>> (lively music) >> Narrator: From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got a 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois is representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Paul: Thanks Lisa. >> Dominique: Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

(lively music) >> From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of may doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois are representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Thanks Lisa. >> Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

>> Narrator: Live from San Diego, California, it's theCUBE covering KubeCon and CloudNativeCon. Brought to you by Red Hat, the CloudNative Computing Foundation and its ecosystem partners. >> Docker, Docker, Docker. No, you're in the right place. This is KubeCon CloudNativeCon 2019 here in San Diego. I'm Stu Miniman kicking off three days of live, wall to wall coverage. My co-host for most of the week this week is John Troyer. Justin Warren's also in the house. He'll be hosting for me. And a big shout out to John Furrier who's back at the corporate ranch in Palo Alto keeping an eye on all the CloudNative stuff with us. The reason that I actually mentioned Docker is because it is the first thing that is on our lips this week. Just this week, Docker, which is the company that, if it wasn't for Docker, we wouldn't have 12,500 people here at this event. Really democratized containers. But the company itself built out a platform, millions and millions of companies using containers. But when the orchestration layer came in there was some contention, there's lots of politics. I'm waiting for Docker the Broadway musical to come out to talk about all the ins and outs there because Kubernetes really sucked the air out of the CloudNative world. Spawned tons of projects here. As you can see behind us, this ecosystem is massive and swelling. Last year it was 8,000 people, year before it was 4,000 people, so many people here, so. And John, so, let's start. This is your first time at this show, you've done many shows with us, definitely covered some of the cloud-native, you've worked with many of the companies that are in this ecosystem here. Give me your first impressions here of KubeCon CloudNativeCon. >> Sure, sure. Well, I mean Stu, 12,000 people, it's pretty crowded here. We're right by the t-shirt line, on day one of the conference. Look, a conference this big, especially an open source conference, there's several jobs to be done, right. This is an active set of open source projects and open source communities. So a lot of the keynote this morning was updating people on details about the latest releases, the latest features, what's in, what's out, what's going on. CNCF is a very broad umbrella for a very broad number of projects, not a coherent opinionated stack, it's a lot of different things that all contribute to a set of CloudNative technologies. So, that's job one. Job two, it's a trade show, and it's an industry show, and people are coming here to figure out how to build and learn and operate. So, that wasn't particularly well served by the keynote this morning. There was certainly a lot of hands-on this week. There's a huge number of breakouts, there's a huge number of tracks. Even day zero, which is a set of specialty breakout workshops and sessions, everything was packed. There were over a dozen of those. So, what strikes me is the breadth here is that it's a mile wide. I won't say it's an inch deep, because there's some, but it is a mile wide. >> Yeah, yeah, John you are right, there's so much going on. The day zero tracks are amazing. I think there were over two dozen, maybe even more of the sessions where, you know, half-day or full day deep dives. Even talk, there was some other small events even that went on for two or three days leading up to this. So, sprawling ecosystem. Last year at this show in Seattle, I actually said that this show is the independent cloud show that we've been looking for. John, I was at Microsoft Ignite just a couple of weeks ago, and absolutely, Satya Nadella, they're not talking about the bits and the bytes. It's a, you know, Microsoft is your trusted partner for everything you're going to do, including building 50 billion new applications. Amazon Reinvent will just be right after Thanksgiving, and we will hear a very different message from Amazon and where they play. But this is not a company, it is a lot of different projects. The CNCF is the steward of this, and so Kubernetes is the one that gets all the attention. I think for this group to even grow more, it needs to be focused more on the CloudNativeCon, because how do we do cloud-native? You know, what does that mean? We heard, you know, Sugu was up on stage talking about Vitess, and he said, look, if you bake your database directly in fully Kubernetes cloud-native, that means that when you want to move between clouds you bring your data with you. So, data, security, networking, messaging, there's so many pieces here. It's a lot of work to be done to mature this stack, but it definitely is getting more mature. You start hearing many of these projects with a million or more downloads a month. So many pieces. John, what are you looking to dig into this week, what are you most excited for, what questions do you want answered? >> Well, here on theCUBE I'm always excited when we get to talk to people in production, customers, really see what's going on. There's a lot of stuff in production right now, which is not to say a lot of stuff isn't bleeding edge, right. I hear a lot of stuff, just out of the woodwork, about things that are fragile, things that aren't ready, things that are not quite updated, and I think Kubernetes is an architectural as well as a spiritual home for everything. But there's a lot of pieces that plug in, and there are opinionated ways of doing it, there are best of breed way, there are vertically integrated stacks. What's the best approach, it's not clear to me. I mean if you have to look at it from a company perspective, who are the winners and losers, I don't think that's a very productive way of looking at it. I'm interested in some projects like, we're going to be talking with Rancher, and they've got some announcements, but I'm also interested in K3s, which is their project there. I'm been hearing some really interesting things on the storage front. You know, all these things are really necessary. It's not all just magic containers moving around. You got to actually get the bits