>> Announcer: Live from San Francisco, it's theCUBE covering Informatica World 2017. Brought to you by Informatica. (light techno music) >> Welcome back to Informatica World 2017. I'm Peter Burris, and once again theCUBE is broadcasting morning to night two days in a row to bring you The Signal from the Noise, this very very important conference. There's a lot going on here as we talk about the increasing role that data's playing in the world. Now, to get a user perspective, and not just any user perspective, a leading user perspective, on some of these issues, we've asked Mel Kirk to come on board. Mel, welcome to theCUBE . >> Thank you sir. Glad to be here. >> Mel is the senior vice president chief information officer for Ryder Systems. For those of you who don't know Ryder, it's a trucking company, a trucking and leasing company. >> Mel: Absolutely. >> And my background is I used to actually do a lot of research around transportation-related things, and I always found the ability to use queuing theory, >> Mel: Ah. in both technology and in transportation, >> Yes. to be very fascinating. So again, Mel, welcome here, but tell us a little bit about what you're here at Informatica World for, and what's your interest in all this? >> You know it's interesting, this was one of the conferences that I set out this year that I wanted to come to because I wanted to learn more about where Informatica is going in terms of leveraging data. Transportation company, we generate a lot of data. We have three business units, we have a fleet management company, a 3PL traditional transportation, supply chain company, and a dedicated transportation company. All three of those businesses generate a lot of data, and we're on a journey to try to figure out how, what's the best way of using that data to improve business outcomes. So that's what I'm here for this week, is to learn more about the tools that are here, the applications that are here, that we can use to do just that. >> So one of the things that I'm fascinated, often the new branding of Informatica, which we think is good: enterprise, Cloud, data management, leader. We know what enterprise is, we know what Cloud is, we know what leader is. One of the dynamics is, what is the new data management? We've talked to a couple of people about it. From your perspective, all this data coming in, what is the new data management function at Ryder, or the new requirements and capabilities? >> I think the biggest thing for us, from a data management standpoint, is mastering our data. Like I said, we generate a lot of data. We've got two really important domains in which that data revolves around. It's a customer and it's a vehicle. And so our objective this year is to master both the customer and the vehicle, the information around those, so that our marketing team can create better solutions by understanding all of the ways that a particular customer may interact with our business. It's also our operating team is leveraging that same data to win at the local level on a day-to-day basis. When a driver comes to one of our facilities, and he wants work done on his truck, our account people and our service people at that location will be able to pull up specific information about that customer and perform the work that they need based on the contract they have with us. That's a win for the customer and a win for our local team. >> So key, handle the customers, handle the crucial assets. That seems to be a general trend in the industry, is you look across both the conversations that you're having here at Informatica World, but also beyond. Where do you think the industry is going, from a trend standpoint, with some of these questions around data? >> I think we're all on a journey to try to figure out the best ways to leverage the data, treat data as an enterprise asset, right? A real enterprise asset that may have more value to it than some of the physical assets that sit in our business. And as I've talked to people during the week here, it's really about that journey of trying to figure out how do you get better value out of the investment that you make, and understanding, cleansing, liberating your data. And for us, again that's creating products, new products, from the data that we have, and it's improving productivity and efficiency in our operations with that data. >> So you must be excited about some of the new capabilities Informatica's announcing about being able to discover, you know, inventory, and then use metadata in new and different ways. What do you think about some of the metadata issues that Informatica's talking about here? >> Yeah, I think, you know, both metadata and Cloud for me is very important. The metadata is important because, again, we've got multiple business units, right, that are operating with elements of data that are not associated across the enterprise. And so, you know, getting more deliberate about understanding the data at the metadata level will help us as we try to bridge everything together across our enterprise. The Cloud's important because more and more of our customers are moving from a batch world to a near real time world. And what's happening there is we need the ability to spin up operations in a very quick way, receive data in large swaths. So having burst capacity is what the Cloud is going to give us. The immediacy of capacity is important to us, so the Cloud-based applications that I've seen here, even the enterprise information catalog is important because as we go through and we cleanse and harness our data, having it in a structured, governed pattern is important to us as well. >> So you had been in the business. You're ex-GE before you came to Ryder, a couple iterations before, you know, Master Black Belt, Six Sigma, that kind of stuff. You're an operations guy. >> I'm an operations guy. >> So as you think about going from an operations guy, and great operations guys are very focused on data, into the CIO, how was that transition? >> It was more than what I thought. You know it's interesting, I've said that as an operator, I'm not sure that I would've been effective in this role five, ten years ago, because it was a different type of role. >> Peter: Right. >> Today I don't know how you'd not do this role, how you could do this type of role, the CIO role, without having an operational background because the technology is integral to everything we do now. So, you know, where before, companies differentiated themselves on, you know, operational rigor and process, which is what I live in, >> Yep. >> Now it's about data. Now it's about data and the technology tools that can free up capacity, create productivity, and again, generate products. And so, this has been a great exercise for me, a great learning experience for me getting involved in technology at a time when it's moving so fast, right? Every day is a different day from a technology standpoint, and bridging that with my operating background, I think it's been a great experiment for both me and Ryder. >> Well a lot of CIOs that have great job satisfaction at heart are operations people who have figured out how to be operations people as opposed to people who often, CIOs who often don't have that satisfaction are spending their days putting out fires, and they never get into that groove. But think about as the role of the CIO changes at Ryder, but just in general, how do you see yourself organizing your groups around data assets, because it used to be that the key assets were, you know, the hardware. >> Right. >> Or the network. How is that catalyzing a new way of thinking about getting your talent mobilized to do what Ryder needs your function to do? >> You know, the big shift is away from keeping the lights on and keeping the phones working to delivering outcomes for the business. So that's that operational view, right? It's really whether there's an application development team or a talent on our, employee on our infrastructure team, it's about delivering outcomes for the