>> Over and welcome to this special cube conversation here in the Palo Alto Studios of Cube. Part of our People. First project with Mayfield Fund and Co creation with Cuban John Very your host. Very special guest. Judy Estrin. She's the CEO of J Labs and author of the book Closing the Innovation Gap. She's also well known for being an Internet entrepreneur. Pioneer worked on the initial TCP IP protocol with Vin Cerf from When the A Stanford Great History Computer Science. You have computer systems in your blood, and now you're mentoring a lot of companies. Author you a lot of work, and you're lending your voice to some cutting edge issues here in Silicon Valley and around the world. Thanks for joining me today for the conversation. >> Thank you. It's fun to be here, >> So I love the fact that you're here. You're a celebrity in the commute computer industry circles. You were there at the beginning, when the computer systems or the Internet were being connected as they built out of stone of the whole system's revolution in the eighties, and the rest is history. Now we have cloud computing, and now we're seeing a whole nother level step function of scale. And so you've kind of seen it all. You've seen all all the waves. Actually, something like make is they have seen some of the ways, but you've seen all of them. The most compelling thing I think that's happening now is the convergence of social science and computer science. Kind of our motto. Silicon Angle. You recently wrote to Post on Medium that that has been kind of trending and going viral. I want to get your perspective on that. And they're They're interesting because they they bring a little bit of computer science called the authoritative Authority Terrian Technology Reclaiming Control far too attention, part one. We go into great detail to lay out some big picture computer industry discussions. What's it all about? What's what's the What's the idea behind these stories? >> So let me back up a little bit in that, a Sze Yu said. And we can go into this if you want. I was very involved in a lot of thie, ah, innovation that happened in the Village Valley in terms of microprocessors, the Internet, networking, everything that laid the foundation for a lot of the things we see today incredible opportunities for my career for problems we solved over the last ten years. Ten, twelve years. Um, I began to see a shift and a shift in the culture and a shift in the way technology was impacting us. And it's not all good or bad. It's that it felt like we were out of balance and that we were becoming shorter and shorter, term focused and actually my book in two thousand eight closing the innovation gap. The main message there is let's not forget about the seeds you plant that all of this comes from because we're reaping the benefit of those seeds. We're not planning new seats and that we were becoming in the Valley in the nation the way we thought about things more and more short term focused and technology was causing some of that and benefitting and not been and at a disadvantage because of that. So that started with my book in two thousand eight and then in twenty fourteen, I think it was I did a Ted talk a Ted X talk called Balancing our Digital Diets, and I was even Mohr concerned that we were out of whack in terms of the consequences of innovation, and I drew an analogy to our food's systems, where so much innovation and creating cheap calories and energy and things like high fructose corn syrup that it took years to realize that, Oh, there's some negative consequences of that innovation. And so that was kind of a warning that, um, we weren't thinking enough about the consequences of at that point. Social media. That was before fake news, and I talked about tweets and how truth that lies went faster than truth, not knowing how bad that situation was going to be and then leading up to the election and after the election. We all know and have all learned now about the impacts of these technologies on our democracy, and I believe on our society and humanity. And I don't think it's just about our election system. I think it's about our psyches and how the technology's air impacting the way we think our fear and anxiety level of our kids and us is adults. So I been talking to people about it and advising, and I finally decided as, uh, I was collaborating with people that I felt that a lot of the awareness was in pockets that we talked about data privacy or we talked about addiction. But these air things were all interrelated, and so I wanted to one ad. My voices is technologists because I think a lot of the people who are writing the building, the awareness and talking about it if you are in government or a journalist's or even a social scientist people, it's really easy to say, Yeah, you say that, but you don't understand. It's more complicated than that. You don't understand the technology. So one, I do understand that technology. So I felt adding my voice as a technologist. But I'm also, uh, just increasingly concerned about what we do about it and that we take a more holistic view. So that's what, what what the pieces are about. And the reason I broke it into two pieces is because they're too long for most people, even the way they are. But the first is to build awareness of the problems which we can dig into it a high level if you want. And then the second is to throw out ideas as we move towards discussing solutions. So let me take a breath because you were goingto jump in, and then I can. >> No, it's just because you're connecting the foundational of technology foundation technology, identifying impact, looking at pockets of awareness and then looking at how it's all kind of coming together when you talk like that The first time I saw O subsystem interrupt us connection so someone could get like a operating system. And I think the society that you're pointing out in the article, the first one intention was there only to relate. And I think that's the key part. I think that's interesting because we run into people all the time when we do our cue broadcasts that have awareness here and don't know what's going on this. So this context that's highly cohesive. But there's no connection, right? So the decoupled right but highly cohesive, That's kind of systems. Architecture concept. So how do we create a robust technology's society system where technology and I think that's a threat that we're seeing this? What I cleaned out of the articles was your kind of raising the flag a little bit to the notion of big picture right system, kind of a foundational, but let's look at consequences and inter relationships, and how can we kind of orchestrate and figure out solutions? So what was the reaction to expand on that concept? Because this is where I was. It was provocative to me, >> right? So I think there are two thought trains that I just went down. One is that one of the problems we have that has been created by technology and technology is suffering from again. It's causing both cause and effect is not enough seats, system thinking and so one issue, which is not just this is not just about social media and not just about a I, but over the last twenty years we've increasingly trained, I think, are, Ah, engineers and computer scientists in Mohr transactional thinking. And as we move quicker and quicker to solve problems, we are not training our leaders or training our technologist to think in terms of systems. And so what I mean by systems is two things that you can break, that any problems have pieces. But those pieces air inter connected. We are interconnected, and that you, if you don't keep those things in mind, then you will not design things in a way, I believe that have the longevity and make the right type decisions. The second is the law of consequences when you have a system, if you do something here, it's going to impact something here. And so that whole notion of taking was thinking through consequences. I'm afraid that we're training people as we are focusing on being more and more agile, moving more and more quickly that it's in technology and in society that we're losing some of that system, thinking >> that they kind of think that's the trade off is always around. Whenever he had systems conversations in the past, but my old systems had on trade offs, we have overhead, so we have more memory. How do we handle things? So this is kind of That's just what happens. You tell about consequence, but >> we don't have all those we I'm older than you. But we started at a time when that we were limited. We were limited by memory. We were limited by processing. We were limited by band with and a different times. As thie industry emerged, the constraints were in different areas. Today, you don't have any of those constraints. And so, if you don't have any of those constraints. You don't get trained in thinking about trade offs and thinking about consequences. So when when we come into just what drove me to write, this one set of things are foundational issues and what I mean by foundational it's it's our relationship to technology. And the fact of the matter is, as a society, um, we put technology on a pedestal, and we have, uh, this is not to be taken out of Cut is not to be taken the extreme of talking about people, but overall, our relationship with technology is a bullying, controlling relationship. That's why I called it authoritarianism. >> Upgrade your iPhone to the new version. >> Well, whether it's as a user that you're giving up your your your authority to all these notifications and to your addiction, whether it is the fact that it is the control with the data, whether it is predictive ai ai algorithms that are reading your unconscious behaviors and telling you what you think, because if it's suggesting what you by putting things in front of you. So there are all of these behaviors that our relationship with technology is not a balanced relationship and you could one. You have a culture where the companies that are that have that power are driving towards. It's a culture of moving fast growth only don't think about the consequences. It's not just the unintended consequences, but it's the consequences of intended use. So the business models and at which we don't need to go into, because I think a lot of other people talk about that all end up with a situation which is unhealthy for us as people and humanity and for us as a society. So you take that part and it is. There's a parallel here, and we should learn from what happened with industrial Ah, the industrial revolution. We want progress. But if we don't pay attention to the harm, the harmful byproducts and trade offs of progress, it's why we have issues with climate. It's why we have plastic in our oceans. It's because you, you judge everything by progresses just growth and industrialization without thinking about well being or the consequences. Well, I believe we now face a similar challenge of digitization, so it's not industrialization. But it's digitalization that has byproducts in a whole number of areas. And so what the the article does is get into those specifics, whether it's data or anxiety, how we think our cognitive abilities, our ability to solve problems, All of those things are byproducts of progress. And so we should debate um, where we what we're willing to give up one last thing. And then I'll have to come in, which is one of the problems with both of these is is humans value convenience. We get addicted to convenience, and if somebody gives us something that is going to make things more convenient, it sure is held to go backward. And that's one of the reasons the combination of measuring our goodness as a country or a CZ. Globalization by economic growth and measuring our personal wellness by convenience, if something is more convenient, were happier. Take those two together, and it makes a dangerous cop combination because then our need for community convenience gets manipulated for continued economic growth. And it doesn't necessarily end up in, Ah, progress from, ah, well being perspective. >> It's interesting point about the digitization, because the digital industrial revolution, when the digital revolution is happening, has consequences. We're seeing them and you point them out in your post Facebook and fake news. There's also the global landscape is the political overlay. There's societal impact. There's not enough scholars that I've been trained in the art of understanding into relationships of technology, and Peg used to be a nerd thing. And now my kids are growing up. Digital natives. Technology is mainstreams, and there it is. Politics. You know, the first hack collection, Some of the control, The first president actually trolled his way. That president, I said that I'm the kid. That was my position. He actually was a successful troll and got everyone he trolled the media and you got the attention. These air new dynamics, This is reality. So is you look forward and bring these ideas, and I want to get your thoughts on ideas on how to bring people together. You've been on a CTO Cisco Systems. I know you've been sleeping on a board. This is a cross pollination opportunity. Bring people together to think about this. How do you do You look at that? How do you view how to take the next steps as a as an industry, as a society and as a global nations? It eventually, because cyber security privacy is becoming polarized. Also on a geography bases in China they have. GPR is hard core there. In Europe, he got Asia. With Chinese. You got America being American. It's kind of complicated as a system architecture thinking. How do you look at this? What is the playing field where the guard rails? What's your thoughts on this? Because it's a hard one, >> right? So it is a hard one and it isn't. It isn't easy to pave out a path that says it's solvable. Um, nor does Climate right now. But you have to believe we're going to figure it out because we have to figure it out. So I think there are a lot of pieces that we need to start with, and then we need to adjust along the way. And, um, one piece is and let me back up. I am not. I don't believe we can leave this up to the industry to solve the incentives and the value systems and the understanding of the issues. The industry is coming from an industry perspective, and you can't also. You also can't leave it just two technologists because technologists have a technology person perspective. I don't believe that you just can have government solve it for a variety of reasons. One is, if it takes a spectrum of things to legislation, tends to be retroactive, not forward looking. And you need to be really careful not to come up with regulation that actually reinforces the status quo as opposed to making something better. But I think we need to. We do need to figure out how to govern in a way that includes all of these things. So once >> it's running, it's clear that watching the Facebook hearing and watching soon dark sky in front of the house. Our current elected officials actually don't even know how the Internet works, so that's one challenge. So you have a shift in its every beat >> and it and it's actually, if you think about the way legislation often gets made one of the problems with our democracy right now, I'm not going to put it in quotes. But I want to put it >> out. >> Is that the influence of money on our democracy means that so often the input toe legislation comes from industry. So whether it's again big tech, big pharma, big Oil, big. That's the way this cycle works in places where we have had successful legislation that industry input, what you need industry input. You just don't want industry to be the on ly input that is balanced with other input. And so we need infrastructure in the world. In the country that has policy ideas, technology. This needs to come from civil society, from the academy from non profits. So you need the same way we have environmental sciences. We need to fund from government, not just industry funded that science. That's number one. And then we need ways to have conversations about influencing companies to do the right thing. Some of it is going to be through legislation some of it is going to be for through pressure. This, in some ways is like tobacco in some ways, like it's like food. In some ways, it's like climate on DH. It's so and an underlying any of this to happen. We need people to understand and to speak up because awareness amongst whether it's individuals, parents, teachers, we need to give people the information to protect themselves and to push back on companies and to rally pushback on government. Because if if there's not an awareness of people are walking around saying, Don't take away my service, don't make this less convenient don't tax my soda. Don't tell me my text messages. That's right, so and I'm not saying taxes of the way. But if there isn't what what I'm focused on is, how do we build awareness? How do we get information out? How do we get companies like yours and others that this becomes part of >> our >> messaging of understanding so we can be talking about I >> think it's, you know back, Teo, The glory days of the TCP epi Internet revolution. He sent a package from here to there. It's a step. Take a first step. I personally listening to you talk feel and I said, It's on The Cuban people know that. You know, my my rap know that I've been pounding this. There's a counter culture in there somewhere. Counter culture's is where action happens, and I think you know, tax regulation and, you know, the current generations inherited. It is what it is we have. You're laying out essentially the current situation. John Markoff wrote a great book, What the door Mail said, talking about how the sixties counterculture influence the computer industry from breaking in for getting computer time for time sharing, too hippy revolution question I have for you put you on the spot. Is Is there a counterculture in your mind? Coming a digital hippie quotes is because I feel it. I feel that that let the air out of the balloon before it pops. Something has to happen and I think has to be a counterculture. I yet yet can't put my finger on it. Maybe it's a digital kind of a revolution, something compelling that says Whoa time out. >> All right? I think we need a couple of counter culture's in that in layers of it, because, um, I think there is going to be or is starting to be a counterculture amongst technologist and the technology industry and entrepreneurs who are some it's still small who are saying, You know what? This chasing unicorns and fastest growth and scale, you know, move faxed and break things. But, um, we want to move fast, but we want to think about whether we're breaking what we're breaking is really dangerous, you know, move fast and break things is fine, but if it's oops, we broke democracy. That isn't something that, uh that is I'm sorry you have to think about and adapt more quickly. So I think there is Are people who are talking about let's talk openly about the harm. Let's not just be tech optimists. Let's understand that it's small, but it's beginning and you're seeing it in a I for instance, the people who are saying Look, were technologists, we want to be responsible. This is a powerful weapon or tool. And let's make sure we think about how we use it. Let me just say one thing, which is, I think we needed another kind of counterculture, which I'm hoping is happing in a number of areas, which is societally saying, You know, we have a slow food movement. Maybe we just need a slow down, a little bit movement. So if you look at mindfulness, if you look at kids who are starting to say, You know what? I want to talk to someone in person, I don't. So we we need some of that counter movement where I'm hoping the pedestal starts to come back. In terms of people looking for real connectivity and not just numbers of connections, >> it's interesting, You know, everything has a symmetrical, responsible thing about it. For every fake news payload and network effect is potentially an opposite reaction of quality network effect. It's interesting, and I don't know where it is, but I think that's got it could be filled, certainly on the economic side, by new entrepreneurial thinking, like one observation I'm making is you know this. Remember, they'll bad boys of tech and he's smiling. Now It's bad gals, too, which is growing still lower numbers. So I think there's gonna be a shift to the good, the good folks right moment. But she's a she's a good entrepreneur. She's not just out there to make a quick buck or hey, mission driven za signal we're seeing. So you start to see a little bit more of a swing to Whoa, hey, let's recognize that it's not about, you know, could Buck or >> so, yes, but between you and I, it's teeny compared to the other forces. So that's what those of us who believe that needs to happen need to continue to >> one of those forces money making. >> I think it's a combination of, Ah, money and how much money, Dr. Celebrity culture, um, the forces, the power that's in place is so strong that it's hard to break through, um, short term thinking, not even being trained. So like so many things in our culture, where you have entrenched power and then you see uprising and you get hope and that's where you need the hope. But, um, we've seen it so often in so many movements, from race to gender, where you think, Oh, that's solved, it's not solved and then you come back in and come back at it. So I just I would argue that there is little bits of it, but it needs fuel. It needs continuity. It it. And the reason I think we need some government regulation is it needs help because it's not gonna >> happen. You should question, you know, some successes that I point out Amazon Web services, Google even having a long game kind of narrative they're always kind of were misunderstood at first. Remember, Google was loud by search is not doing too well. Then the rest is history. Amazon was laughed. Amazon Web services was laughed at. So people who have the long game seemed to be winning in these transitions. And that's kind of what you're getting at. You think long term, the long game. If you think in terms of the long term vision, you then going look at consequences differently. How many people do you run in? The valleys actually think like that. Okay, >> so we're talking about two different things. One is long term thinking, and I do think that apple, Google, Amazon have taken long term thinking's. So there are a good example. But if you look at them, if we look at the big companies in terms of the way they approached the market and competition and their potential negative impacts on overall society, they're part of the power. They're not doing anything to change the systems, to not >> have good and continue to benefit. The rich get richer. >> So there this This is why it's complicated. There are not good guys and bad guys there are. These people are doing this and that. So do I think overall dough? I see more long term thinking. Um, not really. I think that the incentives in the investment community, the incentives in the stock market. The incentives culturally are still very much around shorter term thinking. Not that there aren't any, but >> yeah, I would agree. I mean, it tends to be, you know, Hey, we're crushing it. We're winning, you know? Look at us. Growth hack. I mean, just the languages. Semantics. You look at that. I think it's changed. I think Facebook is, I think, the poster child of short term thinking growth hacks move fast, break stuff and look where they are, you know, they can't actually sustaining and brand outside of Facebook, they have to buy Instagram and these other companies to actually get the kind of growth. But certainly Facebook is dominate on the financial performance, but they're kind of sitting in their situation. I think you know the bro Grammer movement, I think is kind of moving through the white common ear culture of Okay, let's get some entrepreneurship going. Great. Rod. I think that's stabilising. I think we're seeing with cloud really science and thinking for good. That's a positive sign. >> Well, I'm I'm glad to hear that from you, you know, and all >> you're probably going with. >> No, no, no, I'll take that and take that into feeding my hope because I hope, >> well, the movement is classic. Look, we're not gonna tolerate this anymore. I think transparency in my final question to you before you get to some of the more entrepreneur Question says, If you look at the role of community on data, science and connectedness, one of the things about being connected is you got potential potential for collective intelligence. So if you look at data, as I said, networks, what if there was a way to kind of hone that network to get to the truth fast? Esther, something we've been working on here, and I think that's something that, you know changes media. It changes the game. But collective intelligent, the role of the community now becomes a stakeholder and potentially laying out. So his problems and you're part of the Mayfield community was co created this video with roll community, super important people. The rule of the of the person your thoughts on >> so I community is a word that is has takes on a lot of meetings, and the problem is when you mean it one way and use it the other way, the same as data driven. So I think there's at one level which is community and conductivity that has to do with collecting input from lots of sources. And when you talk about investigative journalism or they're in environmental situations or all sorts of areas where the ability to collect information from lots of sources that air interested and analyze that information that is one level of community and connectivity and networking because of people you know which is great, there's another type. When people talk about community, they mean a sense of community in terms of what humans need and what that connectivity is. And most online networks don't give you that level. The online needs to be augmented by, Ah, inter personal understanding. And one of the problems. I think with today's technology is we're fitting humans into bits that technology Khun Support, as opposed to recognizing what are our human needs that we want to hold on to and saying There are some things that are not going to fit into somebody's data set. So in that first type of community than absolutely, I think there's lots of benefits of the cloud and wisdom of the crowd. But if you're talking about humans connecting in people. You don't have the same type of, uh, that that really community online tools can help. But we should never confuse what happens in our online world >> with your final question for, you know, we got We're pushing the time here. Thank you for spending time. First of all, it's great conversation. You've seen the movie with venture capital from the beginning. You know, all the original players seeing what is now just where's that come from? Where are we? What's the state of VC? Then? He hope to the future, they all adding value. How do you see that evolving and where are we with? >> You know, I would. I think venture capital has gone through a lot of different phases. And like so many things, especially those of us who want computers, we liketo lump them all together. They're not altogether. There are some small, Yes, like they field. And the I do think, though, that something shifted in the lead up to the dot com. Ah, and later the burst. And what shifted is venture capitalists. Before that time were company builders. They were the financiers, but they saw themselves with the entrepreneur building companies because of the expansion leading up to two thousand, and the funds grew and the people coming into the field were, they became more bankers and they took more financial supposed to balancing financing and entrepreneurship. It felt like it moved. Maurin toe. This is a private equity play, Um, and I think the dynamic with entrepreneurs and the methodology overall shifted, and I don't know that that's changed Now again, not across the board. I think there are some, uh, those firms that have identified our partners within firms who still very much want Teo filled companies and partner with entrepreneurs. But I think the dynamic shifted, and if you view them as that's what they are, is private equity investors. And don't expect something else. If people need money, that's a good pick. Ones that are the best partner >> is your partner. If you want a banker, go here. If you want Builder, go their key distinction. Judy. Thanks for sharing that insight. We're Judy Estrin. Sea of Jail as author of Closing Innovation. Gabbas Wellman's well known entrepreneur advisor board member formally CTO of Cisco. And again, Great gas. Thanks for coming on I'm John for Herewith. Cube conversation. Part ofmy Mayfield. People first with the Cube. Thanks for watching.