and bytes into the right place at the right time and backed up. >> Yeah, I love that you brought up K3s. Edge is definitely something that I hear talking a lot, because if you talk about cloud-native, it's not just about public cloud. Many of these things can run in my on-premises data centers and everything like that. >> And Edge fits in all of these environments, so. Right, winners and losers, I remember two years ago, first time I got a chance to interview Kelsey Hightower, who we do have on the program. He had actually taken a couple shows off, but he's back here at the show. I said Kelsey, why are we spending so much talking about Kubernetes? Doesn't this just get baked into every platform? And he's like, yeah totally, that's not the importance of it. It's not about distributions, and not about who's who, any of the software companies, it's how do they pull all of the pieces together. How do they add value on top of it. One of the terms I've heard mentioned a lot is, we need to think a lot about day two. Heck, there was even one of the companies that was heavy in this space, Mesosphere, they renamed the company Day Two IQ, spelled D2IQ. No relation to R2D2. But you know, that's what they are focused on to help these things really go together. So yeah, we talk about multicloud, and how do I get my arms around all of these pieces, how do I manage a sprawling environment. You add Edge into it. I've got a huge surface of attack for security issues. So, John, remember cloud was supposed to be simple and cheap, and it really isn't either of those things anymore, so yeah, a lot for us to dig into. >> Yeah, it'll be an interesting mix. Developers, experts, people brand new, probably half the people here they're the first time, and people coming over from the IT space as well as people coming from the open source space and I even saw this morning this is the biggest conference I've ever been to. So it's a many, it's different parts of the elephant, I'd say. >> Yeah, absolutely. It is a good sized conference, especially for open source it probably is the largest. But Salesforce Dreamforce is going on this week, which is more than an order of magnitude bigger, so my condolences to anybody in San Francisco right now, because we know the BART and everything else completely swamped with too many people. One other thing, you know, CNCF, what's really interesting for me always is when you look at a lot of these projects, the people that we saw up on stage were companies, it was the person that oh, I started this project and I'm the technical lead on it, and that's where I'm going. We've interviewed many of the people that start these projects, and they come many times out of industry. It's not a vendor that said, hey, I built something and I'm selling it. It is companies like Uber and Lyft that said, we did things at massive scale, we had a problem, we built something, we thought it was useful for us. Open source seemed a good way to help us get broader visibility and maybe everybody could help, and other people not only pitch in, but say this is hugely valuable, and that's where we go with it. So, it's something we, a narrative I've heard for years about everybody's going to be a software company, well, almost everybody at this conference is building software. We've heard about 30 to 40% of the people attending this show are developers, and therefore many of them are going to build products. A question I have and I'll give you is, with Docker, we just kicked off talking about Docker. You know, Docker created this huge wave of what happens there, but to put it bluntly, Docker the business failed. So, they are not dead, there's the piece that's in Mirantis, there's the piece doing the developer piece. We wish all of them the best of luck, but they had the opportunity to be the next VMware, and instead they are the company that gave us this wave, but did not capitalize on it. So, I look around and I see so many companies, and you say, "Hey, what are you?" "Oh, we're the creators of X technology in this project," and my question is, are you actually going to be able to make money and do a business, or is this just something that gets fit into the overall ecosystem. John, any thoughts and advice for those kind of companies. >> Well, I mean we are here, even though there's 12,000 people here, this is still very leading edge, right. There's a lot of pieces, parts here. We're not sure how they're all going to fit together. A lot of the projects have come out of real use cases, like you say, but they're, it's commercial viability is a different beast than utility. Docker was very good at developer experience, but the DNA of actually selling an enterprise management stack is a whole different beast, and there are a lot of those too. So I mean I think a lot of the companies here may not be around, but their technologies will live on. I think if you're here, and the interviews here at the show I think will be a, you'll want to have your antenna out to see like, okay, does this give you a feeling like this is solving a real problem and is incorporated in a real ecosystem. You know, the big company, it cuts both ways, right. Some of the times those technologies get absorbed and become the standard, sometimes they disappear. So the advice is you just put one foot in front of the other and try to find people in production. That's the only way at the end of the day that you could move ahead as a small company. >> All right, John, I gave you one piece of advice when we came here and I said, you know one thing we don't talk about at this show, we don't talk about OpenStack. So, I'm going to break that rule for a second here, just 'cause I feel we have as an industry learned some of the lessons. There is some of the irrational exuberance around some of these. There's lots of money being thrown at these environments, but I do feel that we are reaching maturity and adoption so much faster, because we are not trying to replacing something. The early days of OpenStack was, you know, we're your alternative for AWS, and we're going to get you off of VMware licensing. And both of those things were, they didn't happen for the most part. And OpenStack did fit in certain environments, especially outside of North America there's lots of OpenStack deployments. The telecommunications environment OpenStack is used a bunch. Telecom, another area, talk about Edge, that plays in here and we have a number of conversations. But there are both the big and the small companies when I look at our list of