, we finally got the nod of approval from Dr. DeLisle Worrell to operate a fiat example of a digital dollar in Barbados. And since then, we have been working with several central banks around the world, bitt.com today is the leading central bank provider for digital dollars. A lot has changed, I've developed other tools since, and other businesses, but bitt.com continues to be the best friend for central banks looking to move and transition into the digital arena. >> Why, I mean other than a closed mindset, why wouldn't every government around the world want to move in this direction? Initiate some kind of FedCoin, for example. >> Education, education, it's the fear that the system may not be scalable, it's the fear that the system could be hacked, it's the fear that they could be cut out, their control, at the end of the day, monetary authorities, like the Federal Reserve, they have a control on the money supply. Whereas, something like decentralized cryptographic currencies, there is nobody in control of the money supply. Hence, inflation versus deflation systems. Then there's the issue of hacking and the threat of digital and cybersecurity. Typically, the head of these monetary authorities are older gentlemen who are traditionally conservative. And who are not (mumbles) with cybersecurity. So the fear of hacking is very real for someone like them, whereas someone like me who is trained as a network security expert, those fears can be mitigated with good policy and procedure, cold wallets, and the right process, to ensuring the environment can run without the risk or the fear of malicious attacks. So it really boils down to education. The educated governors of central banks, like there's one, for example, Timothy Antoine. Dr. Antoine is the governor of the Eastern Caribbean Central Bank. And they govern and mandate the currency union of eight islands below them. St. Lucia, Grenada, Antigua, et cetera. Now, he's a governor that gets this and has wrapped his head around it, and understands that this is the future. He gets it so much that he signed an agreement with bitt.com to begin exploring a pilot for his currency union to have a digital dollar implemented in it. You also have governors and presidents like that of Curacao. Or the central bank of Curacao, where we've just signed an agreement to move forward with a phase of looking at the implications of rolling out a digital dollar in a society like Curacao and St. Maarten. What is the ramifications? What is the feasibility study behind that? So, to answer your question, it's not every single regulator, governor, and central bank manager is going to head toward this technology tomorrow. But with more education, and more lobbying, you will see more and more central bank governors moving in this direction, because it's better, cheaper, faster, makes their job easier, gives them more control, gives them more oversight, and provides all the things that they would want as a central bank to continue to do their job for their society. Which is to protect their dollar from alien threats. And to ensure that the dollar remains stable, and to just generally ensure that the society is functioning the way it should. >> Gabriel, what's your vision on what this will enable for the citizens? What's the impact that you see happening? If this continues down the trajectory, what is the adoption look like, impact to people's lives on a everyday basis. >> Well, for a very starting point, you democratize payment. Right now, if I want to make a payment, I have to go through a utility company called a bank. And this bank typically has frictional costs, and frictional overheads and time. That's one of the biggest problems, is that these monopolistic infrastructures hinder the ability for the average participation of a free-flowing payment system. So what you end up having is rather than me being able to make a digital payment in seconds, with no cost, I have to wait days, I have to use manual-based systems whether it's check, cash or the bank's Visa Mastercard system. And then it has frictional costs. So right off the bat, you democratize payment. What does that do for a society in a developing nation? It empowers people. And you're empowered because now as a developer, I can build on this payment system. As an entrepreneur, I can tap in to this payment system. As a merchant, I can utilize this low-cost payment system. As a society, I now have GDP growth because of financial inclusion. The underbanked, who do not have access to banking facilities for one reason or another, maybe they don't like the bank, maybe the banks don't like them. Maybe they don't have two proofs of ID. Maybe they don't have a fixed place of abode. Maybe they don't have the minimum deposit amount. All of these features keep the poor and the underbanked out of the system. Whereas, in developed nations, we have mobile penetration rates that are through the roof. In some cases, like Barbados, over 100 percent. So if you have 100 percent penetration rate of this mobile platform, this thing in my pocket, but I cannot access the banking system, well flip that around, democratize the payment system, allow payments to exist on this mobile phone, and watch how quickly society becomes banked. So what you end up having is full adoption. Why would we not have full adoption when it's cheaper, it's faster, it's more inclusive. >> And the data from that collective intelligence only creates a digital nation >> A more responsible environment. >> Wealth creation environment. >> It creates a more traced, tracked, and accountable society so that the monetary authorities in the government can now start making educated decisions on data. They now know who's buying milk, who's gambling, who's paying their taxes and who's not. >> The downstream benefits of this are massive. >> The downstream benefits are massive, enormous. They're disruptive. This is a brand new fiscal tool, a monetary tool, being given to central banks to start eroding the field of private e-money systems, and to start bringing about a uniform standard towards payments. Plain and simple. We're going to the central banks and introducing a new monetary instrument, that they're in control of. That now the commercial banks, the financial institutions, the corporatocracies, the citizens, and the merchants can all fall under one roof issued by their monetary authority. And this is not a cell phone company or a bank building their own private system that I have to jump through some hoops and some red tape and sign away my first born and give away my left arm to enter. This is a free and open source standard system. >> And it's networked, as you said, penetration is 100 percent on mobile or roughly that, it's a network society that now has digital fabric built into it. This is the future. >> But I played this out in terms of, when you talked about this in your panel, now every device, every thing, every physical asset will be instrumented. >> Yes. >> And as a result, theory can be coconuts. >> You're building the deep infrastructure. I remember we met with World Bank back in 2014 and they coined this term for me. Because they were saying we want to help entrepreneurs and it's important to help entrepreneurs in developing nations because they're the lifeblood of it. But what we are building is the deep infrastructure. And that's exactly what it is. It's the infrastructure that would allow the entrepreneur and the developer to now have a framework that they can build against to provide more uplift. So in essence, it's really going to be exponential growth once systems like this are implemented. The stock market can move digital, and people could buy stocks using digital dollars. E-commerce can occur because I can now buy things online or sell things online with digital dollars. I can now be part of a global, financial ecosystem, with my smartphone and my wallet. >> That's a great use case, congratulations on amazing success, so much is on your plate, you've had great success in this new era, what's on your plate now, what are you working on, what's happening in your world now? >> So in 2017, we realized Bitt was entering a new growth phase. It was no longer a battle of trying to convince regulators and central banks, our product had been proven. Our reputation had been proven. It was time now to scale the company into a professional level of dealing with these regulators around the world. At the end of the day, we would like to digitize cash, wherever cash exists. And to provide those tools for central banks around the world. That would require professional management, and that is not I. >> (laughs) >> So, our investors and shareholders were quite comfortable with our proposal of bringing on that professional management, so in 2017 I resigned as CEO, retained a board position and still single largest shareholder, but with the idea of what other types of infrastructure can I build, now that a deep infrastructure had been put in place. So I've been attacking three major markets, the banking sector, an actual commercial banking enterprise working with a group from the United States towards looking at deploying the future of where we think commercial banking is going. I think that the community, the crypto community in general, there's a lot of noise happening in the chats. And therefore we built a machine learning chat bot to start looking at market sentiments and aggregating market information and of course building common tools for community members. So we've launched a agent called Gabby, the form to gab. My name's Gabriel and my mom calls me Gabby, so it works out quite well. >> You have the gift of gab that's for sure. >> And then I launched a mutual fund with a very sophisticated former managing director of JPMorgan. A guy named Richard Galvin. And we launched the world's first protocol-only fund. We focus only on protocols. And that's called Digital Asset Fund. And we launched that in late 2017 and got full regulatory approval to become a professional fund, that handles 100 percent, solely crypto. And that's basically been my ride, and then outside of that, just your standard consulting, because everybody from World Bank, to IADB, to some government agency to some private organization wants to know about blockchain they want advice, and they need a team of people to give them that advice. So it's just been, all around, looking at how I can be an entrepreneur in this space, while finding great leaders, and partnering with those leaders to build out great companies. While still focusing on ensuring bitt.com becomes the solution for dollars, digital dollars, worldwide. >> Got a great mission, entrepreneur, builder, congratulations. >> Thank you. >> Industry's lucky to have you, congratulations. >> Thanks for coming on. >> Thanks for coming on theCUBE. >> Thank you guys. >> CUBE coverage here, live in Toronto for the first Global Cloud and Blockchain Summit in concert with the Blockchain Futures Conference happening in the next two days after today. More coverage from theCUBE we're live here, stay with us for more great coverage after this short break. (upbeat electronic music)

