>> "theCUBE," presents Dell Technologies World, brought to you by Dell. >> Hey, welcome back to "theCUBE's" live coverage of Dell Technologies World 2022. Live from the Venetian in Las Vegas, Lisa Martin, Dave Vellante joins me. Dan O'Brien joins us next. The senior vice president of technology solutions at Presidio. Dan, welcome to "theCUBE." >> It's great to be here. Great to be in Vegas too. >> Is it great to be back live in person, three dimensional? >> You have no idea. >> Oh, I know. >> Yeah. >> Just the seeing people again and the vibe here day-one is already fantastic. >> Yeah. >> Talk to us about Presidio and Dell's relationship? What's going on with Presidio? >> Yeah, so I'll tell you just Presidio as as a whole, and part of why I joined about a year ago and I'm still just excited as I was on day one. We're a digital services and solutions provider with deep engineering expertise in networking, cloud, security, collaboration, and modern technologies. And we'll help our customers acquire, deploy, and then operate and manage the solutions that we have. So, we're a Dell titanium black partner. We just got that, we're a super excited about it. And they're a critical part of how we deliver solutions to our customers. >> So, you joined during an interesting time during the pandemic. What are some of the challenges your customers are facing now? Aging infrastructure, labor shortages, supply chain. What do you, what are you seeing from the customers lens? >> Yeah, you know, all of the above. I think when the pandemic first hit, every customer that we spoke with basically said, Cash is king. We want to preserve it, we don't know what the future holds. So, all of the spend that happened was on the things that drove their business forward. So, I got a distributed workforce. How do I go invest in technology to make them productive? A lot of them had to take a digital agenda that was five years long and do it in three months to survive, so they spent it and that generally meant cloud. But what they didn't spend money on, was infrastructure inside the data center. And now what they're finding, is things are old, maintenance bills are going up, the cost to get it is going up. And sometimes supply chain is over 12 months long to be able to actually do something about it. >> You know, when "theCUBE" first started in 2010, it was EFC World 2010 now, 'cause Dell is really our legacy here. So, we said that companies that sell, it's kind of a pejorative, but sell boxes are going to be in trouble because of the cloud. Interesting, right? So, it was partly true because the cloud just intermediated a lot of that sort of box selling business. We said they have to become more value added players, identify. And so, when I watched Presidio, the transformation that you guys went through, and you're relatively new. Cloud has actually become an opportunity. And you're doing stuff around digital, a lot of stuff around security. It's cyber, probably automation, life cycle management. >> True. >> Talk about that transformation? And I'm interested in why you joined Presidio? >> So, I'll tell you why I joined Presidio, is I was talking to a lot of customers every day in my old role, I love doing that part. And the conversation started with, "Dan, I can't spend money on my data center right now because we're in a pandemic. I've got to innovate faster and the answer is to cloud. I don't know how to actually make my workforce productive because they're all over the place now. And we didn't invest in technology. And now I've got a threat surface with people working everywhere in workloads in different places. I don't know how to approach that." And I looked at what Presidio had built, I'm like, that's exactly what we did. But what's been fun for me, has been the answer to most of our customers is this the end? It's not the public cloud, it's not the private cloud. It's, you need to do both of them really well and have the skills and expertise to leverage 'em for the right application, or workload, or use case. And that's why I'm super excited to be here, 'cause we're really helping our customers in both areas. >> You mentioned security. We've seen a number of announcements today from Dell Technologies with respect to cybersecurity. We know the stats are what they are. It's no longer a matter of, if we're going to get hit by a cyber attack, it's when? Most organizations are going to get hit by 2025. Where is security in the conversation now? How high up in the priority is it? >> I would say it's, we don't have a single customer meeting without having that conversation. And what we're finding, is you look at the stats that say, you know, 30% of companies that have a cyber attack, don't come back from it. 20% pay the ransom, and then they don't even get their data back. So, while we want to stop the attacks, I think you're right on that the answer is, it's not a matter of if, it's a matter of when? But what's great about Dell Technologies, is we have a complete portfolio that can meet any SLA of our customers. It's in backup technology, it's in primary storage, they can do a mutable backups and recoveries everywhere. But what happened this week, where they announced partnerships with the cloud, that's huge because the same resource constraints that customers have in their data centers today, are the same ones you have to deploy infrastructure to be able to make this work and be able to accelerate recovery. So, the