people we're going to be talking on the program. You know, I love first the customers. We've got Fidelity, Bloomberg, Red Cross, and Ford Motor Company all on the program, and we've got big companies, mega giants like Cisco, Hewlett Packard Enterprise, as well as couple of companies that came out of stealth like in the last week, including Render and Chronosphere. So, you know, broad spectrum of what's going on. You've done some of the OpenStack shows with me. You've got a long community and ecosystem viewpoint, John. What do you think and what do you hear, yeah. >> You know, this is, I guess yeah, this is a next generation, you could look at it that way. Anytime you bring together one of these open source foundations, you know, it is kind of a new style of development. You do have differing agendas. People do again have to have their antenna up to see, is this person promoting this open source project and what is their commercial interest in it. Because there are different agendas here. But it looks pretty healthy. Look, there's probably a million engineers worldwide that are going to have to know the guts of Kubernetes, but it's a different job to be done than OpenStack. OpenStack community is actually, that exists, is still thriving. It is good for the job to be done there. This job to be done's a little different. I think it's going to be an engine, you know, the engine that's embedded in everything else. So there's going to be a hundred million engineers that don't need to know anything about Kubernetes, but people here are the people that pop the hood open and start to you know, mess with the carburetor and this is a carburetor show. And so for the coverage here we're going to try to up level it to talk about the business a little bit, but this feels important. It feels cross-cloud, it feels outside of any one silo, and I'm really interested to see what we're going to learn this week. >> Okay, and thank you John. I really appreciate it to get it right final. It's like what is our job here? We are an independent media organization. Yes, we did bring our own stickers here to be able to, you know, we know everybody here loves stickers, so we've got theCUBE and we've got the fun gopher one, our friends at Women Who Go that support this, because, you know, inclusion, diversity, something that this community definitely embraces, we are huge supporters of their, but right, we want to be able to give that broad viewpoint of everything. We're not going to be able to get into every project. We're not going to go as deep as the day zero content web, but give a good flavor for everything going on in the show. I've found of all the shows I've gone to in recent years, this is some of the biggest brains in the industry. There's a lot of really important stuff, so I appreciate bringing my PHD holding co-host with me, John. Looking forward to three days with you to dig into all the environment. All right, so we will be wall to wall coverage, three days. If you're at the event, we are here in the expo hall. You can't miss us, we've got the big lights right next to the CloudNativeCon store. If you're online of course reach out to us. I'm @stu, S-T-U on Twitter. He's @jtroyer, and hit us up, see us in person, come grab some stickers, let us know who you want to talk to and what question you have, and as always, thank you for watching theCUBE. (upbeat music)

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)

>> Live from Boston, Massachusetts, it's The Cube! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to The Cube's live coverage of IBM's Chief Data Strategy Summit, I'm your host Rebecca Knight, along with my cohost Dave Vellante. We have a big panel today, these are our social influencers. Starting at the top, we have Christopher Penn, VP Marketing of Shift Communications, then Tripp Braden, Executive Coach and Growth Strategist at Strategic Performance Partners, Mike Tamir, Chief Data Science Officer at TACT, Bob Hayes, President of Business Over Broadway. Thanks so much for joining us. >> Thank you. >> So we're talking about data as a way to engage customers, a way to engage employees. What business functions would you say stand to benefit the most from using data? >> I'll take a whack at that. I don't know if it's the biggest function, but I think the customer experience and customer success. How do you use data to help predict what customers will do, and how do you then use that information to kind of personalize that experience for them and drive up recommendations, retention, upselling, things like that. >> So it's really the customer experience that you're focusing on? >> Yes, and I just released a study. I found that analytical-leading companies tend to use analytics to understand their customers more than say analytical laggards. So those kind of companies who can actually get value from data, they focus their efforts around improving customer loyalty by just gaining a deeper understanding about their customers. >> Chris, you want to jump in here with- >> I was just going to say, as many of us said, we have three things we really care about as business people, right? We want to save money, save time, or make money. So any function that meets those qualifications, is a functional benefit from data. >> I think there's also another interesting dimension to this, when you start to look at the leadership team in the company, now having the ability to anticipate the future. I mean now, we are no longer just looking at static data. We are now looking at anticipatory capability and seeing around corners, so that the person comes to the team, they're bringing something completely different than the team has had in the past. This whole competency of being able to anticipate the future and then take from that, where you take your organization in the future. >> So follow up on that, Tripp, does data now finally trump gut feel? Remember the HBR article of 10, 15 years ago, can't beat gut feel? Is that, we hit a new era now? >> Well, I think we're moving into an era where we have both. I think it's no longer an either or, we have intuition or we have data. Now we have both. The organizations who can leverage both at the same time and develop that capability and earn the trust of the other members by doing that. I see the Chief Data Officer really being a catalyst for organizational change. >> So Dr. Tamir I wonder if I could ask you a question? Maybe the whole panel, but so we've all followed the big data trend and the meme, AI, deep learning, machine learning, same