>> From Times Square, in the heart of New York City, it's theCUBE. Covering Imagine 2018. Brought to you by Automation Anywhere. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan at the Automation Anywhere Imagine 2018. 1100 people milling around looking at the ecosystem, looking at all the offers that all the partners have. And we're excited to have one of the strategic advisors from the company, he's Bill Raduchel. Strategic advisor, been in the industry for >> 50 years, 40 years, 50 years, whatever. Forever. >> So Bill, thanks for takin' a few minutes. >> My pleasure. >> So how did you get involved with Automation Anywhere? >> Oh the way most things happen in life, friends, right? You get involved, and got to talking to Mihir, and we got, we see the world much the same way. And see the importance of bots and bringing productivity back to the economy. And no other way to do it. So just ya know, it grew. >> It grew. So it's interesting right? Cause I though ERP was supposed to have rung out all the efficiency that, and waste in the system, but clearly that was not the case. >> I won both CIO of the year and CTO of the year, and I put in an ERP system, and I understand it. It also failed three times going in. It was incredibly painful, but it produced over a billion dollars in cash saving. So it did. The problem is the world changes. And the world changes now at a pace far faster than you can possibly change your ERP system. >> Right. >> I mean ERP systems are built to be changed every I don't know, 15 to 25 years. And the world in 25 years is gonna look very different than the world does today. So we just have a huge disconnect between how fast we can create and deploy software, and how fast the world is changing to which that software has to relay. >> Right. And still so many of the processes that people actually do in their day job, are still spreadsheet based, you know, my goodness. How much of the world's computational horsepower is used on Excel on stand alone little reports and projects? >> Another question to ask is how many errors are in those spreadsheets? >> That's right. Not enough copy paste. >> I mean, I was on a study for the National Academy of Sciences, and we looked at why productivity growth wasn't happening. And one answer, which we just talked about, is Legacy software. I mean, you just couldn't change it, you couldn't, you know when you had to rewrite the software all productivity growth just slowed to a crawl. The other thing is something that economists call lore. And lore is basically oral tradition. But it's the way the company really works. >> Right. >> You have all these processes and all these procedures but when you get down and you start talking and sort of like, what is it the secret boss show? I mean, you learn the little things that the people down at the bottom know. Well, so far, Automation has never really penetrated that. And yet that becomes the barrier to almost all change. So what RPA does, is RPA actually begins to go after lore. RPA allows companies to begin to understand lore, and understand how to optimize it. Understand how to record it. I mean, you know, it's not written down. It's below the level that people bother to document and yet, if you don't change the lore, you're not gonna matter. >> You're not changing anything. >> You're not changing anything. So this is why this is so exciting because for the first time, companies, organizations, people, I mean we see all this stuff coming out just to help us in our everyday lives. You get to go at the lore. I mean, you know that, well you don't put that field in, no you wait 20 seconds after you filled in this field before you go and do that, because it takes that long for that and you get an error over here. That's how things really work. And this is the kind of technology that can actually address that. And so for that point of view it's really revolutionary because we've never been able I mean, oral tradition has never been subject to a whole lot of scientific studies. >> Well the other thing is just so impressive when you've been in the business a long time, you know we're talking about AOL before we turn on the cameras and shipping CDs around. >> Right. >> As we get closer and closer to ya know, infinite compute, infinite storage, infinite networking, 5G just around the corner. At a price point that keeps absutodically getting closer and closer to zero, the opportunity for things like AI, and to really apply a lot more horsepower to these problems, opens up a whole different opportunity. >> Two comments to that. One is, about 15 years ago the National Science Foundation funded Monica Lamb at Stanford to do a project on the open mobile internet, POMI. And one of their conclusions was that at some point in the future, which may be happening now, we would all have a digital butler. And everybody would have, basically a bot. They would be living 24/7 operating on our behalf, doing the things that help make our life better. And that is you know, really what's gonna happen. Now you see AI, and if you saw there was a report that got a lot of news from the speech given at the Federal Reserve Bank at Dallas, I think. Where the guy said well productivity is fine, it's just that the AI technology hasn't been able to find a way to be effective, or made real. Well the way it's gonna be made real is these bots because you still got your ERP system. Now granted I can have AI over here, but if it doesn't talk to the ERP system, how is the order gonna get placed? How is the product gonna get mailed? How is it gonna get shipped? So something has to go bring these together. So again, you're not gonna have impact from AI unless you have an impact from bots. Because they're the interface to the real world. >> Well the other huge thing that happened, right, was this mobile. And the Googles and the Amazons of the world resetting our expectations of the way we should be interacting with our technology. And you know, it's funny but there's little things that are in our day all the time. I mean, Ways is just a phenomenal example, right? And auto fill on an address. You know, this is the address you typed in, this is the one that USPS says is the official address from your home. So it's all these little tiny things that are just happening >> Spell check. >> Without even, spellcheck. >> Spell check, I mean, the inventor of spell check is John Seely Brown. And he was giving a speech at the University of Michigan 15 years ago and the graduates weren't pleased. Here was a computer scientist gonna come talk to them and it's at the Michigan stadium, and they're throwing beach balls and no one's paying any attention. And the person who introduced him said and I wanna introduce John Seely Brown, the man who invented spell check. And he had a standing ovation from 100,000 people because that got their attention. They all knew that that was really important. No you're right. I mean, the iPhone is 10 years old. Well I mean smart phones are 20 years old. The iPhone is 10 years old, 10 and a half now. I mean, it's changed how we live our lives, how we do business, how everything goes. Anybody who thinks that the next 10 years is gonna be less change >> No, it's only accelerating. >> There's so many vectors. I mean a year ago, a friend coined the Cambric Extinction, basically a play on words on the Cambrian Extinction. And it's Cloud, AI, mobile, big data, robotics, Internetive things, and cyber security. And he pointed out that any one of those would be incredibly disruptive, they were all hitting at the same time. The thing that's amazing is that's a two year old comment. Block chain wasn't around. >> Right. >> And today, block chain may be more disruptive than any of those. And yet, how do all of those connect to the Legacy systems for some long period of time? It's what's going on in this room. >> Right. Well cause I was gonna ask you, cause you advise a ton of companies, so you've seen it and you continue to see it across a large spectrum. What's special about this company? what's special about this leadership team that keeps you excited, that keeps you involved? >> It's the people side of this, right. I mean, I have been to more computer related conferences in my life than I can count. I've never seen as much enthusiasm as there is here. Maybe, at a Mac conference. But I mean it's that same level of enthusiasm, it's passion. How does technology get adopted when you have to go invest in it? It takes passion. You gotta get people who believe. People who are committed. People who wanna go and do something with it. And that's what they've been able to do. That's what Mihir has done. And it's been brilliant in bringing that on board. >> Yeah, you can certainly feel it here in the room. Especially when it's still relatively intimate. >> Right. >> You know, people are sharing ideas, you know they're excited. It's really not kind of a competitive vendor fair, it's more of a community that's really trying to help each other out. >> Well that, I mean, they're at that stage. It may get a little bit, you know this, well no I'm not gonna tell you about my bot. It's a great bot and it does great things, but nope, I'm not gonna tell you how it works. >> Right. So just last parting word, you know as you see kind of the bot economy. We've seen they got the bot store, I guess they have a hundred bots, they've only had it open for a very short period of time. You can buy, sell, free. What do you see kind of the next short term evolution of this space? >> I think that bots are probably worth somewhere around a point in productivity growth. Well, a point >> Not a basis point, but a point point. >> A point. That's what Makenzie says, that's what, I mean because this is allowing you to capture benefits that you should of and you haven't. A point in global productivity is about a trillion dollars. So then your question for the bot economy is okay, if the value of the bots is a trillion dollars, what portion of that can the bot economy capture? And that you know, I mean 20 30 percent is certainly a reasonable number to go look at. The real world lives over here, all this technology change lives over here, and bots are gonna be the bridge by which you bring those two things together. So yes, it should be big and growing for a long time. >> Well Bill, thanks for taking a minute. I really appreciate the conversation. >> Great, thank you. >> Alright, he's Bill, I'm Jeff. You're watchin' theCUBE from Automation Anywhere Imagine 2018. Thanks for watching. (electronic music)