partnership and the integration with the public cloud, really gives a great integration point for a lot of our customers. >> At the analyst of the event today, we had a meeting with Jen Felch, the CIO of Dell. And I said to her, you know, our survey data from ETR shows that security now, number one priority, it kind of always was, but it's distance itself from the number two, which is cloud migration. And I asked her, I said, "Obviously, cloud migration is not your number two, 'cause number security number one was number two?" And she said, "Let me help you interpret that data. Because for us, we have the scale, we can do our own cloud essentially." What her interpretation, was what those customers are really saying is modernization. Now, you must see that. Now, of course, you're leaning into cloud. Dell is not defensive really more about cloud, like, hey, we could take advantage of it as well. So, what are you seeing in terms of the changing priorities of IT kind of pre-post pandemic? Is it like a rubber band that goes and then comes back to where it was, or is it kind of permanent? >> I think that the both worlds together are absolutely permanent. And there's no way we're going to go back from one or the other. And then we're always going to have a world where you might lean more into one. To innovate, you might lean more into one for disaster recovery. But I truly think the world and the answer for us and our perspective, has to be both. But you said something to interesting earlier, is the key I think to what customers are doing is you can't just pick up a workload and move it to the cloud, it doesn't solve a problem. You use that term modernize. And we've invested, acquisitions and continued engineering resources that were hiring around modernization because the economics and the true benefit of actually running a workload and running right at the right SLAs and meeting your customer's objectives, aren't going to work right if you're just picking an application up and moving it over there. So, we're really focused there. >> So, Couch Base, just ran a survey. We did a power panel on it with a bunch of database analysts. And it was a survey of 650 CIOs and CTOs. And it was really interesting 'cause it's an IT bias. But they said like 2/3 of the survey base said that IT is responsible for setting the digital transformation strategy of the company. And I went, "Well, I wonder what the business guys say to that. It was sort of a red flag to me. But I wonder what you're seeing 'cause there's obviously you get a difference when you talk to different worlds. So, I guess what is modernization, was kind of one of the big questions that came out of it? And who's driving the agenda? >> So, it really depends upon the customer, right? But the key to what you said, and there was an article that came out. I won't say where it was from, but it really kind of opened my eyes. But the article was titled that, "It's Time To Get Rid of the IT Department." And for someone like me and a lot of customers, that kind of scares people. But the whole underpin of it, was they were studying customers that took IT and actually disparaged, like broke 'em apart and put them into business units. So, it said, it's your turn to wake up every day and figure out what that business unit needs to be successful. Because the answer is, David, it's both, right? You need both parties on board, right? Where, you've got a business stakeholder that clearly knows want to do, understands technology's the answer but you need IT to be able to go make it work and be a true partner, and help go actually make it work. >> It reminds me of when Nicholas Carr wrote that article if you're, you guys are probably too young to remember, "But Does IT Matter?" It was kind of post Y2K, right? And then everybody went crazy. All the CIO was when nuts. And in fact, IT matters more than ever, but it's a different context, as you're saying. A question on things like skill shortages, supply chain, I mean, obviously, top of mind. >> Yeah. >> Are you helping people with that? And if so, how so? >> Yeah, so two ways I would look at this, is when you look at the supply chain, I mean, Intel I think spent a $100 million on standing up new Silicon plants. We won't see a benefit from that from 2025. So, it's real. So, a lot of what we're doing on a supply chain is how can we help a customer reach in and have certain targeted ways to leverage the cloud? Because we can't physically solve for the physics issue. The other part of it, the people shortage. I mean, it's real. I mean, everyone's sitting at home they're pondering whether or not, you know, what they're doing is fulfilling their dreams. Now, geography doesn't matter, you can do a job from anywhere. And technology is the heart of everything. So, the people shortage is real. So, we're finding that our focus on managed services we're essentially allowing our customers to run and deploy things across every technology aspect, is something that we used to have to drive to our customers. And now, we can't get out of a conversation without them asking for it 'cause they just don't have the people- >> Yeah, they're calling you into that need. >> Yeah. >> Can you share that customer example that you think really articulates the value of the Dell Technologies that Presidio is delivering? It's really been able to truly modernize in the last couple of years? >> Yeah, so looking specifically to Dell, I