wine, new bottle, or is there something substantive behind it? >> So certainly our capabilities are growing, our capabilities in machine learning, and I think that's part of why now there's this new branding of AI. AI is not what your mother might have thought AI is. It's not robots and cylons and that sort of thing that are going to be able to think intelligently. They just did intelligence tests on the different, like Siri and Alexa, quote AIs from different companies, and they scored horribly. They scored much worse than my, much worse than my very intelligent seven-year old. And that's not a comment on the deficiencies in Alexa or in Siri. It's a comment on these are not actually artificial intelligences. These are just tools that apply machine learning strategically. >> So you are all thinking about data and how it is going to change the future and one of the things you said, Tripp, is that we can now see the future. Talk to me about some of the most exciting things that you're seeing that companies do that are anticipating what customers want. >> Okay, so for example, in the customer success space, a lot of Sass businesses have a monthly subscription, so they're very worried about customer churn. So companies are now leveraging all the user behavior to understand which customers are likely to leave next month, and if they know that, they can reach out to them with maybe some retention campaigns, or even use that data to find out who's most likely to buy more from you in the next month, and then market to those in effective ways. So don't just do a blast for everybody, focus on particular customers, their needs, and try to service them or market to them in a way that resonates with them that increases retention, upselling, and recommendations. >> So they've already seen certain behaviors that show a customer is maybe not going to re-up? >> Exactly, so you just, you throw this data in a machine learning, right. You find the predictors of your outcome that interest you, and then using that information, you say oh, maybe predictors A, B, and C, are the ones that actually drive loyalty behaviors, then you can use that information to segment your customers and market to them appropriately. It's pretty cool stuff. >> February 18th, 2018. >> Okay. >> So we did a study recently just for fun of when people search for the term "Outlook, out of office." Yeah, and you really only search for that term for one reason, you're going on vacation, and you want to figure out how to turn the feature on. So we did a five-year data poll of people, of the search times for that and then inverted it, so when do people search least for that term. That's when they're in the office, and it's the week of February 18th, 2018, will be that time when people like, yep, I'm at the office, I got to work. And knowing that, prediction and data give us specificity, like yeah, we know the first quarter is busy, we know between memorial Day and Labor Day is not as busy in the B to B world. But as a marketer, we need to put specificity, data and predictive analytics gives us specificity. We know what week to send our email campaigns, what week to turn our ad budgets all the way to full, and so on and so forth. If someone's looking for The Cube, when will they be doing that, you know, going forward? That's the power of this stuff, is that specificity. >> They know what we're going to search for before we search for it. (laughter) >> I'd like to know where I'm going to be next week. Why that date? >> That's the date that people least search for the term, "Outlook, out of office." >> Okay. >> So, they're not looking for that feature, which logically means they're in the office. >> Or they're on vacation. (laughter) Right, I'm just saying. >> That brings up a good point on not just, what you're predicting for interactions right now, but also anticipating the trends. So Bob brought up a good point about figuring out when people are churning. There's a flip side to that, which is how do you get your customers to be more engaged? And now we have really an explosion in reinforcement learning in particular, which is a tool for figuring out, not just how to interact with you right now as a one off, statically. But how do I interact with you over time, this week, next week, the week after that? And using reinforcement learning, you can actually do that. This is the the sort-of technique that they used to beat Alpha-Go or to beat humans with Alpha-Go. Machine-learning algorithms, supervised learning, works well when you get that immediate feedback, but if you're playing a game, you don't get that feedback that you're going to win 300 turns from now, right now. You have to create more advanced value functions and ways of anticipating where things are going, this move, so that you see things are on track for winning in 20, 30, 40 moves, down the road. And it's the same thing when you're dealing with customer engagement. You want to, you can make a decision, I'm going to give this customer a coupon that's going to make them spend 50 cents more today, or you can make decisions algorithmically that are going to give them a 50 cent discount this week, next week, and the week after that, that are going to make them become a coffee drinker for life, or customer for life. >> It's about finding those customers for life. >> IBM uses the term cognitive business. We go to these conferences, everybody talks about digital transformation. At the end of the day it's all about how you use data. So my question is, if you think about the bell curve of organizations that you work with, how do they, what's the shape of that curve, part one. And then part two is, where do you see IBM on that curve? >> Well I think a lot of my clients make a living predicting the future, they're insurance companies and financial services. That's where the CDO currently resides and they get a lot of benefit. But one of things we're all talking about, but talking around, is that human element. So now, how do we take the human element and incorporate this into the structure of how we make our decisions? And how do we take this information, and how do we learn to trust that? The one thing I hear from most of the executives I talk to, when they talk about how data is being used in their organizations is the lack of trust. Now, when you have that, and you start to look at the trends that we're dealing with, and we call them data points verses calling them people, now you have a problem, because people become very, almost analytically