operating team, for the business team. And so an example of that is in our fleet management business, right, we run 850 shops around the US and Canada, repair centers, and our core application in that business, our technicians in those shops say, "Mel, if you can do one thing for us, "make the application faster." That's both an application problem and an infrastructure problem. >> Peter: Sure. >> Right? In terms of trying to find the right solve. What I've been able to do and what I've been focusing on is translating that ask, of give me more speed, to the infrastructure team and the application team in a way that they understand that that incremental speed means better customer service, better outcomes for the business as well as our customer. That driver that comes to that repair center, he or she is on the clock. >> Peter: Right. And they want to get out as fast as, they are more, of more value to the customer when they're on the road doing their job. >> And a truck is typically not a cheap thing. >> Mel: It's not a cheap thing. >> So a truck's on the clock too. >> Mel: Absolutely. >> So as you think about the new, these new disciplines, and then acculturating the application team to, at least in this case, speed, the infrastructure team to speed, are there any new skills or any new disciplines that you are finding need to be filled within your shop? >> You know, the thing that's been interesting, and I'm going to go back to my Six Sigma background, the thing that's really been interesting, and when I take into consideration the pace of change of technology, it's been change management, right? I mean, the application team can come up with the best, the absolute best solution. I'm going to add two, it's change management and the UI, the user interface is important to that journey, right? >> Peter: Absolutely. >> And so they can come up with the greatest application, it could be the best solution ever, but you've got to get people, like in our organization it's nothing to see employees that have been with the company for 20 years. And getting them to fundamentally change how they do work, that's a challenge. And so we, what we've been focusing on is educating both the IT organization as well as the business team on how to drive change, especially in an organization with such a long, rich heritage. >> So as these changes start to manifest themselves, your relationship with the executive staff, how's that evolving? >> Yes, so when I went over to, when I came over into this role, you know, I'd left the operating role as a peer, and I came over to the IT role, and I think they felt sorry for me because of all of the challenges. But what's evolved is that as I've learned more about the technology and how to deploy, I've been able to actually balance between communicating with the technology team on the needs of the operating side of the business, and then translating the technical challenges to the operating team so that they've got a better sense of if we're going to launch a new product, or if we're going to onboard a new account, right, there's some lead time, there's some pre-thinking that needs to happen to get the technology right for you to be successful when you deploy for that customer. So I think bridging the gap between the two sides of the company has been very important for us, especially now given that, again, the pace of change with technology. >> Peter: So does Ryder have a COO? >> Ryder actually doesn't have a COO at the corporate level. We have a COO in our fleet management business, but I'm playing kind of a hybrid role I'd say. >> Peter: Yes. >> You know, kind of a CIO/COO because I can blend the two. >> Excellent! And how's that, how's that going? >> It's actually good. When I first moved into the CIO role, I was very deliberate about not encroaching on the role of the operating teams, right, even though my heritage and all of the things I had done in the company was around operations, I didn't want to make operating decisions from the CIO role. What I'm realizing now is the best value, the best benefit for Ryder and the customers is for me to bring all of the skills that I have, right, plus the talents of the team, to bear on a problem for the company. So I've thought less about boundaries and more about delivering outcomes. And if that means I have to put a, you know, a little bit of an operating perspective on a technical challenge, so be it. >> Which is really quite frankly what any real great Chief anything does. >> Yes. >> How do I take shareholder capital and translate it into an outcome through my purview. >> Mel: Right. >> So, Mel, let's pretend we got five CIOs sitting here, >> Mel: Okay. >> All about ready to start the journey that you're quite a ways along. What is the one thing you want to say to them? Say, here's how you're going to get started, and here's the pothole that you have to look out for. >> You know, I think one of the most important things that I would advise is to divide, especially if you're like me coming from a different purview and even folks that have been in technology for a while, establish a board of directors, right, your own personal board of directors. For me that was, I had to identify, you know, a couple of folks that had been in this role before that I could call and reach out to and get unfiltered advice, right? It was also identifying, the second one was identifying a short list of vendor partners that I could go to for technical questions in their domain, plus beyond their domain where I felt comfortable with the autonomy of the answer. >> Good ideas. >> Right, just good ideas. No sale, just good ideas. Then I had to reach inside of my team and figure out who are the one or two people in the organization that I'd go bounce ideas across for the sake of the change management that I talked about, right? Some for technology but also from a change management standpoint. And then build a couple of key partners at the leadership level within the organization, again to help with some of the concepts and the ideas. A lot of what a CIO is going to bring to bear now is going to be disruptive to the way a business, a company does business today, and so they're going to need constituents or partners from the executive leadership team. >> Yeah, none of it happens if the CIO doesn't recognize the change management that they have to drive. >> Absolutely. >> About their role within the business. >> Absolutely. So I used my board of directors, this board of directors, as a way of getting smarter about the job, you know, secondly, to help facilitate the change that we need, and three, just to bounce ideas. For sanity. >> Awesome. Fantastic. Mel Kirk is senior vice president, chief information officer of Ryder Systems Inc. Mel, great conversation. Thank you very much for being here in theCUBE . >> Okay, thank you for your time. >> Once again, Peter Burris, Informatica World 2017, we'll be back with more in a moment. (light techno music)