mean, for us, one of the taking technical data out of the data center and modernizing, their HCI portfolio together with VMware, is a complete home run. It takes multiple products, brings it into a single common solution, uses a common tool set for all the operators that are there so you don't need the number of people to run it. But if you do it right, it solves for the portability issue in some of the public cloud options, especially with things like VMC where you can have an on and off-prem and an automation between 'em, so you can pick and choose dynamically. That for us has been a home run in driving modernization strategies. >> From a multi-cloud perspective, it's going to be a big focus of this event the next couple of days. What are you seeing from customers' perspective? They're probably in multi-cloud environments for a variety of reasons, that's going to be persisting. The hyperscalers are all growing. What's going on there? How are you helping customers to manage the multi-cloud environment with just much more simplicity? >> Yeah, so I think there's a couple parts to that, right? I mean, obviously, Dell together with VMware has a great set of technologies to be able to manage the deployment of that. But what we're trying to do, is number one, help a customer determine which workload should be running in which place, right? Understand application dependencies. But as we work through a migration strategy with a lot of our customers, the key part that a lot of people don't realize, is we all think security but the networking is probably the hardest part if you want to have portability in a well running cloud. So, having years and years in network heritage, it's been a great synergy on us kind of moving in that direction to help our cloud customers make sure that the right SLA, the right connectivity, and the right availability to make that world work. >> Yeah, so multicloud, obviously, a big topic of of discussion this morning with Chuck Whitten. And that's another one of those, well, what do you mean by that? I have a sort of a premise I want to test on you, Dan. I've always said, it just comes from talking to customers, multi-cloud is kind of multi-vendor. I got to run some workloads in AWS, I run some On Prem. I run some in Google, some in Azure, and many of them, a handful like the big banks, for instance, they say, "Well we're building our own abstraction layer so we can control the policies, the security." And it seems like that's a direction that the industry generally in Dell specifically is headed. Do you buy that? And what's driving that need? >> Yeah, so I would buy it based on the size of the customer. So, when you take a big bank, a lot of what drives them to go to one cloud or the other, is that the big cloud providers they're innovating constantly. Every day there's a new tool or capability that exists there. And certain ones of them are going to match, a use case that, that large customer has- >> You can't resist? >> So, they're going to end up with multiple clouds, so it makes perfect sense. When you get into smaller customer, they really have to want to be successful. They got to pick one, right? They can't afford the people, and the scale, and the process. So, I think that's... The answer would depend based on the customer. The larger ones, I think they're going to build a full orchestration stack and small customers are going to look for one and someone maybe with managed services to help them augment the skills and staffing to make it work. >> For a while, I haven't heard it much lately, but you'd hear about repatriation, people come to me like, "Dave, you got to look into this repatriation thing." And I did, and I was like, "Eh, I really see, it a little bit, little pockets." But I do see hybrid. I mean, that's very clear. And I do see a lot of people went into the cloud, they didn't have a great experience. And okay, so there's some of that going on. I guess you could call that repatriation. But what are you seeing in terms of both of those? Is repatriation a trend or is it really an hybrid? >> So, I've interesting perspective coming from Dell, right? Where we're a very infrastructure focused in there. I see a little bit of repatriation in like a workload, like virtual desktops where you picked it up and you threw it in the cloud and make your workforce productive. But generally speaking, what we're seeing is not repatriation, which is, "Hey I move things. My cost is out of control, I don't know how to manage it. Can you help me get better controls on cost? Can you help me automate a lot of the things that are running here so I've got better control of cost and we're where things are running in my security posture?" So, it's much more about optimization that we're finding than it is. Let's bring it back. >> So, it's fine tuning the knobs? >> There you go. >> Right? And that seems to be the trend over the next couple of years? >> 110%. Yeah. >> Excellent. >> Have you seen any industries, in particular the last year that you've been with Presidio really leading edge in terms of modernization? >> Yeah. I mean, it's so interesting enough. I mean, I could give you a few examples, right? When we look in our public sector business, a lot of the educational institutions had to invest in new platforms they interact and engage with students. Our financial institutions, believe it or not, continue to innovate. I mean, what people don't realize, is the mainframe