challenged, right? So how do we get people to start saying, okay, let's look at this from the point of view of, it's not an either or solution in the world we live in today. Cognitive organizations are not going to happen tomorrow morning, even the most progressive organizations are probably five years away from really deploying them completely. But the organizations who take a little bit of an edge, so five, ten percent edge out of there, they now have a really, a different advantage in their markets. And that's what we're talking about, hyper-critical thinking skills. I mean, when you start to say, how do I think like Warren Buffet, how do I start to look and make these kinds of decisions analytically? How do I recreate an artificial intelligence when machine-learning practice, and program that's going to provide that solution for people. And that's where I think organizations that are forward-leaning now are looking and saying, how do I get my people to use these capabilities and ultimately trust the data that they're told. >> So I forget who said it, but it was early on in the big data movement, somebody said that we're further away from a single version of the truth than ever, and it's just going to get worse. So as a data scientist, what say you? >> I'm not familiar with the truth quote, but I think it's very relevant, well very relevant to where we are today. There's almost an arms race of, you hear all the time about automating, putting out fake news, putting out misinformation, and how that can be done using all the technology that we have at our disposal for disbursing that information. The only way that that's going to get solved is also with algorithmic solutions with creating algorithms that are going to be able to detect, is this news, is this something that is trying to attack my emotions and convince me just based on fear, or is this an article that's trying to present actual facts to me and you can do that with machine-learning algorithms. Now we have the technology to do that, algorithmically. >> Better algos than like and share. >> From a technological perspective, to your question about where IBM is, IBM has a ton of stuff that I call AI as a service, essentially where if you're a developer on Bluemix, for example, you can plug in to the different components of Watson at literally pennies per usage, to say I want to do sentiment analysis, I want to do tone analysis, I want personality insights, about this piece, who wrote this piece of content. And to Dr. Tamir's point, this is stuff that, we need these tools to do things like, fingerprint this piece of text. Did the supposed author actually write this? You can tell that, so of all the four magi, we call it, the Microsoft, Amazon, Google, IBM, getting on board, and adding that five or ten percent edge that Tripp was talking about, is easiest with IBM Bluemix. >> Great. >> Well, one of the other parts of this is you start to talk about what we're doing and you start to look at the players that are doing this. They are all organizations that I would not call classical technology organizations. They were 10 years ago, look at a Microsoft. But you look at the leadership of Microsoft today, and they're much more about figuring out what the formula is for success for business, and that's the other place I think we're seeing a transformation occurring, and the early adopters, is they have gone through the first generation, and the pain, you know, of having to have these kinds of things, and now they're moving to that second generation, where they're looking for the gain. And they're looking for people who can bring them capability and have the conversation, and discuss them in ways that they can see the landscape. I mean part of this is if you get caught in the bits and bites, you miss the landscape that you should be seeing in the market, and that's why I think there's a tremendous opportunity for us to really look at multiple markets of the same data. I mean, imagine looking and here's what I see, everyone in this group would have a different opinion in what they're seeing, but now we have the ability to see it five different ways and share that with our executive team and what we're seeing, so we can make better decisions. >> I wonder if we could have a frank conversation, an honest conversation about the data and the data ownership. You heard IBM this morning, saying hey we're going to protect your data, but I'd love you guys, as independents to weigh in. You got this data, you guys are involved with your clients, building models, the data trains the model. I got to believe that that model gets used at a lot of different places, within an industry, like insurance or across retail, whatever it is. So I'm afraid that my data is, my IP is going to seep across the industry. Should I not be worried about that? I wonder if you guys could weigh in. >> Well if you work with a particular vendor, sometimes vendors have a stipulation that we will not share your models with other clients, so you just got to stick to that. But in terms of science, I mean you build a model, right? You want to generalize that to other businesses. >> Right! >> (drowned out by others talking) So maybe if you could work somehow with your existing clients, say here, this is what we want to do, we just want to elevate the waters for everybody, right? So everybody wins when all boats rise, right? So if you can kind of convince your clients that we just want to help the world be better, and function better, make employees happier, customers happier, let's take that approach and just use models in a, that may be generalized to other situations and use them. If if you don't, then you just don't. >> Right, that's your choice. >> It's a choice, it's a choice you have to make. >> As long as you're transparent about it. >> I'm not super worried, I mean, you, Dave, Tripp, and I are all dressed similarly, right? We have the model of shirt and tie so, if I put on your clothes, we wouldn't, but if I were to put on your clothes, it would not be, even though it's the same model, it's just not going to be the same outcome. It's going to look really bad, right, so. Yes, companies can share the models and the general flows and stuff, but there's so much, if a company's doing machine learning well, there's so much feature engineering that's unique to that company that trying to apply that somewhere else, is just going to blow up. >> Yeah, but we could switch ties, like Tripp has got a really cool tie, I'd be using that tie on July 4th. >> This is turning into a different kind of panel (laughter) Chris, Tripp, Mike, and Bob, thanks so much for joining us. This has been a really fun and interesting panel. >> Thank you very much. Thank you. >> Thanks you guys. >> We will have more from the IBM Summit in Boston just after this. (techno music)