(upbeat music) >> In 1946, the acerbic manager of the Dodgers, Leo the Lip Durocher famously said of baseball, great Mel Ott who was player manager of the Giants at the time. You know what happens to nice guys. They finished in last place. The phrase nice guys finish last was born. It became popular outside of baseball. Well joining me today is someone who was a consummate gentlemen and a nice guy who proves that idiom absolutely isn't true at all. He's also written a new book "Play nice and Win" Michael Dell chairman and CEO of Dell technologies, welcome back to the CUBE. >> Thank you very much, Dave, always great to be with you. Wonderful to be on the CUBE and thanks for your great coverage of Dell technologies world. >> Yeah. We're very excited to be covering the virtual version this year, next year we're back face to face I'm Sure. And we're going to talk about your book but I want to start by asking you to comment on the past 12 months, how are you going to remember 2020? >> I'm going to remember it by the resiliency of the world and our team, the adaptability the acceleration of digital transformation which is pretty amazing around the world. The vital role that technology played in addressing some of the biggest challenges, whether it was the creation of vaccines or, you know, decoding the virus itself or just addressing all the challenges that the world had. You know, I think it's a game changer in terms of disease identification and how we prevent these kinds of things going forward. You know, there's still a long way to go in terms of how do we get 7.5 billion people vaccinated and safe. I also think it exposed, you know some of the fault lines in our society. And that's a great learning for all of us in terms of access to healthcare and education and, you know, the digital resources that power the world. And so, yeah, those are some of the things that really stand out for me. >> Well, I mean, I think leaders like yourself and position of influence, absolutely passionate about some of those changes that we see coming in society. So hopefully we'll have time to talk about that but I wanted to get into the business. I think a lot of people, myself included felt that 2020 was going to be a down year for big tech companies like yours and that relied heavily on selling products that data centers and central offices but the remote work trend and the laptop, boom offset, some of those on-prem softness and headwinds combined with VMware the financial performance of Dell technologies was actually quite amazing. Why were you able to do so well last year? >> Well, first of all, you're right. We did, we had record pretty much everything record revenues, record operating income, record cashflow and be also paid down a record amount of debt. And so I think the strength and resiliency of our supply chain, as well as the broad diversified nature of what we provide our customers continue to serve us very well as they moved to this sort of do anything from anywhere in the world. And it continues the first part of this year, business is very strong >> You know, a few weeks ago, of course you officially announced the spinoff of Dell technologies. Wasn't a huge surprise but the 81% equity ownership of VMware are you worried about untethering VMware from Dell or maybe you can share more on what this means for the future of, your two companies and your customers. >> Right? So, I think this will drive additional growth opportunities for both Dell Tech and VMware, while it unlocks a lot of value for our stakeholders. What we've done is to formalize the commercial relationship into a series of agreements and those are unique and differentiated and they provide lots of flexibility and we've driven a tremendous amount of innovation together and that's going to continue and it will, one of the things we said back in 2015 you'll remember is our commitment to keep the VMware ecosystem open and independent and working across the whole industry. We've done that. You'll continue to see us innovate together with Edge solutions, certainly all the great work we've done with VxRail SD LAN, you know Tanzu creates this platform to modernize applications and VMware Cloud and Dell technologies are the easy path to a multi-cloud architecture. And, that continues to work super well and is not going to be slowed down at all. So... and of course, I'll continue to be a chairman of both companies and we're not selling VMware we're distributing our ownership to our shareholders. >> Well, of course, Dell is the largest sort channel if you will, for VMware. So that's ... you guys got a tight relationship but I want to ask you about digital transformation and everybody talked about it pre COVID but nobody really knew exactly what it was but COVID sort of brought that into focus very quickly. If you weren't a digital business, you were out of business. So going forward, how do you see that whole digital transformation playing out? >> You know I think the plot of any company is to figure out how it can use its data and turn that into insights and outcomes and better results and ultimately competitive advantage faster. And as you said, you know, if it's not able to do that, it's probably going to go out of business. And that agenda just got massively accelerated because it was kind of digital was sort of the only thing that worked during this, this past period. So every organization has figured out that technology is not the IT department, it's actually the fulcrum of progress in the entire company. And so we're seeing sort of across the board a dramatic acceleration in the investment in digital technologies, you know, Edge is growing very fast. I think 5G just accelerates this and, you know you're seeing it in all the demand trends. It's quite positive and, you know, I think you'll see even a more rapid separation from those companies that are able to take advantage of this and quickly adjust their businesses their organizations, and those that are >> You better hop on board or get left behind, you know, the Edge. You mentioned the Edge it's a little bit like digital transformation, you know kind of pre COVID and even post COVID. It means a lot of things to a lot of different people but the telecoms transformation and 5G they have there certainly real. How do you see the Edge? >> You know, the Edges is ... think of it as actually the real world, right? It's, not a data center sitting in the center of the universe somewhere. And look today, you know only 10% of data is processed outside of the data center, but, you know, it's estimated by 2025 you got 75% of enterprise data will be processed outside of a traditional data center or a Cloud. And so as everything becomes intelligent connected 5G accelerates that it's going to be a huge acceleration of this whole process of digital transformation. And you know, again, think about this. I mean, the cost of making something intelligent used to be really expensive. Now it's asymptotically approaching zero. And of course all those things are connected. They're talking to each other and exactly what does this mean for every industry. Nobody's really quite sure and not everything is going to work, but, you know we're seeing it in manufacturing, in retail, in healthcare and the growth on the Edge is really accelerating in a meaningful way. And it's not so much about, you know people talking people with machines, we know how to do that. Now it's about the thing right And, you know you've got like 200 billion arm processors, you know out there in the last couple of years, all those things talking to the other things, generating data it happens in the real world. That's what the Edge is. >> Yeah as you know, we're a big fans of the arm model. And I think it just presents huge opportunities for companies like Dell. I want to ask you about Cloud. And I have to say, I think, you know companies like Dell have been maybe a little bit defensive over the last several years when it comes to Cloud but I think you starting to see the Cloud as a gift with all that CAPEX that's being built out by these hyperscalers. You know, thank you. It seems to me, you can build on top of that. How are you thinking about the Cloud as an opportunity for you and your customers especially as the definition of Cloud evolves? >> Well, first, you know, what we see is and the Edge is kind of the third place or the third premise, right? You got Clouds in the public form, you've got the Colo which is really growing fast and, you know the private hybrid Clouds, and now you've got the Edge. And so you've got infrastructure all over the place with Edge being the fastest growing. You know, one of the big things we see is that customers want a consistent way to operate and execute across that whole platform. And, you know, one of the other things that we've been focused on at Dell technologies is how can we move our business to more of a service and subscription on demand and provide customers that flexibility to to pay as they consume. And so, to some extent this is an evolution of, you