>> Announcer: From New York, it's theCUBE. Covering Blockchain Week. Now, here's John Furrier. >> Hello, everyone, I'm John Furrier with theCUBE. We are here on the ground in New York City for Consensus 2018, part of Blockchain Week New York. #BlockchainweekNY. We're here, with Judy Gordon with OmniSparx. It' a startup, they just looking for an ICO, getting it going, welcome to theCUBE. >> Thank you so much for having us. >> We're in media row here, all the action going on at the Hilton, there's a packed event. What's it like? You're navigating this sea of growth. You guys are a startup, it must be amazing. >> It is amazing. And what's been the best part for us is meeting all these amazing projects that our company is hoping to support. >> Take a minute to explain what you guys do, stage of the company, how many employees, what you guys are doing, looking for some funding partners. Take a minute to explain what's going on. >> Yeah, so we are a community management platform. We are a product for community managers of all kinds of projects to help manage this very difficult problem that they have. With so many community members, so many are anonymous, so many are causing problems, but yet they need community members to really build their projects. We are just finishing up our angels, our friends and family round, and we're starting our seed round. >> Where are you guys located? >> We're located in Chicago. >> Okay, cool, Chi-town. >> Yes, we have about, we have five members of the team in Chicago and we have a development team in Serbia. >> What's your background? How did you get into this role? What's your role in the industry? How did you get here? >> So, I've been in marketing for large corporations and small startups. And one of my old bosses from Motorola started the company and invited me to come on in and do marketing. And it's been, it's an amazing space right now. >> Interesting opportunity for startups here with Blockchain and decentralized applications. But you mentioned community software. When was the last time the technology stack in community software's been modernized. I mean Slack is like a poster child. It's essentially an IRC message group with a user interface with great APIs. I love Slack. We use it, but that's not really modern software. >> Right. >> So how are you applying Blockchain and decentralized applications for a new modern community approach? >> So first of all, we're letting community members and media managers use whichever tool they want. So from our perspective, you can use Telegram, Slack, Twitter, Facebook, all the tools that you use today. But right now they have to go from channel to channel and manage all these different channels. So now they'll be able to do it from one space. But the way we're revolutionizing it is, and the challenge with crypto is that there are all these anonymous participants. So there's all these token holders out there, but you don't know who they are. Well, we have an app where people can go in, they sign in for the app, they tell you if they want to that they're your token holder, what their social handles are, and so that you can do direct outrates. >> So you guys actually going to have a token? Is it going to be an ICO, public, private, security token, utility token, can you just share some insight into what the strategy is. >> Yeah, so our plan is to do an ICO. We're following all the US regulations. And we'll have a token. Our token is going to be, it's a security token, and crypto projects will be able to use it as a way to do community outreach and do campaigns. Community campaigns. >> Any good leads here at the show? >> Oh, yeah, every single community manager we talked to has been interested. There's so many great projects out there. They all want to build a community, they all need community to thrive, and they all need a tool like ours. >> Well, since you said as an industry veteran, I want to get your take while you're here on the event. What's your experience here? What's the main content? The people who couldn't make it here, obviously they sold out, what's the show about? What's the core themes? What's resonating from a content thematic standpoint that you've observed? >> Well, I think a couple of things. First of all, there's so much excitement, so much growth, so much opportunity. I think what struck me, as I was waiting to be interviewed here, so many languages. People from all over the world are here to learn, to network. And what I've always found is so wonderful about the crypto community is it's really a community. People want each other to thrive. >> It's a tight-knit community. I got to say, it's very strong. They're very opinionated. They're not afraid to share opinions. We just had Jimmy Song on from Blockchain Capital, and he's really vocal, but it's cordial, it's civil, and there's some civil discourse which moves the needle. >> Yeah, and everyone wants everyone to succeed. >> Right, awesome. One of the things I noticed was a lot of the women in tech panels going on, still it's a sea of men here. You're a woman here. What's it like, we need more women in tech. >> Yes. >> What's your, what are you doing to change that? Obviously you're here. Is there more women coming on board? Is there groups out there within this community? What's the women in tech angle? >> Yeah so, I was surprised and I knew there were very few women in Bitcoin, but looking around at Blockchain, there really aren't that many women here. And so, but I think it's a great space for women. I think there's a lot of opportunity for women. And there are several organizations working to promote women in this space. >> It's really rockin'. >> Hopefully next year it'll be different. >> We need more women. So more women out there. Judy Gordon is here, she's with OmniSparx startup. Changing the game with new infrastructure for communities. I'm John Furrier here on the ground here at Blockchain Week Consensus 2018. Thanks for watching. We'll be right back. (upbeat music)

(energetic music) >> Everyone, welcome back to theCUBE's coverage of WiDS 2023. This is the eighth annual Women in Data Science Conference. As you know, WiDS is not just a conference or an event, it's a movement. This is going to include over 100,000 people in the next year WiDS 2023 in 200-plus countries. It is such a powerful movement. If you've had a chance to be part of the Livestream or even be here in person with us at Stanford University, you know what I'm talking about. This is Lisa Martin. I have had the pleasure all day of working with two fantastic graduate students in Stanford's Data Journalism Master's Program. Hannah Freitag has been here. Tracy Zhang, ladies, it's been such a pleasure working with you today. >> Same wise. >> I want to ask you both what are, as we wrap the day, I'm so inspired, I feel like I could go build an airplane. >> Exactly. >> Probably can't. But WiDS is just the inspiration that comes from this event. When you walk in the front door, you can feel it. >> Mm-hmm. >> Tracy, talk a little bit about what some of the things are that you heard today that really inspired you. >> I think one of the keyword that's like in my mind right now is like finding a mentor. >> Yeah. >> And I think, like if I leave this conference if I leave the talks, the conversations with one thing is that I'm very positive that if I want to switch, say someday, from Journalism to being a Data Analyst, to being like in Data Science, I'm sure that there are great role models for me to look up to, and I'm sure there are like mentors who can guide me through the way. So, like that, I feel reassured for some reason. >> It's a good feeling, isn't it? What do you, Hannah, what about you? What's your takeaway so far of the day? >> Yeah, one of my key takeaways is that anything's possible. >> Mm-hmm. >> So, if you have your vision, you have the role model, someone you look up to, and even if you have like a different background, not in Data Science, Data Engineering, or Computer Science but you're like, "Wow, this is really inspiring. I would love to do that." As long as you love it, you're passionate about it, and you are willing to, you know, take this path even though it won't be easy. >> Yeah. >> Then you can achieve it, and as you said, Tracy, it's important to have mentors on the way there. >> Exactly. >> But as long as you speak up, you know, you raise your voice, you ask questions, and you're curious, you can make it. >> Yeah. >> And I think that's one of my key takeaways, and I was just so inspiring to hear like all these women speaking on stage, and also here in our conversations and learning about their, you know, career path and what they learned on their way. >> Yeah, you bring up curiosity, and I think that is such an important skill. >> Mm-hmm. >> You know, you could think of Data Science and think about all the hard skills that you need. >> Mm, like coding. >> But as some of our guests said today, you don't have to be a statistician or an engineer, or a developer to get into this. Data Science applies to every facet of every part of the world. >> Mm-hmm. >> Finances, marketing, retail, manufacturing, healthcare, you name it, Data Science has the power and the potential to unlock massive achievements. >> Exactly. >> It's like we're scratching the surface. >> Yeah. >> But that curiosity, I think, is a great skill to bring to anything that you do. >> Mm-hmm. >> And I think we... For the female leaders that we're on stage, and that we had a chance to talk to on theCUBE today, I think they all probably had that I think as a common denominator. >> Exactly. >> That curious mindset, and also something that I think as hard is the courage to raise your hand. I like this, I'm interested in this. I don't see anybody that looks like me. >> But that doesn't mean I shouldn't do it. >> Exactly. >> Exactly, in addition to the curiosity that all the women, you know, bring to the table is that, in addition to that, being optimistic, and even though we don't see gender equality or like general equality in companies yet, we make progress and we're optimistic about it, and we're not like negative and complaining the whole time. But you know, this positive attitude towards a trend that is going in the right direction, and even though there's still a lot to be done- >> Exactly. >> We're moving it that way. >> Right. >> Being optimistic about this. >> Yeah, exactly, like even if it means that it's hard. Even if it means you need to be your own role model it's still like worth a try. And I think they, like all of the great women speakers, all the female leaders, they all have that in them, like they have the courage to like raise their hand and be like, "I want to do this, and I'm going to make it." And they're role models right now, so- >> Absolutely, they have drive. >> They do. >> Right. They have that ambition to take something that's challenging and complicated, and help abstract end users from that. Like we were talking to Intuit. I use Intuit in my small business for financial management, and she was talking about how they can from a machine learning standpoint, pull all this data off of documents that you upload and make that, abstract that, all that complexity from the end user, make something that's painful taxes. >> Mm-hmm. >> Maybe slightly less painful. It's still painful when you have to go, "Do I have to write you a check again?" >> Yeah. (laughs) >> Okay. >> But talking about just all the different applications of Data Science in the world, I found that to be very inspiring and really eye-opening. >> Definitely. >> I hadn't thought about, you know, we talk about climate change all the time, especially here in California, but I never thought about Data Science as a facilitator of the experts being able to make sense of what's going on historically and in real-time, or the application of Data Science in police violence. We see far too many cases of police violence on the news. It's an epidemic that's a horrible problem. Data Science can be applied to that to help us learn from that, and hopefully, start moving the needle in the right direction. >> Absolutely. >> Exactly. >> And especially like one sentence from Guitry from the very beginnings I still have in my mind is then when she said that arguments, no, that data beats arguments. >> Yes. >> In a conversation that if you be like, okay, I have this data set and it can actually show you this or that, it's much more powerful than just like being, okay, this is my position or opinion on this. And I think in a world where increasing like misinformation, and sometimes, censorship as we heard in one of the talks, it's so important to have like data, reliable data, but also acknowledge, and we talked about it with one of our interviewees that there's spices in data and we also need to be aware of this, and how to, you know, move this forward and use Data Science for social good. >> Mm-hmm. >> Yeah, for social good. >> Yeah, definitely, I think they like data, and the question about, or like the problem-solving part about like the social issues, or like some just questions, they definitely go hand-in-hand. Like either of them standing alone won't be anything that's going to be having an impact, but combining them together, you have a data set that illustrate a point or like solves the problem. I think, yeah, that's definitely like where Data Set Science is headed to, and I'm glad to see all these great women like making their impact and combining those two aspects together. >> It was interesting in the keynote this morning. We were all there when Margot Gerritsen who's one of the founders of WiDS, and Margot's been on the program before and she's a huge supporter of what we do and vice versa. She asked the non-women in the room, "Those who don't identify as women, stand up," and there was a handful of men, and she said, "That's what it's like to be a female in technology." >> Oh, my God. >> And I thought that vision give me goosebumps. >> Powerful. (laughs) >> Very powerful. But she's right, and one of the things I think that thematically another common denominator that I think we heard, I want to get your opinions as well from our conversations today, is the importance of community. >> Mm-hmm. >> You know, I was mentioning this stuff from AnitaB.org that showed that in 2022, the percentage of females and technical roles is 27.6%. It's a little bit of an increase. It's been hovering around 25% for a while. But one of the things that's still a problem is attrition. It doubled last year. >> Right. >> And I was asking some of the guests, and we've all done that today, "How would you advise companies to start moving the needle down on attrition?" >> Mm-hmm. >> And I think the common theme was network, community. >> Exactly. >> It takes a village like this. >> Mm-hmm. >> So you can see what you can be to help start moving that needle and that's, I think, what underscores the value of what WiDS delivers, and what we're able to showcase on theCUBE. >> Yeah, absolutely. >> I think it's very important to like if you're like a woman in tech to be able to know that there's someone for you, that there's a whole community you can rely on, and that like you are, you have the same mindset, you're working towards the same goal. And it's just reassuring and like it feels very nice and warm to have all these women for you. >> Lisa: It's definitely a warm fuzzy, isn't it? >> Yeah, and both the community within the workplace but also outside, like a network of family and friends who support you to- >> Yes. >> To pursue your career goals. I think that was also a common theme we heard that it's, yeah, necessary to both have, you know your community within your company or organization you're working but also outside. >> Definitely, I think that's also like how, why, the reason why we feel like this in like at WiDS, like I think we all feel very positive right now. So, yeah, I think that's like the power of the connection and the community, yeah. >> And the nice thing is this is like I said, WiDS is a movement. >> Yes. >> This is global. >> Mm-hmm. >> We've had some WiDS ambassadors on the program who started WiDS and Tel Aviv, for example, in their small communities. Or in Singapore and Mumbai that are bringing it here and becoming more of a visible part of the community. >> Tracy: Right. >> I loved seeing all the young faces when we walked in the keynote this morning. You know, we come here from a journalistic perspective. You guys are Journalism students. But seeing all the potential in the faces in that room just seeing, and hearing stories, and starting to make tangible connections between Facebook and data, and the end user and the perspectives, and the privacy and the responsibility of AI is all... They're all positive messages that need to be reinforced, and we need to have more platforms like this to be able to not just raise awareness, but sustain it. >> Exactly. >> Right. It's about the long-term, it's about how do we dial down that attrition, what can we do? What can we do? How can we help? >> Mm-hmm. >> Both awareness, but also giving women like a place where they can connect, you know, also outside of conferences. Okay, how do we make this like a long-term thing? So, I think WiDS is a great way to, you know, encourage this connectivity and these women teaming up. >> Yeah, (chuckles) girls help girls. >> Yeah. (laughs) >> It's true. There's a lot of organizations out there, girls who Code, Girls Inc., et cetera, that are all aimed at helping women kind of find their, I think, find their voice. >> Exactly. >> And find that curiosity. >> Yeah. Unlock that somewhere back there. Get some courage- >> Mm-hmm. >> To raise your hand and say, "I think I want to do this," or "I have a question. You explained something and I didn't understand it." Like, that's the advice I would always give to my younger self is never be afraid to raise your hand in a meeting. >> Mm-hmm. >> I guarantee you half the people weren't listening or, and the other half may not have understood what was being talked about. >> Exactly. >> So, raise your hand, there goes Margot Gerritsen, the founder of WiDS, hey, Margot. >> Hi. >> Keep alumni as you know, raise your hand, ask the question, there's no question that's stupid. >> Mm-hmm. >> And I promise you, if you just take that chance once it will open up so many doors, you won't even know which door to go in because there's so many that are opening. >> And if you have a question, there's at least one more person in the room who has the exact same question. >> Exact same question. >> Yeah, we'll definitely keep that in mind as students- >> Well, I'm curious how Data Journalism, what you heard today, Tracy, we'll start with you, and then, Hannah, to you. >> Mm-hmm. How has it influenced how you approach data-driven, and storytelling? Has it inspired you? I imagine it has, or has it given you any new ideas for, as you round out your Master's Program in the next few months? >> I think like one keyword that I found really helpful from like all the conversations today, was problem-solving. >> Yeah. >> Because I think, like we talked a lot about in our program about how to put a face on data sets. How to put a face, put a name on a story that's like coming from like big data, a lot of numbers but you need to like narrow it down to like one person or one anecdote that represents a bigger problem. And I think essentially that's problem-solving. That's like there is a community, there is like say maybe even just one person who has, well, some problem about something, and then we're using data. We're, by giving them a voice, by portraying them in news and like representing them in the media, we're solving this problem somehow. We're at least trying to solve this problem, trying to make some impact. And I think that's like what Data Science is about, is problem-solving, and, yeah, I think I heard a lot from today's conversation, also today's speakers. So, yeah, I think that's like something we should also think about as Journalists when we do pitches or like what kind of problem are we solving? >> I love that. >> Or like kind of what community are we trying to make an impact in? >> Yes. >> Absolutely. Yeah, I think one of the main learnings for me that I want to apply like to my career in Data Journalism is that I don't shy away from complexity because like Data Science is oftentimes very complex. >> Complex. >> And also data, you're using for your stories is complex. >> Mm-hmm. >> So, how can we, on the one hand, reduce complexity in a way that we make it accessible for broader audience? 'Cause, we don't want to be this like tech bubble talking in data jargon, we want to, you know, make it accessible for a broader audience. >> Yeah. >> I think that's like my purpose as a Data Journalist. But at the same time, don't reduce complexity when it's needed, you know, and be open to dive into new topics, and data sets and circling back to this of like raising your hand and asking questions if you don't understand like a certain part. >> Yeah. >> So, that's definitely a main learning from this conference. >> Definitely. >> That like, people are willing to talk to you and explain complex topics, and this will definitely facilitate your work as a Data Journalist. >> Mm-hmm. >> So, that inspired me. >> Well, I can't wait to see where you guys go from here. I've loved co-hosting with you today, thank you. >> Thank you. >> For joining me at our conference. >> Wasn't it fun? >> Thank you. >> It's a great event. It's, we, I think we've all been very inspired and I'm going to leave here probably floating above the ground a few inches, high on the inspiration of what this community can deliver, isn't that great? >> It feels great, I don't know, I just feel great. >> Me too. (laughs) >> So much good energy, positive energy, we love it. >> Yeah, so we want to thank all the organizers of WiDS, Judy Logan, Margot Gerritsen in particular. We also want to thank John Furrier who is here. And if you know Johnny, know he gets FOMO when he is not hosting. But John and Dave Vellante are such great supporters of women in technology, women in technical roles. We wouldn't be here without them. So, shout out to my bosses. Thank you for giving me the keys to theCube at this event. I know it's painful sometimes, but we hope that we brought you great stories all day. We hope we inspired you with the females and the one male that we had on the program today in terms of raise your hand, ask a question, be curious, don't be afraid to pursue what you're interested in. That's my soapbox moment for now. So, for my co-host, I'm Lisa Martin, we want to thank you so much for watching our program today. You can watch all of this on-demand on thecube.net. You'll find write-ups on siliconeangle.com, and, of course, YouTube. Thanks, everyone, stay safe and we'll see you next time. (energetic music)

>>Welcome back to the cubes coverage of women in data science, 2022. We've been live all day at Stanford at the Arriaga alumni center. Lisa Martin, John furrier joins me next, trying to, to cure your FOMO that you have. >>I love this events. My favorite events is 2015. We've been coming, growing community over 60 countries, 500 ambassadors and growing so many members. Widths has become a global phenomenon. And it's so exciting to be part of just being part of the ride. Judy and Karen, the team have been amazing partners and it's been fun to watch the progression and international women's day is tomorrow. And just the overall environment's changed a lot since then. It's gotten better. I'm still a lot more work to do, but we're getting the word out, but this year seems different. It seems more like a tipping point is happening and real-time cultural change. A lot of problems. COVID pulled forward. A lot of things, there's a war going on in Europe. It's just really weird time. And it's just seems like it's a tipping point. >>I think that's what we felt today was that it was a tipping point. There was a lot of our guests on the program that are first time with attendees. So in seven, just seven short years, this is the seventh annual width it's gone from this one day technical conference to this global movement, as you talked about. And I think that we definitely felt that women of all ages and men that are here as well understand we're at that tipping point and what needs to be done next to push it over the edge. >>Well, I'm super excited that you are able to do all the amazing interviews. I watched some of them online. I had to come by and, and join the team because I have FOMO. I love doing the interviews, but they're including me. I'm happy to be included, but I got to ask you, I mean, what was different this year? Because it was interesting. It's a hybrid event. It's in part, they didn't have it in person last year, right? So it's hybrid. I showed the streams where everywhere good interviews, what was some of the highlights? >>Just a very inspiring stories of women who really this morning's conversation that I got to hear before I came to set was about mentors and sponsors and how important it is for women of any age and anybody really to build their own personal board of directors with mentors and sponsors. And they were very clear in the difference between a mentor and a sponsor and John something. I didn't understand the difference between the two until a few years ago. I think it was at a VMware event and it really surprised me that I have mentors do ask sponsors. And so that was a discussion that everybody on this onset talked about. >>It was interesting. We're doing also the international women's day tomorrow, big 24 interviews, including the winds of content, as well as global women leaders around the world and to new J Randori, who runs all of AWS, Amy are your maps. And she told me the same thing. She's like, there's too many mentors, not enough sponsors. And she said that out loud. I felt, wow. That was a defining moment because he or she is so impressive. Worked at McKinsey, okay. Was an operator in, in running businesses. Now she heads up AWS saying out loud, we have too many mentors, this get down to business and get sponsors. And I asked her the same thing and she said, sponsors, create opportunities. Mentors, give feedback. And mentors go both ways. And she said, my S my teenage son is a mentor to me for some of the cool new stuff, but ventures can go both ways. Sponsors is specifically about opportunities, and I'm like, I felt like that comment hit home. >>It's so important, but it's also important to teach girls. And especially the there's younger girls here this year, there's high school and middle, I think even middle school girls here, how to have the confidence to, to find those mentors, those sponsors and cultivate those relationships. That's a whole, those are skills that are incredibly important, as important as it is to understand AI data science, machine learning. It's to be able to, to have the confidence in a capability to create that and find those sponsors to help you unlock those opportunities. >>You know, I feel lucky to do the interviews, certainly being included as a male, but you've been doing a lot of the interviews as females and females. I got to ask you what was the biggest, because every story is different. Some people will it's about taking charge of their career. Sometimes it's maybe doing something different. What some of the story themes did you see in your interviews out there? What were some of the, the coverings personal? Yeah. >>Yeah. A lot of, a lot of the guests had stem backgrounds and were interested in the stem studies from when they were quite young and had strong family backgrounds that helps to nurture that. I >>Also heard that role models. Yes, >>Exactly, exactly. A strong family backgrounds. I did talk to a few women who come from different backgrounds, like international business and, but loved data and wanted to be able to apply that and really learn data analytics and understand data science and understand the opportunities that, that it brings. And also some of the challenges there. Everybody had an inspiring story. >>Yeah. It's interesting. One of the senior women I interviewed, she was from Singapore and she fled India during a bombing war and then ended up getting her PhD. Now she's in space and weld and all that stuff. And she said, we're now living in nerd, native environment, me and the younger generation they're nerds. And I, you know, were at Stanford dirt nation. Of course we're Stanford, it's nerd nerd nation here. But her point is, is that everything's digital now. So the younger generation, they're not necessarily looking for programmers, certainly coding. Great. But if you're not into coding, you can still solve society problems. There's plenty of jobs that are open for the first time that weren't around years ago, which means there's problems that are new to that need new minds and new, fresh perspectives. So I thought that aperture of surface area of opportunities to contribute in women in tech is not just coding. No, and that was a huge, >>That was, and we also, this morning, I got to hear, and we've talked about, we talked with several of the women before the event about data science in healthcare, data science, in transportation equity. That was a new thing for me, John, that I didn't know, I didn't, I never thought about transient equity and transportation or lack thereof. And so w what this conference showed, I think this year is that the it's not just coding, but it's every industry. As we know, every company is a data company. Every company is a tech company. If they're not, they're not going to be here for a long. So the opportunities for women is the door is just blown. >>And I said, from my interviews, it's a data problem. That's our line. We always say in the cube, people who know our program programming, we say that, but it actually, when we get the data on the pipeline and the pipeline, it has data points where the ages of drop-off of girls and young women is 12 to 14 and 16 to 18, where the drop-off is significant. So attack the pipelining problem is one that I heard a lot of. And the other one that comes out a lot, it's kind of common sense, and it's talked about it, but it's nuanced, but it became very elevated this year in the breaking, the bias theme, which was role models are huge. So seeing powerful women in leadership positions is really a focus and that's inspires people and they can see themselves. And so I think when people see role models of women and, and folks on in positions, not just coded, but even at the executive suite huge focus. So I think that's going to be a next step function in my mind. That's that's, if I had to predict the trend, it would be you see a lot more role modeling, flexing that big time. >>Good that's definitely needed. You know, we, we often used to say she can't be what she can't see, but one of the interviews that I had said, she can be what she can see. And I loved the pivot on that because it put a positive light, but to your point, there needs to be more female role models that, that girls can look up to. So they can see, I can do this. Like she's doing leading, you know, YouTube, for example, or Sheryl Sandberg of Facebook. We need more of these role models to show the tremendous amount of opportunities that are there, and to help those, not just the younger girls, those even that are maybe more mature find that confidence to build. >>And I think that was another king that came out role models from family members, dad, or a relative, or someone that could see was a big one. The other common thread was, yeah. I tend to break stuff and like to put it together. So at a young age, they kind of realized that they were kind of nerdy and they like to do stuff very engineering, but mind is where math or science. And that was interesting. Sally eaves from in the UK brought this up, she's a professor and does cyber policy. She said, it's a stems gray, but put the arts in there, make it steam. So steam and stem are in two acronyms. Stem is, is obviously the technical, but adding arts because of the creativity needs, we need creativity and problem solving with technical. Yes. So it's not just stem it's theme. We've heard that before, but not as much this year, it's amplified big >>Time. Sally's great. I had the chance to interview her in the last couple of months. And you, you bring up creativity, which is an incredibly important point. You know, there are the, obviously the hard skills, the technical skills that are needed, but there's also creativity. Curiosity being curious to ask a question, there's probably many questions that we haven't even thought to ask yet. So encouraging that curiosity, that natural curiosity is as important as maybe someone say as the actual technical knowledge, >>What was the biggest thing you saw this year? If you zoom out and you look at the forest from the trees, what was the big observation for you this year? >>I think it's the growth of woods. We've decided seven years. It's now in 60 countries, 200 events, 500 ambassadors, probably 500 plus. And the number of people that I had on the program, John, that this is their first woods. So just the fact that it's growing, we, we we've seen it for years, but I think we really saw a lot of the fresh faces and heard from them today had stories of how they got involved and how they met Margo, how she found them. I had a younger Alon who'd just graduated from Harvard back in the spring. So maybe not even a year ago, working at Skydio, doing drone work and had a great perspective on why it's important to have women in the drone industry, the opportunities Jones for good. And it was just nice to hear that fresh perspective. And also to S to hear the women who are new to woods, get it immediately. You walk into the Arriaga alumni center in the morning and you feel the energy and the support and that it was just perpetuated year after year. >>Yeah, it's awesome. I think one of the things I think it was reflecting on this morning was how many women we've interviewed in our cube alumni database now. And we yet are massing quite the database of really amazing people and there's more coming in. So that was kind of on a personal kind of reflection on the cube and what we've been working on together. All of us, the other thing that jumped out at me was the international aspect this year. It just seems like there's a community of tribal vibe where it's not just the tech industry, you know, saying rod, rod, it's a complete call to arms around more stories, tell your story. Yes. More enthusiasm outside of the corporate kind of swim lanes into like more of, Hey, let's get the stories out there. And the catalyst from an interview turned into follow up on LinkedIn, just a lot more like viral network effect so much more this year than ever before. So, you know, we just got to get the stories. >>Absolutely. And I think people given what we've been through the last two years are just really hungry for that. In-person collaboration, the opportunity to see more leadership to get inspired and any level of their career. I think the women here this today have had that opportunity and it's been overwhelmingly positive as you can imagine as it is every year. But I agree. I think it's been more international and definitely much more focused on teaching some of the other skills, the confidence, the creativity, the curiosity. >>Well, Lisa, as of right now, it's March 8th in Japan. So today, officially is kicking off right now. It's kicking off international women's day, March 8th, and the cube has a four region portal that we're going to make open, thanks to the sponsors with widths and Stanford and AWS supporting our mission. We're going to have Latin America, AMIA Asia Pacific and north America content pumping on the cube all day today, tomorrow. >>Exactly. And we've had such great conversations. I really enjoyed talking to the women. I always, I love hearing the stories as you talked about, we need more stories to make it personal, to humanize it, to learn from these people who either had some of them had linear paths, but a lot of emergency zig-zaggy, as you would say. And I always find that so interesting to understand how they got to where they are. Was it zig-zaggy, was it zig-zaggy intentionally? Yes. Some of the women that I talked to had very intentional pivots in their career to get them where they are, but I still thought that story was a very, >>And I like how you're here at Stanford university with winds the day before international Wednesday, technically now in Asia, it's starting, this is going to be a yearly trend. This is season one episode, one of the cube covering international women's day, and then every day for the rest of the year, right? >>What were some of your takeaways from some of the international women's day conversations that you had? >>Number one thing was community. The number one vibe was besides the message of more roles or available role models are important. You don't have to be a coder, but community was inherently the fabric of every conversation. The people were high energy, highly knowledgeable about on being on point around the core issue. It wasn't really politicized was much more of about this is really goodness and real examples of force multipliers of diversity, inclusion and equity, when, what works together as a competitive advantage. And, you know, as a student of business, that is a real change. I think, you know, the people who do it are going to have a competitive advantage. So community competitive advantage and just, and just overall break that bias through the mentoring and the sponsorships. >>And we've had a lot of great conversations about, I loved the theme of international women's day, this year breaking the bias. I asked everybody that I spoke with for international women's day and for width. What does that mean to you? And where are we on that journey? And everyone had a really insightful stories to share about where we are with that in their opinions, in their fields industries. Why, and ultimately, I think the general theme was we have the awareness now that we need, we have the awareness from an equity perspective, that's absolutely needed. We have to start there, shine the light on it so that the bias can be broken and opportunities for everybody can just proliferate >>Global community is going to rise and it's going to tend to rise. The tide is rising. It's going to get better and better. It was a fun year this year. And I think it was relief that COVID kind of going out, people getting back into physical events has been, been really, really great. >>Yep, absolutely. So, John, I, I appreciate all the opportunities that you've given me as a female anchor on the show. International women's day coverage was fantastic. Widths 2022 coming to an end was fantastic. Look forward to next year. >>Well, Margo, Judy and Karen who put this together, had a vision and that vision was right and it was this working and when it gets going, it has escape, velocity unstoppable. >>It's a rocket ship. That's a rocket. I love that. I love to be part of John. Thanks for joining me on the wrap. We want to thank you for watching the cubes coverage of international women's day. The women's showcase as well as women in data science, 2022. We'll see you next time.