still has the transaction where your money lives in the ledger, but all the supporting ecosystem is digitalized and is completely modernized to interact with you. And, of course, retail for us. I mean, retail, they had to change their business model in many cases overnight, not even to survive, but to serve the communities they were working in. >> Yeah, I think one of the things that we've all learned in the last couple of years, is just the access, the e-commerce, the access online. We expect that now in the brick and mortar stores to be able to deliver that connected store, make sure that they have the inventory that I'm looking for with a frictionless experience. >> Yeah, and I tell you my favorite one, is you look at the healthcare industry, and while obviously with loans, and healthcare, and billing, all had to change. But that was really exciting for us, I mean, as consumers, right? Is the fact that we can interact with doctors online at the click of a button now. I mean, that part for us has been super exciting. >> Everything's at the click of the button now. >> Yeah. >> Oh, my gosh. Well, Dan, thank you so much for joining Dave and me on the program today, sharing what's new with Presidio, what you guys are doing together with Dell, and how you're helping companies in every industry to modernize. >> Perfect. I appreciate it. >> Great to have you. >> Likewise. >> Thank you. >> With Dave Vellante, I'm Lisa Martin, and you're watching "theCUBE's" coverage of Dell Technologies World live from the Venetian in Las Vegas. Stick around, and Dave and I will be right back with our next guest. (bright upbeat music)

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the Cubes. Continuing coverage of the fourth annual Women and Data Science Conference with Hashtag with twenty nineteen to join the conversation. Lisa Martin joined by one of the speakers on the career panel today at Stanford. Natalie Evans Harris, the cofounder and head of strategic initiatives at right hive. Natalie. It's a pleasure to have you on the program so excited to be here. Thank you. So you have, which I can't believe twenty years experience advancing the public sectors. Strategic use of data. Nearly twenty. I got more. Is your career at the National Security Agency in eighteen months with the Obama administration? You clearly were a child prodigy, of course. Of course, I was born in nineteen ninety two s. So tell me a little bit about how you got involved with was. This is such an interesting movement because that's exactly what it is in such a short time period. They of a mask. You know, they're expecting about twenty thousand people watching the live stream today here from Stanford. But there's also fifty plus countries participating with one hundred fifty plus a regional events. You're here on the career panel. Tell me a little bit about what attracted you to wits and some of the advice and learnings that you're going to deliver this afternoon. Sure, >> absolutely So Wits and the Women and Data Science Program and Conference on what it's evolved to are the exact type of community collective impact initiatives we want to say. When we think about where we want data science to grow, we need to have diversity in the space. There's already been studies that have come out to talk about the majority of innovations and products that come out are built by white men and built by white men. And from that lens you often lose out on the African American experience or divers racial or demographic experiences. So you want communities like women and data science to come together and show we are a part of this community. We do have a voice and a seat at the table, and we can be a part of the conversation and innovation, and that's what we want, right? So to come together and see thousands of people talking and walking into a room of diverse age and diverse experience, it feels good, and it makes me hopeful about the future because people is what the greatest challenge to data science is going to be in the future. >> Let's talk about that because a lot of the topics around data science relate to data privacy and ethics. Cyber security. But if we look at the amount of data that's generated every day, two point five quintillion pieces of data, tremendous amount of impact for the good. You think of cancer research and machine learning in cancer research. But we also think, Wow, we're at this data revolution. I read this block that you co authored it about a year ago called It's time to Talk About Data Ethics, and I found it so interesting because how how do we get control around this when we all know that? Yes, there is so many great applications for data that were that we benefit from every day. But there's also been a lack of transparency on a growing scale. In your perspective, how do what's the human capital element and how does that become influenced to really manage data in a responsible way? I think that >> we're recognizing that data can solve all of these really hard problems and where we're collecting these quintillion bytes of data on a daily basis. So there's acknowledgment that there's things that humans just can't d'oh so a I and machine learning our great ways to increase access to that data so we can use it to start to solve problems. But we also need to recognize is that no matter how good A I gets, there's still humans that need to be a part of that context because the the algorithms air on Lee as strong