>> Announcer: Live from Las Vegas, it's The Cube covering IBM Think 2018. Brought to you by IBM. >> Hello and welcome to The Cube here in IBM Think 2018, I'm John Furrier. It's The Cube, our flagship program, we go out to the events and extract the signal in the noise. We're the number one live event coverage. We're here with The Cube with IBM Think 2018. Our next guess is Jesse Lund who's the vice president of IBM Blockchain. He's in the financial services side. Into blockchain, into crypto, into token economics, seeing the future, how money flows, Jesse great to have you on The Cube, thanks for joining me. >> Yeah, thanks for having me. It's great to be here. >> We were talking before on camera about blockchain, and we love blockchain, IBM certainly put it out there as part of the innovation sandwich. Blockchain, data, AI, kind of making that innovation, but it's really what it enables, and I want to talk to you about. You are involved in payments. We've been saying on The Cube that the killer app is money in this market. >> I agree, yeah. >> You agree, and you talk about it. This is a new market, so a stack is kind of developing. You got blockchain, then you got crypto which as protocols and you got infrastructure, then you got decentralized applications which you could call ICOs up top, certainly a little bit scammy and bubbly, but that's as arbitraging and optimizing the capital markets, you could argue that. But so this is a really big dynamic. Your thoughts on this trend. >> Sure, well so I joined IBM from 18 years at Wells Fargo. I spent really the majority of my career in financial services and when blockchain came along, I sort of immediately saw the impact, the potential for, I'll call it positive disruption, disruption in the positive sense. Transformational paradigm shift kind of stuff in terms of how money moves around the world and how we classify assets and how we transfer ownership of assets, I mean that's just, it's, the possibilities are limitless. And you're right, IBM is the place where I think blockchain has started as a mainstream focus for enterprises around building private networks, but that's really just the beginning. What we talked about earlier was it gets really interesting when data and money are connected together and they move at high velocities together. >> Let's get into that. I mean first let's just address the IBM thing. They got to put a stake in the ground, blockchain, it's a safe harbor to say supply chain stuff because that's their business, they've been building technologies for supply chains for companies, that's what enterprises do, that's IBM. But the game is where the money is and that's where the businesses are going to be transformed. We're talking about disrupting structural industries. This is where the money power comes in. Money's flowing, I mean if you want to move money from China, go to bitcoin. If you want to move it from anywhere, this is what's happening. >> Yeah, so think about bitcoin. It's kind of what started it all. It's a little bit of a bad word in banks and in regulated financial circles, but let's face it, the only real mainstream blockchain application today is still bitcoin, but you know we're only three years in to the blockchain industry, right? I mean think about when we were three years in to the internet industry, where we were still talking about which browser is going to win and then it went on to which application server's going to win, and it wasn't til a decade later we were really focused on what are the applications, the killer apps that are enabled by an interconnected world and that's exactly what's happening now. Other industries have already been completely disrupted. Look at retail, it's just, it's banking's turn. It's financial services turn. >> One of the founders, the co-founders of Ethereum, Anthony Diiorio, who I interviewed a couple weeks ago at the Bahamas, he said "While it is the new browser," to your points, browser wars, if you think about the payment, wallets are now becoming part of the mechanism for money transfer. If you don't have a wallet, if you want to send me some Ripple, you want to send me some Ethereum, I need a wallet. This is a no brainer, right? I mean if you want to leverage any money, that's one thing. The second thing I want to get your thoughts on besides the wallets, the fiat conversion, right? These are two threshold conversations that are going on. Your thoughts, wallet and conversion to fiat. >> Well I mean I think wallets are really important because this whole thing is based on key management, this whole concept is based on cryptography. It only works on a public, private key notion and you got to keep that private key private, but you got to keep it, right? You got to keep it safe and you got to keep it, it's like your wallet. You've got a wallet, you've got cash in your wallet, you lose your wallet, you lose your cash. It's the same kind of analogy, so wallets are really important and you're going to want to turn to providers who have made their business in encryption, who have made their business in security, I mean-- >> And cold storage, old school is kind of coming back, people are taking their keys and they're spreading them across multiple lock boxes, multiple states. People are getting broken into their house or their PCs are getting broken into. >> Right, yeah. >> I mean security, going old school. >> And why not? I mean, it works. >> Because if someone knows you got 100 million dollars in your house, they're going to get it if you don't lock it. Okay back to the reality of the money transfer. We were talking before you came on, I've been saying on The Cube, token economics really is where the action is, at least in my opinion. I want to get your thoughts because really the business model innovation is on the table because whoever can innovate the business model has more of a chance to disrupt an existing industry. This is where tokenization becomes part of the money piece of it, so how do you convert that value into capture? Is that token? Is that where you see it? What's your thoughts? >> Yeah so well first of all, I mean if you think of tokens as another form of currency, and by the way, I think we have to be careful about what we say, cryptocurrencies, the industry talks about thousands of cryptocurrencies out there where there's really not. There's maybe dozens and they're all derivatives of just a few models, bitcoin being one prominent model and there's a lot of offshoots off of that. But the rest of what we call cryptocurrencies are really tokens that represent primarily securities, which is why the SCC's getting involved. But the really interesting thing about this is these tokens move at high velocity because they're digital and so, but these digital things represent a claim on real world value, and that's where it becomes really interesting. IBM's built and launched as kind of its first foray into the solution space of financial services where IBM is an investor in this technology, a cross-border payment solution that inherently re-engineers this whole correspondent banking, this international wire process, and where FX, foreign exchange, becomes a real time capability in a series of operations that execute as an atomic unit. That's novel today. When you want to send money from here to somewhere else in the world, you go to your bank, your bank sends an instruction to another bank, and they respond and say "Yeah you know it's okay "because the person you're sending it to is not a terrorist, "is not on a some sort of sanctions list," great, now the bank has to actually go settle and it settles through another network, so the novelty is why can't the messages and the data and the value itself, the digital asset, why can't they exist and move together at the same time? That's what we've really built. But as we've built and deployed that and are getting banks and non-bank financial institutions to sign up for it because the cost of moving money goes way, way, way down and the user experience goes way, way, way up because instead of taking two or three days and you don't know how much it's going to cost until it gets there, it takes 10 or 15 seconds and you know before you even press send how much it's going to cost to get there. It all boils down to this notion of digital assets, that's what it all comes down to, is the way to settle value with finality in real time is for one party to exchange a digital asset with another party. Today, initially, the only form of negotiable digital assets are cryptocurrencies which has banks a little scared, but as we start talking through what we've learned in the enterprise blockchain space, we realized that we can tokenize all sorts of other asset classes, commodities, securities, and even fiat currencies where central banks or commercial banks can issue a token that represents a claim on deposits held at some financial institution and that's, that's a-- >> So you see tokenization as a big deal. >> It's a huge deal. I mean it's everything, I think it's-- >> It's the economic value of the ... >> I think it's the tipping point for blockchain. The irony is it goes back to bitcoin kind of started this all. You know we said "Well we like the idea of the technology "underneath bitcoin, but we want to focus on blockchain," I mean forget for a second blockchain is actually terminology that's invented by the bitcoin primer that was published nine years ago by Satoshi, so yeah it's their, whoever they are, it's their terminology, and it's kind of coming back full circle where you're seeing the convergence of all of these cool optimization capabilities, you know, immutability and workflow optimization, supply chain management-- >> And there's a lot of work to be done on performance and whatnot, but the concept of decentralized immutability data is fine, store the data. Now there's, it's got to get fixed, but I think that what that enables and I think you agree that tokenization's critical. So for a company that wants to token their business or raise money via tokens or get involved in this new economic value creation, innovation trend, how do they do it? And by the way are there tools available? You mentioned banking, and the banking business got to where it was because you had to build the picks and shovels to make it happen, you had to do a swift and you had to have this stuff go on. Now developers don't necessarily have the tools, so there's a picks and shovel market and there's also the real innovation. >> Yeah and that's I think the value contribution that IBM brings. I mean we bring 107 years of credibility in developing and operating mission critical, transactional, and financial systems, and I could do just an ad for a second, that's what the IBM blockchain platform is all about and as the industry evolves, as our platform offering evolves, what we want to be able to bring to small business, medium sized businesses, large businesses is the ability to develop solutions using our toolkit. >> So Jesse I want you to put your financial hat on and at the same time put your payments hat on and your token economics hat on, three hats. Hey I want to tokenize my business, I really want to get in. So we have an innovative team, we're seeing new business model formulas and logic that we want to disrupt, what do I do? I got an existing, growing business that I know has assets and I'm not a startup, but I'm not trying to pivot like Kodak, so I'm not dying, throwing