>> Narrator: Live from San Francisco, it's the CUBE. Covering Samsung Developer Conference 2017, brought to you by Samsung. >> Okay, welcome back everyone. We are here live with the CUBE coverage where Cloud Native and the SmartThings Conference from Samsung Developer Conference. I'm John Furrier, the founder, the co-founder of SiliconANGLE Media. Co-host of the cube here with Redg Snodgrass, who's the chairman of ReadWrite and ReadWrite Labs. >> Hello everybody. >> Also been an entrepreneur, he's done the Wearable World events, done a lot of things in tech, riding the waves. You seen them, a lot of action going on, Redg. Want to get your the thoughts as we wrap up day one of two days of wall-to-wall coverage of the cubes, Samsung Developer Conference, a lot going on. You know Samsung, they're trying to play their best hand that possible. Obviously, they're not going to come out and say, We're not really ready for primetime, for the cloud. But the reality is, they're not ready for primetime for the cloud and IoT. However, huge strides in positioning, messaging, and the self awareness of their stove pipes. They are series of stove pipes that they've recognized, We've got to make this a 2.0 Bixby that crosses across all of Samsung, open up IoT. >> Redg: Which I thought was great. >> Open ecosystem, everything else, to me, is a work in progress, kind of, cover the, hide the ball, a little bit, I mean, what's your thoughts? Do you agree or what's your reaction? >> Oh man, I was on a panel earlier today. And somebody was like, oh, this is great. And I wanted to go back to, back when we did the open API service with Alcatel-Lucent, when we roll out all this stuff for the telcos. I mean, it's just, it's a lot of hype, initially. But what I do like about it is it seems like there's a dogged commitment to creating all the different documentations necessary and bringing that in, I mean, if they really put the full marketing weight behind it, this could get really interesting really fast. I mean, they own almost every device in your home already. >> Well, I said the word hide-the-ball. Maybe I should take a step back and not be too harsh. What I mean by that is, they're not hiding the ball on purpose, I think they're, by design, and I think Greg mentioned this earlier. Greg Narain said, they're doing it by design. And I think that that's a good call. SmarterThings is a good positioning because it highlights multiple devices and connecting it together. I think if they played the data card and the cloud too much, they would've overplayed their hand, and it's not needed. I mean, do you think it's needed? I mean, I don't think it's needed. >> Well, one of the biggest problems with IoT right now is that you have multiple different silos creating data. And then all those data silos have to figure out how to come together and talk about it. I mean, it seems like they're taking a step out, and saying, hey, we want to build that solution. Which is great, I'm more interested in the orchestration between different OSs, like, how are they really going to do that? Because it, we talked a lot about, when you build one of these ecosystems, you're really just building an economy. And the more open that you let your economy, right, the more business models come in, the more people that can be there. And so, if we were to start thinking about these OSs as real economies, like what do you need to have economy work? >> Well, I think this is why, we were talking earlier, I think that you had a good point. I think that validates what I'm thinking out loud here, which is, why play the data card. They don't need to because it's still open-book. They still got to figure it out, and that's not a bad thing. They play with their best hand, which is the consumer hand. >> Redg: It's consumerism is where they're at >> The devices are awesome, the screen on the phones are phenomenal, they got TVs. They got a little bit of a family hub going on with the living room, kitchen thing, with the refrigerators. That's IoT, they got healthcare because it's a device issue. So they're working their way from the consumer edge into the industrial edge. Now, if you're in the IT world, you have security problems. So most people that we talk to, at the humans, they say, hey John, my plate is full, I got to staff up my DevOps and my application developers. I got to unbolt security from my IT department, make that report to the board as a profit center now. And I got all this machine learning and Cloud Ops, and you want me to do what? Like, instrument my entire factory with this IoT thing? So people are holding the brakes. >> Well, I mean think about it. Every day, right, you're confronted with another executive that has like fallen on a sword of a major security hack, a major security issue. And so, as an executive of a major like business unit, with a technology group in front of you, you're sitting there making all these decisions every day. And it used to, you used to come and say, okay, we're going to make decisions every eight, nine months. And you have this big waterfall thing in front of you. And you know that, from your vendors, that. >> John: It's predictable >> Everything was predictable, and now it's like, oh man, I got to get into this Google Glass stuff, and I've got, no, now it's wearables, and wearables, that doesn't work, I need my IoT infrastructure stuff. And so we're moving the court, you know, away from all these CIO, CTOs consistently of what they need to think about next. >> It's interesting, if you look at the stack, go back to the old 80s OSI model, you got the lower level stack, middleware, and then application stacks. If you follow the data, and the networks, and the packets, how it moves, you can almost see the trends, batch versus real time. And I think what we've seen in the big data world, in data sciences, which can be analytics, obviously specialty industry. But the role of data and realtime, self-driving cars, really highlights this really huge wave coming, which is how that people dealt with data and software, the relationship between software and data was different. You store it in a database, build the database, call the database, get the data out, load it in, slow, monolithic, siloed. But