as the people that have developed them. So we need data scientist. We need women with diverse experiences. We need people with diverse thoughts because they're the ones we're going to create, those algorithms that make the machine learning and the and the algorithms in the technology more powerful, more diverse and more equal. So we need to see more growth and experiences and people and learning the things that I talk about. When I when others asked me and what I'll mention on the career panel is when you think about data science. It's not just about teaching the technical skills. There's this empathy that needs to be a part of it. There's this skill of being able to ask questions in really interesting ways of the data. When I worked at National Security Agency and helped build the data science program there, every data scientist that came into the building, we, of course taught them about working in our vitamins. But we also made every single one of them take a class on asking questions. The same class that we had our intelligence analyst take so the same ways of the history and the foreign language experts needed to learn how to ask questions of data we needed, Our data scientist told. Learn that as well. That's how you start to look beyond just the ones and zeros and start to really think about not just data but the people that are impacted by the use of the data. >> Well, it's really one of the things I find interesting about data. Science is how diverse on I use that word, specifically because we talked about thought diversity. But it's not just the technical skills as you mentioned. It's empathy. It's communication. It's collaboration on DH those air. So it's such a like I said, Diverse opportunity. One of the things I think I read about in your blawg. If we look at okay, we need to not just train the people on how to analyze the data but howto be confident enough to raise their hand and ask questions. How do you also train the people? >> Two. >> Handle data responsibly. You kind of mentioned there's this notion of sort of like a Hippocratic oath that medical doctors take for data scientist. And I thought that was really intriguing. Tell me a little bit more about that. And how do you think that data scientists in training and those that are working now can be trained? Yeah, influenced to actually take something like that in terms of really individualizing that responsibility for ethical treatment of data. So, towards the >> end of my time at the White House, we it was myself deejay Patil and a number of experts and thought leaders in the space of of news and ethics and data science came together and had this conversation about the future of data ethics. And what does it look like? Especially with the rise of fake news and misinformation and all of these things? And born out of that conversation was just this. This realization that if you believe that, inherently people want to do the good thing, want to do the right thing? How do they do that? What does that look like? So I worked with Data for Democracy and Bloomberg to Teo issue a study and just say, Look, data scientist, what keeps you up at night? What are the things that as you as you build these algorithms and you're doing this? Data sharing keeps you up at night. And the things that came out of those conversations and the working groups and the community of practice. Now we're just what you're talking about. How do we communicate responsibly around this? How do we What does it look like to know that we've done enough to protect the data, to secure the data, to, to use the data in the most appropriate ways? And when we >> see a problem, what do >> we do to communicate that problem and address it >> out of >> that community of practice? And those principles really came the starts of what an ethics. Oh, the Hippocratic oath could look like it's a set of principles. It's not the answer, but it's a framework to help guide you down. Your own definition of what ethical behaviour looks like when you use data. Also, it became a starting point for many companies to create their own manifestos and their own goals to say as a company, these are the values that we're going to hold true to as we use data. And then they can create the environments that allow for data scientists to be able to communicate how they feel about what is happening around them and effect change. It's a form of empowerment. Amazing. I love >> that in the last thirty seconds, I just want to get your perspective on. Here we are spring of twenty nineteen. Where are we as a society? Mon data equaling trust? >> Oh, I love that we're having the conversation. And so we're at that point of just recognizing that data's more than ones and zeroes. And it's become such an integral part of who people are. And so we need some rules to this game. We need to recognize that privacy is more than just virus protection, that there is a trust that needs to be built between the individuals, the communities and the companies that are using this data. What the answers are is what we're still figuring out. I argue that a large part of it is just human capital. It's just making sure that you have a diverse set of voices, almost a brain trust as a part of the conversation. So you're not just going to the same three people and saying, What should we d'Oh But you're growing and each one teach one and building this community around collectively solving these problems. Well, >> Natalie's been such a pleasure talking with you today. Thank you so much for spending some time and joining us on the Cuban. Have a great time in the career panel this afternoon. Atwood's. >> Thank you so much. This is a lot of fun. >> Good. My pleasure. We want to thank you. You're watching the Cube from the fourth annual Women and Data Science Conference alive from Stanford University. I'm Lisa Martin. I'll be back with my next guest after a short break