the hail Mary, or I'm not a startup and got to build a whole product. I'm a real business, I'm growing, and I see tokenization as a way for me to be successful. What do I do? What's your advice? >> Well I think you look at it from all potential angles. If you look at any business, they're always looking to improve the bottom line by shrinking costs, right? They're also looking to improve the bottom line by increasing the top side, increasing revenue, and I think as a mid-sized business or a growing business, you have the opportunity to use tokenization, to use blockchain and digital currencies to do both of those things. You have the ability to accelerate the adoption of whatever your good or service or product is by if it's tokenizable, and most things are whether it's a utility, access to some service you provide, or whether it's an asset, some widget that you sell, you enable primary and secondary markets by creating a digital asset that can be bought by anybody anywhere around the world. I mean that's one way to do it and so I think getting people to realize the potential there-- >> You got programs, they call up IBM or get some developers, make it happen. Okay so killer apps money, that's going to be a 30 plus year trend and certainly this highlights that, but the other thing that's happened, it's coming out of either, in the open source community as well as cloud, the notion of marketplaces and communities so marketplaces and communities become a very important role in the token economics piece. What's your thoughts and opinion on that narrative? >> Well again for me, it goes back, I always go back to digital assets. We in the U.S. and around the world, when we start talking about financial instruments, we classify assets differently, but when it comes to an ecosystem and a community that becomes inherently peer to peer and inherently democratic, it's about an asset class agnostic distributed exchange where I can sell you my security token in exchange for your fiat token, or I can sell you my commodity token or utility token for the same. I think the ecosystem gets built automatically by way of new assets coming to a common network or interoperable set of networks, and that's what's missing today by the way, same in capital markets, right? The holy grail in the capital market space today is how do I shrink the time between trade and settlement? There's this whole t plus three and we're spending billions of dollars to go to t plus two, we gain a day, so the trade day and the settlement date are two days apart. I mean you just think about kind of the absurdity of that. If you just say well if the security that you're buying is a digital asset, and the money that you're buying it with is a digital asset, and they both exist on either the same network or an interoperable network, the transfer of ownership and the transfer of value happen together as two operations or a single operation in one atomic transaction, you've solved the problem. >> Speed of light can make it happen. >> Right, delivery versus payment, that's what the capital markets industry is trying to optimize for, right? Because it improves the balance sheet of all sorts of finance-- >> You had a phrase you mentioned before we came on camera, something about money, the future of money. What was that phrase? >> Programmable money? >> Programmable money. >> Yeah, right, right. >> I want you to take a minute to explain. Love this concept, Miko Matsumura, thought leader friend of ours, has a vision called open source money which is more of an open source, this hey money's flowing, it's open, it's out there, but you have a different perspective which I like too which is programmable money. What does that mean? Describe the concept and take a minute to unpack that. >> The concept of programmable money comes out of a paper that I jointly authored with Jed McCaleb who is the founder of Stellar and was the co-founder of Ripple and is a really smart guy so I feel like I have a small brain when I'm around him but we really wrote it in the context of central banking and the ultimate issuer of an asset because central banks are the issuers of currencies. Right now the primary dealers, if you will, for currencies are commercial banks and so that whole commercial, central, fractional reserve banking model has been replicated from the western world to everywhere else in the world and you can't get access to central bank money as they say. But if the central banks were to issue digital currencies which is essentially a token of fiat currency, so you own the token, you own a claim of fiat deposits held on the balance sheet of the central bank, now you have the ability to move that around. You can actually program the movement of money because it's a digital thing, it's a digital asset that's as good as cash and if you are working with a central bank who's issuing it, not only is it electronic money, it's actually legal tender because if the central bank issues it, it becomes legal tender which means everybody who accepts it has to accept that form of payment. That's pretty profound if we can get to that point and we're working with-- >> And software's a big driver in that because you need software to manage digital assets. >> Oh yeah, absolutely. >> The software's driving it. Bill Tai is an investor, I interviewed him, and he had an interesting topic and I made a highlight of it. He said after World War II, we talked about the oil situation when the dala was pegged to OPEC, that was essentially tokenizing oil. Then okay that's good, so that was their ICO. >> Right, right, yeah, essentially. >> That's what you're saying, you can actually put fiat to the digital token and take advantage of the efficiencies of digital. >> Right, yeah, okay-- >> Taking down all the structural inefficiencies that were built prior to digital. Is that ... >> It is. You fast forward a little bit and think where that takes us. It's no secret that the U.S. dollar is the trade currency of the world, and I want to be careful what I say because, you know, I'm an American patriot here but there are other large G20 nations who wouldn't mind dethroning the U.S. dollar as the trade currency of the world and so as you see central banks starting to get involved in the issuance of digital currency, you create a situation where all of a sudden well maybe oil could be traded heresy in other currencies besides the U.S. dollar which is all it's traded in today. Goes back to your ecosystem question. >> This is a great point. We could riff on this stuff, let's riff on this. The UK just signed a deal with Coinbase, this is a major signal. >> Sign, yeah. >> You got a legitimate country saying we're going to give a license to Coinbase, now they have Brexit to deal with so they're looking at it as an opportunity. Outside of the UK coming in and doing that deal with Coinbase, it's on the web, look up Coinbase in the UK, you'll see the deal. You have other companies trying to jockey for who's going to be the Wall Street for crypto? Meaning I want to convert crypto to fiat, where do I go? Do I go to Estonia? Do I go to Dubai? Bahrain? Armenia? China? There is no place yet. Your thoughts, what's going to happen? What shoe will drop first? Is there a domino effect? >> Yeah, well there's a couple things as it relates to the UK and kind of the extension to Coinbase of access to the national payment system which is really what enables them to then convert fiat to crypto and back. That's pretty interesting. Going back to the programmable money thing, though. If you have a central bank issued token, you've essentially extended the real time gross settlement system which has been only accessible by commercial banks to anybody that holds that token, right? It's a trend, I think the UK sees it coming, I think the Federal Reserve sees it coming. It's going to happen. >> Is it winner take all or winner take most? >> I think it creates a much more purely efficient market. It's a democratic system so I don't think there is going to be a new Wall Street, I think it's going to be-- >> John: Decentralized. >> Exactly, I mean that's the beauty of it. It's scary though for establishments like Wall Street to look at this and it-- >> I mean are the banks scared? You're dealing with the banks right now. >> Yes, they're scared. I mean I've actually read a recent article that Bank of America, the headline was "Bank of America's afraid of digital currency." You've seen Jamie Dimon who came out with a kind of a hard stance against bitcoin and has since kind of backed away from that. >> Of course you probably bought in when it dropped and now it's back up again. >> Well I think part of the bank was actually facilitating their clients and trading bitcoin so that might've been it. There's a natural reaction to it, especially if you're part of the mainstream establishment. >> There's no proof of that, I'm just saying we're posting on Reddit and whatnot. >> No we're just joking around. Jamie's a, he's a good guy, right? >> Can I get your thoughts on digital nations? We've been talking about this. Just a few years ago, smart cities, IoT was kind of the narrative, oh be a smart city, control the traffic lights, and instrument the physical goods and services. Now with crypto and blockchain front and center conversation is digital nations with sovereignty around their cash. This is kind of your point earlier. How are you seeing that? What's your view? Are you seeing that trend? Are there dots connecting for you? Because again, people are jockeying for a position on the global digital backbone to be a major part of the money flow, the fiat conversion, what is the goods and services? Who's going to clear the values? All digital, it's a perfect storm. >> Well I think there's always going to be the need for trusted entities to be the issuers of these assets because it all comes down to trust at the end of the day. The thing with bitcoin is that it's purely autonomous and people are a little bit skeptical of it because they're like, "Well who's controlling "the monetary policy?" and the answer is the market, you know, the users of the network are controlling it and that's why you see such volatility, right? Because the traders love it, they can go in and trade the up trends and the down trends. As long as there's volatility, traders are making money. I think there is still going to be a place for central authorities to add value, but that's going to be the pressure, is for them to prove that they're adding value not, you know, bureaucracy masquerading as process. >> I was reading an article that Telegram, which is doing a huge ICO, just got shut down by the Russian government, they went to turn over their keys, their private keys of their users. Say goodbye to the-- >> Jesse: I didn't read that, that's crazy. >> It's really crazy, so that's going to put a damper on their ICO but regulatory and then government issues around countries becomes a big deal. In your experience as Wells Fargo, at a bank, looking forward in the new digital world, is it one of