know, products to services to managed services, to everything as a service. And so, you know, looking at our balance sheet you'll see over $40 billion in remaining performance obligations as we moved the business to that kind of model and it's been growing double digits for several quarters in a row. And so, you know, we're embracing Cloud and on-demand, and as a service, and obviously here at Dell technologies world we're talking a lot about Apex and our continuing initiatives to move our whole business in that direction. >> Yeah. Apex is a real accelerator for that model. I want to switch topics a little bit. I got a long list of things I want to talk about ESG, sustainability, inclusion, you know, is another topic that, that I'm interested in. I want it. And I said before, people like yourself in a position of influence to influence public policy and obviously the employees and your ecosystem why is it not just the right thing to do? Why is... why are those things good business, Michael? >> Well, it's good business because people want to be part of something that is important and purposeful. You know, it's not just make a profit and earn a living right? You know, people want to be inspired and feel that they're part of something special. And look, I think if you look at the positive changes that have occurred in the world certainly you could turn on the news and see the horrible things that happened in the last 24 hours or something like that. But if you step back and think about the amazing progress that's happened in the last several decades, you know a lot of it's been driven by technology and by businesses that have stepped up and made a difference and made commitments. And, you know, we're one of those companies that has made a series of commitments you know, 10 years ago, we set out with our 2020 goals. We accomplished significant majority of those retired those. Now we set out our progress made real 2030 goals all around the ESD themes. And it's not only the right thing to do but it is good for business. It inspires our team members, our customers and I think initiatives like progress made real at Dell and thousands of other companies. Ultimately, those are the things that are going to drive progress forward. I believe, you know, more so than government edicts or regulation, those can play a role. But I think, companies voluntarily driving things like the circular economy and how we include everyone in our business and provide opportunities for everyone to succeed no matter where they come from. I think those are the things that are really going to drive the world forward. >> Well, I want to ask you about public policy because as you say, it's not just the government, but of course sometimes the government can get in the way. You're seeing a lot of vitriol around Val break up big tech but the same time, you're seeing the US government and the EU very willing to help out with the semiconductor competitiveness in the like I know you were tapped with the new administration President Biden, tapping, you know, the best minds in tech and you were asked to part sort of participate give feedback. What can you tell us about, you know your advice to the US government? >> Well, you know, lots of great discussion with the new administration and it's a delight to see that they're focused on semiconductors and sort of the industries of the future. This is a big deal. I mean, you know, we've got some big global competitors out there other nations that are with a deterministic strategy very focused on the industries of the future. But US, you know if you think about the atomic age and, you know the Apollo missions that created the whole semiconductor industry ARPANET and ultimately the Internet and that kind of stopped right there, you know, there wasn't as much government investment in some of those big R and D initiatives that really drove an enormous creation of industries and success for the United States and its citizens. And so I think focusing on semiconductors and how you build the infrastructure of the future really important for the United States to continue to be a leader in that you know, we were, you know, producing a one point about 37% of the world's semiconductors. It's now down to 12% and dropping and really important that more investments are made in that area. It's a combination of capital, talent, you know education knowledge, and also, you know, the policies that promote the development of these kinds of businesses. >> Yah well, Pat's got a very big challenge ahead of them. And so that's why but we've said Intel's too strategic to fail in our view but I wanted to plug your book a little bit. My former boss, you and I have talked about this. He was also a gentleman who proved Leo Durocher wrong. He was very nice guy, but also a winner, Play Nice But Win, why did you decide to write another book? >> Well, you know, Dave, a lot has happened in the last 20 years and especially the last nine or so years since we went private and, you know merged with EMC and VMware and went public again. And, you know, I'd say we... first of all, you know when I wrote the first book in 1998 I wasn't comfortable disclosing a lot. And, and I wasn't vulnerable enough and didn't feel, you know, able to do that. Now I do, you know, I'm older, you know hopefully a little wiser. And so I think everybody's going to like hearing some of the fun stories about not only my childhood but you know, the dorm room and beyond, and leading up to, you know the pivotal changes that have occurred the last decade my alligator wrestling with Carl Icahn and other, you know there's lots of fun stories in there. I got arrested one time. It was only for speeding tickets, don't worry but you know, lots of fun. I'm really looking forward to the book coming out and being able to talk about it. >> I can't wait. You know, I've said many times anybody who could beat the great icon is interesting to me. I wanted to ask you, I mentioned my old boss, Pat McGovern. I used to say to them all the time, "Pat how come you don't buy more companies?" And he'd say," Dave, you know the vast majority of acquisitions and mergers they failed to meet their objectives." Did you ever imagine, I mean... I did the EMC acquisition. Did... how could it not have exceeded your expectations? I wonder if you could give us your final thoughts on that. >> You know, and I talk about this a lot in the book. I mean, these are kind of the ultimate considered decisions. And in the case of the EMC combination it was something that we had thought about going back to 2008, 2009. And then, you know, started thinking about it in 2014 worked on it for a full year before it got announced in 2015 and finally closed in 2016. But yeah, I mean, you know, we thought it would be great. It turned out to be even better than We thought the revenue synergies were far greater. The teams were quite energized. Customers liked what we were providing and you know it's ... and, of course the markets were supportive Right? You know, we were paying close attention to interest rates and how we could structure the merger in a attractive way. And, you know, thank goodness, lots of hard work lots of determination, you know, it's worked out quite well. >> Yeah, great commitment from the Dell team as well. Congratulations on that. Go ahead, please. >> And any adventure continues right? It's...( both chuckles) >> I can't wait to see the next chapter and I can't wait to get the book, but congratulations on that, all your tremendous success you're you are a winner and a gentleman and a friend of the CUBE, Michael Dell. Thanks so much. >> Thank you so much Dave. >> And thank you for watching. And this is the CUBE continuous coverage of Dell tech world 2021, the virtual edition. Keep it right there, right back. (upbeat music)