>>Hello, and welcome to today's event, dealing government emergency responses beyond the pandemic. This is Bob Wooley, senior fellow for the center for digital government and formerly the chief tech clerk for the state of Utah. I'm excited to serve as moderator for today's event. And just want to say, thank you for joining us. I know we're in for an informative session over the next 60 minutes before we begin a couple of brief housekeeping notes or recording of this presentation will be emailed to all registrants within 48 hours. You can use the recording for your reference or feel free to pass it along to colleagues. 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We would like to hear what you think if you're unable to see with us for the entire webinar, but we're just like to complete the survey. As much as you're able, please click the survey widget at the bottom of the screen to launch the survey. Otherwise it will pop up once the webinar concludes at this time, we recommend that you disable your pop-up blockers, and if you experiencing any media player issues or have any other problems, please visit our webcast help guide by clicking on the help button at the bottom of the console. >>Joining me today to discuss this very timely topic are Karen revolt and Tim Burch, Kim Berge currently serves as the administrator of human services for Clark County Nevada. He's invested over 20 years in improving health and human service systems of care or working in the private public and nonprofit sectors. 18 of those years have been in local government in Clark County, Las Vegas, where you served in a variety of capacities, including executive leadership roles as the director of department of social services, as well as the director for the department of family services. He has also served as CEO for provider of innovative hosted software solutions, as well as chief strategy officer for a boutique public sector consulting firm. Karen real-world is the social program management offering lead for government health and human services with IBM Watson health. Karen focuses delivering exciting new offerings by focusing on market opportunities, determining unmet needs and identifying innovative solutions. >>Much of her career has been in health and human services focused on snap, TANIF, Medicaid, affordable care act, and child welfare prior to joining IBM. Karen was the senior director of product management for a systems integrator. She naturally fell in love with being a project manager. She can take her user requirements and deliver offerings. Professionals would use to make their job easier and more productive. Karen has also found fulfillment in working in health and human services on challenges that could possibly impact the outcome of people's lives. Now, before we begin our discussion of the presentation, I want to one, we'd like to learn a little more about you as an audience. So I'm going to ask you a polling question. Please take a look at this. Give us an idea of what is your organization size. I won't bother to read all these to you, but there are other a range of sizes zero to 250 up to 50,000. Please select the one that is most appropriate and then submit. >>It looks like the vast majority are zero to two 50. Don't have too many over 250,000. So this is a very, very interesting piece of information. Now, just to set up our discussion today, what I want to do is just spend just a moment and talk about the issue that we're dealing with. So when you look the COVID-19 pandemic, it's put immense pressure on States. I've been a digital state judge and had been judging a lot of the responses from States around the country. It's been very interesting to me because they bifurcate really into two principle kinds of reactions to the stress providing services that COVID environment present. One is we're in a world of hurt. We don't have enough money. I think I'm going to go home and engage as little as I have to. Those are relatively uncommon. Thankfully, most of them have taken the COVID-19 pandemic has immense opportunity for them to really do a lot more with telework, to do more with getting people, employees, and citizens involved with government services. >>And I've done some really, really creative things along the way. I find that to be a really good thing, but in many States systems have been overloaded as individuals and families throughout the country submitted just an unprecedented number of benefit applications for social services. At the same time, government agencies have had to contend with social distance and the need for a wholly different approach to engage with citizens. Um, overall most public agencies, regardless of how well they've done with technology have certainly felt some strain. Now, today we have the opportunity to go into a discussion with our speakers, have some wonderful experience in these areas, and I'm going to be directing questions to them. And again, we encourage you as you hear what they have to say. Be sure and submit questions that we can pick up later at the time. So Tim, let's start with you. Given that Las Vegas is a hub for hospitality. An industry hit severely as a result of this pandemic. How's the County doing right now and how are you prioritizing the growing needs of the County? >>Thanks Bob. Thanks for having me. Let me start off by giving just a little, maybe context for Clark County too, to our audience today. So, uh, Clark County is, you know, 85% of the state of Nevada if we serve not just as a regional County by way of service provision, but also direct municipal services. Well, if, uh, the famous Las Vegas strip is actually in unincorporated Clark County, and if we were incorporated, we would be the largest city in the state. So I say all of that to kind of help folks understand that we provide a mix of services, not just regional services, like health and human services, the direct and, and missable, uh, services as well as we work with our other five jurisdiction partners, uh, throughout the area. Uh, we are very much, um, I think during the last recession we were called the Detroit of the West. >>And, uh, that was because we're very much seen as a one industry town. Uh, so most like when the car plants, the coal plants closed back East and in the communities fuel that very rapidly, the same thing happens to us when tourism, uh, it's cut. Uh, so of course, when we went into complete shutdown and March, uh, we felt it very rapidly, not just on, uh, uh, tax receipts and collectibles, but the way in which we could deliver services. So of course our first priority was to, uh, like I think you mentioned mobilized staff. We, we mobilized hundreds of staff overnight with laptops and phones and cars and the things they needed to do to get mobile and still provide the priority services that we're mandated to provide from a safety standpoint. Um, and then we got busy working for our clients and that's really where our partnership with IBM and Watson, uh, came in and began planning that in July. And we're able to open that portal up in October to, to really speed up the way in which we're giving assistance to, to our residents. Um, re focus has been on making sure that people stay housed. We have, uh, an estimated, uh, 2.5 million residents and over 150,000 of those households are anticipated to be facing eviction, uh, as of January one. So we, we've got a, a big task ahead of us. >>All of this sounds kind of expensive. Uh, one of the common threads as you know, runs throughout government is, ah, I don't really have the money for that. I think I'd be able to afford that a diaper too, as well. So what types of funding has been made available for counties, a result of a pandemic, >>Primarily our funding stream that we're utilizing to get these services out the door has been the federal cares act. Uh, now we had some jurisdictions regionally around us and even locally that prioritize those funds in a different way. Um, our board of County commissioners, uh, took, um, a sum total of about $85 million of our 240 million that said, this will go directly to residents in the form of rental assistance and basic needs support. No one should lose their home or go hungry during this pandemic. Uh, so we've really been again working through our community partners and through our IBM tools to make sure that happens. >>So how does, how does, how does the cares act funding then support Clark County? Cause it seems to me that the needs would be complex, diverse >>Pretty much so. So as you, as folks may know him a call there's several tronches of the cares act, the original cares act funding that has come down to us again, our board, uh, identified basic needs or rental assistance and, and gave that the department of social service to go to the tunicate, uh, through the community. We then have the cares act, uh, uh, coronavirus relief funds that have, uh, impacted our CDBG and our emergency solutions grants. We've taken those. And that's what we was going to keep a lot of the programs and services, uh, like our IBM Watson portal open past January one when the cares act dollars expire. Uh, our initial response was a very manual one, uh, because even though we have a great home grown homeless management information system, it does not do financials. Uh, so we had 14 local nonprofits adjudicating, uh, this rental assistance program. >>And so we could get our social service visitor portal up, uh, to allow us to take applications digitally and run that through our program. Uh, and, uh, so those partners were obviously very quickly overwhelmed and were able to stand up our portal, uh, which for the reason we were driving so hard, even from, uh, beginning of the conversations where after going into lockdown into contracting in July and getting the portal open in October, which was an amazing turnaround. Uh, so the kudos that IBM team, uh, for getting us up and out the door so quickly, uh, was a tie in, uh, to our, uh, Curam IBM, uh, case management system that we utilize to adjudicate benefits on daily basis in Clark County for all our local indigent population, uh, and high needs folks. Uh, and then that ties into our SAP IBM platform, which gets the checks out the door. >>So what, what we've been able to do with these dollars is created in Lucian, uh, that has allowed us in the last 60 days to get as much money out the door, as our nonprofits were able go out the door in the first six months pandemic. So it really has helped us. Uh, so I'm really grateful to our board of County commissioners for recognizing the investment in technology to, to not only get our teams mobile, but to create ease of access for our constituents and our local residents to give them the help they need quickly and the way that they need it. >>Just to follow up question to that, Tim, that I'm curious about having done a lot of work like this in government, sometimes getting procurement through in a timely way is a bit challenging. How were you able to work through those issues and getting this up and provision so quickly? >>Uh, yeah, so we, we put together a, what we call a pandemic playbook, which is kind of lessons learned. And what we've seen is the folks who were essential workers in the first 60 days of the, uh, pandemic. We were able to get a lot done quickly because we were taking full advantage of the emergency. Uh, it may sound a little crass to folks not inside the service world, but it was, uh, you know, don't want you to crisis. It was things we've been planning or trying to do for years. We need them yesterday. We should have had them yesterday, but let's get them tomorrow and get it moving very quickly. Uh, this IBM procurement was something we were able to step through very quickly because of our longstanding relationship. Our countywide, uh, system of record for our financials is SAP. Uh, we've worked with Curam, uh, solution, uh, for years. >>So we've got this long standing relationship and trust in the product and the teams, which helped us build the business case of why we did it, no need to go out for competitive procurement that we didn't have time. And we needed something that would integrate very quickly into our existing systems. Uh, so that part was there. Now when the folks who were non essential came back in June and the reopening, it was whiplash, uh, the speed at which we were moving, went back to the pace of normal business, uh, which feels like hitting a wall, doing a hundred miles an hour when you're used to having that, uh, mode of doing business. Uh, so that's certainly been a struggle, uh, for all of those involved, uh, in trying to continue to get things up. Um, but, uh, once again, the teams have been great because we've probably tripled our licensure on this portal since we opened it, uh, because of working with outside vendors, uh, to, uh, literally triple the size of our staff that are processing these applications by bringing on temporary staff, uh, and short-term professionals. Uh, and so we've been able to get those things through, uh, because we'd already built the purchasing vehicle during the early onset of the crisis. >>That's very helpful. Karen, IBM has played a really pivotal role in all of this. Uh, IBM Watson health works with a number of global government agencies, raging from counties like Clark County to federal governments. What are some of the major challenges you've seen with your clients as a result of the pandemic and how is technology supporting them in a time of need and give us some background Watson health too. So we kind of know a little more about it because this is really a fascinating area. >>Yeah. Thank you, Bob. And thanks Tim for the background on Clark County, because I think Clark County is definitely also an example of what federal governments and global governments are doing worldwide today. So, um, Watson health is our division within IBM where we really focus on health and human services. And our goal is to really focus in on, um, the outcomes that we're providing to individuals and families and looking at how we use data and insights to really make that impact and that change. And within that division, we have our government health and human services area, which is the focus of where we are with our clients around social program. But it also allows us to work with, um, different agencies and really look at how we can really move the ball in terms of, um, effecting change and outcomes for, um, really moving the needle of how we can, uh, make an impact on individuals and families. >>So as we look at the globe globally as well, you know, everything that Tim had mentioned about how the pandemic has really changed the way that government agencies operate and how they do services, I think it's amazing that you have that pandemic playbook because a lot of agencies in the same way also had these set of activities that they always wanted to go and take part on, but there was no impetus to really allow for that to happen. And with the pandemic, it allowed that to kind of open and say, okay, we can try this. And unfortunately I'm in a very partial house way to do that. And, um, what Tim has mentioned about the new program that they set up for the housing, some of those programs could take a number of years to really get a program online and get through and allowing, uh, the agencies to be able to do that in a matter of weeks is amazing. >>And I think that's really gonna set a precedent as we go forward and how you can bring on programs such as the housing and capability in Canada with the economic, uh, social, um, uh, development and, and Canada need that the same thing. They actually had a multi benefit delivery system that was designed to deliver benefits for three programs. And as part of the department of fisheries and oceans Canada, the, um, the state had an emergency and they really need to set up on how they could provide benefits to the fishermen who had been at that impacted, um, from that. And they also did set up a digital front-end using IBM citizen engagement to start to allow the applications that benefits, um, and they set it up in a matter of weeks. And as I mentioned, we, uh, Clark County had a backend legacy system where they could connect to and process those applications. And this case, this is a brand new program and the case management system that they brought up was on cloud. And they had to set up a new one, but allow them to set up a, what we used to call straight through processing, I think has been now turned, turned or coined contact less, uh, processing and allowing us to really start to move those benefits and get those capabilities out to the citizens in even a faster way than has been imagined. Uh, pre pandemic. >>Karen, I have one follow-up question. I want to ask you, having had a lot of experience with large projects in government. Sometimes there's a real gap between getting to identified real requirements and then actions. How do you, how do you work with clients to make sure that process time to benefit is shortened? >>So we really focus on the user themselves and we take a human centered design focus and really prioritizing what those needs are. Um, so working with the clients, uh, effectively, and then going through agile iterations of brain, that capability out as, um, in, in a phased approach to, so the idea of getting what we can bring out that provides quality and capability to the users, and then over time starting to really roll out additional functions and, um, other, uh, things that citizens or individuals and families would need >>Very helpful. Tim, this is an interesting partnership. It's always good to see partnerships between private sector and government. Tell us a little bit about how the partnership with IBM Watson health was established and what challenges or they were brought into assist, where they brought into assist with back to requirements. Again, within the requirements definitely shifted on us. You know, we had the con looking at, uh, Watson on our child welfare, uh, side of the house that I'm responsible for and how that we could, uh, increase access to everything from tele-health to, to, uh, foster parent benefit, uh, kinship, placement benefits, all those types of things that, that right now are very manual, uh, on the child welfare side. Uh, and then the pandemic kid. And we very quickly realized that we needed, uh, to stand up a, um, a new program because, uh, a little bit for context, uh, the park County, we don't administer TANIF or Medicaid at the County level. >>It is done at the state level. So we don't have, uh, unemployment systems or Medicaid, 10 of snap benefits systems to be able to augment and enroll out. We provide, uh, the indigent supports the, the homelessness prevention, referee housing continuum of care, long-term care, really deep emergency safety net services for our County, which is a little bit different and how those are done. So that was really our focus, which took a lot of in-person investigation. We're helping people qualify for disability benefits so they can get into permanent supportive housing, uh, things that are very intensive. And yet now we have a pandemic where we need things to happen quickly because the cares act money expires at the end of December. And people were facing eviction and eviction can help spread exposure to, to COVID. Uh, so, uh, be able to get in and very rapidly, think about what is the minimal pelvis to MVP. >>What's the minimum viable product that we can get out the door that will help people, uh, entrance to a system as contactless as possible, which again was a complete one 80 from how we had been doing business. Um, and, uh, so the idea that you could get on and you have this intelligent chat bot that can walk you through questions, help you figure out if you look like you might be eligible, roll you right into an application where you can upload the few documents that we're going to require to help verify your coat would impact and do that from a smartphone and under, you know, 20 minutes. Um, it, it, it is amazing. And the fact that we've stood that up and got it out the door in 90 days, it's just amazing to me, uh, when it shows the, uh, strength of partnership. Um, I think we can, we have some shared language because we had that ongoing partnership, but we were able to actually leverage some system architects that we had that were familiar with our community and our other products. So it really helped expedite, uh, getting this, uh, getting this out to the citizens. >>So, uh, I assume that there are some complexities in doing this. So overall, how has this deployment of citizen engagement with Watson gone and how do you measure success other than you got it out quick? How do you know if it's working? >>Yeah. Right. So it's the adage of, you know, quick, fast and good, right. Um, or fast, good and cheap. So, uh, we measure success in this way. Um, how are we getting access as our number one quality measurement here? So we were able to collect, uh, about 13,000 applications, uh, manual NRC, manually folks had to go onto our website, download a PDF, fill it out, email it, or physically drop it off along with their backup. One of their choice of 14 non-profits in town, whichever is closest to them. Um, and, uh, and then wait for that process. And they were able to get 13,000 of those, uh, process for the last six months. Uh, we have, I think we had about 8,000 applications the first month come into the portal and about an equal amount of folks who could not provide the same documentation that it was needed. >>And self-selected out. If we had not had the, the tool in place, we would have had 16,000 applications, half of which would have been non-eligible would have been jamming up the system, uh, when we don't have the bandwidth to deal to deal with that, we, we need to be able to focus in on, uh, Judy Kenny applications that we believe are like a 95% success rate from the moment our staff gets them, but because we have the complex and he was on already being dependent upon the landlord, having to verify the rent amount and be willing to work with us, um, which is a major hurdle. Um, but, uh, so w we knew we could not do is go, just reinvent the manual process digitally that that would have been an abject failure on our behalf. So, uh, the ideas that, uh, folks had can go on a very, had this very intuitive conversation to the chat bot, answer some questions and find out if they're eligible. >>And then self-select out was critical for us to not only make sure that the citizens got the help they needed, but not so burnt out and overload our workforce, which is already feeling the strain of the COVID pandemic on their own personal lives and in their homes and in the workplace. Um, so that was really critical for us. So it's not just about speed, ease of access was important. Uh, the ability to quickly automate things on the fly, uh, we have since changed, uh, the area median income, a qualifier for the rental assistance, because we were able to reallocate more money, uh, to the program. So we were able to open it up to more people. We were able to make that, uh, change to the system very quickly. Uh, the idea that we can go on the home page and put updates, uh, we recognized that, uh, some of our monolingual Hispanic residents were having difficulty even with some guidance getting through the system. >>So we're able to record a, a Spanish language walkthrough and get done on the home page the next day, right into the fordable, there'll be a fine, so they could literally run the YouTube video while they're walking through their application. Side-by-side so things like that, that those are how we are able to, for us measured success, not just in the raw dollars out the door, not just in the number of applications that have come in, but our ability to be responsive when we hear from our constituents and our elected officials that, Hey, I want, I appreciate the 15,000 applications as you all, a process and record time, I've got three, four, five, six, 10 constituents that having this type of problem and be able to go back and retool our systems to make them more intuitive, to do, be able to keep them responsive for us is definitely a measure of success and all of this, probably more qualitative than here we're looking >>For, but, uh, that's for us, that's important. Actually the qualitative side is what usually gets ignored. Uh, Karen, I've got a question that's a follow up for you on the same topic. How does IBM facilitate reporting within this kind of an environment given the different needs of stakeholders, online managers and citizens? What kinds of things do you, are you able to do >>So with, um, the influx of digitalization? I think it allows us to really take a more data-driven approach to start looking at that. So, as, as Tim was mentioning, you can see where potentially users are spending more time on certain questions, or if they're stuck on a question, you can see where the abandoned rate is. So using a more data-driven approach to go in to identify, you know, how do we actually go and, um, continue to drive that user experience that may not be something that we drive directly from the users. So I would say that analytics is really, uh, I think going to continue to be a driving force as government agencies go forward, because now they are capturing the data. But one thing that they have to be careful of is making sure that the data that they're getting is the right data to give them the information, to make the right next steps and decisions. >>And Tim, you know, use a really good example with, um, the chatbot in terms of, you know, with the influx of everything going on with COVID, the citizens are completely flooded with information and how do they get the right information to actually help them decide, can I apply for this chap program? Or should I, you know, not even try and what Tim mentioned just saved the citizens, you know, the people that may not be eligible a lot of time and going through and applying, and then getting denied by having that upfront, I have questions and I need answers. Um, so again, more data-driven of how do we provide that information? And, you know, we've seen traditionally citizens having to go on multiple website, web pages to get an answer to the question, because they're like, I think I have a question in this area, but I'm not exactly sure. And they, then they're starting to hunt and hunt and hunt and not even potentially get an answer. So the chocolate really like technology-wise helps to drive, you know, more data-driven answers to what, um, whether it's a citizen, whether it's, um, Tim who needs to understand how and where my citizens getting stuck, are they able to complete the application where they are? Can we really get the benefits to, um, this individual family for the housing needs >>Too many comments on the same thing. I know you have to communicate measures of success to County executives and others. How do you do that? I mean, are you, do you have enough information to do it? Yeah, we're able to, we actually have a standup meeting every morning where the first thing I learn is how many new applications came in overnight. How many of those were completed with full documentation? How many will be ported over into our system, assigned the staff to work, where they're waiting >>On landlord verification. So I can see the entire pipeline of applications, which helps us then determine, um, Oh, it's, it's not, you know, maybe urban legend is that folks are having difficulty accessing the system. When I see really the bottleneck there, it got gotten the system fine, the bottlenecks laying with our landlord. So let's do a landlord, a town hall and iterate and reeducate them about what their responsibilities are and how easy it is for them to respond with the form they need to attest to. And so it lets us see in real time where we're having difficulties, uh, because, uh, there's a constant pressure on this system. Not just that, uh, we don't want anyone to lose their home, uh, but these dollars also go away within a December. So we've got this dual pressure of get it right and get it right now. >>Uh, and so th the ability to see these data and these metrics on, on a daily basis is critical for us to, to continue to, uh, ModuLite our response. Um, and, and not just get comfortable are baked into well, that's why we developed the flowchart during requirements, and that's just the way things are gonna stay. Uh, that's not how you respond to a pandemic. Uh, and so having a tool and a partner that helps us, uh, stay flexible, state agile, I guess, to, to, to leverage some terminology, uh, is important. And, and it's, it's paid dividends for our citizens. Karen, again, is another up to the same thing. I'm kind of curious about one of the problems of government from time to time. And Tim, I think attest to this is how do you know when Dunn has been reached? How did you go about defining what done would look like for the initial rollout with this kind of a customer? >>So I think Doug, I guess in this case, um, is, is this, isn't able to get the benefits that they're looking for and how do we, uh, you know, starting from, I think what we were talking about earlier, like in terms of requirements and what is the minimum viable, um, part of that, and then you start to add on the bells and whistles that we're really looking to do. So, um, you know, our team worked with him to really define what are those requirements. I know it's a new program. So some of those policy decisions were still also being worked out as the requirements were being defined as well. So making sure that you are staying on top of, okay, what are the key things and what do we really need to do from a compliance standpoint, from a functionality, and obviously, um, the usability of how, uh, an assistant can come on and apply and, um, have those, uh, requirements, make sure that you can meet that, that version before you start adding on additional scope. >>Very helpful. Jim, what's your comment on this since I know done matters to you? Yeah. And look, I I've lived through a, again, multiple, uh, county-wide it implementations and some department wide initiatives as well. So I think we know that our staff always want more so nothing's ever done, uh, which is a challenge and that's on our side of the customer. Um, but, uh, for this, it really was our, our experience of recognizing the, the time was an essence. We didn't have a chance. We didn't have, uh, the space to get into these endless, uh, conversations, uh, the agile approach, rather than doing the traditional waterfall, where we would have been doing requirements tracking for months before we ever started coding, it was what do we need minimally to get a check in the hands of a landlord on behalf of a client, so they don't get evicted. >>And we kept just re honing on that. That's nice. Let's put that in the parking lot. We'll come back to it because again, we want to leverage this investment long term, uh, because we've got a we, and we've got the emergency solutions and CDBG, and then our, uh, mainstream, uh, services we brought on daily basis, but we will come back to those things speed and time are of the essence. So what do we need, uh, to, to get this? So a chance to really, um, educate our staff about the concepts of agile iteration, um, and say, look, this is not just on the it side. We're gonna roll a policy out today around how you're doing things. And we may figure out through data and metrics that it's not working next week, and we'll have to have that. You want it. And you're going to get the same way. >>You're getting updated guidance from the CDC on what to do and what not to do. Uh, health wise, you're getting the same from us, uh, and really to helping the staff understand that process from the beginning was key. And, uh, so, and, and that's, again, partnering with, with our development team in that way was helpful. Um, because once we gave them that kind of charter as I am project champion, this is what we're saying. They did an equally good job of staying on task and getting to the point of is this necessary or nice. And if it wasn't necessary, we put it in the nice category and we'll come back to it. So I think that's really helpful. My experience having done several hundred sheet applications also suggest the need for MBP matters, future stages really matter and not getting caught. My flying squirrels really matters. So you don't get distracted. So let's move on to, let's do a polling question before we go on to some of our other questions. So for our audience, do you have a digital front ends for your benefit delivery? Yes, no. Or we're planning to a lot of response here yet. There we go. Looks like about half, have one and half note. So that's an interesting question. What's going to one more polling question, learn a little more here. Has COVID-19 >>Accelerated or moved cloud. Yes, no. We already run a majority of applications on cloud. Take a moment and respond if you would, please. So this is interesting. No real acceleration was taken place and in terms of moving to cloud is not what I was expecting, but that's interesting. So let's go onto another question then. And Karen, let me direct this one to you, given that feedback, how do you envision technologies such as citizen engagement and watching the system will be used, respond to emergency situations like the pandemic moving forward? I mean, what should government agencies consider given the challenges? This kind of a pandemic is brought upon government and try to tie this in, if you would, what, what is the role of cloud in all of this for making this happen in a timely way? Karen, take it away. >>Okay. Thanks Bob. So as we started the discussion around the digital expansion, you know, we definitely see additional programs and additional capabilities coming online as we continue on. Um, I think, uh, agencies have really seen a way to connect with their citizens and families and landlords, um, in this case an additional way. And he prepared them like there were, uh, presuppose assumptions that the, um, the citizens or landlords really wanted to interact with agency face-to-face and have that high touch part. And I think, um, through this, the governments have really learned that there is a way to still have an impact on the citizen without having a slow, do a face to face. And so I think that's a big realization for them to now really explore other ways to digitally explain, expand their programs and capabilities. Another area that we touched on was around the AI and chat bot piece. >>So as we start to see capabilities like this, the reason why Clark County was able to bring it up quickly and everything was because it was housed on cloud, we are seeing the push of starting to move some of the workloads. I know from a polling question perspective that it's been, um, lighter in terms of getting, uh, moving to the cloud. But we have seen the surge of really chatbots. I think we've been talking about chatbots for a while now. And, um, agencies hadn't really had the ability to start to implement that and really put it into effect. But with the pandemic, they were able to bring things up and, you know, very short amount of time to solve, um, a big challenge of not having the call center be flooded and have a different way to direct that engagement