those situations where path of least resistance, the countries that go more friendly get around that in a sovereignty where you domicile, where you start your company, where you do your banking. I mean I could start a company in Gibraltar and bank in Switzerland. >> Well transparency is part of the benefit or the downside of this, right? I think there may be advantages that pop up but I think they will equalize over time. I've been around the world now for IBM talking to 20 plus central banks, and I had a really interesting conversation with one of them recently in Asia. We're in the room with deputy director level people who are responsible for things like the NA money laundering policy and the economics and monetary policy and things like that and one person said, "You know, we're really torn "between two equally unacceptable decisions. "One is to ignore cryptocurrencies altogether, "and the other end of the spectrum is "to make them illegal, to ban them." I thought it was poignant that they see those as unacceptable, they have to do something in the middle. >> Do they weigh or ban? I mean look, the banning's happening. >> But okay so you saw that Trump used the executive order to prevent Americans from using or trading in the Venezuelan crypto that was issued on Ethereum, right? I saw that Venezuelan thing as a publicity stunt more than anything, an active of global defiance. So there's precedent now for, and the Russia thing with Telegram-- >> The United States of America has to step up its game because look at it, we have a lot of, I mean I remember back in the crypto days when I was just getting into the business, late 80s, early 90s, you couldn't even do it in the U.S., you go to Canada, that's why Canada's got a lot of innovation up there. We're risking our country, and I had one guy tell me in Puerto Rico, he's from South Africa, and he shouldn't be throwing any stones either but his point was, he says, "America's becoming Europe. "There's a shrinking middle class "while other emerging markets have a growing middle class," so the global impact of blockchain, cryptocurrency, and these applications are significant and have to be factored into policy decision making for governments. The U.S. can't just think about itself anymore in a vacuum. >> Right, not anymore. >> Because there's implications otherwise the U.S. will turn into Europe, regulated, all these rules, byzantine stuff. It's a real problem. Your thoughts on that. >> It is. It's cliche, but we live and work in a global economy. The flow of information globally in real time has been around now for a while and it's about time it came to money. The internet of money is a term I've heard. It's just, it's unavoidable. >> Jesse Lund here inside The Cube. Great guest, great conversation. >> Yeah, thanks. >> How do people get ahold of you on IBM's, you mentioned you got some great stuff going on, you've written a paper, you've got a lot of content, where does someone go to discover some of the stuff that you're working on they could get involved with you guys? >> Yeah well I mean the best place to go is IBM.com/blockchain, that'll tell you a lot about what we're doing and the different industry-- >> And the programmable money paper you wrote, is that there? >> It's out there as well, there's a link to that. >> On IBM.com? >> You can get me directly on LinkedIn, I try to be pretty responsive with that because I really enjoy the dialogue. This is a revolution of the peoples, man, it's all over the world, so it's great, it's great to be a part of it. >> And people tokenizing their business, there's real opportunities to change the game to bring consensus, data driven, new kind of supply chain whatever to the markets you're in, great opp-, and you need banking. >> Yeah of course. >> You need to have money. Money, marketplaces, and communities, that's my mantra. >> I subscribe to it. >> Thanks for coming on. >> Thank you, thanks for having me. >> Jesse Lund. I'm John Furrier here at IBM Think 2018. Cube coverage continues after this short break. (upbeat music)

(upbeat music) >> Welcome to this special CUBEConversation here in the Palo Alto CUBE studio. I'm John Furrier, the co-host, theCUBE co-founder of SiliconANGLE Media. In theCUBE we're here with Alan Cohen, CUBE alumni, joining us today for a special segment on the future of technology and the impact to society. Always good to get Alan's commentary, he's the Chief Commercial Officer for Illumio, industry veteran, has been through many waves of innovation and now more than ever, this next wave of technology and the democratization of the global world is upon us. We're seeing signals out there like cryptocurrency and blockchain and bitcoin to the disruption of industries from media and entertainment, biotech among others. Technology is not just a corner industry, it's now pervasive and it's having some significant impacts and you're seeing that in the news whether it's Facebook trying to figure out who they are from a data standpoint to across the board every company. Alan, great to see you. >> Always great to be here, I always feel like, I can't tell whether I'm at the big desk at ESPN or I've got the desk chair at CNBC, but that's what it's like being on theCUBE. >> Great to have you on extracting the signal noises, a ton of noise out there, but one of things of the most important stories that we're tracking is, that's becoming very obvious, and you're seeing it everywhere from Meed to all aspects of technology. Is the impact of technology to people in society, okay you're seeing the election, we all know what that is, that's now a front and center in the big global conversation, the Russian's role of hacking, the weaponizing of data, Facebook's taking huge brand hits on that, to emerging startups, and the startup game that we're used to in Silicon Valley is changing. Just the dynamics, I mean cryptocurrency raises billions of dollars but yet (laughs) something like 10, 20% of it's been hacked and stolen. It's a really wild west kind of environment. >> Well it's a very different environment. John, you and I have been in the technology industry certainly for a whole bunch of lines under our eyes over the years have gone there. My friend Tom Friedman has this phrase that he says, "Everybody's connected and nobody's in control," so the difference is that, as you just said, the tech industry is not a separate industry. The tech industry is in every product and service. Cryptocurrency is like, the concept of that money is just code. You know, our products and services are just code, it raises a couple of really core issues. Like for us on the security point of view, if I don't trust people with the products they're selling me, that I feel like they're going to be hacked, including my personal data, so your product now includes my personal information, that's a real problem because that could actually melt down commerce in a real way. Obviously the election is if I don't trust the social systems around it, so I think we're all at an, and I'd like to say world is still kind of like iRobot moment, and if you remember iRobot, it's like, people build all these robots to serve humankind and then one day the robots wake up and they go, "We have our own point of view on how things are going to work" and they take over, and I think whether it's the debate about AI, whether cryptocurrency's good or bad, or more importantly, the products and services I use, which are now all digitally connected to me, whether I trust them or not is an issue that I think everyone in our industry has to take a step back because without that trust, a lot of these systems are going to stop growing. >> Chaos is an opportunity, I think that's been quoted many times, a variety-- >> You sound like Jeff Goldblum in like Jurassic Park, yeah. (laughing) >> So chaos is upon us, but this is an opportunity. The winds are shifting, and that's an opportunity for entrepreneurs. The technology industry has to start working for us but we've got to be mindful of these blind spots and the blind spots are technology for good not necessarily just for profits, so that also is a big story right now. We see things like AI for good, Intel has been doing a lot of work on that area, and you see stars dedicated to societal impact, then young millennials, you see the demographic shift where they want to work on stuff that empowers people and changes society so a whole kind of new generation revolution and kind of hippie moment, if you look at the 60s, what the 60s were, right? >> Well there's people out in the street protesting, right? There were a couple of million women out in the street this weekend, so we are in that kind of moment again, people are not happy with things. >> And I believe this is a signal of a renaissance, a change, a sea change at enormous levels, so I want to get your thoughts on this. As technology goes out in mainstream, certainly from a security standpoint, your business Illumio is in that now where there's not a lot of control, just like you were mentioning before we came on that all the spends happening but no one has more than 4% market share. These are dynamics and this is not just within one vertical. What's your take on this, how do you view this sea change that's upon us, this tech revolution? >> Well, you know, think about it. You and I grew up in the era where clients server took over from main frame, right? So remember there was this big company called IBM and they owned a lot of the industry, and then it blew up for client server and then there were thousands of companies and it consolidated its way down, but when those thousands of new companies, like you didn't know what was going to be Apollo and what was going to be Oracle right? Like you didn't know how that was going to work out, there was a lot of change and a lot of uncertainty. I think now we're seeing this on a scale like that's 10x of this that there's so much innovation and there's so much connectedness going on very rapidly, but no one is in control. In the security market, you know, what's happening in our world is like, people said, okay I have to reestablish control over my data, I've lost that control, and I've lost it for good reasons, meaning I've evolved to the cloud, I've evolved to the app economy, I've done all of these things, and I've lost it for bad reasons because like am I, like I'm not really running my data center the way I should. We're in the beginning of a move in of people kind of reasserting that control, but it's very hard to put the genie back in the bottle because the world itself is so much more dynamic and more distributed. >> It's interesting, I've been studying communities and online communities for over a decade in terms of dynamics. You know, from the infrastructural level, how packets move to a human interaction. It's interesting, you mentioned that we're all connected and no one's in control, but you now see a ground swell of organic self-forming networks where communities are starting to work together. You kind of think about the analog