>>from the >>Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation >>over and welcome to this special cube coverage of AWS Partner Awards show. I'm John Furrier, host of The Cube. We're here in our Palo Alto, California studio is doing the remote interviews with our quarantine Cruelty during this time of covert were remote with the best remote Work solution award for AWS Partner Awards goes to Tyler Technologies in the city of Alvin Municipal Court. And we have Sandy, Peter's vice president, general manager of virtual courts and in code court system. Sandy's here to talk about that. And Sonya Gates, who is a city of albums. Mutual court court administrator. Welcome. And congratulations for the best promote work solution. We're remote. Congratulations. Okay, so, CNI, I'll start with you. Tyler Technologies, You're the general manager of the encode Court. This is a vert. This is a solution that you're deploying with the city of Alvin to do some things. Take a minute to explain what you guys are doing together. What is your group of Tyler do And how is it working with City of Album? >>John Tyler Technologies is just completely focused on ah, local, state and federal government software and services. And, uh, particularly the code court application focuses on municipal court, which is what Sonya is the court administrator for Calvin. We have about 900 clients across the U. S that do that same thing. We had this idea about coming up with a remote solution for, ah, ability for someone toe instead of having to go to court to see a judge that they could do that remotely and really have the same experience. And so we sort of launched off on that Ah, and worked with several different of our clients and came up with a way for for that happens on you. I got involved in it very early on and has been instrumental in helping us continue to make it successful. >>When you talk about the city of albums based court system I've seen with Koven, people are sheltering in place and they're not moving around much. You have to have a solution. Talk about the partnership with Tyler. How did this come together? How do you guys were? Take us through that. >>Well, we we have a great relationship with Tyler Technologies. They are very instrumental in our day to day processing. They send out an email with the idea due to Coben, And as soon as we receive the email, we decided that was the best solution for for our court. And we just immediately jumped on board with it so we could resolve cases and not get behind. >>So the virtual court means okay, I get a ticket, I want to appeal it. No way would show up. And now I can't. So it interfaces and take me through the solution. And what is a best fit involved in some some things on the cloud. >>It definitely is on the cloud, John. And, um and that's exactly right. So if you get ah, you get a citation, sometimes you may want to appeal that sometimes you just wanna find out what your options are, and you are going to go appear before a judge. You can do that remotely now, through this through our application, it supports all the video. You can upload documents, exchange those ah, supporting documents. Ah, and ah. And then it interfaces with our case management system so that a sea change is we made on the case. They're reflected and the defendant can see those. And so it just really the whole idea is remotely being ableto go before the judge find out what your options are. Go through that process. And then at the very end, it gives them a way. The completely take care of that case on Within a few minutes, it could be completely resolved. >>So take us through the city of Alvin's court system there. What's the challenges that you have? Um And what was some of the feedback when you first brought this out? Take us through what happened? >>Well, to be honest, it was for us, it was unknown territory. We were a little nervous. We were a little scared to do something of this sort. But with the situation at hand, we had to figure out something, and this was the best fit for us. There was other options available, but we we prefer to stay within Tyler and utilize the system to its fullest. So that why we just said, Okay, let's do this. I have a judge. That's amazing. That is very tech savvy. And he was on board and my city manager. So just working with Tyler each way. You know, each step of the way, you know, in them comforting us in a sense, you know, to let us know. Hey, it's okay. We're here. Each step of the way will be built this together. And that's kind of where we started with the whole project. >>So this is a low hanging fruit. Obviously, it's not Jury, I'm assuming not a jury kind of situations. More of other non jury activities, right? >>It's the day to day court, you know, non jury. We're not doing any during Charles right now until after the governor allows us. So it's just the regular, you know, pre trials, the attorney dockets, arrangements and those sorts of cases. >>I'd be love to be on the planning sessions As you start to roll out the software for jury selection. We'll go into that kind of like what you're looking to look like, You know, it's going to be a digital surveillance. I don't know. It could be crazy, but this >>is the >>future. This is what we're talking about here. This is cloud scale. One of the benefits of cloud is is taking things and doing experiments. We hear that all the time. What's take us through the judge. So you see these tech savvy of these, like Zoom like, calls it like Is there a workflow trying? Envision what stood up in terms of the encode virtual courtside? Sandy, Sonia, What's What's it like? What's that? Take me through the experience? >>Well, everything's tied in together where a zoom and other options out there it's separated from your software so that, you know, that was one of the parts of going through Tyler with this virtual port is because everything's tied into one. We don't have to enter data or anything. After the dock, it's over. It's all live our forms. As soon as the defendant and the judge make an agreement, it put into TCM where the defendant can see it live, signed the orders and immediately get it back to us. And there's no delay time. There's no downtime, Um, and it's housed in one. So we're not having the mis data or, you know, it eliminates a lot of errors. Clerical errors are cases from being miss, >>and the judge handles everything right. He just he deals with the personal interactions reviews the data the defendant makes >>the clarity do a lot to. He's talking. And as he's talking, we're entering his orders as we speak. >>So it's real time thing. This is true agility. Sadie, this is the future. This is where the solutions start to get the scale. So what's next? What is the vision? How do you guys see the next step? Because, I mean, we all know that, you know, Kobe will be over soon. We hope faster than it's happened. But it will be a hybrid world. And I think this shows a template for efficiency. >>Yes. Yeah, I think that's a great point. And it is the future. We're going to continue to leverage our relationship with AWS, which has just been incredible to this process, and and, uh, we went way beyond what we were expecting just in terms of resource is and, uh, and helping us even just within our own development processes as we as we brought something to scale on in learning how to have a low test and, uh, really build applications that can scale out. And so we believe it is the future. And ah, Sonia makes a great point many times because they live in an area where sometimes there's other natural disasters, like hurricanes that can disrupt what's going on for them. Ah, but then also as you, as you just think about really what I would call a responsibility. As we move forward, we have a responsibility to provide ways that people can take care of things Ah, and not put themselves at risk. And a swee move into the future past Covad. Then s O. We're going to continue to leverage the technology that AWS provides the scalability, the how we can load test and everything. And, uh and it was really a no brainer for us toe run this application on the AWS services for us >>and Sonia. It's also not just about justice, not only