between the citizen and the government. >>So really building a different type of channel for them to engage rather than having to call or to come into an office, which wasn't really allowed in terms of, um, the pandemic. Um, the other thing I'll touch on is, um, 10 mentioned, you know, the backlog of applications that are coming in and we're starting to see the, um, the increase in automation. How do we automate areas where it's administratively highly burdened, but it's really a way that we can start to automate those processes, to give our workers the ability to focus on more of those complex situations that really need attention. So we're starting to see where the trends of trying to push there of can we automate some of those processes, um, uh, uploading documents and verification documents is another way of like, trying to look at, is there a way that we can make that easier? >>Not only for the applicant that's applying, but also for the caseworker. So there's not having to go through that. Um, does the name match, um, the applicant, uh, information and what we're looking on here, and Bob, you mentioned cloud. So behind the scenes of, you know, why, uh, government agencies are really pushing the cloud is, um, you heard about, I mean, with the pandemic, you see a surge of applicants coming in for those benefits and how do we scale for that kind of demand and how do you do that in an inappropriate way, without the huge pressures that you put on to your data center or your staff who's already trying to help our citizens and applicants, applicants, and families get the benefits they need. And so the cloud, um, you know, proposition of trying, being able to be scalable and elastic is really a key driver that we've seen in terms of, uh, uh, government agencies going to cloud. >>We haven't really seen during a pandemic, the core competencies, some of them moving those to cloud, it's really been around that digital front end, the chat bot area of how do we start to really start with that from a cloud perspective and cloud journey, and then start to work in the other processes and other areas. Um, security is also huge, uh, focus right now with the pandemic and everything going online. And with cloud allows you to be able to make sure that you're secure and be able to apply the right security so that you're always covered in terms of the type of demand and, um, impact, uh, that is coming through >>Very helpful. Tim, I'm going to ask to follow up on this of a practical nature. So you brought this up very quickly. Uh, there's a certain amount of suspicion around state government County government about chatbots. How did you get a chat much and be functional so quickly? And were you able to leverage the cloud in this process? Yeah, so on the trust is important. Uh, and I'll go back to my previous statement about individuals being able to see upfront whether they believe they're eligible or not, because nothing will erode trust more than having someone in hours applying and weeks waiting to find out they were denied because they weren't eligible to begin with, uh, that erodes trust. So being able to let folks know right up front, here's what it looks like to be eligible, actually help us build some of that, uh, cause they don't feel like, uh, someone in the bureaucracy is just putting them through the ringer for no reason. >>Um, now in regard to how do we get the chat bot out? I will say, uh, we have a, uh, dynamic it and leadership, uh, team at the highest level of County government who we have been already having conversations over the last year about what it meant to be smart government, uh, the department of social service and family services that I'm responsible for. We're already, uh, hands up first in line, you know, Guinea pigs volunteering to be on the front end of, uh, certain projects. So w we have primed ourselves for, for some of this readiness in that aspect. Um, but for citizen trust, um, the timeliness of application right now is the biggest element of trust. Uh, so I've applied I've I feel like I put my housing future in your hands. Are you going to deliver and having the ability for us to rapidly scale up? >>Uh, we typically have 120 staff in the department of social service that, that are adjudicating benefits for programs on daily basis. We've doubled that with temporary staff, uh, through some partnerships, uh, we're, we're gonna, as of next week, probably have more temporary per professional staff helping an adjudicator applications. No, do full-time County staff, because again, this rush to get the dollars out, out the door. So having a system where I can easily, uh, ramp on new users and manage them without having to be solely dependent upon an already, uh, overworked it staff who were trying to support 37 other departments in the County, um, around infrastructure needs has been greatly helpful. Sounds to me like a strong outcome focus and one that seems to work. Let's move on now to our audience questions. We're getting close to the end of our time. So let's jump into some questions from the audience. A number of you have been asking about getting copies of today's presentation within the next 48 hours. Government technology will provide all attendees with the link to the recording for your reference, or to share with colleagues. Well, let's go to our first question. So this is an interesting one. And Karen, this is for you did IBM work with other counties and States to provide digital engagement portals. >>We did Bob, uh, we've worked, um, so globally we've provided guidance on this. We work closely with New York city. They've been the integral part of the development also with our citizen engagement offering. Um, we work closely with the States. So we worked with New York city. Um, North Carolina was also another state who, um, improved their, uh, citizen engagement piece, bring up their Medicaid and snap, um, applications along with Medicaid. COVID testing along that. And I mentioned, um, the economic and social development in Canada as well. And we also work with the ministry of social development in Singapore. So a number of our customers had put up, uh, a global, uh, or sorry, a citizen engagement frontend. And during this timeframe, >>Very helpful. I don't know how much did you hear your mom provide you, but how much did it cost for initial deployment and what are the ongoing costs in other words, is this thing going to be sustainable over time? >>Yeah, absolutely. So total, uh, to date, we've spent about a $1.8 million on development implementations and licensure. A big chunk of that again has been the rapid extended of licensure, uh, for this program. Um, I think over a third of that is probably licensing because again, we need to get the dollars out and we need staff to do that and making the short term several hundred thousand dollar investment in a professional support staff and having them be able to work this portal is much cheaper than the long-term investment of bringing on a staff, printing a job, uh, during a financial difficulty that we're facing, uh, the single largest fiscal cliff let's get into that us history. Um, so it's not smart to create jobs that have a 30 year, one way to retirement, uh, inside our in unionized government environment here. So having this, the staff that would come on and do this and get out the door on these federal dollars was critical for us. Um, and there is a $800,000 a year, I believe so ongoing costs associated with licensure and, and the programming support. Uh, but once again, we're going to be moving, um, our traditional services into this digital front end. We'll be continuing this because we're, we're, we're facing, it took us, I think, six and a half, seven years to come back from the previous recession. Undoubtedly, take a little longer to get back >>From this one. Here's another interesting question, I guess really primarily Tim Tim was the solution on primarily on premise or in the cloud. >>So we'll, we've done a mix. Uh, the, and I'm starting a lot of feedbacks. I don't know if you all can hear that or not, but the, uh, I think we went on prem for, uh, some people because of the, uh, bridge into our service case manager system, which is on prem. So we did some management there. I do believe the chat bot piece of it though is in the cloud. So we're bringing it down to, from one system to the other. Uh, and, and part of that was a student negotiations and costs and worrying about what long-term is that we have a very stated goal of moving, uh, our Curam platform, which is on-prem, this is the backend. So how are we? We, we set our IBM Watson, uh, portal up, uh, and moving all of that on cloud, uh, because I mean, we've got, uh, a workforce who, uh, has the ability to retire at a very high rate over the next five years. >>And, uh, having 24 seven support in the cloud is, is as a, someone who would be called to respond to emergency situations like the is, is a much better Cod deal for, for myself and the citizen. So migrating, uh, and, um, our typical on-prem stuff up into the cloud, uh, as we continue on this, uh, evolution of what IBM Watson, uh, and the plug into our Curam, uh, system looks like Karen related question for another user is the portal provided with Clara County and others linked to other third-party backend office apps, or can it be, >>Yeah, the answer is it can be it's interoperable. So through APIs, uh, rest, uh, however, um, assistance that they need to be integrated with can definitely be integrated with, uh, like, uh, Tim mentioned, we, we went to the case management solution, but it can be integrated with other applications as well. >>Tim, did you use some other backend third party apps with yours? Uh, we did not. Uh, again, just for speed of getting, uh, this MVP solution out the door. Uh, now what we do with that on the go forward, it is going to look different and probably will include some, another practical question. Given the cares funding should be expended by December. Can this application even be employed at this late date? And you want to take a cut at that? Yeah, for us, uh, once again, we brought up earlier, um, the emergency solutions grants and the community development block grants, which have a Corona virus, uh, CV traunch, each one of those, and those have two to three year expenditure timeframes on them. Uh, so we were going to leverage those to keep this system and some of these programs going once again, that the housing needs, uh, will outstrip our capacity for years to come. >>I guess probably I should have said upfront Las Vegas has one of the worst affordable housing inventories in the nation. Uh, so we know we're going to be facing a housing issue, um, because of this for, for a long time. So we'll be using those two traunches of dollars, ESE, ESPs, uh, CV CDBG, CB funds, uh, in addition to dollars earmarked through some, uh, recreational marijuana license fees that have been dedicated to our homelessness. And when you consider this housing, uh, stability program was part of that homelessness prevention. That's our funding mix locally. Very helpful. So questions maybe for bolts for you on this one, you can probably also teach respond is the system has been set up helping the small business community. Um, this user's been canvassing and the general feeling is that small businesses have been left behind and they've been unable to access funds. What's your response on that? Karen, do you want to take that first? >>Um, yes. So in terms of, uh, the security and sorry. Um, but, uh, can you repeat the last part of that? I just missed the last part when you >>Behind it, but unable to access funds. >>Uh, yeah, so I think from a funding perspective, there's different types of, I think what Tim mentioned in terms of the cares funding, there was different types of funding that came out from a government perspective. Uh, I think there were also other grants and things that are coming out one, uh, that we're still looking at. And I think as we go into the new year, it'll be interesting to see, you know, what additional funding, um, hopefully is, is provided. Uh, but in terms of creativity, we've seen other creative ways that organizations come together to kind of, uh, help with the different agencies, to provide some, some guidance to the community, um, and helping to, uh, provide efforts and, uh, maybe looking at different ways of, um, providing, uh, some of the capabilities that the, either at the County or at the state level that they're able to leverage. But Tim happy to maybe have you chime in here too. >>Yeah. So I'll first start with my wheelhouse and I'll expand out to, to some of my partners. Uh, so the primary, small business, we knew the idea was a daily basis inside this realm is going to be landlords. Uh, so actually this afternoon, we're doing a town hall with folks to be able to roll out, uh, which they will go to our portal to find a corporate landlord program. Uh, so that I seem a landlord for Camille the application pack and on behalf of a hundred residents, rather than us having to adjudicate a hundred individual applications and melon a hundred checks. Uh, so that is because we were listening to that particular segment of the, uh, the business community. Now I know early on, we were, we were really hoping that the, the paycheck protection program federally would have, uh, been dispersed in a way that helped our local small businesses. >>Uh, more we did a, our economic development team did a round of small business supports through our cares act. Uh, our quarterly unfortunate was not open yet. It was just about 15, 20 days shy. So we use, uh, another traditional grant mechanism that we have in place to dedicate that. Uh, but on a go forward board, willing to Congress passes something over the next 30 days, um, that if there's a round two of cares or some other programs, we absolutely now have a tool that we know we can create a digital opening for individuals to come figure out if they're eligible or not for whatever program it is, the it housing, the it, uh, small business operations supports, uh, and it would apply through that process and in a very lightweight, so we're looking forward to how we can expand our footprint to help all of the needs that are present in our community. This leads to another question which may be our last one, but this is an interesting question. How can agencies use COVID-19 as a proof point providing a low cost configurable solutions that can scale across government. Karen, do you want to respond to that? And then Tim also, >>Thanks, Bob. So I believe like, you know, some of the things that we've said in terms of examples of how we were able to bring up the solution quicker, I definitely see that scaling as you go forward and trying to really, um, focus in on the needs and getting that MVP out the door. Uh, and then Tim alluded to this as well. A lot of the change management processes that went into re-imagining what these processes look like. I definitely see a additional, you know, growth mindset of how do we get better processes in place, or really focusing on the core processes so that we can really move the ball forward and continuing to go that path of delivering on a quicker path, uh, leveraging cloud, as we mentioned of, um, some, some of the capabilities around the chat bot and other things to really start to push, um, uh, the capabilities out to those citizens quicker and really reduce that timeline that we have to take on the backend side, um, that that would be our hope and goal, um, given, you know, sort of what we've been able to accomplish and hoping using that as a proof point of how we can do this for other types of, uh, either programs or other processes. >>Yeah, I think, um, the, you know, the tool has given us capability now there, whether we use local leaders leverage that to the fullest really becomes a coming upon us. So do we take a beat, uh, when we can catch our breath and then, you know, work through our executive leadership to say, look, here's all the ways you can use this tool. You've made an enterprise investment in. Um, and I know for us, uh, at Clark County, we've stood up, uh, enterprise, uh, kind of governance team where we can come and talk through all of our enterprise solutions, uh, encourage our other department head peers, uh, to, to examine how you might be able to use this. Is there a way that, um, you know, parks and rec might use this to better access their scholarship programs to make sure that children get into youth sports leagues and don't get left out, uh, because we know youth suicide on the rise and they need something positive to do when this pandemic is clear, I'm there for them to get out and do those things. >>So the possibilities really are out there. It really becomes, um, how do we mind those internally? And I know that being a part of listservs and, uh, you know, gov tech and all the magazines and things are out there to help us think about how do we better use our solutions, um, as well as our IBM partners who are always eager to say, Hey, have you seen how they're using this? Um, it is important for us to continue to keep our imaginations open, um, so that we continue to iterate through this process. Um, cause I, I would hate to see the culture of, um, iteration go away with this pandemic. >>Okay. We have time for one final question. We've already addressed this in part two, and this one is probably for you and that you've used the cares act to eliminate some of the procurement red tape that's shown up. Well, how do you somehow that's been very positive. How do you see that impacting you going forward? What happens when the red tape all comes back? >>Yeah, so I think I mentioned a little bit, uh, about that when some of the folks who are deemed non essential came back during our reopening phases and they're operating at the speed of prior business and red tape where we had all been on this, these green tape, fast tracks, uh, it, it was a bit of a organizational whiplash. Uh, but it, for us, we've had the conversation with executive management of like, we cannot let this get in the way of what our citizens need. So like keep that pressure on our folks to think differently. Don't and, uh, we've gone so far as to, uh, even, uh, maybe take it a step further and investigate what had been done in, in, in Canada. Some other places around, um, like, like going right from in a 48 hour period, going from a procurement statement through a proof of concept and doing purchasing on the backside, like how can we even get this even more streamlined so that we can get the things we need quickly, uh, because the citizens don't understand, wait, we're doing our best, uh, your number 3000 and queue on the phone line that that's not what they need to hear or want to hear during times of crisis. >>Very helpful. Well, I want to be respectful of our one hour commitment, so we'll have to wrap it up here in closing. I want to thank everyone for joining us for today's event and especially a big, thank you goes to Karen and Tim. You've done a really great job of answering a lot of questions and laying this out for us and a special thanks to our partners at IBM for enabling us to bring this worthwhile discussion to our audience. Thanks once again, and we look forward to seeing you at another government technology event,

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Welcome back to the cubes. Live coverage for ADFS reinvent 2020 I'm John Ford with the cube, your host. We are the cube virtual. We're not there in person this year. We're remote with the pandemic and we're here for the keynote analysis for Verner Vogels, and we've got some great analysts on and friends of the cube cube alumni is Rob Hirschfeld is the founder and CEO of Rakin a pioneer in the dev ops space, as well as early on on the bare metal, getting on the whole on-premise he's seen the vision and I can tell you, I've talked to him many times over the years. He's been on the same track. He's on the right wave frog. Great to have you on. I'm going to have to start Veatch, come on. Y'all come on as well, but great to see you. Thanks, pleasure to be here. Um, so the keynote with Verna was, you know, he's like takes you on a journey, you know, and, and virtual is actually a little bit different vibe, but I thought he did an exceptional job of stage layout and some of the virtual stage craft. Um, but what I really enjoyed the most was really this next level, thinking around systems thinking, right, which is my favorite topic, because, you know, we've been saying, going back 10 years, the cloud is just, here's a computer, right. It's operating system. And so, um, this is the big thing. This is, what's your reaction to the keynote. >>Wow. So I think you're right. This is one of the challenges with what Amazon has been building is it's, you know, it is a lock box, it's a service. So you don't, you don't get to see behind the scenes. You don't really get to know how they run these services. And what, what I see happening out of all of those pieces is they've really come back and said, we need to help people operate this platform. And, and that shouldn't be surprising to anyone. Right? Last couple of years, they've been rolling out service, service service, all these new things. This talk was really different for Verner's con normal ones, because he wasn't talking about whizzbang new technologies. Um, he was really talking about operations, um, you know, died in the wool. How do we make the system easier to use? How do we expose things? What assistance can we have in, in building applications? Uh, in some cases it felt like, uh, an application performance monitoring or management APM talk from five or even 10 years ago, um, canaries, um, you know, Canary deployments, chaos engineering, observability, uh, sort of bread and butter, operational things. >>We have Savi Joel, who's a influencer cloud computing Xtrordinair dev ops guru. Uh, we don't need dev ops guru from Amazon. We got Sarpy and prop here. So it'd be great to see you. Um, you guys had a watch party. Um, tell me what the reaction was, um, with, of the influencers in the cloud or ADI out there that were looking at Vernon's announcement, because it does attract a tech crowd. What was your take and what was the conversation like? >>Yeah, we kinda geeked out. Um, we had a watch party and we were commenting back and forth, like when we were watching it. I think that the general consensus is that the complexity of AWS stack itself is, is increasing. Right. And they have been focused on developers a lot, I think a lot longer than they needed to be a little bit. I think, uh, now they need to focus on the operations. Like we, we are, we all love dev ops talks and it's very fancy and it's very modern way of building software. But if you think deep down that, like once we developed software traditionally and, and also going forward, I think we need to have that separation. Once you develop something in production, it's, it's, it's operating right. Once you build a car, you're operating car, you're not building car all the time. Right? >>So same with the software. Once you build a system, it should have some stability where you're running it, operating it for, for a while, at least before you touch it or refactoring all that stuff. So I think like building and operating at the same time, it's very good for companies like Amazon, AWS, especially, uh, and, and Google and, and, and Facebook and all those folks who are building technology because they are purely high-tech companies, but not for GM Ford Chrysler or Kaiser Permanente, which is healthcare or a school district. The, they, they need, need to operate that stuff once it's built. So I think, uh, the operationalization of cloud, uh, well, I think take focus going forward a lot more than it has and absorbable Deanna, on a funny note, I said, observability is one of those things. I, now these days, like, like, you know, and the beauty pageants that every contestant say is like, whatever question you asked, is it Dora and the answer and say at the end world peace, right? >>And that's a world peace term, which is the absorbability. Like you can talk about all the tech stuff and all that stuff. And at the end you say observability and you'll be fine. So, um, what I'm making is like observability is, and was very important. And when I was talking today about like how we can enable the building of absorbability into this new paradigm, which is a microservices, like where you pass a service ID, uh, all across all the functions from beginning to the end. Right. And so, so you can trace stuff. So I think he was talking, uh, at that level. Yeah. >>Let me, let's take an observer Billy real quick. I have a couple of other points. I want to get your opinions on. He said, quote, this three, enabling major enabling technologies, powering observability metrics, logging and tracing here. We know that it would, that is of course, but he didn't take a position. If you look at all the startups out there that are sitting there, the next observability, there's at least six that I know of. I mean, that are saying, and then you got ones that are kind of come in. I think signal effects was one. I liked, like I got bought by Splunk and then is observability, um, a feature, um, or is it a company? I mean, this is something that kind of gets talked about, right? I mean, it's, I mean, is it really something you can build a business on or is it a white space? That's a feature that gets pulled in what'd you guys react to that? >>So this is a platform conversation and, and, you know, one of the things that we've been having conversations around recently is this idea of platforms. And, and, you know, I've been doing a lot of work on infrastructure as code and distributed infrastructure and how people want infrastructure to be more code, like, which is very much what, what Verna was, was saying, right? How do we bring development process capabilities into our infrastructure operations? Um, and these are platform challenges. W what you're asking about from, uh, observability is perspective is if I'm running my code in a platform, if I'm running my infrastructure as a platform, I actually need to understand what that platform is doing and how it's making actions. Um, but today we haven't really built the platforms to be very transparent to the users. And observability becomes this necessary component to fix all the platforms that we have, whether they're Kubernetes or AWS, or, you know, even going back to VMware or bare metal, if you can't see what's going on, then you're operating in the blind. And that is an increasingly big problem. As we get more and more sophisticated infrastructure, right? Amazon's outage was based on systems can being very connected together, and we keep connecting systems together. And so we have to be able to diagnose and troubleshoot when those connections break or for using containers or Lambdas. The code that's running is ephemeral. It's only around for short periods of time. And if something's going wrong in it, it's incredibly hard to fix it, >>You know? And, and also he, you know, he reiterated his whole notion of log everything, right? He kept on banging on the drum on that one, like log everything, which is actually a good practice. You got to log everything. Why wouldn't you, >>I mean, how you do, but they don't make it easy. Right? Amazon has not made it easy to cross, cross, and, uh, connect all the data across all of those platforms. Right? People think of Amazon as one thing, but you know, the people who are using it understand it's actually a collection of services. And some of those are not particularly that tied together. So figuring out something that's going on across, across all of your service bundles, and this isn't an Amazon problem, this is an industry challenge. Especially as we go towards microservices, I have to be able to figure out what happened, even if I used 10 services, >>Horizontal, scalability argument. Sorry. Do you want to get your thoughts on this? So the observability, uh, he also mentioned theory kind of couched it before he went into the talk about systems theory. I'm like, okay. Let's, I mean, I love systems, and I think that's going to be the big wake up call here for the next 10 years. That's a systems mindset. And I think, you know, um, Rob's right. It's a platform conversation. When you're thinking about an operating system or a system, it has consequences when things change, but he talked about controllability versus, uh, observability and kinda T that teed up the, well, you can control systems controls, or you can have observability, uh, what's he getting at in all of this? What's he trying to say, keep, you know, is it a cover story? Is it this, is it a feature? What was the, what was the burner getting at with all this? >>Uh, I, I, I believe they, they understand that, that, uh, that all these services are very sort of micro in nature from Amazon itself. Right. And then they are not tied together as Rob said earlier. And they, he addressed that. He, uh, he, uh, announced that service. I don't know the name of that right now of problem ahead that we will gather all the data from all the different places. And then you can take a look at all the data coming from different services at this at one place where you have the service ID passed on to all the servers services. You have to do that. It's a discipline as a software developer, you have to sort of adhere to even in traditional world, like, like, you know, like how you do logging and monitoring and tracing, um, it's, it's your creativity at play, right? >>So that's what software is like, if you can pass on, I was treating what they gave an example of Citrix, uh, when, when, when you are using like tons of applications with George stream to your desktop, through Citrix, they had app ID concept, right? So you can trace what you're using and all that stuff, and you can trace the usage and all that stuff, and they can, they can map that log to that application, to that user. So you need that. So I think he w he was talking about, I think that's what he's getting too. Like we have to, we have to sort of rethink how we write software in this new Microsoft, uh, sort of a paradigm, which I believe it, it's a beautiful thing. Uh, as long as we can manage it, because Microsoft is, are spread across like, um, small and a smaller piece of software is everywhere, right? So the state, how do we keep the state intact? How do we, um, sort of trace things? Uh, it becomes a huge problem if we don't do it right? So it it's, um, it's a little, this is some learning curve for most of the developers out there. So 60 dash 70% >>Rob was bringing this up, get into this whole crash. And what is it kind of breakdown? Because, you know, there's a point where you don't have the Nirvana of true horizontal scalability, where you might have microservices that need to traverse boundaries or systems, boundaries, where, or silos. So to Rob's point earlier, if you don't see it, you can't measure it or you can't get through it. How do you wire services across boundaries? Is that containers, is that, I mean, how does this all work? How do you guys see that working? I just see a train wreck there. >>It's, it's a really hard problem. And I don't think we should underestimate it because everything we toast talked about sounds great. If you're in a single AWS region, we're talking about distributed infrastructure, right? If you think about what we've been seeing, even more generally about, you know, edge sites, uh, colo on prem, you know, in cloud multi-region cloud, all these things are actually taking this one concept and you're like, Oh, I just want to store all the log data. Now, you're not going to store all your log data in one central location anymore. That in itself, as a distributed infrastructure problem, where I have to be able to troubleshoot what's going on, you know, and know that the logs are going to the right place and capture the data, that's really important. Um, and one of the innovations in this that I think is going to impact the industry over the next couple of years is the addition of more artificial intelligence and machine learning, into understanding operations patterns and practices. >>And I think that that's a really significant industry trend where Amazon has a distinct advantage because it's their systems and it's captive. They can analyze and collect a lot of data across very many customers and learn from those things and program systems that learn from those things. Um, and so the way you're going to keep up with this is not by logging more and more data, but by doing exactly what we're talking through, which was how do I analyze the patterns with machine learning so that I can get predictive analysis so that I can understand something that looks wrong and then put people on checking it before it goes wrong. >>All right, I gotta, I gotta bring up something controversial. I can't hold back any longer. Um, you know, Mark Zuckerberg said many, many years ago, all the old people, they can do startups, they're too old and you gotta be young and hungry. You gotta do that stuff. If we're talking systems theory, uh, automated meta reasoning, evolvable systems, resilience, distributed computing, isn't that us old guys that have actually have systems experience. I mean, if you're under the age of 30, you probably don't even know what a system is. Um, and, or co coded to the level of systems that we use to code. And I'm putting my quote old man kind of theory, only kidding, by the way on the 30. But my point is there is a generation of us that had done computer science in the, in the eighties and seventies, late seventies, maybe eighties and nineties, it's all it was, was systems. It was a systems world. Now, when you have a software world, the aperture is increasing in terms of software, are the younger generation of developers system thinkers, or have we lost that art, uh, or is it doesn't matter? What do you guys think? >>I, I think systems thinking comes with age. I mean, that's, that's sort of how I think, I mean, like I take the systems thinking a greater sort of, >>Um, world, like state as a system country, as a system and everything is a system, your body's a system family system, so it's the same way. And then what impacts the system when you operated internal things, which happened within the system and external, right. And we usually don't talk about the economics and geopolitics. There's a lot of the technology. Sometimes we do, like we have, I think we need to talk more about that, the data sovereignty and all that stuff. But, but even within the system, I think the younger people appreciate it less because they don't have the, they don't see, um, software taught like that in the universities. And, and, and, and by these micro micro universities now online trainings and stuff like sweaty, like, okay, you learn this thing and you're good at it saying, no, no, it's not like that. So you've got to understand the basics and how the systems operate. >>Uh, I'll give you an example. So like we were doing the, the, the client server in early nineties, and then gradually we moved more towards like having ESB enterprise services, bus where you pass a state, uh, from one object to another, and we can bring in the heterogeneous, uh, languages. This