world when we grew up without computers and networks, you kind of knew everyone, you knew your neighbor, you knew who the town loony was, you kind of knew things and people watch each other's kids and parents sat from the porch, let the kid play, that's the way that I grew up, but it was still chaotic but yet somewhat controlled by the group. So I got to ask you, when you see things like cryptocurrency, things like KYC, know your customer, anti money laundering, which is, you know these are policy based things, but we're in a world now where, you know, people don't know who their neighbors are. You're starting to see a dynamic where people are-- >> Put the phone down. >> Asserting themselves to know their neighbor, to know their customer, to have a connected tissue with context and so your trust and reputation become super important. >> Well I think people are really, so like every time there is a shift in technology, there's scary stuff. There's the fuddy-duddy moment where people are saying, "Oh we can't use that," or "I don't know that," and you know, clearly we're in this kind of new kam-ree and explosion of this cloud mobile blah blah blah type of computing thing and ... Blah blah blah is always a good intersection when you don't have a term. Then things form around it, and just as you said, so if you think about 25 years ago, right, people created The WELL and there was community writing first bulletin boards and like now we have Facebook and you go through a couple of generations and for a while, things feel out of control and then it reforms. I personally am an optimist. Ultimately I believe in the inherent goodness of people, but inherent goodness leaves you open and then, you know, could be manipulated, and people figure these things out. Whether it's cryptocurrency or AI, they are really exciting technologies that don't have any ground rules, right? What's going to happen I believe is that people are going to reestablish ground rules, they're going to figure out some of the core issues, and some of these things may make it, and some of these things may not make it. Like cryptocurrency, like I don't know whether it makes it or not, but certainly the blockchain as a technology we're going to be incorporating in what we do, and maybe the blockchain replaces VPNs and last generation's way of protecting zeros and ones. If AI is figuring out how to read an MRI in five minutes, it's a good thing, and if the AI is teaching you how to exclude old folks for me finding jobs, it's a bad thing. I think as technology forms, there's always Spectre and 007, right? There's always good and bad sides and you know, I think if you believe-- >> I'm with you on that. I think value shifts and I think ultimately it's like however you want to look at it will shift to something, value activity will be somewhere else. Behind me in the bookshelf is a book called The World is Flat and you're quoted in it a lot as a futurist because you have inherently that kind of view, well that's not what you do for a living, but you're kind of in an opt-- >> Alan: Marketing, futurist, kind of same thing. >> Thomas Friedman, the book, that was a great book and at that time, it was game changing. If you take that premise into today where we are living in a flat world and look at cryptocurrency, and then over with the geo political landscape, I mean I just can't see why the Federal Reserve wouldn't reign in this cryptocurrency because if Japan's going to control a bunch of, or China, it's going to be some interesting conversations. I mean I would be like all over that if I was in the Federal Reserve. >> I think people-- Look, cryptocurrency's really interesting and I think people a little over-rotated. If you look at the amount of GDP that's invested in cryptocurrency, it's like, I don't know, there might've been, you know 20 years ago the same amount involved invested in Beanie Babies, right? I mean things show up for a while and the question is is it sustainable over time? Now I'm trained as an economist, you and I have had this conversation, so I don't know how you have a series of monetary without kind of governmental backing, I just don't understand. But I do understand that people find all kinds of interesting ways to trade, and if it's an exchange, like I mean what's the difference between gold and cryptocurrency? Somebody has ascribed a value to something that really has no efficacy outside of its usage. Yeah I mean you can make a filling or bracelets out of gold but it doesn't really mean anything except people agree to a unit of value. If people do that with cryptocurrency, it does have the ability to become a real currency. >> I want to pick your perspective on this being an economist, this is is the hottest area of cryptocurrency, it's also known as token economics, is a concept. >> Alan: Token economics. >> You know that's an area that theCUBE, with CUBE coins, experimenting with tokens. Tokens technically are used for things in mobile and whatnot but having a token as a utility in a network is kind of the whole concept, so the big trend that we're seeing and no one's really talking about this yet is instead of having a CTO, Chief Technology Officer, they're looking for a CEO, a Chief Economist Officer, because what you're seeing with the MVP economy we're living in and this gamification which became growth hack which didn't really help users, the notion of decentralized applications and token economics can open the door for some innovation around value and it's an economic problem, how you have a fiscal policy of your token, there's a monetary policy, what's it tied to? A product and a technology, so you now have a now a new, twisted, intertwined mechanism. >> Well you have it as part of this explosion, right? We're at a period of time, it feels like there's a great amount of uncertainly because everything's, you know, there's a lot of different forces and not everybody's in control of them, and you know, it's interesting. Google has this architecture, they call it BeyondCorp, where the concept is like networks are not trusted so I will just put my trust in this device, Duo Security's a great example of a company that's built a technology, a security technology around it which is completely antithetical to everything we know about networks and security. They're saying everything's the internet, I'll just protect the device that it's on. It's a kind of perfect architecture for a world like where nobody is in charge, so just isolate those, buy this, what is a device? It's a token too, it's a person, your iPhone's your personal token. Then over time, systems will form around it. I think we just have to, we always have to learn how to function in a different type of economy. I mean democracy was a new economy 250 years ago that kind of screwed around with most of the world, and a lot of people didn't think it would make it, in fact we went through two World War wars that it was a little on the edge whether democracy was going to make it and it seems to have done okay, like it was pretty good IPO to buy into. You know, in 1776. But it's always got risks and struggles with it. I think if, ultimately it comes together, it's whether a large group of people can find a way to function socially, economically, and with their personal safety in these systems. >> You bring up a great point, so I want to go to the next level in this conversation which is around-- >> Alan: You've got the wrong guy if you're going to the next level because I just tapped out. >> No, no, no we'll get you there. It's my job to get you there. The question is that everyone always wants to look at, whether it's someone looking at the industry or actors inside the industries across the board, mainly the tech and we'll talk about tech, is the question of are we innovating? You brought up some interesting nuances that we talk about with token economics. I mean Steve Jobs had the classic presentation where he had street signs, technology meets liberal arts. That's a mental image that people who know Steve Jobs, know Apple, was a key positioning point for Apple at that time which was let's make computers and technology connect with society, liberal arts. But we were just talking about is the business impact of technology, the economics, and that's just not like just some hand waving, making technology integrate with business. You're in the security business, There are some gamification technology, gamification that's business built into the products. So the question is, if we have the integration of business, technology, economics, policy, society rolling into the product definitions of innovation, does that change the lens and the aperture of what innovation is? >> I think it does, right? The IT industry's somewhere between three and four trillion dollars depends on how it counts in. It grows pretty slowly, it grows by a low single digit. That tells me as composite, like is that, that slow growth is a structural signal about how consumers of technology think in a macro sense. On a micro sense, things shift very rapidly, right? New platforms show up, new applications show up, all kinds of things show up. What I don't think we have done yet, to your point, is in this new integrated world, the role of technology is not just technology anymore. I don't think, you know you said you need Chief Economical Officer, what about Chief Political Officer? What about a Chief Social Officer? How many heads of HR make decisions about the insertion of systems into their business? And that's what this kind of iRobot concept is in my mind which is that you know, we are exceeding control of things that used to be done by human beings to systems and when you see control, the social mores, the political mores, the cultural mores, and the human emotional mores have to move with it. We don't tend to think about things like that. We're like, "I win and my competitors lose." Like technology used to be much more of a zero sum, my tech's better than yours. But the question is not just is my tech better than yours, is my customer better off in their industry for the consumption of my technology of inserting it into their offering or their service? You know what, that is probably going to be the next area of study. The other thing that's very important in whether, any of you have read Peter Thiel's book Zero to One, the nature of competition technology used to feel like a flat playing field and now the other thing that's rising is do you have super winners? And then what is the power of the super winners? So you mentioned whether it's Facebook or Google or Amazon or you know, or Microsoft, the FANG companies right? Their roles are so much more significant now than the Four Horsemen of the Nasdaq were in 2000 when you had Intel and Cisco and Oracle and Saht-in it's a different game. >> You're seeing that now. That's a good point, so you're reinforcing kind of this notion that the super players if you will are having an impact, you're mentioning the confluence of these new sectors, you know, government, policy, social