getting the folks who are speeding and taking care of the penalties there, but it's also potentially for justice. If someone is not guilty or they want to get business has to continue, right? So this extends into the use case of remote hybrid the future because our work can be distributed now you have efficiencies. This is going to create a connected system which ultimately can be a connected community. >>Yeah, and it's going to reduce the failure to a rate here for court cases. Also, um, so that'll be less warrant more compliant, Um, in the easier. Well, it's a better relationship between us, the court and our defendants because they have the option of not having to leave work or miss appointments. You know, they can still attended their case and do other things that they need to do without taking a spin. A, you know, a couple of hours and sit in a room. And you know the court. >>That's a huge point. Sandy. This is about resource utilization on both sides, not just the court's and the city of Alvin on the municipal side. The citizens, it's efficiency. I mean, how many people don't show up because they can't get out of work or they need to make their paycheck or they have their their family? These need to be met. So all these things play into the psychology of of the way of life. This is digital life, virtualization of of the of life. It really is a big thing. >>Yeah. Yeah, I think I think you're exactly right. I mean you're hitting on some of the some great points. That's exactly right. And when you think about what has to happen for you to go and maybe go before a judge and ah, take off work, you've got to go buy traffic, find parking. You may have to have someone that takes care of your Children. There's there's all sorts of things that you're having to go through just to get down and and be in front of a judge that this can help with. And I think it's just one aspect to your point, really trying to think of, uh, really starting to help government think about how to be more customer centric out of provide some ways for people Teoh take care of of what they need to take care of. Uh and, uh and so we're really trying in your your point about connected communities. Is is a huge key point for us at Tyler, as we think of ways that we can help a community be more connected for sure. >>Well, you know, I'm huge into whole civic relationships and having a productive government and having citizens be served for that reasons and having it be a community. And this and now more than ever, transparency is helpful, right? This only helps things. So you guys are doing a really great job of one enabling a work environment remotely. In this case, it's for the courts to be operational. Is they need to be, But it clearly can extend. So, Sanjay, I gotta ask you the question. I'd love to get your commentary on surprises when you rolled this out. You know where people like Oh, my God, no one's ever going to use it or it's just too techy. Or has there been any pleasant surprises or things that surprised you that you didn't think was gonna happen to >>give us >>some kind of commentary on some observations that you've seen from from remote working, rolling out the best remote work solution? >>It's been very interesting. Um, we read our actual first defendant. He was elderly, and so we were kind of concerned. Okay, well, we know how to connect, you know, and he did amazing. So that's kind of where we knew if if we could reach the older generation and he can connect all these younger defendants and you know, younger people what shouldn't have any issues. So he was, you know, we explained to him, Hey, you're our first defendant. This is new to us. It's new to you. And he did awesome. So that kind of gave us the confidence we needed to pursue it even more and push it out there and give the defendants options. There's been, um we've looked. Some people forget, and so do I. That were on camera. And, you know, we see up with this, um, they forget their vehicle, you know, made it a few bumps, but it was like walking in the background. Yeah. Um, so it's been It's been an experience, but a pleasant experience. And it gave us where we didn't want a backlog of cases. There are over and having the virtual option through Tyler has We were like, Oh, it first started. We got behind until we launched about. We had about 800 cases we got behind on. And then as soon as we launched out virtual port. Now we're caught up, my courts running smooth, everything's great, and there's no backlog of cases. >>Clear. The backlog of the question I want to ask is that elderly first a user that did he or she get an early adopter discount on the sentence? >>Fine. Yeah, I was shocked. >>I kind of resent the elderly remark. I think he's referring to me. >>No, no, no, he was and he was in his eighties. >>Okay, I feel I feel young men while you guys congratulations. I like to get your parting thoughts. Just with cloud technology. A lot of other folks out there are looking at re imagining public service specifically around these times where there's a lot of emotional stress, like you got back long. You don't want to have the court get back. You can see that people don't want tickets hanging out there. But that kind of encapsulate people's feelings right now. And I think remote citizenship is coming. Just your thoughts on how you see this as a beginning starting point for cloud computing enabling the efficiencies, the solutions and the applications for more connected community experience. So we'll start with you. >>Okay. Um, I can see this. This is the way we're going to keep things. We like the option. The flexibility that are defendants or citizens have, um it it's opened our eyes And if you're if there's other courts out there that are kind of hesitant to go ahead and jump in and do it, I strongly recommend Just do it. It's It's scary in the very beginning because a lot of us, we're not used to it. But after you get through it and you go through the changes, it's It's so working in the end and you'll see such a more of a compliance for both sides and you know, it reduces the stress on staff. Having to send out Mel notice is, you know, for fire to appears and stuff of that sort produced warrants. So it's been a win win all the way around. Um, so if I could reach any court out there, that's kind on the line of doing that. Just just do it, >>Alright? Yeah, great. Sandy >>Gun and yeah, John. For us, Cloud is the future. I mean, every every application we have. Ah, we're actively working. If it's not already a cloud based solution, it will be Ah, and And we're a huge believer in the scalability. But But when you look at applications like this is as an example, Ah Tyler, virtual court, where it's really a win win situation. It's it's better for the court. They can continue to carry on their business. It's better for the citizen because now they can actually take care of something that they weren't going to be able to take care of in the past. And, Ah, and as we continue to find Win Win, uh, solutions cloud based solutions, they're going to be at the core of that in terms of just how easy it is to say excess and roll out. So it's a big part of our future, and we believe it's a big part of of our customer future as well. >>Well, congratulations. Modernization has positive impacts if done right, more times freed up to work on maybe personal things and connect those communes and bring people together. Congratulations. Tyler Technologies in the City of Album for the best remote work solution. It's the court system. Get those tickets paid, clear that backlog. And now you've got all the time in the world. So you take I work on other things. What do >>you do with your free time? I'm gonna take a vacation. Thank >>you so much. For thanks. Conversation and again. Congratulations. Thanks for time. >>Thank you. >>Okay, this is the Cube's coverage of AWS Public Sector Partners. Awards show I'm John Furrier with best remote work solution. Thanks for watching. Yeah. Yeah, yeah, yeah, yeah.