>> 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)

>> Narrator: Live from Manhattan, It's theCUBE. Covering AWS Summit, New York City, 2017. Brought to you by Amazon Web Services. >> Welcome back to Midtown. We're at the Javits Center here. (sound cuts out) 2017, along with Stu Miniman, I'm John Walls and you're watching The Cube as we continue with what's happening here. About five thousand people on the show floor and they said some twenty thousand registrants. Right Stew? That people came in and wanted to watch the keynotes live. >> It could be ten thousand that walked through before the days-- >> Right, it's hard to tell. >> Yeah. >> And right now half of them are outside looking for a cab I think. That's the way it works here. Ryan Kroonenburg is also here. He's the founder of a company called A Cloud Guru. >> Yes. >> I like Ryan already. I liked him as soon as we met him because he said, "like the beer, Kroonenburg." So you resonated with the two of us, Ryan. >> Ryan like the airline and Kroonenburg like the beer. >> We appreciate that. Alright, so you're a cloud education company. >> Yes. >> And you bill yourself or at least in the conversation as you want to be the Netflix of cloud education. That's what you're doing. Tell us a little bit about the founding of the company. It began with your brother? >> Yes, yeah. >> Just two years ago and now you've grown to some 40 employees. >> Yeah, so I used to be a solutions architect and I was desperate to get a job at AWS so I became obsessed with getting trained in AWS. And at the time, a company I worked for had a training freeze. So we couldn't go out and do in-classroom training. If I had to do that myself, I'd have to pay for it myself. And I found that there wasn't a lot of good on-line training companies two years ago. I didn't get the job with AWS and turned out to be the best thing that ever happened to me. And so I decided to create my own course on AWS. Launched that, started going viral and that was the birth of A Cloud Guru. >> Ryan, bring is in a little inside of building the company, so you're not only teaching cloud, but you're built on cloud and not just any cloud, but using the LAN to server list from pretty early on that. >> Exactly, so we practice what we preach. You know, we are real AWS engineers. We built the entire platform serverlessly. We think we're the world's first serverless start-up. We're certainly the world's first serverless learning management system. So we don't pay for any servers whatsoever. There's no virtual/physical servers. And we're basically, purely AWS native. We do use a bunch of third party services like Xero and PayPal and things like that. But most of our platforms are AWS. >> Yeah, in the keynote this morning, Adrian Cockroft talked about Bustle, A New York based start-up that uses a lot of serverless, but you built the company before you even had funding and now you've got a little bit of funding. Can you give any insight? Do the investors looks at that and say, wow, this is a great model? >> Yeah, so we raised a decent series A. One of the founders of Warby Parker is on our board now so that's really exciting. A guy called Andy and he's helping us scale. One of the reasons we took funding was helping to scale. So our infrastructure scales automatically with AWS because it's built on Lambda and API Gateway. But we as a company are struggling to scale in like finding the right employees and all of that sort of thing, so that's where we're getting some help. >> Alright, what are you hearing from people taking your courses? What new things are they asking for? How are you expanding the scope of your offerings? >> Everyone is obviously very interested in AWS, but they also want to learn other cloud-computing platforms now, especially Azure, so we are expanding the scope of our content to do Azure as well as Guru. The other problem people are having is, AWS innovates so quickly. You know, there's like a thousand updates last year. There's 19 new updates last week. So there having trouble keeping up so we run just a weekly TV show called, AWS This Week, and we basically just tell people what's new this week. And the great thing about New York Summit is there's been like five or six announcements here so I'm going to be busy on Friday, filming. >> Is there any one particular area of training that you see more people drifting toward or following toward? >> I think serverless and big data are the hot topics. Big data, by that I mean AI, machine learning. That's just exploding right now. And just serverless architectures because the future of cloud is serverless. Why pay for virtual, physical machines by the hour or by the minute and have system administrators, network administrators, database administrators when all you actually want to focus on is your code and your end customers and serverless allows you to do that. >> So what's your process then? In terms of you staying on top of it, right? Because now you have to. >> Ryan: Yeah. >> I