are new areas. The question is, this sounds like a strategic imperative for the industry, and we're early so it's not like there's a silver bullet or is there, it doesn't sound like there, so to me that's not really in place yet, I mean. >> Oh no. We're not even in alpha. We have demo code for the new economy and we're trying to get the new model funded. >> John: That's the demo version, not the real version. It's the classic joke. >> Yeah this not the alpha or the beta version that like you're going to go launch it. If people think they're launching it, I think it's a little preliminary and you know, it's not just financial investment, it's like do I buy in? I'll tell you something that's really interesting. I've been visiting a bunch of our customers lately and the biggest change I'd say in the last two years is they now have to prove to their customers they're going to be good custodians of their data. Think about that, like you could go to any digital commerce you do, any website you use and you give them basically the ticket to the Furrier family privacy, you do, but you don't spend a lot of time questioning whether they're really going to protect your data. That has changed. And it's really changing in B2B and in government organizations. >> The role of data to us is regulation, GDPR in Europe, but this is a whole new dynamic. >> It's not just my data because I'm worried about my credit card getting hacked, I'm worried about my identity. Like am I going to show up as a meme in some social media feed that's substituted for the news? I don't want to use the FN word, but you know what I mean? It is a really brave new world. It's like a hyper-democracy and a hyper-risky state at the same time. >> We're living in an area of massive pioneering, new grounds, this is new territory so there's a lot of strategic imperatives that are yet not defined. So now let's take it to how people compete. We were talking before we came on camera, you mentioned the word we're in an MVP economy, minimum viable product concept, and you're seeing that being a standard operating procedure for essentially de-risking this challenge. The old way of you know, build it, ship it, will it work? We're seeing the impact from Hollywood to big tech companies to every industry. >> Well you've got a coffee mug for a company that does both. Amazon does MVP in entertainment, like we'll create one pilot and see if it goes as opposed to ordering a season for 17 million dollars to hey, let's try this feature and put it out on AWS. What's interesting is I don't think we've completely tilted but the question is will buyers of technology, of entertainment products, of any product start to say, "I'll try it." You know like, look, I've done four startups and I always know there's somebody I can go to get and try my early product. There are people that just have an appetite, right? The Jeffrey Moores, early adapter, all the way to the left of the-- >> They'll buy anything new. >> They'll try it, they're interested, they have the time and the resources, or they're just intellectually curious. But it was always a very small group of people in the IT industry. What I think that the MVP economy is starting to do is look, I Kickstarted my wallet. I don't know if I'm the only person who bought that skinny little wallet on Kickstarter, it doesn't matter to me, it had appeal. >> What's the impact of the MVP economy? Is it going to change to the competitive landscape like Peter Thiel was suggesting? Does it change the economics? Does it change the makeup of the team? All of the above? What's your thoughts on how this is going to impact? Certainly the encumbrance will seem to be impacted or not. >> I think two things happen. One, it attacks the structural way markets work. If you go back to classical economics, land, labor, and capital, and people who own those assets, now you add information as a fourth. If those guys were around now they would say that would be the fourth core asset, production, I'm sorry, means of production is the term. The people who can dominate that would dominate a market. Now that that's flattened out, you know, I think it pushes against the traditional structures and it allows new giants to kind of show up overnight. I mean the e-commerce market is rife with companies that have, like look at Stich Fix. A company driven by AI, fashions, tries to figure out what you like, sends it to you every month, just had a monster IPO. We invented, by the way the Spiegal Catalog, except like with a personal assistant and you know, it's changed that in just a short number of years. I think two things happen. One is you'll get new potential giants but certainly new players in the market quickly. Two, it'll force a change in the business model of every company. If you're in a cab in any city in the world, I'm not saying whether the app works there or not, Uber and Lyft has forced every cab company to show you here's the app to call the cab. They haven't quite caught up to the rest of the experience. What I think happens is ultimately, the larger players in an industry have to accommodate that model. For people like me, people who build companies or large technology companies, we may have to start thinking about MVPing of features early on, working with a small group, which is a little what the beta process is but now think about it as a commercial process. Nobody does it, but I bet sure a lot of people will be doing it in five years. >> I want to get your take on that approach because you're talking about really disrupting, re-imagining industry, the Spiegal catalog now becomes digital with technology, so the role of technology in business, we kind of talked about the intertwine of that and its nuance, it's going to get better in my opinion. But specifically the IT, the information technology industry is being disrupted. Used to be like a department, and the IT department will give you your phone on your desk, your PC on your desk or whatever, now that's being shattered and everyone that's participating in that IT industry is evolving. What's your take on the IT industry's disruption? >> Well look, it started 20 years ago when Marc Benioff and Salesforce decided to sell the sales forces instead of IT people, right? They went around to the end buyer. I don't think it's a new trend, I think a lot of technology leaders now figure out how to go to the business buyer directly and make their pitch and interestingly enough, the business buyer, if the IT team doesn't get on board, will do that. >> John: Because of cloud computing and ... >> Because of everything. The modern analog I think in our world is that the developers are increasingly in control. Like my friend Martin Casado up in Andreessen talks about this a lot. The traditional model on our industry is you build a product, you launch it, you launch your company, you work with the traditional analyst firms, you try to get a little bit of halo, you get customer references, those are the things you do and there was a very wall structured, for example, enterprise buying cycle. >> And playbook. >> Playbook, and there's the challenger sale and there's Jeffrey Moore and there's like seeing God. You've got your textbooks on how it's been done. As everything turns into code, the people who work with code for a living increasingly become the front end of your cycle and if you can get to them, that changes. Like I mean think about like, you know, Tom wrote about this actually in The World is Flat, like Linux started as a patchy. It didn't start with the IT department, it started with developers and there was the Linux foundation and now Linux is everything. >> There's a big enemy called the big mini computer, and not operating systems and work stations. >> Wiped out whole parts of Boston and other parts of the world, right? >> Exactly, that's why I moved out here. >> You filed client's server out here. >> I filed a smell of innovation. No but this is interesting because this location of industries is happening, so with that, so they also on the analog, so Martin's at Andreessen, so we'll do a little VC poke there at the VCs because we love them of course, they're being dislocated-- >> I don't (mumbles) my investors. >> Well no, their playbook is being challenged. Here's an example, go big or go home investment thesis seems not to be working. Where if you get too much cash on the front end, with the MVP economy we were just riffing on and with the big super powers, the Amazons and the Googles, you can't just go big or go home, you're going to be going home more than going big. >> I think they know that. I mean Dee-nuh Suss-man who's I think Chief Investment Officer at Nasdaq has a very well known talking line that there are half as many public companies as there were 10 years ago, so the exit scenario for our industry is a little bit different. We now have things like acqui-hires, right we have other models for monetization, but I think what the flip side of it is, we're in the-- >> Adapt or die because the value will shift. Liquidity's changing, which acqui-hires-- >> I think the investment community gets it completely and they spend a lot more time with the developer mindset. In fact I think there's been a doubling down focus on technical founders versus business founders for companies for just that reason because as everything turns to code, you got to hang out with the code community. I think there are actually-- >> You think there'll be more doubling down on technical founders? You do, okay. >> Yeah I think because that is ultimately the shift. There are business model shifts, but it's, you know, I mean like Uber was a business model shift, I mean the technology was the iPhone and GPS and they wrote an app for it, but it was a business model shift, so it can be a business model shift. >> And then scale. >> And then scale and then all of those other things. But I think if you don't think about developers when you're in our, and it's like we built Illumio because a developer could take the product and get started. I mean you can, developers actually can write security policy with our product because there's a class of customers, where as not everyone where that matters. There's other people where the security team is in charge or the infrastructure team is in charge but I think everything is based on zeros and ones and everything is based on code and if you're not sensitive to how code gets bought, consumed, I mean there's a GitHub economy which is I don't even have to write the code, I'll go look at your code and maybe use pieces of it, which has always been around. >> Software disruption is clear. Cloud computing is scale. Agile is fast, and with de-risking capabilities, but the craft is coming back and some will argue, we've talked about on theCUBE before is that, you know, the craftsmanship of software is moving to up the stack in every industry, so-- >> I think it's more like a sports league. I love the NBA, right? In the old days, your professional team, you'd scout people in college. Now they used to scout