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

>> live from Boston, Massachusetts. >> It's the Cube >> covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day villain Day and >> stew minimum. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is the Cube, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the chief data officer office at IBM. And she's joined by Dave Shoot Mel, who's a research director at, uh D. C. And he covers cognitive systems and content analytics. Folks, welcome to the Cube. Good to see you. Thank you. Can't. Then we'll start with you. You were You kicked off the morning and I referenced the Forbes article or CDOs. Miracle workers. That's great. I hadn't read that article. You put up their scanned it very quickly, but you set up the event. It started yesterday afternoon at noon. You're going through, uh, this afternoon? What's it all about? This is evolved. Since, what, 2014 >> it has, um, we started our first CDO summit back in 2014. And at that time, we estimated there were maybe 200 or so CDOs worldwide, give or take and we had 30, 30 people at our first event. and we joked that we had one small corner of the conference room and we were really quite excited to start the event in 30 2014. And we've really grown. So this year we have about 170 folks joining us, 70 of which are CEOs, more acting, the studios in the organization. And so we've really been able to grow the community over the last two years and are really excited to see to see how we can continue to do that moving forward. >> And IBM has always had a big presence at the conference that we've covered the CDO event. So that's nice that you can leverage that community and continue to cultivate it. Didn't want to ask you, so it used that we were talking when we first met this morning. It used to be dated was such a wonky topic, you know, data was data value. People would try to put a value on data, and but it was just a really kind of boring but important topic. Now it's front and center with cognitive with analytics. What are you seeing in the marketplace. >> Yeah, I think. Well, what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence of deep learning. All of those those types of applications are derived and really run by data. So unless you have really good authoritative data to actually make these models work, you know, the systems aren't going to be effective. So we're seeing an emerging marketplace in both people looking at how they can leverage their first party data, which, you know, IBM is really talking about what you know, Bob Picciotto talked about this morning. But also, we're seeing thie emergency of a second party and third party data market to help build these models out even further so that I think that's what we're really seeing is the combination of the third party data along with the first party data really being the instrument for building these kind of predictive models, you know, they're going to take us hopefully, you know, far into the future. >> Okay, so, Caitlin square the circle for us. So the CDO roll generally is not perceived. Is it technology role? Correct. Yet as Davis to saying, we're talking about machine learning cognitive. Aye, aye. These air like heavy technical topics. So how does the miracle worker deal with all this stuff generally? And how does IBM deal with it inside the CDO office? Specifically? >> Sure. So it is. It's a very good point, you know, Traditionally, Seo's really have a business background, and we find that the most successful CDO sit in the business organization. So they report somewhere in a line of business. Um, and there are certainly some that have a technical background, but far more come from business background and sit in the business. I can't tell you how we are setting up our studio office at IBM. Um, so are new. And our first global chief date officer joined in December of last year. Interpol Bhandari, um and I started working for him shortly thereafter, and the way he's setting up his office is really three pillars. So first and foremost, we focused on the data engineering data sign. So getting that team in place next, it's information, governance and policy. How are we going to govern access, manage, work with data, both data that we own within our organization as well as the long list of of external data sources that that we bring in and then third is the business integration filler. So the idea is CDOs are going to be most successful when they deliver those data Science data engineering. Um, they manage and govern the data, but they pull it through the business, so ensuring that were really, you know, grounded in business unit and doing this. And so those there are three primary pillars at this point. So prior >> to formalizing the CDO role at I b m e mean remnants of these roles existed. There was a date, equality, you know, function. There was certainly governance in policy, and somebody was responsible to integrate between, you know, from the i t. To the applications, tow the business. Were those part of I t where they sort of, you know, by committee and and how did you bring all those pieces together? That couldn't have been trivial, >> and I would say it's filling. It's still going filling ongoing process. But absolutely, I would say they typically resided within particular business units, um, and so certainly have mature functions within the unit. But when we're looking for enterprise wide answers to questions about certain customers, certain business opportunities. That's where I think the role the studio really comes in and what we're What we're doing now is we are partnering very closely with business units. One example is IBM analytic. Seen it. So we're here with Bob Luciano and other business units to ensure that, as they provide us, you know, their data were able to create the single trusted source of data across the organization across the enterprise. And so I agree with you, I think, ah, lot of those capabilities and functions quite mature, they, you know, existed within units. And now it's about pulling that up to the enterprise level and then our next step. The next vision is starting to make that cognitive and starting to add some of those capabilities in particular data science, engineering, the deep learning on starting to move toward cognitive. >> Dave, I think Caitlin brought up something really interesting. We've been digging into the last couple of years is you know, there's that governance peace, but a lot of CEOs are put into that role with a mandate for innovation on. That's something that you know a lot of times it has been accused of not being all that innovative. Is that what you're seeing? You know what? Because some of the kind of is it project based or, you know, best initiatives that air driving forward with CEOs. I think what we're seeing is that enterprises they're beginning to recognize that it's not just enough to be a manufacturer. It's not just enough to be a retail organization. You need to be the one of the best one of the top two or the top three. And the only way to get to that top two or top three is to have that innovation that you're talking about and that innovation relies on having accurate data for decision making. It also relies on having accurate data for operations. So we're seeing a lot of organizations that are really, you know, looking at how data and predictive models and innovation all become part of the operational fabric of a company. Uh, you know, and if you think about the companies that are there, you know, just beating it together. You know Amazon, for example. I mean, Amazon is a completely data driven company. When you get your recommendations for, you know what to buy, or that's all coming from the data when they set up these logistics centers where they're, you know, shipping the latest supplies. They're doing that because they know where their customers are. You know, they have all this data, so they're they're integrating data into their day to day decision making. And I think that's what we're seeing, You know, throughout industry is this this idea of integrating decision data into the decision making process and elevating it? And I think that's why the CDO rule has become so much more important over the last 2 to 3 years. >> We heard this morning at 88% percent of data is dark data. Papa Geno talked about that. So thinking about the CEOs scope roll agenda, you've got data sources. You've gotto identify those. You gotta deal with data quality and then Dave, with some of the things you've been talking about, you've got predictive models that out of the box they may not be the best predictive models in the world. You've got iterated them. So how does an organization, because not every organizations like Amazon with virtually unlimited resource is capital? How does an organization balance What are you seeing in terms of getting new data sources? Refining those data source is putting my emphasis on the data vs refining and calibrating the predictive models. How organizations balancing that Maybe