thing is written in Java. This is in.net. This is in Python. And then you can pass it through that. Uh, you're gonna make a state for, right. And that, that was contained environment. Like ESBs were contained environment. We were, I, I wrote software for ESPs myself at commerce one. And so like, we, what we need today is the ESP equallant in the cloud. We don't have that. >>Rob, is there a reverse ageism developers? I mean, if you're young, you might not have systems. What do you think? I, I don't agree with that. I actually think that the nature of the systems that we're programming forces people into more distributed infrastructure thinking the platforms we have today are much better than they were, you know, 20 years ago, 30 years ago, um, in the sense that I can do distributed infrastructure programming without thinking about it very much anymore, but you know, people know, they know how to use cloud. They know how to use a big platform. They know how to break things into microservices. I, I think that these are inherent skills that people need to think about that you're you're right. There is a challenge in that, you know, you get very used to the platform doing the work for you, and that you need to break through it, but that's an experiential thing, right? >>The more experienced developers are going to have to understand what the platforms do. Just like, you know, we used to have to understand how registers worked inside of a CPU, something I haven't worried about for a long, long time. So I, I don't think it's that big of a problem. Um, from, from that perspective, I do think that the thing that's really hard is collaboration. And so, you know, it's, it's hard people to people it's hard inside of a platform. It's hard when you're an Amazon size and you've been rolling out services all over the place and now have to figure out how to fit them all together. Um, and that to me is, is a design problem. And it's more about being patient and letting things, uh, mature. If anything might take away from this keynote is, you know, everybody asked Amazon to take a breath and work on usability and, and cross cross services synchronizations rather than, than adding more services into the mix. And that's, >>That's a good point. I mean, again, I bring up the conversation because it's kind of the elephant in the room and I make it being controversial to make a point there. So our view, because, you know, I interviewed Judy Estrin who helped found the internet with Vince Cerf. She's well-known for her contributions for the TCP IP protocol. Andy Besta Stein. Who's the, who's the Rembrandt of motherboards. But as Pat Gelsinger, CEO of VMware, I would say both said to me on the cube that without systems thinking, you don't understand consequences of when things change. And we start thinking about this microservices conversation, you start to hear a little bit of that pattern emerging, where those systems, uh, designs matter. And then you have, on the other hand, you have this modern application framework where serverless takes over. So, you know, Rob back to your infrastructure as code, it really isn't an either, or they're not mutually exclusive. You're going to have a set of nerds and geeks engineering systems to make them better and easier and scalable. And then you're going to have application developers that need to just make it work. So you start to see the formation of kind of the, I won't say swim lanes, but I mean, what do you guys think about that? Because you know, Judy and, um, Andy better sign up. They're kind of right. Uh, >>Th th the enemy here, and we're seeing this over and over again is complexity. And, and the challenge has been, and serverless is like, those people like, Oh, I don't have to worry about servers anymore because I'm dealing with serverless, which is not true. What you're doing is you're not worrying about infrastructure as much, but you, the complexity, especially in a serverless infrastructure where you're pulling, you know, events from all sorts of things, and you have one, one action, one piece of code, you know, triggering a whole bunch of other pieces of code in a decoupled way. We are, we are bringing so much complexity into these systems, um, that they're very hard to conceive of. Um, and AIML is not gonna not gonna address that. Um, I think one of the things that was wonderful about the setting, uh, in the sugar factory and at all of that, you know, sort of very mechanical viewpoint, you know, when you're actually connecting all things together, you can see it. A lot of what we've been building today is almost impossible to observe. And so the complexity price that we're paying in infrastructure is going up exponentially and we can't sustain infrastructures like that. We have to start leveling that in, right? >>Your point on the keynote, by the way, great call out on, on the, on the setting. I thought that was very clever. So what do you think about this? Because as enterprises go through this transformation, one of the big conversations is the solution architecture, the architecture of, um, how you lay all this out. It's complexity involved. Now you've got on premise system, you've got cloud, you've got edge, which you're hearing more and more local processing, disconnected systems, managing it at the edge with visualization. We're going to hear more about that, uh, with Dirk, when he comes on the queue, but you know, just in general as a practitioner out there, what, what's, what's your, what do you see people getting their arms around, around this, this keynote? What do they, what's your thoughts? >>Yeah, I, I think, uh, the, the pattern I see emerging is like, or in the whole industry, regardless, like if you put, when does your sign is that like, we will write less and less software in-house I believe that SAS will emerge. Uh, and it has to, I mean, that is the solution to kill the complexity. I believe, like we always talk about software all the time and we, we try to put this in the one band, like it's, everybody's dining, same kind of software, and they have, I'm going to complexity and they have the end years and all that stuff. That's not true. Right. If you are Facebook, you're writing totally different kind of software that needs to scale differently. You needs a lot of cash and all that stuff, right. Gash like this and cash. Well, I ain't both gases, but when you are a mid size enterprise out there in the middle, like fly over America, what, uh, my friend Wayne says, like, we need to think about those people too. >>Like, how do they drive software? What kind of software do they write? Like how many components they have in there? Like they have three tiers of four tiers. So I think they're a little more simpler software for internal use. We have to distinguish these applications. I always talk about this, like the systems of record systems of differentiation, the system of innovation. And I think cloud will do great. And the newer breed of applications, because you're doing a lot of, a lot of experimentation. You're doing a lot of DevOps. You have two pizza teams and all that stuff, which is good stuff we talk about, well, when you go to systems of record, you need stability. You need, you need some things which is operational. You don't want to touch it again, once it's in production. Right? And so the, in between that, that thing is, I think that's, that's where the complexity lies the systems are, which are in between those systems of record and system or innovation, which are very new Greenfield. That, that's what I think that's where we need to focus, uh, our, um, platform development, um, platform as a service development sort of, uh, dollars, if you will, as an industry, I think Amazon is doing that right. And, and Azura is doing that right to a certain extent too. I, I, I, I worry a little bit about, uh, uh, Google because they're more tilted towards the data science, uh, sort of side of things right now. >>Well, Microsoft has the most visibility into kind of the legacy world, but Rob, you're shaking your head there. Um, on his comment, >>You know, I, I, you know, I, I watched the complexity of all these systems and, and, you know, I'm not sure that sass suffocation of everything that we're doing is leading to less is pushing the complexity behind a curtain so that you, you, you can ignore the man behind the curtain. Um, but at the end of the day, you know what we're really driving towards. And I think Amazon is accelerating this. The cloud is accelerating. This is a new set of standard operating processes and procedures based on automation, based on API APIs, based on platforms, uh, that ultimately, I think people could own and could come back to how we want to operate it. When I look at what we w we were just shown with the keynote, you know, it was an, is things that application performance management and monitoring do. It's, it's not really Amazon specific stuff. There's no magic beans that Amazon is growing operational knowledge, you know, in Amazon, greenhouses that only they know how to consume. This is actually pretty block and tackle stuff. Yeah. And most people don't need to operate it at that type of scale to be successful. >>It's a great point. I mean, let's, let's pick up on that for the last couple of minutes we have left. Cause I think that's a great, great double-down because you're thinking about the mantra, Hey, everything is a service, you know, that's great for business model. You know, you hand it over to the techies. They go, wait a minute. What does that actually mean? It's harder. But when I talk to people out there and you hear people talking about everything is a service or sanctification, I do agree. I think you're putting complexity behind the curtain, but it's kind of the depends answer. So if you're going to have everything as a service, the common thesis is it has to have support automation everywhere. You got to automate things to make things sassiphy specified, which means you need five nines, like factory type environments. They're not true factories, but Rob, to your point, if you're going to make something a SAS, it better be Bulletproof. Because if you're, if you're automating something, it better be automated, right? You can measure things all you want, but if it's not automated, like a, like a, >>And you have no idea what's going on behind the curtains with some of these, these things, right. Especially, you know, I know our business and you know, our customers' businesses, they're, they're reliant on more and more services and you have no idea, you know, the persistence that service, if they're going to break an API, if they're going to change things, a lot of the stuff that Amazon is adding here defensively is because they're constantly changing the wheels on the bus. Um, and that is not bad operational practice. You should be resilient to that. You should have processes that are able to be constantly updated and CICB pipelines and, you know, continuous deployments, you shouldn't expect to, to, you know, fossilize your it environment in Amber, and then hope it doesn't have to change for 10 years. But at the same time, we'll work control your house. >>That's angle about better dev ops hypothetical, like a factory, almost metaphor. Do you care if the cars are being shipped down the assembly line and the output works and the output, if you have self-healing and you have these kinds of mechanisms, you know, you could have do care. The services are being terminated and stood up and reformed as long as the factory works. Right? So again, it's a complexity level of how much it, or you want to bite off and chew or make work. So to me, if it's automated, it's simple, did it work or not? And then the cost of work to be, what's your, what's your angle on this? Yeah. >>I believe if you believe in systems thinking, right. You have to believe in, um, um, the concept of, um, um, Oh gosh, I'm losing over minor. Um, abstraction. Right? So abstraction is your friend in software. Abstraction is your friend anyways, right? That's how we, humans pieces actually make a lot more progress than any other sort of living things here in this world. So that's why we are smart. We can abstract complexity behind the curtains, right? We, we can, we can keep improving, like from the, the, you know, wooden cart to the car, to the, to the plane, to the other, like, we, we, we have this, like when, when we see we are flying these airplanes, like 90% of the time they're on autopilot, like that's >>Hi, hiding my attractions is, is about evolution. Evolvable software term. He said, it's true. All right, guys, we have one minute left. Um, let's close this out real quick. Each of you give a closing statement on what you thought of the keynote and Verner's talk prop, we'll start with you. >>Uh, you know, as always, it's a perf keynote, uh, very different this year because it was so operationally focused and using the platform and, and helping people run their, their, off their applications and software better. And I think it's an interesting turn that we've been waiting for for Amazon, uh, to look at, you know, helping people use their own platform more. Um, so, uh, refreshing change and I think really powerful and well delivered. I really did like the setting >>Great shopping. And when we found, I found out today, that's Teresa Carlson is now running training and certification. So I'm expecting that to be highly awesomely accelerated a success there. Sorry, what's your take real quick on burners talk, walk away. Keynote thoughts. >>I, I, I think it was what I expected it to be like, he focused on the more like a software architecture kind of discussion. And he focused this time a little more on the ops side and the dev side, which I think they, they are pivoting a little bit, um, because they, they want to sell more AWS stuff to us, uh, to the existing enterprises. So I think, um, that was, um, good. Uh, I wish at the end, he said, not only like, go, go build, but also go build and operate. So can, you know, they all say, go build, build, build, but like, who's going to operate this stuff. Right. So I think, um, uh, I will see a little shift, I think, going forward, but we were talking earlier, uh, during or watch party that I think, uh, going forward, uh, AWS will open start open sourcing the commoditized version of their cloud, which have been commoditized by other vendors and gradually they will open source it so they can keep the hold onto the enterprises. I think that's what my take is. That's my prediction is >>Awesome and want, I'll make sure I'm at your watch party next time. Sorry. I missed it. Nobody's taking notes. Try and prepare. Sorry, Rob. Thanks for coming on and sharing awesome insight and expertise to experts in cloud and dev ops. I know them. And can firstly vouch for their awesomeness? Thanks for coming on. I think Verner can verify what I thought already was reporting Amazon everywhere. And if you connect the dots, this idea of reasoning, are we going to have smarter cloud? That's the next conversation? I'm John for your host of the cube here, trying to get smarter with Aus coverage. Thanks to Robin. Sarvi becoming on. Thanks for watching.

>>from around the globe If the cube with digital coverage of M I t. Chief data officer and Information quality symposium brought to you by Silicon Angle Media >>Hello, everyone. This is Day Volonte and welcome back to our continuing coverage of the M I t. Chief Data Officer CDO I Q event Interpol Bhandari is here. He's a leading voice in the CDO community and a longtime Cubillan Interpol. Great to see you. Thanks for coming on for this. Especially >>program. My pleasure. >>So when you you and I first met, you laid out what I thought was, you know, one of the most cogent frameworks to understand what a CDO is job was where the priority should be. And one of those was really understanding how, how, how data contributes to the monetization of station aligning with lines of business, a number of other things. And that was several years ago. A lot of change since then. You know, we've been doing this conference since probably twenty thirteen and back then, you know, Hadoop was coming on strong. A lot of CEOs didn't want to go near the technology that's beginning to change. CDOs and cto Zehr becoming much more aligned at the hip. The reporting organizations have changed. But I love your perspective on what you've observed as changing in the CDO roll over the last half decade or so. >>Well, did you know that I became chief data officer in two thousand six? December two thousand and six And I have done this job four times four major overnight have created of the organization from scratch each time. Now, in December of two thousand six, when I became chief data officer, there were only four. Chief Data Officer, uh, boom and I was the first in health care, and there were three, three others, you know, one of the Internet one and credit guns one and banking. And I think I'm the only one actually left standing still doing this job. That's a good thing or a bad thing. But like, you know, it certainly has allowed me to love the craft and then also scripted down to the level that, you know, I actually do think of it purely as a craft. That is. I know, going into a mutual what I'm gonna do. They were on the central second. No, the interesting things that have unfolded. Obviously, the professions taken off There are literally thousands off chief data officers now, and there are plenty off changes. I think the main change, but the job is it's, I think, a little less daunting in terms off convincing the senior leadership that it's need it because I think the awareness at the CEO level is much, much, much better than what it waas in two thousand six. Across the world. Now, having said that, I think it is still only awareness and don't think that there's really a deep understanding of those levels. And so there's a lot off infusion, which is why you will. You kind of think this is my period. But you saw all these professions take off with C titles, right? Chief Data officer, chief analytics officer, chief digital officer and chief technology officer. See, I off course is being there for a long time. And but I think these newer see positions. They're all very, very related, and they all kind of went to the same need which had to do with enterprise transformation, digital transformation, that enterprises chief digital officer, that's another and and people were all trying to essentially feel the elephants and they could only see part of it at the senior levels, and they came up with which have a role you know, seemed most meaningful to them. But really, all of us are trying to do the same job, which is to accelerate digital transformation in the enterprise. Your comment about you kind of see that the seat eels and sea deals now, uh, partnering up much more than in the past, and I think that's in available the major driving force full. That is, in my view, anyway. It's is artificial intelligence as people try to infuse artificial intelligence. Well, then it's very technical field. Still, it's not something that you know you can just hand over to somebody who has the business jobs, but not the deep technical chops to pull that off. And so, in the case off chief data officers that do have the technical jobs, you'll see them also pretty much heading up the I effort in total and you know, as I do for the IBM case, will be building the Data and AI Enablement internal platform for for IBM. But I think in other cases you you've got Chief date officers who are coming in from a different angle. You know, they built Marghera but the CTO now, because they have to. Otherwise you cannot get a I infused into the organization. >>So there were a lot of other priorities, obviously certainly digital transformation. We've been talking about it for years, but still in many organisations, there was a sense of, well, not on my watch, maybe a sense of complacency or maybe just other priorities. Cove. It obviously has changed that now one hundred percent of the companies that we talked to are really putting this digital transformation on the front burner. So how has that changed the role of CDO? Has it just been interpolate an acceleration of that reality, or has it also somewhat altered the swim lanes? >>I think I think it's It's It's Bolt actually, so I have a way of looking at this in my mind, the CDO role. But if you look at it from a business perspective, they're looking for three things. The CEO is looking for three things from the CDO. One is you know this person is going to help with the revenue off the company by enabling the production of new products, new products of resulting in new revenue and so forth. That's kind of one aspect of the monetization. Another aspect is the CEO is going to help with the efficiency within the organization by making data a lot more accessible, as well as enabling insights that reduce into and cycle time for major processes. And so that's another way that they have monitor. And the last one is a risk reduction that they're going to reduce the risk, you know, as regulations. And as you have cybersecurity exposure on incidents that you know just keep keep accelerating as well. You're gonna have to also step in and help with that. So every CDO, the way their senior leadership looks at them is some mix off three. And in some cases, one has given more importance than the other, and so far, but that's how they are essentially looking at it now. I think what digital transformation has done is it's managed to accelerate, accelerate all three off these outcomes because you need to attend to all three as you move forward. But I think that the individual balance that's struck for individuals reveals really depends on their ah, their company, their situation, who their peers are, who is actually leading the transformation and so >>forth, you know, in the value pie. A lot of the early activity around CDO sort of emanated from the quality portions of the organization. It was sort of a compliance waited roll, not necessarily when you started your own journey here. Obviously been focused on monetization how data contributes to that. But But you saw that generally, organizations, even if they didn't have a CDO, they had this sort of back office alliance thing that has totally changed the the in the value equation. It's really much more about insights, as you mentioned. So one of the big changes we've seen in the organization is that data pipeline you mentioned and and cycle time. And I'd like to dig into that a little bit because you and I have talked about this. This is one of the ways that a chief data officer and the related organizations can add the most value reduction in that cycle time. That's really where the business value comes from. So I wonder if we could talk about that a little bit and how that the constituents in the stakeholders in that in that life cycle across that data pipeline have changed. >>That's a very good question. Very insightful questions. So if you look at ah, company like idea, you know, my role in totally within IBM is to enable Ibn itself to become an AI enterprise. So infuse a on into all our major business processes. You know, things like our supply chain lead to cash well, process, you know, our finance processes like accounts receivable and procurement that soulful every major process that you can think off is using Watson mouth. So that's the That's the That's the vision that's essentially what we've implemented. And that's how we are using that now as a showcase for clients and customers. One of the things that be realized is the data and Ai enablement spots off business. You know, the work that I do also has processes. Now that's the pipeline you refer to. You know, we're setting up the data pipeline. We're setting up the machine learning pipeline, deep learning blank like we're always setting up these pipelines, And so now you have the opportunity to actually turn the so called EI ladder on its head because the Islander has to do with a first You collected data, then you curated. You make sure that it's high quality, etcetera, etcetera, fit for EI. And then eventually you get to applying, you know, ai and then infusing it into business processes. And so far, But once you recognize that the very first the earliest creases of work with the data those themselves are essentially processes. You can infuse AI into those processes, and that's what's made the cycle time reduction. And although things that I'm talking about possible because it just makes it much, much easier for somebody to then implement ai within a lot enterprise, I mean, AI requires specialized knowledge. There are pieces of a I like deep learning, but there are, you know, typically a company's gonna have, like a handful of people who even understand what that is, how to apply it. You know how models drift when they need to be refreshed, etcetera, etcetera, and so that's difficult. You can't possibly expect every business process, every business area to have that expertise, and so you've then got to rely on some core group which is going to enable them to do so. But that group can't do it manually because I get otherwise. That doesn't scale again. So then you come down to these pipelines and you've got to actually infuse AI into these data and ai enablement processes so that it becomes much, much easier to scale across another. >>Some of the CEOs, maybe they don't have the reporting structure that you do, or or maybe it's more of a far flung organization. Not that IBM is not far flung, but they may not have the ability to sort of inject AI. Maybe they can advocate for it. Do you see that as a challenge for some CEOs? And how do they so to get through that, what's what's the way in which they should be working with their constituents across the organization to successfully infuse ai? >>Yeah, that's it's. In fact, you get a very good point. I mean, when I joined IBM, one of the first observations I made and I in fact made it to a senior leadership, is that I didn't think that from a business standpoint, people really understood what a I met. So when we talked about a cognitive enterprise on the I enterprise a zaydi em. You know, our clients don't really understand what that meant, which is why it became really important to enable IBM itself to be any I enterprise. You know that. That's my data strategy. Your you kind of alluded to the fact that I have this approach. There are these five steps, while the very first step is to come up with the data strategy that enables a business strategy that the company's on. And in my case, it was, Hey, I'm going to enable the company because it wants to become a cloud and cognitive company. I'm going to enable that. And so we essentially are data strategy became one off making IBM. It's something I enterprise, but the reason for doing that the reason why that was so important was because then we could use it as a showcase for clients and customers. And so But I'm talking with our clients and customers. That's my role. I'm really the only role I'm playing is what I call an experiential selling there. I'm saying, Forget about you know, the fact that we're selling this particular product or that particular product that you got GPU servers. We've got you know what's an open scale or whatever? It doesn't really matter. Why don't you come and see what we've done internally at scale? And then we'll also lay out for you all the different pain points that we have to work through using our products so that you can kind of make the same case when you when you when you apply it internally and same common with regard to the benefit, you know the cycle, time reduction, some of the cycle time reductions that we've seen in my process is itself, you know, like this. Think about metadata business metadata generating that is so difficult. And it's again, something that's critical if you want to scale your data because you know you can't really have a good catalogue of data if you don't have good business, meditate. Eso. Anybody looking at what's in your catalog won't understand what it is. They won't be able to use it etcetera. And so we've essentially automated business metadata generation using AI and the cycle time reduction that was like ninety five percent, you know, haven't actually argue. It's more than that, because in the past, most people would not. For many many data sets, the pragmatic approach would be. Don't even bother with the business matter data. Then it becomes just put somewhere in the are, you know, data architecture somewhere in your data leg or whatever, you have data warehouse, and then it becomes the data swamp because nobody understands it now with regard to our experience applying AI, infusing it across all our major business processes are average cycle time reduction is seventy percent, so just a tremendous amount of gains are there. But to your point, unless you're able to point to some application at scale within the enterprise, you know that's meaningful for the enterprise, Which is kind of what the what the role I play in terms of bringing it forward to our clients and customers. It's harder to argue. I'll make a case or investment into A I would then be enterprise without actually being able to point to those types of use cases that have been scaled where you can demonstrate the value. So that's extremely important part of the equation. To make sure that that happens on a regular basis with our clients and customers, I will say that you know your point is vomited a lot off. Our clients and customers come back and say, Tell me when they're having a conversation. I was having a conversation just last week with major major financial service of all nations, and I got the same point saying, If you're coming out of regulation, how do I convince my leadership about the value of a I and you know, I basically responded. He asked me about the scale use cases You can show that. But perhaps the biggest point that you can make as a CDO after the senior readership is can we afford to be left up? That is the I think the biggest, you know, point that the leadership has to appreciate. Can you afford to be left up? >>I want to come back to this notion of seventy percent on average, the cycle time reduction. That's astounding. And I want to make sure people understand the potential impacts. And, I would say suspected many CEOs, if not most understand sort of system thinking. It's obviously something that you're big on but often times within organisations. You might see them trying to optimize one little portion of the data lifecycle and you know having. Okay, hey, celebrate that success. But unless you can take that systems view and reduce that overall cycle time, that's really where the business value is. And I guess my we're real question around. This is Every organization has some kind of Northstar, many about profit, and you can increase revenue are cut costs, and you can do that with data. It might be saving lives, but ultimately to drive this data culture, you've got to get people thinking about getting insights that help you with that North Star, that mission of the company, but then taking a systems view and that's seventy percent cycle time reduction is just the enormous business value that that drives, I think, sometimes gets lost on people. And these air telephone numbers in the business case aren't >>yes, No, absolutely. It's, you know, there's just a tremendous amount of potential on, and it's it's not an easy, easy thing to do by any means. So we've been always very transparent about the Dave. As you know, we put forward this this blueprint right, the cognitive enterprise blueprint, how you get to it, and I kind of have these four major pillars for the blueprint. There's obviously does this data and you're getting the data ready for the consummation that you want to do but also things like training data sets. How do you kind of run hundreds of thousands of experiments on a regular basis, which kind of review to the other pillar, which is techology? But then the last two pillars are business process, change and the culture organizational culture, you know, managing organizational considerations, that culture. If you don't keep all four in lockstep, the transformation is usually not successful at an end to end level, then it becomes much more what you pointed out, which is you have kind of point solutions and the role, you know, the CEO role doesn't make the kind of strategic impact that otherwise it could do so and this also comes back to some of the only appointee of you to do. If you think about how do you keep those four pillars and lock sync? It means you've gotta have the data leader. You also gotta have the technology, and in some cases they might be the same people. Hey, just for the moment, sake of argument, let's say they're all different people and many, many times. They are so the data leader of the technology of you and the operations leaders because the other ones own the business processes as well as the organizational years. You know, they've got it all worked together to make it an effective conservation. And so the organization structure that you talked about that in some cases my peers may not have that. You know, that's that. That is true. If the if the senior leadership is not thinking overall digital transformation, it's going to be difficult for them to them go out that >>you've also seen that culturally, historically, when it comes to data and analytics, a lot of times that the lines of business you know their their first response is to attack the quality of the data because the data may not support their agenda. So there's this idea of a data culture on, and I want to ask you how self serve fits into that. I mean, to the degree that the business feels as though they actually have some kind of ownership in the data, and it's largely, you know, their responsibility as opposed to a lot of the finger pointing that has historically gone on. Whether it's been decision support or enterprise data, warehousing or even, you know, Data Lakes. They've sort of failed toe live up to that. That promise, particularly from a cultural standpoint, it and so I wonder, How have you guys done in that regard? How did you get there? Many Any other observations you could make in that regard? >>Yeah. So, you know, I think culture is probably the hardest nut to crack all of those four pillars that I back up and you've got You've got to address that, Uh, not, you know, not just stop down, but also bottom up as well. As you know, period. Appear I'll give you some some examples based on our experience, that idea. So the way my organization is set up is there is a obviously a technology on the other. People who are doing all the data engineering were kind of laying out the foundational technical elements or the transformation. You know, the the AI enabled one be planning networks, and so so that are those people. And then there is another senior leader who reports directly to me, and his organization is all around adoptions. He's responsible for essentially taking what's available in the technology and then working with the business areas to move forward and make this make and infuse. A. I do the processes that the business and he is looking. It's done in a bottom upwards, deliberately set up, designed it to be bottom