mean, you, you're it, right? You're the point of expertise. So how do you ... I guess, remain in that kind of relationship with AWS that you're the cusp? >> So, I obviously read all the blogs. Our students, We've got 300,000 students right now and our discussion forums are very very active so if they have announced something that I've missed, the students tell me, like, we'll know within a few hours. So, that's it really. It's just forever learning, but I love learning anyway so it's fun to get paid to learn. >> John: Sure. You bet. >> Ryan, how many people have gone through the training so far? Do you know how many of them get certified after they do that? And how many are kind of repeat customers? >> We've got 300,00 have gone through the training so far. We do track our pass rates. Our pass rates vary from anywhere between, normally 80 to 90%. Not everyone will pass on the first go because the exams are tough and it's also quite stressful. Sitting these exams can be quite stressful. In terms of the number of students that actually go on to get certified, that's not something we track just yet, but we're looking to change that as well. But yeah, we have a very good pass rate. >> So how does it work? I want to learn, you know, whatever. I want to dive into AI, whatever it is. I come to you, you've got something for me there right? You've got, I don't know how many hours of work I have to do, but take us through how it really works. >> Yeah so, it's video training. Online video training. So say you want to learn DynamoDB. We have a 19 hour course on that. And we go right into the very depths of DynamoDB. So you watch the videos. we'll show you what we're doing in the labs. We'll give you all the sample code if we're using code and then you can go and do it yourself. We very much believe in, the only way to learn Cloud is by getting your hands dirty. To actually go and do it yourself. So people watch the labs, do the stuff themselves and then complete the course. If it's a certification course, then at the end what they'll do is go and book the exam and hopefully, they'll pass the exam as well. >> So Ryan, you're in there looking at all this stuff, especially things like server lists. What are you looking for, for kind of the maturation? Is there anything that do you give feedback to Amazon? The community give you feedback? I have to imagine that there's some good feedback loops there? >> Yeah, I'm lucky enough to be an AWS community hero. So we get get briefed by Amazon on things that are coming out. You know, under MDA of course. We give a lot of feedback on that. No, I think serverless is the next big revolution. I hate hype and buzz words and things like that, but the thing about serverless is that, now you don't have to worry about servers. You can just focus on your code and you don't need to worry about any of the normal administration behind it and it's like ridiculously cheap. You get a million lambda implications a month for free. That's just part of Free Tier. We actually only just came off of Lambda Free Tier a couple of months ago and we've got 300,000 students. So, it's very very very cheap so its amazing. It's driving new revolution. >> What advice would you give to someone if they were looking to start a business and using serverless as a platform? >> Yeah, definitely check out AWS of course, we build our entire business off AWS. Design, try if you can, architect everything in a serverless fashion because like I keep saying, you don't have to worry about management of operating systems, virus patching, security, any of that. AWS, they take all... They take care of all of the heavy lifting for you. >> So I know you are a big fan of Lambda, but have you looked at some of the other serverless options out there? Is there any concern around, there's open source options out there. >> Ryan: Yeah. >> How do we get compatibility and not be just locked into Amazon? >> Azure Functions looks really good. See, this thing about vendor lock-in, I mean, you've got the serverless framework as well. If you build your applications on the serverless framework, you can move between platforms quite easily. That is coming so you could build it out on AWS and then move over to Azure if you wanted. The founder of serverless frameworks is a good friend of mine. So I definitely recommended checking it out. And that would be my advice. If you are going to go serverless use the serverless framework so then you don't have to worry about vendor lock in. But at the same time, Amazon, they reduce their prices all the time. So it is a good vendor to be with. >> I just think your story is great. I think that the best "no" you ever got in your life was from AWS. And now you're giving them a big "yes". >> Yeah, absolutely, I love AWS. They're such amazing people as well. They've all become my-- through my business and people I used to work with have all become really good friends of mine as well. It's been a great journey in last two years. >> You've done well for them, they've done well for you. It's a good relationship. >> Exactly. >> Ryan, thanks for being with us. >> Thank you. >> And continued success. >> Right, thanks guys. >> Good for you. You bet, Ryan Kroonenburg. The founder of A Cloud Guru. Along with his brother, Sam, making a pretty good business out of things on the AWS platform right now. Back with more here from AWS Summit, right after this. You're watching The Cube. (fast music)