them in high school, now they're scouting kids in middle school. >> (laughs) That's sad. >> Well what it says is that you have to-- >> How can you tell? >> You know but they can, right? I think you know, your point about it craft, you're going to start tracking developers as they go through their career and invest and bet on them. >> Don't reveal our secrets to theCUBE. We have scouts everywhere, be careful out there. (laughs) >> But think about that, imagine it's like there's such a core focus on hiring from college, but we had an intern from high school two years ago. We hire freshman. >> Okay so let's go, I want to do a whole segment on this but I want to just get this point because we're both sports fans and we can riff on sports all day long. >> I'm just not getting the chance >> And the greatness of Tom Brady >> to talk about the Patriots. >> And Tom Brady's gotten his sixth finger attached to his hands for his sixth ring coming up. No but this is interesting. Sports is highly data driven. >> Alan: Yep. >> Okay and so what you're getting at here, with an MVP economy, token economics is more of a signal, not yet mainstream, but you can almost go there and think okay data driven gives you more accuracy so if you can bring data driven to the tech world, that's kind of an interesting point. What's your thoughts on that? >> Yeah I mean look, I think you have to track everything. You have to follow things, and by the way, we have great tools now, you can track people through LinkedIn. There's all kinds of vehicles to tracking individuals, you track products, you track everything, and you know look, we were talking about this before we went on the show right, people make decisions based on analytics increasingly. Now the craft part is what's interesting and I'm not the complete expert, I'm on the business side, I'm not an engineer by training, but look a lot of people understand a great developer is better than five bad developers. >> Well Mark Andris' 10x is a classic example of that. >> There's clearly a star system involved, so if I think in middle school or in high school, you're going to be a good developer, and I'm going to track your career through college and I'm going to try to figure out how to attach. That's why we started hiring freshmen. >> Well my good friend Dave Girouard started a company that does that, will fund the college education for people that they want to bet on. >> Sure, they're just taking an option in them. >> Yeah, option on their earnings. Exactly. >> They are. >> It sounds like token economics to me. (laughs) >> You know you can sell anything. We are in that economy, you can sell those pieces. The good news is I think it can be a great flattener, meaning that it can move things back more to a meritocracy because if I'm tracking people in high school, I'm not worrying whether they're going to go to Stanford or Harvard or Northwestern, right? I'm going to track their abilities in an era and it's interesting, speaking about craft, you know, what are internships? They're apprenticeships. I mean it is a little bit like a craft, right? Because you're basically apprenticing somebody for a future payout for them coming to work for you and being skilled because they don't know anything when they come and work, I shouldn't say that, they actually know a lot of things. >> Alan, great to have you on theCUBE as always, great to come in and get the update. We'll certainly do more but I'd like to do a segment on you on the startup scene and sort of the venture capital dynamics, we were tracking that as well, we've been putting a lot of content out there. We believe Silicon Valley's a great place. This mission's out there, we've been addressing them, but we really want to point the camera this year at some of the great stuff, so we're looking forward to having you come back in. My final question for you is a personal one. I love having these conversations because we can look back and also look forward. You do a lot of mentoring and you're also helping a lot of folks in the industry within just your realm but also startups and peers. What's your advice these days? Because there's a lot of things, we just kind of talked a lot of it. When people come to you for advice and say, "Alan, I got a career change," or "I'm looking at this new opportunity," or "Hey, I want to start a company," or "I started a company," how is your mentoring and your advisory roles going on these days? Can you share things that you're advising? Key points that people should be aware of. >> Well look, ultimately ... I never really thought about it, you just asked the question so, ultimately, I think to me it comes down to own your own fate. What it means is like do something that you're really passionate about, do something that's going to be unique. Don't be the 15th in any category. Jack Welch taught us a long time ago that the number one player in a market gets 70% of the economic value, so you don't want to play for sixth place. It's like Ricky Bobby said, if you're not first, you're last. (John chuckles) I mean you can't always be first, but you should play for that. I think for a lot of companies now, I think they have to make sure that, and people participating, make sure that you're not playing the old playbook, you're not fighting yesterday's battle. Rhett Butler in Gone With the Wind said, "There's a lot of money in building up an empire, "and there's even more money in tearing it down." There are people who enter markets to basically punish encumbrance, take share because of innovation, but I think the really inspirational is you know, look forward five years and find a practical but aggressive path to being part of that side of history. >> So are we building up or are we taking down? I mean it seems to me, if I'm not-- >> You're always doing both. The ocean is always fighting the mountains, right? That is the course of, right? And then new mountains come up and the water goes someplace else. We are taking down parts of the client server industry, the stack that you and I built a lot of our personal career of it, but we're building this new cloud and mobile stack at the same time. And you're point is we're building a new currency stack and we're going to have to build a new privacy stack. It's never, the greatest thing about our industry is there's always something to do. >> How has the environment of social media, things out there, we're theCUBE, we do our thing with events, and just in general, change the growth plans for individuals if you were, could speak to your 23 year old self right now, knowing what you know-- >> Oh I have one piece of advice I give everybody. Take as much risk as humanly possible in your career earlier on. There's a lot of people that have worked with me or worked for me over the years, you know people when they get into their 40s and they go, "I'm thinking about doing a startup," I go, "You know when you got two kids in college "and you're trying to fund your 401K, "working for less cash and more equity may not be "the most comfortable conversation in your household." It didn't work well in my household. I mean I'm like Benjamin Button. I started in big companies, I'm going to smaller companies. Some day it's just going to be me and a dog and one other guy. >> You went the wrong way. >> Yeah I went the wrong way and I took all the risk later. Now I was lucky in part that the transition worked. When I see younger folks, it's always like, do the riskiest thing humanly possible because the penalty is really small. You have to find a job in a year, right? But you know, you don't have the mortgage, and you don't have the kids to support. I think people have to build an arc around their careers that's suitable with their risk profile. Like maybe you don't buy into bitcoin at 19,000. Could be wrong, could be 50,000 sometime, but you know it's kind of 11 now and it's like-- >> Yeah don't go all in on 19, maybe take a little bit in. It's the play and run-- >> Dollar cost averaging over the years, that's my best fidelity advice. I think that's what's really important for people. >> What about the 45 year old executive out there, male or female obviously, the challenges of ageism? We're in economy, a gig economy, whatever you want to call, MVP economics, token economics, this is a new thing. Your advice to someone who's 45 who just says "Hey you're too old for our little hot startup." What should they do? >> Well being on the other side of that history I understand it firsthand. I think that you have an incumbent role in your career to constantly re-educate yourself. If you show up, whether you're a 25, 35, 45, 55, or 65, I hope I'm not working when I'm 75, but you never know right? (mumbles) >> You'll never stop working, that's my prediction. >> But you know have you mastered the new skills? Have you reinvented yourself along the way? I feel like I have a responsibility to feed the common household. My favorite part of my LinkedIn profile, it says, "Obedient worker bee at the Cohen household," because when I go home, I'm not in charge. I've always felt that it's up to me to make sure I'm not going to be irrelevant. That to me is, you know, that to me, I don't worry about ageism, I worry about did I-- >> John: Relevance. >> Yeah did I make myself self-obsolescent? I think if you're going to look at your career and you haven't looked at your career in 15 years and you're trying to do something, you may be starting from a deficit. So the question, what can I do? Before I make that jump, can I get involved, can I advise some small companies? Could I work part time and on the weekends and do some things so that when you finally make that transition, you have something to offer and you're relevant in the dialogue. I think that's, you know, nobody trains you, right? We're not good as an industry-- >> Having a good community, self-learning, growth mindset, always be relevant is not a bad strategy. >> Yeah, I mean because I find increasingly, I see people of all ages in companies. There is ageism, there is no doubt. There's financial ageism and then there's kind of psychological bias ageism, but if you keep yourself relevant and you are the up to speed in your thing, people will beat a path to want to work for you because there's still a skill gap in our industry-- >> And that's the key. >> Yeah, make sure that you're on the right side of that skill gap, and you will always have something to offer to people. >> Alan, great to have you come in the studio, great to see you, thanks for the commentary. It's a special CUBEConversation, we're talking about the future of technology impact the society and a range of topics that are emerging, we're on a pioneering, new generational shift and theCUBE is obviously covering the most important stories in Silicon Valley from figuring out what fake news is to impact to the humans around the world and again, we're doing our part to cover it. Alan Cohen, CUBEConversation, I'm John Furrier, thanks for watching. (upbeat music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.