up. So what I mean by that is the team on my side is fully empowered to move forward. Why did they find a like minded team on the other side and go ahead and do it? They don't have to come back for funding they don't have, You know, they just go ahead and do it. They're basically empowered to do that. And that particular set up enabled enabled us in a couple of years to have one hundred thousand internal users on our Central data and AI enabled platform. And when I mean hundred thousand users, I mean users who were using it on a monthly basis. We company, you know, So if you haven't used it in a month, we won't come. So there it's over one hundred thousand, even very rapidly to that. That's kind of the enterprise wide storm. That's kind of the bottom up direction. The top down direction Waas the strategic element that I talked with you about what I said, Hey, be our data strategy is going to be to create, make IBM itself into any I enterprise and then use that as a showcase for plants and customers That kind of and be reiterated back. And I worked the senior leadership on that view all the time talking to customers, the central and our senior leaders. And so that's kind of the air cover to do this, you know, that mix gives you, gives you that possibility. I think from a peer to peer standpoint, but you get to these lot scale and to end processes, and that there, a couple of ways I worked that one way is we've kind of looked at our enterprise data and said, Okay, therefore, major pillars off data that we want to go after data, tomato plants, data about our offerings, data about financial data, that s and then our work full student and then within that there are obviously some pillars, like some sales data that comes in and, you know, been workforce. You could have contractors. Was his employees a center But I think for the moment, about these four major pillars off data. And so let me map that to end to end large business processes within the company. You know, the really large ones, like Enterprise Performance Management, into a or lead to cash generation into and risk insides across our full supply chain and to and things like that. And we've kind of tied these four major data pillars to those major into and processes Well, well, yes, that there's a mechanism they're obviously in terms off facilitating, and to some extent one might argue, even forcing some interaction between teams that are the way they talk. But it also brings me and my peers much closer together when you set it up that way. And that means, you know, people from the HR side people from the operation side, the data side technology side, all coming together to really move things forward. So all three tracks being hit very, very hard to move the culture fall. >>Am I also correct that you have, uh, chief data officers that reporting to you whether it's a matrix or direct within the division's? Is that right? >>Yeah, so? So I mean, you know, for in terms off our structure, as you know, way our global company, we're also far flung company. We have many different products in business units and so forth. And so, uh, one of the things that I realized early on waas we are going to need data officers, each of those business units and the business units. There's obviously the enterprise objective. And, you know, you could think of the enterprise objectives in terms of some examples based on what I said in the past, which is so enterprise objective would be We've gotta have a data foundation by essentially making data along these four pillars. I talked about clients offerings, etcetera, you know, very accessible self service. You have mentioned south, so thank you. This is where the South seven speaks. Comes it right. So you can you can get at that data quickly and appropriately, right? You want to make sure that the access control, all that stuff is designed out and you're able to change your policies and you'd swap manual. But, you know, those things got implemented very rapidly and quickly. And so you've got you've got that piece off off the off the puzzle due to go after. And then I think the other aspect off off. This is, though, when you recognize that every business unit also has its own objectives and they are looking at some of those things somewhat differently. So I'll give you an example. We've got data any our product units. Now, those CEOs right there, concern is going to be a lot more around the products themselves And how were monetizing those box and so they're not per se concerned with, You know, how you reduce the enter and cycle time off IBM in total supply chain so that this is my point. So they but they're gonna have substantial considerations and objectives that they want to accomplish. And so I recognize that early on, and we came up with this notion off a data officer council and I helped staff the council s. So this is why that's the Matrix to reporting that we talked about. But I selected some of the key Blair's that we have in those units, and I also made sure they were funded by the unit. So they report into the units because their paycheck is actually determined. Pilot unit and which makes them than aligned with the objectives off the unit, but also obviously part of my central approach so that I can disseminate it out to the organization. It comes in very, very handy when you are trying to do things across the company as well. So when we you know GDP our way, we have to get the company ready for Judy PR, I would say that this mechanism became a key key aspect of what enabled us to move forward and do it rapidly. Trouble them >>be because you had the structure that perhaps the lines of business weren't. Maybe is concerned about GDP are, but you had to be concerned with it overall. And this allowed you to sort of hiding their importance, >>right? Because think of in the case of Jeannie PR, they have to be a company wide policy and implementation, right? And if he did not have that structure already in place, it would have made it that much harder. Do you get that uniformity and consistency across the company, right, You know, So you will have to in the weapon that structure, but we already have it because way said Hey, this is around for data. We're gonna have these types of considerations that they are. And so we have this thing regular. You know, this man network that meat meets regularly every month, actually, and you know, when things like GDP are much more frequently than that, >>right? So that makes sense. We're out of time. But I wonder if we could just close if you could address the M I t CDO audience that probably this is the largest audience, Believe or not, now that it's that's virtual definitely expanded the audience, but it's still a very elite group. And the reason why I was so pleased that you agreed to do this is because you've got one of the more complex organizations out there and you've succeeded. And, ah, a lot of the hard, hard work. So what? What message would you leave the M I t CDO audience Interpol? >>So I would say that you know, it's it's this particular professional. Receiving a profession is, uh, if I have to pick one trait of let me pick two traits, I think what is your A change agent? So you have to be really comfortable with change things are going to change, the organization is going to look to you to make those changes. And so that's what aspect off your job, you know, may or may not be part of me immediately. But the those particular set of skills and characteristics and something that you know, one has to, uh one has to develop or time, And I think the other thing I would say is it's a continuous looming jaw. So you continue sexism and things keep changing around you and changing rapidly. And, you know, if you just even think just in terms off the subject areas, I mean this Syria today you've got to understand technology. Obviously, you've gotta understand data you've got to understand in a I and data science. You've got to understand cybersecurity. You've gotta understand the regulatory framework, and you've got to keep all that in mind, and you've got to distill it down to certain trends. That's that's happening, right? I mean, so this is an example of that is that there's a trend towards more regulation around privacy and also in terms off individual ownership of data, which is very different from what's before the that's kind of weather. Bucket's going and so you've got to be on top off all those things. And so the you know, the characteristic of being a continual learner, I think is a is a key aspect off this job. One other thing I would add. And this is All Star Coleman nineteen, you know, prik over nineteen in terms of those four pillars that we talked about, you know, which had to do with the data technology, business process and organization and culture. From a CDO perspective, the data and technology will obviously from consent, I would say most covert nineteen most the civil unrest. And so far, you know, the other two aspects are going to be critical as we move forward. And so the people aspect of the job has never bean, you know, more important down it's today, right? That's something that I find myself regularly doing the stalking at all levels of the organization, one on a one, which is something that we never really did before. But now we find time to do it so obviously is doable. I don't think it's just it's a change that's here to stay, and it ships >>well to your to your point about change if you were in your comfort zone before twenty twenty two things years certainly taking you out of it into Parliament. All right, thanks so much for coming back in. The Cuban addressing the M I t CDO audience really appreciate it. >>Thank you for having me. That my pleasant >>You're very welcome. And thank you for watching everybody. This is Dave a lot. They will be right back after this short >>break. You're watching the queue.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Hi. We're right outside of the Boston Haba. You're watching >> the cube on stew Minimum in. And this is active Geo data driven. 2019 due date. Two days digging into, You >> know, the role of data inside Cos on, you know, in an ever changing world, happy to welcome to the program of first time guests are China Oven countrymen who's a research manager at I. D. C. Coming to us from across the pond in London. Thanks so much for joining us. Pleasure. So tell us a little bit. I d c. We know. Well, you know, the market landscapes, you know, watching what's happening. Thie said it 77 Zita bites that was put up in the keynote. Came came from I D. C. Tells you you're focused. >> Yeah, so I'm part of the data protection and storage research team, But I have, ah, European focus. I covered the Western European markets where data protection is almost off a neurotic interest to us. So a lot of our investment is actually made on the context of data protection. And how do I become data driven without compromising on security and sovereignty and data locality. So that's something that I look at. I'm also part of our broader multi cloud infrastructure team on also develops practice. I'm looking at all these modern new trends from data perspective as well. So it's kind of nice being >> keeping you busy, huh? Yeah. So about a year ago, every show that I went to there would be a big clock up on the Kino stage counting down until gpr went way actually said on the Q. Many times it's like we'll know when GPR starts with lawsuits. Sister and I feel like it was a couple of days, if not a couple of weeks before some of the big tech firms got sued for this. So here we are 2019. It's been, you know, been a while now since since since this launch. How important is GDP are you know what? How is that impacting customers and kind of ripple effect? Because, you know, here in the States, we're seeing some laws in California and beyond that are following that. But they pushed back from the Oh, hey, we're just gonna have all the data in the world and we'll store it somewhere sure will protect it and keep it secure. But but But >> yeah, yeah, so it's suggestive. Here is a game changer and it's interesting you said this big clock ticking and everybody has been talking about it. So when the European Commission >> announced repairs >> coming, organizations had about two years to actually prepare for it. But there were a lot of naysayers, and they thought, This is not gonna happen. The regulators don't have enough resources to actually go after all of these data breaches, and it's just too complicated. Not everyone's going complaints just not gonna happen. But then they realised that the regulators we're sticking to it on towards the end. Towards the last six months in the race to GDP, and there was this helter skelter running. Their organizations were trying to just do some Die Ryan patch of exercise to have that minimum viable compliance. So there they wanted to make sure that they don't go out of business. They don't have any major data breaches when Jean Pierre comes a difference that that was the story of 2018 although they have so much time to react they didn't on towards the end. They started doing a lot of these patch up work to make sure they had that minimum by the compliance. But over time, what we're seeing is that a lot off a stewed organizations are actually using GDP are as to create that competitive differentiations. If you look at companies like Barclays, they have been so much on top of that game on DH. They include that in their marketing strategies and the corporate social responsibility to say that, Hey, you know our business is important to us, but your privacy and your data is much more valuable to us, and that kind of instantly helps them build that trust. So they have big GDP, our compliance into their operations so much and so well that they can actually sell those kind of GPR consultancy services because they're so good at it. And that's what we are seeing is happening 2019 on DH. Probably the next 12 to 18 months will be about scaling on operational izing GDP are moving from that minimum viable compliance. >> Its interest weighed a conversation with Holly St Clair, whose state of Massachusetts and in our keynote this morning she talked about that data minimalist. I only want as much data as I know what I'm going to do. How I'm goingto leverage it, you know, kind of that pendulum swing back from the I'm goingto poured all the data and think about it later. It is that Did you see that is a trend with, you know, is that just governments is that, you know, you seeing that throughout industries and your >> interesting. So there was seven gpr came into existence. There were a lot of these workshops that were happening for on for organizations and how to become GDP. And there was this Danish public sector organization where one of the employees went to do that workshop was all charged up, and he came back to his employer and said, Hey, can you forget me on it Took that organization about 14 employees and three months to forget one person. So that's the amount of data they were holding in. And they were not dilating on all the processes were manual which took them so long to actually forget one person on. So if you don't cleanse a pure data act now meeting with all these right to be forgotten, Andi, all these specific clauses within GPR is going to be too difficult. And it's going to just eat up your business >> tryingto connecting the dots here. One of the one of the big stumbling blocks is if you look at data protection. If I've got backup, if I've got archive, I mean, if I've taken a snapshot of something and stuck that under a mountain in a giant tape and they say forget about me Oh, my gosh, Do I have to go retrieve that? I need to manage that? The cost could be quite onerous. Help! Help us connect the dots as to what that means to actually, you know, what are the ramifications of this regulation? >> Yeah, So I think so. Judy PR is a beast. It's a dragon off regulations. It's important to dice it to understand what the initial requirements are on one was the first step is to get visibility and classified the data as to what is personal data. You don't want to apply policies to all the data because I might be some garbage in there, so you need to get visibility on A says and classified data on what is personal data. Once you know what data is personal, what do you want to retain? That's when you start applying policies too. Ensure that they are safe and they're anonymous. Pseudonym ized. If you want to do analytics at a later stage on DH, then you think about how you meet. Individual close is so see there's a jeep airframe, but you start by classifying data. Then you apply specific policies to ensure you protect on back up the personal data on. Then you go about meeting the specific requirements. >> What else can you tell us about kind of European markets? You know, I I know when I look at the the cloud space, governance is something very specific to, and I need to make sure my data doesn't leave the borders and like what other trends in you know issues when you hear >> it from Jenny Peered forced a lot ofthe existential threat to a lot of companies. Like, say, hyper scale. Er's SAS men does so they were the first ones to actually become completely compliant to understand their regulations, have European data data hubs, and to have those data centres like I think At that time, Microsoft had this good good collaboration with T systems to have a local data center not controlled by Microsoft, but by somebody who is just a German organizations. You cannot have data locality more than that, right? So they were trying different innovative ways to build confidence among enterprises to make sure that cloud adoption continues on what was interesting. That came out from a research was that way thought, Gee, DPR means people's confidence and cloud is going to plunge. People's confidence in public cloud is going to pledge. That didn't happen. 42% of organizations were still going ahead with their cloud strategies as is, but it's just that they were going to be a lot more cautious. And they want to make sure that the applications and data that they were putting in the cloud was something that they had complete visibility in tow on that didn't have too much of personal data and even if it had, they had complete control over. So they had a different strategy off approaching public cloud, but it didn't slow them down. But over time they realised that to get that control ofthe idea and to get that control of data. They need to have that multiple multi cloud strategy because Cloud had to become a two way street. They need to have an exit strategy. A swell. So they tried to make sure that they adopted multiple cloud technologies and have the data interoperability. Ahs Well, because data management was one of their key key. Top of my prayer. >> Okay, last question I had for you. We're here at the active you event. What? What do you hear from your customers about Octavio? Any research that you have relevant, what >> they're doing, it's going interesting. So copy data management. That's how active you started, right? They created a market for themselves in this competition, a management and be classified copy data management within replication Market on replication is quite a slow market, but this copy data management is big issue, and it's one of the fastest growing market. So So So they started off from a good base, but they created a market for themselves and people started noticing them, and now they have kind of grown further and grown beyond and tried to cover the entire data management space. Andi, I think what's interesting and what's going to be interesting is how they keep up the momentum in building that infrastructure, ecosystem and platform ecosystem. Because companies are moving from protecting data centers to protecting centers of data on if they can help organizations protect multiple centers of data through a unified pane of glass, I have a platform approach to data management. Then they can help organizations become data drivers, which gives them the competitive advantage. So if they can keep up that momentum there going great guns, >> Thank you so much for joining us in Cheshire, sharing the data that you have in the customer viewpoints from Europe. So we'll be back with more coverage here from Active EO data driven 2019 in Boston. Mess fuses on stew Minimum. Thanks for watching the Q. Thank you.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas, Lisa Martin. With the cue, we're live Day one of our duel set coverage of Del Technologies World twenty nineteen student a menace here with me, and we're welcoming back a couple of alumni. But for the first time together on our set, we've got Terry Pellegrino, the BP of high performance computing at Delhi Emcee and Nick, who grew VP of Data Analytics and Cyber Securities just at MasterCard. Did I get that right? All right, good. So, guys, thanks for joining Suited me this afternoon, by the way. So we will start with you High performance computing. Talk about that a lot. I know you've been on the Cube talking about HPC in the Innovation lab down in in Austin, high performance computing, generating a ton of data really requiring a I. We talk a lot of it II in machine learning, but let's look at it in the context of all this data. Personal data data from that word, you know, it turns out do with mastercard, for example How are you guys working together? Dell Technologies and MasterCard to ensure that this data is protected. It secure as regulations come up as fraud, is a huge, expensive >> issue. Well, I think make way worked together to really well worry about the data being secure, but also privacy being a key item that we worry about every day you get a lot of data coming through, and if we let customer information or any kind of information out there, it can be really detrimental. So we've really spent a lot of time not only helping manage and worked through the data through the infrastructure and the solutions that we've put together for. For Nick, who also partnered with the consortium project that got started Mosaic Crown to try to focus even more on data privacy on Mosaic Crown is is really interesting because it's getting together and making sure that the way we keep that privacy through the entire life cycle of the data that we have the right tools tio have other folks understand that critical point. That's that's how we got all the brains working together. So it's not just Delon DMC with daily emcee and MasterCard It's also ASAP We have use of Milan, you're sort of bergamot and we'Ll solve the only three c and all together back in January decided to get together and out of Nick's idea. Think about how we could put together with all those tools and processes to help everybody have more private data. Other. >> I think this was your idea. >> I can't say it was my idea. The European Union itself with what? The advent of Judy parent privacy. Their biggest concern was we don't want people to stop sharing. Data began with artificial intelligence. The great things that we do with it from the security, you know, carrying diseases all the way through, making sure transactions are safe and secure. Look, we don't want people to stop our organizations to stop sharing that data because they have fear of the regulations. How do we create a date on market? So the U has something called Horizon twenty twenty on one of their initiatives. Wass Way wanted to understand what a framework for data market would look like where organizations can share that data with confidence that they're complying to all the regulations there, doing the anonymous ization of that data, and the framework itself allows someone to say, I could do analysis without worrying that if it's surfacing personally identifiable information or potentially financial information, but I can share it so that it can progress the market data economy. So as a result of that, what we did is we put the guilt. I said, This is a really good idea for us. Went to the partners at del. That's it, guys, this is something we should consider doing now. Organization always been looking at privacy, and as a result, we've done a very good job of putting that consortium together. >> So, Nick, we've talked with you on the Cuba quite a few times about security. >> Can you just give >> us? You know, you talked about that opportunity of a I We don't want people to stop giving data in. There was concerned with GPR that Oh, wait, I need you to stop collecting information because I'm going to get sued out of existence. If it happened, how do we balance that? You know, data is the new oil I need, you know, keep not flowing and oh, my God. I'm going to get hacked. I'm going to get sued. I'm going to have the regulation, You know, people's personal information. I'm goingto walk down the grocery store and they're going to be taking it from me. How do we balance that? >> Well, the nice part is, since State is the new oil, well, we considered it is artificial intelligences that refinery for that oil. So, for our perspective, is the opportunity to say we can use a eye to help. Somebody says, Hey, I don't want you to share my data information. I want to be private, but I can use a I d. S. Okay, let's filter those out so I can use a I'd actually sit on top of that. I can sit down and say, Okay, how do I keep that person's safe, secure and only share the necessary data that will solve the problem again, using artificial intelligence through different types of data classifications, whoever secure that data with different methods of data security, how we secure those types of things come into play. And again, there's also people say, I don't ever want my data to be we identified so we can use different methods to do complete anonymous ation. >> How do you do that when there are devices that are listening constantly, what Walmart's doing? Everybody that has those devices at home with the lady's name. I won't say it. I know it activates it. How How do you draw the line with ensuring that those folks that don't want certain things shared if they're in the island Walmart talking about something that they don't want shared? How do you facilitate that? >> Well, part of that is okay. At a certain point, when it comes to privacy, you've gotta have a little bit of parenting. Just because you have that information doesn't mean you need to use that information. So that's where we as humans have to come into play and start thinking about what is the data that we're collecting And how should we use that information on that person and who is walking through a store? And we say we are listening to what their conversations are? Well, I don't need to identify that you or you. I just didn't know what is the top talking about? Maybe that's the case, but again, you have to make that decision again. It's about being a parent at this point. That's the ethical part of data which we've discussed on this program before. Alright, >> so teary. Talkto us some about the underlying architecture that's going to drive all of this. You know, we we love the shift. For years ago, it was like storing my data. You know, Now we're talking about how do we extract the value of the data? We know data's moving a lot, So you know what's changing And I talk every infrastructure company I talked to, it's like, Oh, well, we've got the best ai ai, you know, x, whatever. So you know what kind of things should custom be looking for To be able to say, Oh, this is something, really. It's about scale. It's about, you know, really focused on my data. Yeah, absolutely. Well, I will say first, the end of underlying infrastructure. We have our set of products that have security intrinsic in the way they're designed. I really worry about ki management for software we have silicon based would have trust throughout a lot of our portfolio. We also think about secure supply chain, even thinking through security race. If you lose your hard drive on, we can go and make sure that the data is not removed. So that's on the security front. On the privacy side, as a corporation, William C. Is very careful about the data that we have access to on. Then you think about a HBC. So being in charge of H. P. C for Cordelia emcee way actually are part of how the data gets created, gets transferred, gets generated, curated and then stored. Of course, storage s O. What we want to make sure is our customers feel like where that one company that can help them through their journey for their data. And as you heard Michael this morning during keynote, >> uh, getting that value out of the data because it's really where that little transformation is going to get everybody to the next level. But right now there's a lot of data. Has Nick stated this data has more personal information at times? Andan i'll add one more thing way. Want to really make sure that innovation is not stifled and the way we get there is to make sure >> that the data sets are as broad as possible, and today it's very difficult to share data. Sets mean that there are parts of the industry there are so worried about data that they will not even get it anywhere else than their own data center and locked behind closed doors. But if you think about all the data scientists, they're craving more data. And the way we can get there is with what make it talked about is making sure that the data that is collected is free of personal information and can still be qualified for some analysis and letting all the data scientists out there to get a lot of value out of it. >> So HBC can help make the data scientist job simpler or simplify evaluating this innumerable amun of data. >> Correct. So what in the days you had an Excel spreadsheet and wanted to run and put the table on it, you could do that on a laptop for end up tablet. When you start thinking about finding a black hole in the galaxy, you can do that on tablet. So you're gonna have to use several computers in a cluster with the right storage of the right interconnect. And that's why it's easy comes in place. >> I mean, if I man a tactical level, what you'LL see with HBC computing is when someone's in the moment, right? You want to be able to recognize that person has given me the right to communicate to them or has not given me the right to communicate to them, even though they're trying to do something that could be a transaction. The ability to say Hey, I have I know that this person's or this device is operating here is this and they have given me these permissions. You've got to do that in real time, and that's what you're looking for. HBC competing to do. That's what you're saying. I need my G p you to process in that way, and I need that cpt kind of meat it from the courts. The edges say Yep, you can't communicate. No, you can't. Here's where your permissions like. So, >> Nick, what should we >> be looking for? Coming out of this consortium is people are watching around the industry. You know what, what, what >> what expect for us? The consortium's about people understand that they can trust that they're data's being used properly, wisely, and it's being used in the way it was intended to be used so again, part of the framework is what do you expect to do with the data so that the person understands what their data is being used for the analysis being done? So they have full disclosure. So the goal here is you can trust your data's being used. The way was intended. You could trust that. It's in a secure manner. You can trust that your privacy is still in place. That's what we want this construction to create that framework to allow people to have that trust and confidence. And we want the organization to be able to not, you know, to be able to actually to share that information to again move that date economy forward. >> That trust is Nirvana. Well, Nick Terry, thank you so much for joining suing me on the cue this afternoon. Fascinating conversation about HPC data security and privacy. We can't wait to hear what's in store next for this consortium. So you're gonna have to come back. Thank >> you. We'LL be back. Excellent. Thanks so much. >> Our pleasure. First Minutemen, I'm Lisa Martin. You're watching us live from Las Vegas. The keeps coverage of day one of del technology World twenty nineteen. Thanks for watching