now you have data that you need in really low latency at any given time, in any different app, from any different database, in less than a millisecond how do you do that. >> Well, think of it. >> John: That takes intelligence. >> About two years ago, I had a great conversation with a big packet moving company that managed most of the packet movement for most of the internet. And we were talking about, what does it look like per person in the US in the next like three or four years? And it could be up to a petabyte a day at a per person. Now that sounds awesome because if you look at all the different like videos we watch, it's like, oh, that's great, really cool flying car. You know, connecting windows, no one's really doing the math on that. And if it's a petabyte per day per person, like in the US a year even, or you know. I could see models where it could be a month. Think about what that does to the network load. We just don't have the math to be able, you know, possibility to handle that. >> This is why the decentralization with Blockchain is interesting. Even though Blockchain is hyped up, I think it's fundamental to the internet, as this Dr. Wong from Alibaba, who told me that last week. He said it was like a TCP/IP, I agree with him because you have distributed computers, which we know about. We've been there, done that, but now you got decentralized and distributed, two different concepts at the same time. That's a fundamental paradigm shift. >> Well, I mean it's just, so, I mean, you got to. >> It's intoxicating to think about what that disrupts. >> No, no, I love it, I mean, honestly, I've fallen in love with narrow band networks the last week. For some reason, I'm the weirdest person on the planet. Because it's such a solution for security. It's such a solution for a lot of this back calling and data that we're going to have. It'll be interesting to look at, but when you think about the pure math on this. >> John: Are you back calling data or are you back calling compute? >> Oh, well it's so. >> That's a different conversation. The trend is, don't move the data. Throw the compute at it because compute is, this is an architectural renaissance happening, people are re-imagining. >> How many, how many startup. >> In global infrastructure. >> Execs can even like draw architecture? Right, with all the lame startups, I mean, when was the last time you saw like somebody pitch. When they came to pitch, it's like, let me talk about my architecture. >> John: That should be the first slide. >> It should be the slide that you talk about as an executive and everything, I don't see. >> If he can't get on the whiteboard. >> Startups deliver architecture. >> If you can't get on the whiteboard and lay out an architecture on fundamentally the core engine of your technology, you shouldn't get funded. >> Well, so that is a major issue that's happening right now because I do think that we have this group think where we've disallowed a lot of R&D thinking. We don't do longterm R&D before we get a product to market. And now, like all. >> John: Sometimes you can't. Sometimes you have to sprint out and put a stake in the ground and iterate. >> Think about all of the connected device product. How do you test the connected device product to scale? Right, I mean the iPhone, you know Samsung, everybody has all these devices out there, they're getting this data, it's coming in they can actually iterate on that product and make decisions, right? >> Well, that brings up a good point. We saw this at the Cube at VMWorld. For the first time we heard people grumbling in the hallways like, you know, I love the ENC tries, but they just haven't tested this use case. And the use case was a new workload that had unique characteristics. In this case they needed low latency. It was an edge device, so it was mandatory to have no latency with all this was trickling data in. But in this case, they had set up their virtual SAN in a tiered basis. And they needed a certain hardware configuration with vSAN. And they've never tested the hardware stack with the software stack. So it's just one of those things that the hardware vendor just never imagined, you can't QA the unknown. So this is where I'd see Samsung doing things like in-chip and seeing what Intel's doing with some of their FPGA stuff. You can see that these infrastructure guys got to bring that DevOps concept to the consumer world. >> Redg: Oh, it's going to be so hard. >> Which is programming the. >> Redg: So hard. >> The hardware at will. >> Yeah, well. >> John: Like the cloud DevOps ethos. What do you think of that? >> Yeah, no, no, no, look, I mean, I'm such a big fan of being able to get your product in people's hands, to be able to see the use cases, develop them out and push that forward. You know, big corporations can do that. You have 10 iterations of almost every iPhone right now, with thousands of engineers iterating on it. So when you look at like the competitor, which is your device right now, versus every other piece of IoT technology that isn't been perfected or anything. Our biggest issue is we're driven by the success of the smartphone for every other piece of technology today. And that's, that makes it hard to drive adoption for any other devices. >> So I get your thoughts on this, 'cause we wrap up day one. Obviously, let's talk about the developers that they're targeting, okay. >> Okay. >> The Samsung developers that they're targeting is the same kind of developers that Apple's targeting. Let's just call it out, however, you see voice-activated touch, you're seeing the services tools, now they're bringing in an IoT. You're not hearing Apple talk about IoT. This is unique, you got Google onstage, wink, wink, hey, everybody we're here, we're Google, Android, coming together. What is in the mind of the developer in the Samsung ecosystem right now, what's your take on it, what's the psychology of that developer? >> I built an app at one point in time. It was dating app a long time ago, right, with some other guys, they built it, I was just the mouth. It's called Scout and we were on the Simian platform, and the iPhone, and we were on web, we were on mobile web. And in the iPhone app store, all with one engineer. And it was really hard because we had real-time chat. It was just so much crazy things. At the end of the day, what always matters is, again, you're building economies, you're not building fun playgrounds or anything else like that. And if your economy is, your platform is the easiest to use, it has the