we start with how IBM is doing. It's what you're seeing in the field. >> So So I would say, from what we're doing from a setting up the chief data office role, we've taken a step back to say, What's the company's monitor monetization strategy? Not how your mind monetizing data. How are how are you? What's your strategy? Moving forward, Um, for Mance station. And so with IBM we've talked about it is moved to enabling cognition throughout the enterprise. And so we've really talked about taking all of your standard business processes, whether they be procurement HR finance and infusing those with cognitive and figuring out how to make those smarter. We talking examples with contracts, for example. Every organization has a lot of contracts, and right now it's, you know, quite a manual process to go through and try and discern the sorts of information you need to make better decisions and optimize the contract process. And so the idea is, you start with that strategy for us. IBM, it's cognitive. And that then dictates what sort of data sources you need. Because that's the problem you're trying to solve in the opportunity you're chasing down. And so then we talk about Okay, we've got some of that data currently residing today internally, typically in silos, typically in business units, you know, some different databases. And then what? What are longer term vision is, is we want to build the intelligence that pulls in that internal data and then really does pull in the external data that we've that we've all talked about. You know, the social data, the sentiment analysis, analysis, the weather. You know, all of that sort of external data to help us. Ultimately, in our value proposition, our mission is, you know, data driven enablement cognition. So helps us achieve our our strategy there. >> Thank you, Dad, to that. Yeah, >> I mean, I think I mean, you could take a number of examples. I mean, there's there's ah, uh, small insurance company in Florida, for example. Uh, and what they've done is they have organized their emergency situation, their emergency processing to be able to deal with tweets and to be able to deal with, you know, SMS messages and things like that. They're using sentiment analysis. They're using Tex analytics to identify where problems are occurring when hurricane happens. So they're what they're doing is they're they're organizing that kind of data and >> there and there were >> relatively small insurance company. And a lot of this is being done to the cloud, but they're basically getting that kind of sentiment analysis being ableto interpret that and add that to their decision making process. About where should I land a person? Where should I land? You know, an insurance adjuster and agent, you know, based on the tweets, that air coming in rather than than just the phone calls that air coming into the into the organization, you know? So that's a That's a simple example. And you were talking about Not everybody has the resources of an Amazon, but, you know, certainly small insurance companies, small manufacturers, small retail organizations, you, Khun get started by, you know, analyzing your You know what people are saying about you. You know, what are people saying about me on Twitter? What are people saying about me on Facebook? You know how can I use that to improve my customer service? Uh, you know, we're seeing ah whole range of solutions coming out, and and IBM actually has a broad range of solutions for things like that. But, you know, they're not the only points out there. There's there's a lot of folks do it that kind of thing, you know, in terms of the dark data analysis and barely providing that, you know, as part of the solution to help people make better decisions. >> So the answers to the questions both You're doing both new sources of data and trying to improve the the the analytics and the models. But it's a balancing act, and you could come back to the E. R. A. Y question. It sounds like IBM strategies to supercharge your existing businesses by infusing them with new data and new insights. Is >> that correctly? I would say that is correct. >> Okay, where is in many cases, the R A. Y of analytics projects that date have been a reduction on investment? You know, I'm going to move stuff from my traditional W two. A dupe is cheaper, and we feels like Dave, we're entering a new wave now maybe could talk about that a little bit. >> Yeah. I mean, I think I think there's a desk in the traditional way of measuring ROI. And I think what people are trying to do now is look at how you mentioned disruption, for example. You know what I think? Disruption is a huge opportunity. How can I increase my sales? How can I increase my revenue? How can I find new customers, you know, through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to see start ups that are dedicated to providing this level of disruption and helping address new markets. You know, by using these kinds of technologies, uh, in in new and interesting ways. I mean, everybody uses the airbnb example. Everybody uses uber example. You know that these are people who don't own cars. They don't know what hotel rooms. But, you know, they provide analytics to disrupt the hotel industry and disrupt the taxi industry. It's not just limited to those two industries. It's, you know, virtually everything you know. And I think that's what we're starting to see is this height of, uh, virtual disruption based on the dark data, uh, that people can actually begin to analyze >> within IBM. Uh, the chief data officer reports to whom. >> So the way we've set up in our organization is our CBO reports to our senior vice president of transformation and operations, who then reports to our CEO our recommendation as we talked with clients. I mean, we see this as a CEO level reporting relationship, and and oftentimes we advocate, you know, for that is where we're talking with customers and clients. It fits nicely in our organization within transformation operations, because this line is really responsible for transforming IBM. And so they're really charged with a number of initiatives throughout the organization to have better skills alignment with some of the new opportunities. To really improve process is to bring new folks on board s. So it made sense to fit within, uh, organization that the mandate is really transformation of the company of the >> and the CDO was a peer of the CIA. Is that right? Yes. >> Yes, that's right. That's right. Um, and then in our organization, the role of split and that we have a chief data officer as well as a chief analytics officer. Um, but, you know, we often see one person serving both of those roles as well. So that's kind of, you know, depend on the organizational structure of the company. >> So you can't run the business. So to grow the business, which I guess is the P and L manager's role and transformed the business, which is where the CDO comes. >> Right? Right, right. Exactly. >> I can't give you the last word. Sort of Put a bumper sticker on this event. Where do you want to see it go? In the future? >> Yes. Eso last word. You know, we try Tio, we tried a couple new things. Uh, this this year we had our deep dive breakout sessions yesterday. And the feedback I've been hearing from folks is the opportunity to talk about certain topics they really care about. Is their governance or is innovation being able to talk? How do you get started in the 1st 90 days? What? What do you do first? You know, we we have sort of a five steps that we talk through around, you know, getting your data strategy and your plan together and how you execute against that. Um And I have to tell you, those topics continue to be of interest to our to our participants every year. So we're going to continue to have those, um, and I just I love to see the community grow. I saw the first Chief data officer University, you know, announced earlier this year. I did notice a lot of PR and media around. Role of studio is miracle workers, As you mentioned, doing a lot of great work. So, you know, we're really supportive. Were big supporters of the role we'll continue to host in person events. Uh, do virtual events continue to support studios? To be successful on our big plug is will be world of Watson. Eyes are big IBM Analytics event in October, last week of October in Vegas. So we certainly invite folks to join us. There >> will be, >> and he'll be there. Right? >> Get still, try to get Jimmy on. So, Jenny, if you're watching, talking to come on the Q. >> So we do a second interview >> and we'll see. We get Teo, And I saw Hillary Mason is going to be the oh so fantastic to see her so well. Excellent. Congratulations. on being ahead of the curve with the chief date officer can theme. And I really appreciate you coming to Cube, Dave. Thank you. Thank you. All right, Keep right there. Everybody stew and I were back with our next guest. We're live from the Chief Data Officers Summit. IBM sze event in Boston Right back. My name is Dave Volante on DH. I'm a longtime industry analysts.