>> Live from Stanford University, it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE I'm Lisa Martin. We've been live all day at the fourth annual Women in Data Science Conference. I'm with John Furrier, John, this is not just WiDS fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Gerritsen, Co-Founder stopped by this afternoon and was chatting with me saying, there's over 20,000 people they expect today just to watch the WiDS livestream from Stanford. Another 100,000 engaging in over 150 regional WiDS events, and 50 countries, CUBE's been there since the beginning tell us a little bit about that. >> Well what's exciting about this event is that we've been there from the beginning, present at creation with these folks. Great community, Judy Logan, Karen Matthys, Margot. They're all been great, but the vision from day one has been put together smart people, okay, on a stage, in a room, and bring it, syndicate it out to anyone who's available, meet ups and groups around the world. And if you bet on good content and quality people the community with self-form. And with the Stanford brand behind it, it really was a formula for success from day one. And this is the new model, this is the new reality, where, if you have high quality people in context, the global opportunity around the content and community work well together, and I think they cracked the code. Something that we feel similar at theCUBE is high quality conversations, builds community so content drives community and keep that fly wheel going this is what Women in Data Science have figured out. And I'm sure they have the data behind it, they have the women who can analyze the data. But more importantly is a great community and it's just it's steamrolling forward ahead, it's just great to see. 50 countries, 125 cities, 150 events. And it's just getting started so, we're proud to be part of it, and be part of the creation but continue to broadcast and you know you're doing a great job, and I wish I was interviewing, some of the ladies myself but, >> I know you do >> I get jealous. >> you're always in the background, yes I know you do. You know you talk about fly wheel and Margot Gerritsen we had her on the WiDS broadcast last year, and she said, you know, it's such a short period of time its been three and a half years. That they have generated this incredible momentum and groundswell that every time, when you walk in the door, of the Stanford Arrillaga Alumni Center it's one of my favorite events as you know, you feel this support and this positivity and this movement as soon as you step foot in the door. But Margot said this actually really was an idea that she and her Co-Founders had a few years ago. As almost sort of an anti, a revenge conference. Because they go to so many events, as do we John, where there are so many male, non-female, keynote speakers. And you and theCUBE have long been supporters of women in technology, and the time is now, the momentum is self-generating, this fly wheel is going as you mentioned. >> Well I think one of the things that they did really well was they, not only the revenge on the concept of having women at the event, not being some sort of, you know part of an event, look we have brought women in tech on stage, you know this is all power women right? It's not built for the trend of having women conference there's actual horsepower here, and the payload of the content agenda is second to none. If you look at what they're talking about, it's hardcore computer science, its data analytics, it's all the top concepts that the pros are talking about and it just happens to be all women. Now, you combine that with what they did around openness they created a real open environment around opening up the content and not making it restrictive. So in a way that's, you know, counter intuitive to most events and finally, they created a video model where they livestream it, theCUBE is here, they open up the video format to everybody and they have great people. And I think the counter intuitive ones become the standard because not everyone is doing it. So that's how success is, it's usually the ones you don't see coming that are doing it and they think they did it. >> I agree, you know this is a technical conference and you talked about there's a lot of hardcore data science and technology being discussed today. Some of the interesting things, John, that I really heard thematically across all the guests that I was able to interview today is, is the importance, maybe equal weight, maybe more so some of the other skills, that, besides the hardcore data analysis, statistical analysis, computational engineering and mathematics. But it's skills such as communication, collaboration collaboration was key throughout the day, every person in academia and the industry that we talked to. Empathy, the need to have empathy as you're analyzing data with these diverse perspectives. And one of the things that kind of struck me as interesting, is that some of the training in those other skills, negotiation et cetera, is not really infused yet in a lot of the PhD Programs. When communication is one of the key things that makes WiDS so effective is the communication medium, but also the consistency. >> I think one of the things I'm seeing out of this trend is the humanization of data and if you look at I don't know maybe its because its a women's conference and they have more empathy than men as my wife always says to me. But in seriousness, the big trend right now in machine learning is, is it math or is it cognition? And so if you look at the debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of thought where it's all math all math, and then you have, you know kind of another school of thought where learning machines and unsupervised machine learning kicks in. So, machines have to learn, so, in order to have a humanization side is important and people who use data the best will apply human skills to it. So it's not just machines that are driving it, it's the role of the humans and the machines. This is something we have been talking a lot in theCUBE about and, it's a whole new cutting edge area of science and social science and look at it, fake news and all these things in the mainstream press as you see it playing out everyday, without that contextual analysis and humanization the behavioral data gets lost sometimes. So, again this is all data, data science concepts but without a human application, it kind of falls down. >> And we talked about that today and one of the interesting elements of conversation was, you know with respect to data ethics, there's 2.5 trillion data sets generated everyday, everything that we do as people is traceable there's a lot of potential there. But one of the things that we talked about today was this idea of, almost like a Hippocratic Oath that MDs take, for data scientists to have that accountability, because the human component there is almost one that can't really be controlled yet. And it's gaining traction this idea of this oath for data science. >> Yeah and what's interesting about this conference is that they're doing two things at the same time. If you look at the data oath, if you will, sharing is a big part, if you look at cyber security, we are going to be at the RSA conference this week. You know, people who share data get the best insights because data, contextual data, is relevant. So, if you have data and I'm looking at data but your data could help me figure out my data, data blending together works well. So that's an important concept of data sharing and there's an oath involved, trust, obviously, privacy and monitoring and being a steward of the data. The second thing that's going on at this event is because it's a global event broadcast out of Stanford, they're activating over 50 countries, over 125 cities, they're creating a localization dynamic inside other cities so, they're sharing their data from this event which is the experts on stage, localizing it in these markets, which feeds into the community. So, the concept of sharing is really important to this conference and I think that's one of the highlights I see coming out of this is just that, well, the people are amazing but this concept of data sharing it's one of those big things. >> And something to that they're continuing to do is not just leverage the power of the WiDS brand that they're creating in this one time of year in the March of the year where they are generating so much interest. But Margot talked about this last year, and the idea of developing content to have this sustained inspiration and education and support. They just launched a podcast a few months ago, which is available on iTunes and GooglePlay. And also they had their second annual datathon this year which was looking at palm oil production, plantations rather, because of the huge biodiversity and social impact that these predictive analytics can have, it's such an interesting, diverse, set of complex challenges that they tackle and that they bring more awareness to everyday. >> And Padmasree Warrior talked about her keynote around, former Cisco CTO, and she just ran, car, she's working on a new start up. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew it changed the architecture. Now mobile phones, that's changing the architecture. Now you have a global AI market, that's going to change the architecture of the solutions, and she mentioned at the end, an interesting tidbit, she mentioned Blockchain. And so I think that's something that's going to be kind of interesting in this world is, because there's, you know about data and data science, you have Blockchain it's the data store potentially out there. So, interesting to see as you start getting to these supply chains, managing these supply chains of decentralization, how that's going to impact the WiDS community, I'm curious to see how the team figures that out. >> Well I look forward to being here at the fifth annual next year, and watching and following the momentum that WiDS continues to generate throughout the rest of 2019. For John Furrier, I'm Lisa Martin, thanks so much for watching theCUBE's coverage, of the fourth annual Women in Data Science Conference Bye for now. (upbeat electronic music)

live from San Francisco it's the cube covering IBM thing 2019 brought to you by IBM good afternoon and welcome back to the cubes continuing coverage of IBM think 2019 I'm Lisa Martin and sake San Francisco with Dave Volante hey Dave hey Lisa we're staying dry though we are the most part exactly there are there looks like the Moscone notices maybe having a few little areas of improvement I think just running water through the pipes as we would say is a little trial that's true so we're welcoming back to the queue but guess that hasn't been with us for a while Paul is a couple of vice president of Big Data at cognitive systems at IBM Paul welcome back oh thank you and thanks for get my name right that was good so you are an accomplished author I talked to you on Twitter 19 books ever 350 articles I know you do a lot of speaking you've been IBM a long time this events massive great 30,000 people or so yesterday was standing room only in fact they shut the doors to Judy's keynote because there were so many people I'm curious some of the announcements that came out with cognitive yesterday what are some of what are some of the things that you saw yesterday that kind of piqued your interest well the Watson the Watson anywhere was I person have said that's a long time coming and they come on we got to have Watson on any cloud right not just the IBM cloud so that was I thought a big deal and then there were a bunch of announcements around enabling hybrid I think there were 20 plus services so you know it's not kind of vogue you know we're in this multi cloud world I need a way to get to hybrid so those are two standouts so your group's been busy basically that's right that's right I mean you hit it right Watson anywhere cloud everywhere so it's about AI in that drink I have to tell you that when I hear all the announcements there's tons of them right one of my favorite ones probably doesn't go as notice and it was Watson machine learning accelerator and that is really about looking at the journey for AI and clients over the next course of the years on that journey see most clients are just getting started there's some clients in the middle phase and there's some clients now that are hitting what I call the enterprise worthiness stage of AI right and so when we look at our announcements they're actually taking you from just getting started all the way to enterprise hardened explainable and algorithms and how to manage that because we're gonna go from this world where AI is sitting in the corner offices for the privileged few we have to democratize for the many but today it's like here's a little data science team they have their own server here's our programmer on their laptop you know and hanging out working there we want to bring this all together for enterprise so things like workload management which is what watching machine learning accelerated really does is how do I get everything together and working in a concurrent environment as organizations go from having 10 20 algorithms to trying to deploy thousands of them that's all they'll define themselves well you know when you get a bunch of data scientists in the room and you talk about citizens data scientists they kind of look at he like me there's no such thing but the fact is that if you can operationalize a you can open it up to a lot more people you know as a line of business person you'd much rather not have to go to a data scientist every time you want to do something with a because otherwise you're just kind of repeating the old decision-support world cells right what do you guys do when to operationalize yeah so it's a great question we're trying to taking the friction and so a lot of people will come and say oh gee p you acceleration so yeah it's about training stuff faster it's an open architecture and power and so you've seen the work with NVIDIA and that's unique to what Nvidia can do with with our cognitive systems is to accelerate the CPU GPU communications but there's a broader pipeline when you go to as the say I journey and we want to flatten that curve so one is how do I get up and running I don't know if you remember open source changes all the time so we're Enterprise hardening back testing getting you ready for here's the platform to deploy built on open source and where 80% of a data scientist time is spent right now is in what I call data preparation wrangling data labeling data gets stuff together now none of that is data science like none of that is data science at all and that's where the time and once I get the data ready I train the model ok so you've heard a lot about that and then the next thing I do is have to optimize the model so I think about where data scientist should be spending their time and that's on stage for we call that exploring the hyper parameter space another thing that Watson machine learning accelerator is all about how do we make the model perform now for data science geeks perform means how well is it classifying or how accurate is Hardware people often think performance means how fast you go right and then finally go to inference so we're looking at all five of those stages and one of them the biggest one is that 80% sink time we're trying to drop that to 20% and open it up for the rest of the enterprise so how do you democratize AI you mentioned that a lot of enterprises are really at the beginning of that journey yeah but when you're out talking with customers is there some sort of paralysis there where they're like Paul where do we start right right I think there's two areas where I see inertia or friction and so one is where do we start so let me say that start with the data you have you don't have to step up to the plate and hit a homerun you just get started and it's the things the little things you do every day not the big things you do once in a while and we always hear about disruption disruption you hear about uber and airbnb as the disruptors I actually believed they were the disruptors of yesterday I think right now we're in this list shift rift or cliff moment the disruptors of tomorrow will be those at the head of the analytics Renaissance that work with the data they have we know the outcomes we call that supervised learning and that's where you get started and the other piece is how do I get more people to participate talk about the lift shift rift or cliff intersection I saw that you've seen talked about that on social media can you break that down a little bit more and also talk to us about how you're helping customers actually kind of break through that or maybe it's avoid that altogether yeah well I mean you want to take two of those four and not take the other two right and I think that we do this lift if cliff moment in two ways one is as individuals so the people in the audience to people watching here all of us as practitioners we have got to get our skills moving forward I always say skill years are like dog years right like they age instantly and so you should be waking up every day like a newbie in this world and learning every single day and if you do that you'll have nothing to worry about as an individual and as organizations you had better put analytics at the forefront that means from the boardroom that means we encourage the culture of analytics everywhere and so those that's what I mean by lift chef rift or cliff moment so what comes back to sort of opening it up for average everyday line of business people you got a you got a demo yeah I'm gonna see what can I show to you all right so you know you were talking about the data scientist and citizen data scientist so I'm gonna propose to you this thing I call the wisdom of the crowd right today data scientists have to build things they're not domain experts imagine if I could invite the many to participate in this storyline and in this story line everyday line of business people could create an application based on an idea or a model and maybe we'd have thousands of them and out of those thousands we might vet I don't know 50 or hundred and out of that we would team up with data science deploy ten or twenty into production and then do the whole thing over again so let me show you how I could create this application here without building a single line of code and I actually use you Dave as an example because I wanted to see how much face time you get on the cube when John is up here with you doing this I get the short end of the stick the data tell the truth right so I had this intuition as a line of business user and I went to explore this so you can see here that we'll have two videos here and on the first video see where I put this here will say host screen time that's actually gonna measure the amount of time that you're on screen and I will be like that yeah and I actually built that in this modern way that democratizes for the many I'll just start it out here and on the bottom I built it the old-fashioned way so you can see we got John in there and they start out pretty good to start right there both recognizing both of them so let me go in pause these now the first thing you should notice is I've got a timer on the bottom I got a timer on the bottom cuz actually I had time to build that my dev ops team kind of put that in there for me so we'll continue this move it over here and let these things run now look at the accuracy of these models do you notice on the top you guys are both identified increasing this green counter and on the bottom I can't see you so in computer vision is very interesting if I wanted to teach a computer to tell me what the number eight was I could show it a picture of an eight but no more when I moved it sideways it would have no idea what it was I need to train it with lots and lots of data and so the bottom is the way the data scientists work so what did I have to do to do that I had to go collect some video had to reformat it had to put it down to a 480 and I had to write some code fire away and you see the code there now in order to get just to MVP so this model clearly doesn't score well Dave turns his head and it doesn't know who it is anymore all I said is your Dave Valente and if you're not then you're John so what do you do if you've got a third person in there all right and this is where we democratize it so this is our power I vision we've been talking a lot about this and I want to kind of invite everybody to take part in this kind of data science Renaissance all you do is you would go and upload some video here and you go capture some frames we could auto capture those frames every five seconds and let's say I wanted to add a new person like Arvin into this list here so I want to go develop and figure out how the algorithm can find out Arvin is now my last demo I showed you that was a linear classifier that wasn't easy here we'll go type in Arvind add Arvind and then I'm just gonna highlight it and box Arvind and now I've started to train the model there's no code at all you just train them all you just said this is Arvind when I see this so I'm leaving the model and then I'd have to go set it off to training and I'll look I'll do one other thing for you here I'll go and say well here's the think logo and maybe I want to track some logo detection that's it that's how I built the model now it's all about how much supervised label data you have so I asked I said who are the disruptors of the future and it's all about the compute power and the workload management power to train this stuff so economy systems is really all about both so we obviously know about the power in the workload management how do I go and actually generate the data so once I train this model I could click auto label it'll actually go through the rest of the video and go and find out from what it saw but here's where things get beautiful and everything I've showed you is someone writing lines of code now replaced with a clicker so I click on mint data we call these morphological operations I want you to notice something we have a hundred nineteen images labeled of Dave and John so as I click here I'm gonna apply these morphological operations Gaussian blurs sharpening blur that all means stuff to data scientists now I have four thousand two hundred and forty nine data points and I will generate that automatically that's all driven by line of business and finally we can come over here and go actually look at the model here's my model this model is actually scoring pretty really well but even if it wasn't scoring well and that's seventy percent this is now when I pass it to the data scientist team to do what their exceptional at the the hyper parameter tuning for the performance score the algorithm and so here I'll just finish this off by I think I had a picture of you I'll just drag it in here and now it's actually going out and scoring it we're scoring at 96% okay accuracy and I can expose this as a rest of API with the click of a button so I just have one thing the way I found out with the AI for you Dave at the end of it from what I can see John is getting about 50% more screen time than you and it's all good actually yeah oh you thought it was worse and if you notice your name here is Dave dapper Volante because we can't help but notice funny we can't even always tell well-dressed you a scientist you're well-dressed and it's pretty accurate but you're not getting the ROI on those outfits that you need for screen time that's what we found with it stuff with my business partner John but that's that's pretty good now you're saying you wrote the code right to identify either John or Dave and and at what point did you bring the data scientist in yeah so I didn't write any code on the top right on the bottom which the model did not perform well when he turns Ivy conceived that's the code we wrote now would take iterations iterations there was no code written there we built the model and then we brought a dev person in to try to build us a timer it was a couple lines of code took him about half an hour and in this case I didn't really bring the data scientist in yet because I'm scoring at 96% but I can easily pass it on into workflow and that's the story it's a pipeline workflow across so I'll pull the data scientists and I need to but 96% accuracy without a data scientist presence pretty good so a more complex use case you know you might not get 96 percent accuracy you might be at 50 percent forty percent more than 70 percent now you bring the data scientists in for the last mile absolutely let's say I was only scoring 50% and you don't think that's impressive I think it's pretty impressive that I did that in a half an hour and now this is engineer from the wisdom of the crowd I'm a line of business user and I'd like to know what kind of screen time you're getting maybe that's not a sporting event and I'd actually like a new business model where I charge Toyota by the second that they show up on the screen that's my idea data scientist never gonna think that I get it started and then they join the Renaissance that's how you democratize AI for the money yeah so maybe you could talk a little bit about how what was the compute power behind this the infrastructure behind this and then maybe we could talk about power and how you're applying that for AI infrastructure yeah that's a great great question so the bottom video actually trained on my laptop it ran for about a day and a half just so you know who's saying it is my laptop on the top of the video we actually leveraged our para AI architecture and ran that through with Watson machine learning accelerator and I gotta tell you the models train in about 30 minutes and in fact we had trained a model on your last show with your last guest in the amount of time it when you finish to when I came on stage 20 loads yeah so I mean that's the that's the accelerated compute and it's not and I hope what you're seeing here this isn't just a hardware component tree story this is a kind of coexistence in an almost synergy of software and hardware together and that's what's needed in the AI era well it's interesting I know when when you guys change the name of the you know power systems group to cognitive systems they had you know and I inferred of course we got a guy running it who used to run the software business so the different software component so it is clearly more than than software what are some of the sort of more interesting use cases that you guys are seeing with with clients specifically in terms of operationalizing this yeah for sure so in use cases of AI is I think it's we're in this world of precision so we're in precision agriculture precision risk or underwriting precision finance precision retail so the use cases are everywhere and it's really taking in all this kind of data in the operationalizing I think that we're helping people on all the levels you think about it I almost see three segments the first segment is we're not really sure what to do this AI and everyone says they're doing AI reminds me of the Hadoop days and the big data Lake and you know all that stuff turned out so how do we get you started so you can get down the path and build kind of MVPs and that's what I just showed you is the MVP the next group of people are the folks that have maybe one or two models deployed and now they're trying to say how do we scale out to hundreds and thousands of models what is the path now to make this bigger because we got it moving here and then the final phase with few people are at are those who are getting the challenge of I'm getting to a thousand algorithms deployed and now how do I get all this stuff running and so that entire path goes like this and our story line goes across that entire path how unique is this in the marketplace I'm interested in your commentary on IBM's competitive advantage is this so you guys have only guys who can do this and and how you know why are you winning in the market how how differentiable is this yeah so I think I'll answer that in two ways one is from the brand in which I participate in a larger company called IBM in terms of the acceleration there's nobody doing what we're doing and the reason is you took this kind of power processor and created the open power project and just like software evolved through open innovation that's what hardware is done so you look at Mellanox and Nvidia so I'll give you an example Dave the NV link exists on Intel and exists on power but they operate in two very different ways and nobody realizes that so envy link accelerates GPU to GPU communications does that an Intel does that on power but because of open power Envy link also allows the GPU to talk to the CPU so GPUs accelerate ai training because there's thousands of cores there right but they still got to talk to the CPU on top of that they don't have much memory so there's an example that's completely unique in the industry to make you train faster I think our workflow model is completely unique the tools that I showed you and around the workload management and then you look at the bigger part of IBM and how I can mix this with API calls to clouds clouds based Watson services or local but on top of that is now it's about how do you build the data that you can trust and how do you look at things like the explained ability of the model with their Watson open scale and that kind of stuff so it's a bigger story and nobody else has that end end story well and it's showing up in the in the in in the results we saw last quarter the your line of business was a bright star you know we're seeing some momentum obviously there's a lot of activity going on in Linux clearly you know cognitive is a big play there so congratulations that's it's exciting to see and of course maybe a lot of people don't realize it when you guys did the work to bring in little-endian compatibility and you know and tire you know software Suites now that it's you know it's not just this sort of niche proprietary platform anymore it's mainstream and so it's starting to show up in the business results so that's great to see yeah when I say democratize for the many I mean for the people for the enterprise and across the entire spectrum so well Paul thank you for confirming my suspicions here that John is my partner John Ferrier is sucking up all the camera time John I'm gonna have to elbow my way in a lot more so appreciate that having the data John's very data-driven so appreciate that yeah to have you on yeah as I see you again all right take deep right there everybody we'll be back with our next guest we're live from IBM think 2019 you're watching the cube

>> Announcer: Live from Las Vegas, it's theCube, covering IBM Think 2018. Brought to you by IBM. >> Welcome back to Las Vegas, everybody. We're here at the Mandalay Bay. This is theCube. And we have two days, sorry, three days of live wall-to-wall coverage of IBM Think 2018. My name is Dave Vellante. I'm here with my co-host Peter Burris. Steve Robinson is here; he's the general manager of client technical engagement for IBM, and he's joined by Bala Rajaramen, who's an IBM fellow, expert in Hybrid Cloud. (microphone feedback) Gentlemen, welcome to theCube. >> Oh, good, thanks for having us. Always a pleasure. >> Thank you. >> You're very welcome. So, Steve, let's start with you. >> Sure. >> We were talking off camera about some of the work that we've been doing in what we call true private cloud. You talked about some of the work you've done with BCG, your own internal work. What are you seeing in terms of private cloud and the resurgence of private cloud? >> You know, it's kind of fascinating. You know, over the past, probably two years, we started to see this kind of next definition of private cloud coming about, where most firms had spent a lot of effort on virtualizing their data center, building up these beautiful VMware firms, etc., and then this next level is, how can I start to do more cloud capability back behind the firewall? This notion of CaaS, container as a service, started showing up in RFPs. People wanted to say, hey, can Kubernetes come back as well, could I use private cloud as a parking lot for certain workloads, and could it possibly be the basis for doing true multi-cloud down the road where some of these environments may start landing both on private, multinode private, and even on public as well. So it's been a real resurgence from our side. >> So we made the observation several years ago with our research team that CIOs are realizing they couldn't just put their business into the public cloud. >> Right, right. >> Rather, they wanted the cloud experience and they wanted to bring that experience to their data wherever that lives. >> Exactly. >> So, Bala, what technical challenges does that bring and how do you guys solve them? >> Yeah, it's interesting because, I mean, when you look at cloud, it's about what makes something a cloud. And I think the two things that CIOs are struggling with, which is why public cloud was an attractor initially, was that easy self-service. I can get to things quickly from the business perspective, and I can manage it very consistently because everything works the same. And I think what Steve alluded to was when you bring the cloud to you, it's not just bringing the capability, but it's bringing the experience. And can people get to it easily? Can businesses be competitive in that environment? Can the operations guy manage that environment like they would manage something at a cloud scale? And that, essentially, was the challenges we had to solve, not just in moving things, but in moving all the right pieces around it so that it was a cloud, yeah. >> So, we talked with you about the whole concept is the cloud experience where the data warrants it. And as you said, it's not just bringing technology in, it's also bringing the entire operating model-- >> That's right. >> Of how the cloud works. IBM is a big company, has always been its first customer. What has IBM been learning as you become more of a cloud-first company, or a cloud-oriented company, and how are you bringing that to your customers? >> Well, definitely I think the key thing we've been doing has been in a, you know, spirit of transformation for the past three years, as well. One of the things we picked up critically when we started the private cloud effort is there's a dimension of having to fit in with what an enterprise has already. They've got a strong system management process in place, they've got ticketing, they've got their plasmas up on the hall showing the up time of their applications. The biggest challenge was when they were moving to public cloud, they were kind of giving that to the public cloud vendors and they were losing visibility in that as well. So part of this, we had to respect them to be able to allow them to see their applications, to be able to fit into their existing environments, and be able to fit into the process. We can't leave that system management team behind. >> And just to add to that, I think when you look at the evolution of things like microservices, you're breaking something that was intrinsically a whole and manageable as a whole, into a bunch of individual pieces. That challenge has always existed when you move from mainframes to distributed, because the management challenge more than anything else. You could build applications quickly, but it's really hard to manage them with microservices across multiple clouds, it's a fascinating exercise. So I think our learnings, to your point, was we have to think about it in a different way. Think about from an app-centric way, not from an infrastructure-centric way. And I think that's critical. >> I want to build on that for a second because Judy talked this morning, and we certainly would agree with the concept of your data as an asset. Are we really thinking apps-centric longer term, or data-centric longer term, and apps-centric is more how do we affect the transition because that's where the value proposition is today? >> Right, and that's where your assets are, right? >> Right. >> And your data becomes an integral part, an entangled part of it. As you split your applications, you're also looking at splitting your data, and how do you manage that? How do you manage where the data is placed? Manage where applications are placed? I think the true cloud value, going back to your question, is how does this multi-cloud universe around placement of data, placement of applications, security models, availability models, how does it all come together? And I think that's the biggest challenge, and I think we are doing some interesting work to address this. >> We almost view it always as kind of two planes at the same time. Where do we optimize the application based off of the performance characteristics, you know, how much compute do we need around it, you know if it's a very sophisticated investment banking, let's get that closer. We've even been running private cloud back on the mainframe, Kubernetes clusters back on the mainframe. But then the whole data story now with regulatory, with GDRP, etc., gives you another layer of complexity. So we almost have to look at what's the app doing, and then what's the data doing at the same time? >> You've kind of called it cloud your way. >> Yeah, right. (laughs) >> You used that statement a while back. And so we could define cloud a lot of different ways. We're talking about our data, you talked about some of your studies, and you show them, actually, the private cloud and the public cloud infrastructure's comparable in size. >> It's pretty close. Pretty close. >> We show private cloud smaller but growing twice as fast, so, okay. >> But we also call it two-prong cloud. >> Yeah, so we maybe have a different definition, but let's talk about the customer definition. >> Of course, yeah. >> A cloud is in the eye of the beholder-- >> Beholder, right, right, right. >> Beholder's the customer. So to me, it's about the business impact. Are they seeing an impact on agility? Is it changing their operating model? Because if it is, then it's going to have a bottom line impact, and if it's not, it's just a lift and shift on prem. What are you seeing in terms of the customers? >> Well, I think it's interesting, though, you used the term lift and shift. That's one of these, I call it an urban myth of