capabilities and advantages that are the norm, right, you'll win. Bass Diffusion is great it's this guy out here, he won a Nobel prize, but what Bass Diffusion says, in order for you to win in a market, you need two things, imitation and innovation. Imitation, for instance, in TVs, is your TV black and white, is it color. As things move up, innovation eventually overtakes, and always becomes innovation. So when you look at like what's needed in market, the platform that is the easiest to use, the platform that has the most capable imitative qualities, it's just very easy for you to push things to market universally from OS to OS, along with certain pieces of innovation around business models, certain API capabilities that may make it easier for them to deliver revenues. If those are the things that are delivered, that we see pushed out, a good blend of imitation and innovation, the win. It's that person that actually can deliver it. >> Well, we're seeing gaming in entertainment really driving change, Netflix earnings just came out. They blew it away again, you're seeing the cord cutters are clearly there. >> So much for Disney, right? >> E-commerce, yeah, I mean, Amazon's still got to make some moves too, even though they were still winning. No one's really falling out of the chair for Prime. I mean, no, I don't know a lot of people who rigorously turn on Prime, they shop on Prime, but not necessarily watching any entertainment. So I'm a little critical of Amazon on that. But, then again, but Amazon's doing the right thing. Netflix, Amazon, YouTube, you're seeing a culture of digital entertainment shifting. E-commerce is shifting, and now you got web services. I think Amazon encapsulates, in my mind, a great strategy, retail and services, but if you extend that out to the rest of the world, voice-activated apps, you can blend in commerce entertainment, you can replicate Amazon. I mean, they could replicate everything out there in the open. >> Amazon is so good at understanding where they fit in the stack and then, pushing the edge case further and further and further along. They're really brilliant, versus like VMware that's like, oh man, we can make apps, no problem. They went to make apps, and it didn't work out so well, they're great with VMs, so. >> John: They're great with operators in the enterprise, not so much with DevOps. >> No, no, no, no, and it's. >> They got pivotal for that now. Michael Dell bought everyone up. >> Yeah, exactly. It's understanding where you fit in the stack and being able to take advantage of it strategically. I mean, like I said, I think Samsung's positioned really well, I mean, I wouldn't have come and hung out with everybody if I was like, ah, I'm going to be bored all day. There's a lot of really exciting things. >> We got a lot of eye candy, no doubt about it. I love their TVs, love their screens. The new Samsung phone, is spectacular, you what I mean. >> I'm pretty ecstatic. >> It was the first phone that wanted me to get transferred off my iPhone. And I ended up getting the little junior Samsung here, but. >> Oh no, well it'll be interesting as they start to connect their platform together as all a lot of these other developers start pushing the pieces of their strategy together. Remember, it's like whenever you throw a strategy out here like this, it's like you have a big puzzle with a lot of empty pieces. >> I mean, the question I have for you is, let's just close out the segment. What do you think, what area should Samsung really be doubling down on or peddling faster, I should say. What should be developing faster? Is it the open APIs, is it the cloud? And they got to get the open ecosystem going, in my opinion. That's my take, what do you think they should be working on the most right now? >> Yeah, I mean like look, cloud is going to be really, really, there's a lot of competitors out in cloud. There's a lot of multiple, there's a lot of choices, right. Where I've seen them like really do well, I'll go back to the fact that I firmly believe that Google never really monetized the Android that Samsung did that a lot better. And so, by looking at the different points in the market, where they're good, I mean, their ecosystem is solid. I mean, yes, I mean it seems like the sexy thing is Apple, but I've talked to several developers, and I know where they make their money, and they do a strong amount of revenue, if not equivalent to where the iPhone is, at least from what I've heard so far. >> The android market share it's not shabby at all. >> Not, so. >> Damn good. >> So they've, they've been able to do this, like, from that, taken that Android stack, applying that imitation and innovation on top of it, fascinatingly so, I wouldn't count them out for this. And I'm pretty encouraged to see all the other aspects, but I like the ecosystem built out too. >> Redg Snodgrass, ReadWrite Labs, quick plug for you. What's going on in your world? Got some recent activities happening, please share update. >> So, yeah it's great, so we just launched our IOT revolution event series where we look at the atomic unit of different markets. And what that means is, we find the real buyers and sellers, a lot like what Debbie Lann, who I love, did. And we look at the buyers and sellers together, along with the top series A startups, all around newsworthy issues. And so, whatever it's like, is it hacking and Russia. You know, then we'll get cybersecurity experts up, and we'll talk about those issues from an executive point of view. And that's the thing that's making me most excited because I get to have all these conversations with people. It will be on video, onstage, November 13th, is the first one, it's a private event, but we'll work out anybody. >> Where's it going to be? >> It'll be in San Francisco, around 100 Broadway. So it's kind of a quiet thing, but I'd love for everybody to come if you're interested. >> It's a quiet thing but I want everyone to come. It was, not going there, too many people are going. >> It's like my parties, right? >> It's like a Yogi Berra. Well, thanks for coming out, appreciate, wrapping up day one of coverage The Cube. This is Samsung Developer Conference 2017. Hashtag SDC2017, that's what they're calling it. Lot of great guests today go to YouTube.com/siliconangle for all the great footage. And also check the Twitter sphere, lot of photos. And shout-out to Vanessa, out there has like helped us set everything up. Appreciate it and great to the team. That's day one wrap up, thanks for watching. (upbeat music)