cloud. Nothing is a lift and shift. >> Dave: Right. >> I think part of the challenge for us is could we bring some cloud attributes back behind, and what would that do for you? I know Bala mentioned self service. We, you know, some degree of horizontal scalability. We'll never have ultimate scalability like we have in the public cloud, but we can spin up multiple instances and start to manage pieces in a different way. The other area that we looked at that we had never thought about when we did our Bluemix local product, etc., could this be a path also for their middleware coming forward at the same time? Could we take this opportunity to start to containerize WebSphere, MQ, DB2, so that more workloads could move towards the cloud without having to have them be fully replaced and change up all the dependency chains, etc. So that's been a key thing, to pull the gravity of that middleware forward, while you kind of have it back on premise, as well. >> Yeah, absolutely. I think, going back to the lift and shift point, right, I mean, I think the traditional disadvantage of a lift and shift was you're moving your bad with your good, right? >> Right, right. >> And I think what this gives us in approach is how do you actually decouple that? Your applications are your crown jewels. You have invested a lot of effort over many years. What held you back was the processes you put around it that slowed you down. So being able to, to Steve's point, when you bring WebSphere, for example, onto a cloud platform, you minimize the risk, you enhance the value of building your application or moving your applications as is. That's a valuable lift and shift. But what you're not lifting and shifting is all of your processes, all the bureaucracy, all of the more traditional ways of doing things, and that combination, I think, is really the, to pick on your definition, is a true private cloud because it brings a customer-perceived value of, and a customer-perceived values risk. It is cost, and how do you optimize that? You're minimizing the risk, you're giving them a new operating model, a new self-service model, that takes away the bad and keeps the good. And I think that is, to me personally, I think that's a very exciting thing. >> Well one of the things that people always talk about when they talk about cloud is they talk about elasticity. >> Steve: Right. >> Great idea. But we like to talk about plasticity. >> Steve: Yes. >> Which is a different definition. Elasticity is same workload and scale, plasticity is the ability to consume, bring up, new workloads, do a better job of patches and updates. >> Steve: You got it. >> What do you think about that notion? At what point in time does the industry start to focus more on the fact that you can use cloud to fit your business differently? To snap your business into place differently, as a consequence of these services? >> That's a great insight, and it's one that I think most people just don't realize out of the gate that even bringing some of these cloud capabilities and also some of these more advanced container orchestration capabilities to all of their workloads gives them a lot more flexibility. We use a term pet versus cattle. You know, where in the old days, I would stand up, middleware stack, etc., and I would do everything to make sure that thing stood up, it was never impacted, etc. With some of the orchestration that we find in Kubernetes, I can stand up six versions of those. If one ends up getting knocked down, who cares? I can just automatically launch another one right back up. It changes the way how I manage that environment. It gives you more flexibility. It gives you more dynamic capability as to where I actually put individual pieces, even with my own infrastructure as well. So I think this could open a whole new era of how I manage. The plasticity; I like that idea as well. >> Yeah, that's a great word because I think when we started this discussion, I did not define cloud as being elastic for very much the same reason because from a business perspective, elasticity is a lower down function, or more of a second-order function. Being able to consume it easily, not be worried about how it's deployed-- >> It's a value proposition with a cloud guy's term. >> That's right, that's right. >> Exactly. And so plasticity's a much better word because that is a business impacting statement, which is, all to the point, which is I can deploy it. I can remove it. I'm not locked into particular things. I can evolve it very quickly. I think you're absolutely right and I think it's different. >> So speaking of the cloud guys, I got to ask you. So if the cloud guys were here, the public cloud pure plays wheel, they would say, oh, IBM, and we get this all the time with our true private cloud, that's old-guard thinking. >> Sure. >> Okay, so what we're doing is changing the world, what they're doing is trying to put a little, you know, lipstick on virtualization. How would you respond? >> If you look at the workloads that a typical enterprise, now, trust me, if I was building greenfield applications or doing a brand-new start-up with my BC money, etc., boom, if I had the chance, I would put it on public and run right away. A lot of flexibility, etc., with that. But the enterprises that we've worked with, I tend to say that most of the ones where we come in and we evaluate large number of workloads, you know, we just did one with a bank. We evaluated 900 different workloads. 15% met their regulatory and their risk policy and could move to the public cloud. That leaves 85% that are either going to stay in their legacy state, or are not going to start taking advantage of some of the cloud concepts we have. So, yeah, you've got to come back behind. And I think if you look at the public vendors, they're trying desperately to either send hard drives down or send appliances down. They understand they're going to have to extend down so that they can bring more workloads the right direction here. >> Of course. >> Now, we're advising our clients to focus on what's their value proposition, what activities are most important, what data's required to perform those activities. >> Steve: We say right cloud for the right workload. >> Yeah, and the question with data is latency, regulatory, and IP protection. Does that resonate with you guys? >> Yeah, that resonates very well. And I think, to me, we are trying to impose a strategy on a current state of the universe. So I think we are arguing whether public cloud is the right answer, or private cloud is the right answer, based on how we perceive private and public today. I think it's, in the next 10 years, you're not going to be able to tell the distinction. I mean, it's going to be more like a central office model where you have the core switches, you're going to have distributed switches. That is the cloud. And who manages what, how you delegate it, multiple providers, cross-provider billing, it's going to become a fabric. Then I can't tell the difference between what's public and what's private. I mean, I have the boundaries well-defined. And so, I think I view that as the eventual strategy, and I think we are now predicting a future that we are just guessing. >> Does that suggest, Bala, then, that the capabilities of the on-prem services are going to be substantially similar to what you see in the public? Do you guys benchmark yourselves against your IBM cloud brethren and have a little healthy internal competition, or? >> No, it is contextual. So if you take something very complex like weather, where it is gathering data from a whole bunch of sources, it makes almost no sense to have something that's local. But if you look at some of the other services, even things like machine learning and so on and so forth, there are some that make perfect sense on a cloud. There's things that make sense on closer to the data, on premise. But what's going to be more interesting is how they work together. And over time, you're going to see the programming model evolve to eliminate the distinction between what is private and what's public. And you're going to see an operational model evolve with the right delegation and controls that wipes out the distinction. In 10 years, I think we are not going to be having this discussion of private versus public. It is going to be a cloud with private components, with public components, and the ability for, from a business perspective, for a client to manage it in the right way. >> So things like, sorry, Peter, things like functional programming models will be pervasive. >> That's correct. >> And it will be up to the client to choose which, where their data is, essentially, is going to dictate what they use and what-- >> Well, and I think-- >> The business requires. >> We envision today where it's almost done on a dynamic basis, you know, where you're really to the point where I may have a load that's based on CPU, etc., running predominantly in the private cloud. Then we have a bursting scenario, actually be able to pick that container up and dynamically move it up to public as need be. As my risk and compliance rules begin to change, I could dynamically say, the same application, these three we're running here today, let me do a distributed, distribution of those as well. Not heavy lifting. >> Really quickly, so we're going to focus more on how you get value out of your data where the infrastructure's not the issue, and even the applications are less of an issue. One quick question though. >> Steve: Yeah? >> So we talk about, we talk about self-service, we talk about rolling updates, we talk about new maintenance styles, all associated with the cloud. What about metering? What about pay as you go? At what point in time does pay as you go start to really hit private cloud options? >> Sure, sure. >> I think it'll hit it sooner than later, but I think what's going to be interesting is the economics of it. >> Yeah. >> I think there's a supposition that pay as you go is a better model from an economic perspective. Not always. It depends on the duty cycle of your workloads. We are seeing movement where, when the workload is variable, that pay as you go model is, is a better fit. When things get where you can actually understand the application, optimize the application, optimize the infrastructure behind the application, a different model which is-- >> But doesn't it make sense to give the customer options? >> Yes, it does. >> Of course you do. But I think we always talk about clouded option and cloud transformation. There's both a technical piece and there's a cultural piece as well. I had Forrester on stage with me yesterday, and I said, "What is the one thing that "enterprises have to get right with cloud in 2018?" He said, "Procurement." And I can recall a CIO asking me one time if I could sell him compute by the nanosecond. I said, "Can you buy compute by the nanosecond?" And he said, "Touche." (all laughing) They are used to buying in big blocks in certain circumstances. They're used to the enterprise license in certain instances. So that's going to have to show as much change as, you know, we could do fine-grain billing today. Does it match, and does it fit the need? >> All right, we've got to go, but Steve, I want to give you the last word. We really didn't talk much about Cloud Private, which is your sort of branding and your offering. Maybe you could give us a little commercial on that? >> Sure. Yeah, we launched this last year, early November. We did IBM Cloud Private. So what we did is we took a core Kubernetes base, we extended it with some other compute models, you'll see Cloud Foundry in there, you'll see VM's in there as well. We took our middleware, we did a full containerization of it so you'll see a lot of rich stack of our middlewares, and then you see this automation layer's on top of it, our processes, etc., to kind of help you manage that overall environment. It's gone gangbusters. In just two months we had over 150 of our large enterprise clients. We got some of the great ones here with Hertz, MRN, etc., and getting great value out of it already. So we're very positive. Getting a lot of great press off of it, and we got a sales team extremely excited about it as well. >> Okay, Steve, Bala, great discussion as always. Really appreciate you guys coming on theCube. >> Oh, always great. >> Have a good rest of Think. >> Well, thank you again. >> Thank you, guys. >> We appreciate theCube. >> All right, keep it right there, everybody. We'll be back with our next guest right after this short break. You're watching theCube live from IBM Think 2018. Be right back. (electronic music)

>> Narrator: Live from Stanford University, in Palo Alto California, it's The Cube, Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube, our continuing coverage of Women in Data Science 2018 continues. I'm Lisa Martin, live from Stanford University, and very excited to be joined by our Co-founder, Co-CEO of SiliconANGLE Media and The Cube, John Furrier. John, what an amazing event, the 3rd Annual WiDS event, the third time The Cube has been here, this event, the energy, the momentum, the excitement, you can feel it. >> I really wanted to interview with you all day, but I wanted to make sure that we had the right women in tech, women in data science. (Lisa laughs) You're an amazing host. I thought it was awesome. What a great powerhouse of women. It's just such an honor for The Cube team and SiliconANGLE to be here. We're listed as a global innovative sponsor on there, so it's like the recognition because they have high integrity. The organizers, Judy, Karen, and Margot, when we first met, when they first started, this "Can you bring The Cube?", of course we will! Because we knew the network effect was big here. They were early on, and they took a great approach. They really nailed the positioning of the event. Use Stanford University as a base, establish a global community, which they have now done. It is so successful, this is the future of events, in my opinion. The way they do it, the way they bring in the content curation here at Stanford, but it's open, it's inclusive, they created a network effect with satellite communities around the world. They've created a VIP network of power women, and it's a shortcut to trust. This is the trusted network of women in data science. It's super exciting. I'm so proud to be part of it in a small way. They get all the credit, but just capturing all the data, the interviews are great data. You've done a great job. The conversations were amazing. The hallway conversations went great. It was just fantastic. >> Yeah it was fantastic, and thank you for handing the keys to The Cube to me for this event. The remarkable thing-- One of the remarkable things to me about this event is that they have, in third year, they're going to reach 100,000 people with this event. There were 177 regional events in the last 24 hours, #WiDS2018, in 53 countries. And we were fortunate to have Margot Gerritsen on a few hours ago, and I said, "You must be pleasantly shocked at this massive trajectory, "but where do go from here?" "Sustaining, maintaining, but also reaching out," she said, "to even younger audiences in high schools "and being able to ignite the bunsen burner, "turn it up a little bit higher." What were some of the hallway conversations that you had? >> Well I think the big thing was is that, first of all, the panels on the conversation of the content was not about women, it was about data science, that happen to be women. >> Yes. So the quality of the conversations, if you close your eyes, you'll be like, "There are some serious pros on here". And they had some side discussions around how to be a woman in tech and data science, and how to use your integrity and reputation, but the content program was top-shelf. I mean, it was fantastic, so that was equalizing. The hallway conversations was global. I heard about global impact, I heard that data science is very mission-driven. And you're seeing a confluence of technology and innovation with technology like data analytics, data science, fueling mission-driven, so standard run your business on analytics, but now run society on analytics. So you're seeing a global framework developing around mission-driven, you'll hear the word "impact" a lot, and it was not just speeds-and-feeds data science, although they're plenty to geek out about, but it was more of a higher level order bit around mission, and society. So this is right around what we're seeing at The Cube around cloud computing, cryptocurrency and blockchain, that you're seeing a democracy being rewritten with technology. Data's the new oil. Oil's power in the new global economy, and you're seeing that in all kinds of decentralized forms of blockchain and cryptocurrency, you're seeing businesses transform with data science, so with that comes a lot of responsibility. So, ethics conversation in the hallway. I felt like I was at a TED talk, meets World Economic Forum, meets Stanford Think Tank, meets practitioner. It was like, really exciting. >> And they had keynotes, which we had a few on some tech tracks, and a career panel. Did you get to listen to the career panel? >> John: The career panel was interesting and I'd love to get your thoughts on some of your interviews that crossover, because it was really more about being proud and high integrity. So the word "democratization" came up, and the conversations in the audience when they had the Q&A was, "Isn't it more about respect?", democratization, not that there's anything wrong with that, but "Isn't it about integrity? "What is the integrity of us as a community, "as women in data science, what is the respect, "integrity, and mission of the role?" Of course democratization is a side effect of good news data, so that was super exciting. And then also, stand up, never give up, never worry about the failure, never worry about getting in a blocker, remove that blocker or as Teresa Carlson at Amazon would say. So there was definitely the woman vibe of "Listen, don't take things lying down. "Have a tough skin. "Take names and kick butt, but be proud." >> That's where a lot of the, when I'd ask some of our guests, "What advice would you give your younger self?" and a lot of them said the same thing, of "Don't be afraid to get out of your comfort zone". My mentor says, "Get comfortably uncomfortable." I think that's pretty hard for a lot-- If I look back at myself 20 years ago I wouldn't have been able to do that. It took a mentor, and just as Maria Klawe has said on The Cube before, the best time to reach and inspire the next generation of females to go into STEM is first semester yoo-nuh-ver-zhen, that's exactly when it happened for me and I didn't plan it, but it took someone to kind of go like Maria said this morning, "Don't be focused "on the things you think you're not good at." So that "failure is not a bad F word" was a theme that we heard a number of times today, and I think, incredibly important. >> And the tweets I tweeted out but it was kind of said differently, I don't know the exact tweet, but I'd kind of paraphrase it by saying Maria from Harvey Mudd said, "Look it, there's plenty of opportunities "in data science, go there." And she compared and contrasted her journey in a male-dominated world with "Look, if you're stuck or you're in a rut, "or you're in somewhere you're uncomfortable with, "from a male perspective or dogma, "or structural system that's not working for you, "just get out of it and go to another venue." Another venue being a growth market. So the message here was there's plenty of opportunities in data science than just data analytics. There's math career paths, there's cryptocurrency, there's blockchain, there's all kinds of different elements. Go where the growth is. If you go where the growth is, you can pioneer and find like-minded individuals. That was a great message I thought, for women, because you're going to find men in those markets that love collaborating with anyone who's smart, and since everyone here's smart, they're saying just go where the growth is. Don't try to go to a stagnant pond where all the dogma and the structural stuff is. That's going to take too long to change. That's my take, but I think that's kind of the message I thought was really, really powerful. And that's the message I'm going to tell my two daughters is "Stand tall, and go after the new territory." >> You can do anything, and that was also a theme of "Don't be afraid to take risks". In any way of life if we don't take risks, we risk losing out on something. That was something we heard a lot. >> John: Let me ask you a question then, because you did the interview. I was jealous, 'cause you know I hate to give up the microphone. >> I know you. (laughs) But I love this event, 'cause it's super awesome. What were some of the highlights for you? Was there a notable interview, was there some sound bites? What were some of the things that you found were inspiring, informational, or notable? >> Oh, all of the above. Everybody. I loved talking with Maria Klawe this morning who, to your point earlier, had to from many generations face the gender bias, and has such a... That her energy alone is so incredibly inspiring. And what she has been able to do as the first female president of Harvey Mudd and the transformation that she's facilitated so far is remarkable. Margot Gerritsen also was a great, inspiring guest for me. She had said, they had this idea three years ago, you were there from the beginning and I said how long was it from concept to first event? Six months. Whoa, strap on your seatbelt. And she said it was almost-- >> And they did it on a limited budget too, by the way. >> Sure. She said it was almost like the revenge conference. Tell us we can't do something, and I heard that theme as well, people saying, "Tell me I can't do something, "and I will prove you wrong in spades." (John laughs) And I think it's an important message. There's still such a gap in diversity. Not just in diversity in gender and ethnicity, there's a thought diversity gap that every industry is missing. That was another kind of common theme, and that was kind of a new term for me, thought diversity. I thought, "Wow, it's incredibly important "to bring in different perspectives." >> And on that point, one of the things I did here in the hallway was a conversation of, this is not just a movement, it's a collection of movements. So it's not one movement, this one is, or women in general, it's a collection of movements, but it's really one movement. So that was interesting, I was kind of like "Hmm", as being a guy I'm like, "Can you women-splain that to me please?" (Lisa and John laugh) >> Yeah, well the momentum that they-- >> What kind of movement is this? (laughing) >> They're achieving. (laughing) I'm sure there'll be a hashtag for that, and speaking of hashtags, I did think it was very cool that today is Monday, #MotivationMonday, this whole day was Motivation Monday to me. And I asked Margot, "Where do you go from here? "You've achieved this in the third year." And she said, "Doing more WiDS events throughout the year, "also starting to deliver resources on demand for folks". Not just females, to your point, this is people in data science, globally, to consume, and then going sort of downstream if you will, or maybe it's upstream, and starting to reach more of that high school age, those girls who might have a desire or interest in something but might think, "I don't think I can do this". >> Well I think one of the things that I'm seeing, and I was glad to be one of the men that stood up, and there's men here, is that men being part of it is super important because these newer markets, like I was just in the Bahamas for a cryptocurrency blockchain event, and there's a lot of younger generations, the whole gender thing to them, they think is nonsense. They should be all equal. So in these new growth areas they're kind of libertarian, but also they're really open and inclusive. It's because of their open-source ethos. So I think for the younger generation in the youth, we can kind of set the table now, and men got to be a part of that. So to be that kind of world where the conversation isn't about women in tech, means that it's all good now, >> Yeah. Right? So the question we've had on The Cube is when we're done with the diversity and inclusion discussion, that means we've accomplished the goal, which is there's no longer a need for that discussion because it's all kind of leveled up. So I mean, a long ways to go for sure, but that's the goal, and I think the younger generations are like, "You old people are like... "We don't view it that way", so we hope that structurally, we have these kinds of conferences where the conversation is not about just women, but the topics, and their gurus at their field. To me, that is the shining light that we want to focus on, because that's also inspirational. Now the stuff that needs to be fixed, is hard conversations, and it's tough but you can do both. And I think that's a message that I hear here. Phenomenal. >> Great to hear though from your perspectives, from what you're hearing with the millennials in the next generation going "Why are you even talking about this?" It would be great if we eventually get there, but some other things that are really key, and some of these companies are WiDS sponsors, Intel and SAP, and what they're doing to achieve, really aggressively, much more gender diversity. We heard Intel talk about it. We heard SAP talk about it today, Walmart Labs as well. And it's still obviously quite a need for it is what it's showing. >> The pay gap is still off. Way too off, yes. >> So that is like, the conversation needs to happen, I'm not trying to minimize that with my other point, but we got to get there. The other thing that's really off, the pay has got to get leveled up and people are working on that. That's great, let's see the progress. Let's look at the data. But the other one that no one's talking about is not only is the pay a problem, the big problem is the titles. So, we've been looking at data amongst a lot of the big companies. Women are getting some pay leveled up, but their titles aren't. So there's still a lot of these little things out there that matter. She's only a VP, and he's an SVP, but she's actually operating at an SVP level, or Senior Director, I mean, this is happening. So much more work to do, but again, the more that they come in with the skills that they got like in here, the networks that are forming, the VIP trust influence networks, it's just phenomenal. I think this is going to really accelerate the peer review, the peer relationships, access to the data, and just the more the merrier. Shine the light on it, turn the sunlight on. >> Exactly, shining a light on the awareness that they're generating, and also that we have a chance to share through The Cube, bringing more light to some of these things that you talked about, the faster, like you said, the more we're going to be able to accelerate making this a non-topic. >> It's our mission. The Cube's mission is to open the content up, get the conversations, document the folks, get them ingested into our network, share our networks open content. The more that that meta data and that knowledge can share digitally, that is the mission that we live for. As you know we love doing it. You did a great job today. >> Lisa: Thank you! It was my pleasure. It's an inspiring event, even just getting prepped for it, and you can hear all the buzz around us that it probably feels-- >> Cocktail party time. It is cocktail party time. Feels pretty darn good. Well John, thanks so much for being our fearless leader and allowing us to come here. And we want to thank you for watching The Cube. We have been live all day at WiDS 2018. Join the conversation. Follow us, @thecube. Join the conversation with #WiDS2018, and please join the conversation and share the videos of some of these fantastic leaders and inspirational folks that we had on the show today. For my co-host, John Furrier, I am Lisa Martin. We'll see ya next time. (electronic music)

>> Host: Live from Las Vegas. It's The Cube! Covering Oracle Modern Customer Experience 2017. (electronic music) Brought to you by Oracle. >> Okay, welcome back everyone, we're here live at the Mandalay Bay for Oracle's Modern CX Show, Modern Customer Experience, this is the Cube, I'm John Furrier. My co-host, Peter Burris, two days of wall-to-wall coverage. Day two, my next guest is Katrina Gosek, Senior Director Commerce Product Strategy, (mumbles) Oracle upper world a few years ago, and Alistair Galbraith Sr, Director of CX, Customer Experience Innovation Lab with Oracle. Welcome to The Cube, great to see you. >> Thank you. >> Thanks, welcome. >> So commerce is part of the story, it's just not marketing, there's transactions involved, there's R & D, there's a lot of technology. The show here is the common theme of just modernizing the customer experience, which is good, because it's the outcomes. But commerce is one of them. Give us the update, what's hot for you guys this week? >> Yeah, I think what's different this year, from any other year in the past is the pace of innovation is changing, because I think there's so much disruption in the commerce space, and particularly in retail and also B to B commerce. There's lots of new expectations from customers. I know we've been saying that for years, right? But I think the technologies now, that can enable some new experiences, have rapidly changed. Now it's completely fathomable to leverage AI to drive more high-end personalization or to leverage internet of things, to embed commerce more into everyday experience. >> John: Where's the innovation in retail? 'Cause retail's not a stranger to data. They've had data models going back, but certainly digital changes things, they're at the edge of the networks, so it's a little bit of internet of things meets consumer data, the data's huge if you can get the identity of the person. That seems to be the key conversation: how do you guys enable that to take advantage of the sea of data that you're providing form the data cloud, third party and first party data? >> Well I think there's a lot of fun approaches. Oracle has a technology called the Oracle ID Graph, which starts to merge a lot of identities across channels, so where customers are using data cloud, that can inform those micro interactions as they move between channels, and I think one of the trends we've been seeing this year that we're talking about as My Channel, is that customers no longer really complete one interaction or one transaction in one place. They might start on mobile, move to voice, move into a physical store, and we're trying to track that customer in all of those places, so a lot of our focus, and you see data cloud moves into AI, is enabling brands to move this data around more easily without needing to know everything about the customer themselves. >> John: Well that's the key for the experience of the customer, because they don't want to have to answer the same questions again if they're on a chat bot, and they've already been at a transaction. Knowing what someone's doing at any given time is good contextual data. >> Alistair: Yep. >> Well it's funny you say that, because when we talk to customers or end consumers, they're not thinking, "I need more artificial intelligence, "I need more data around my experience, "I need internet of things", they're thinking, "I want convenience, I want this to be fast and quick, "I want you to know me as a brand, "I don't want to have to re-enter everything. "If I'm talking to a customer service agent, "versus someone in the store, versus interacting online". So data's a huge part of that, the challenge is how do you make it consistent? >> John: Katrina has a great point: it's not the technology, it's about what they're trying to do. >> Katrina: Yeah, exactly, very much. >> Well the experience comes back to, in many respects, convenience, and, "I want you to sustain "the state of where I am in my journey for me". >> Katrina: Correct, yeah. >> Or at least not blow my state up. So it's interesting, the journey used to be a role or a context thing, and now we're adding physical location to it, as well as device. So go back to this notion of new experiences. 'Cause it's got to be more than, you can look at something on your phone and then transact on your phone. What are some of the new experiences on the horizon? 'Cause that is a lot to do with where you guys think digital technology's going to go. >> I think some of those experiences are micro-interactions, so that could be people are using voice shopping, but not for the entire purchase, just a re-order this thing, what's the status of this thing? And brands are also using the data that they're gathering to tweak and adjust those interactions. So we're seeing data coming from real world devices and IOT changing the expectation of the customer, as they, maybe, we showed some stories where people are re-ordering products using voice, and then when they shift between these channels, that micro piece of data is really changing that interaction. The other challenge we're seeing is the consistency of the interaction, you said yourself, not only it's the complexity of "what did I do?", but if I do something here and I do something here, I should get the same experience both times. >> So we're talking mostly at this point about the B to C, the consumer world. In many respects, some of the most interesting experiences, we can envisage in the B to B world, where a community of sellers is selling to a community of buyers, and the state that's really important is how does that buying community interact with each other? As they discover things and share information. So how do you see this notion of new experiences starting to manifest itself in the B to B world? >> Katrina: Yeah, it's interesting you say that, because I often, we work with both B to C and B to B clients, and I actually think B to B has always been more focused on personalization, because they do have so much information about their customers, contract data, a lot of information about the buyer, the companies, they've always done kind of online custom personalized catalogs. So I think there's a lot that B to C can learn from B to B about how to leverage that data to personalize experiences. >> John: And vice-versa too, it's interesting, to that point, the B to C is a leading indicator on the experience side, but B to B's got the blocking and tackling down, if they have the data. 'Cause having the data, you get the goods. Okay, so here's the question for you: with the consumers going to digital, you're seeing massive, we were reporting yesterday, here on The Cube and also on siliconhill.com, as well as Adage, not that we didn't predict this, but ad spend now on digital has surpassed TV for the first time. Which is an indicator, but the ad tech world's changing, because how people are engaging with the customer is changing, so the question is, what technology is going to help transition those ad dollars, from banner ads to older formats to something more compelling and using data? 'Cause you can imagine retail being less about click, buy, to sharing data. So the spend's going to only grow on advertising or reaching consumers. That conversion, that experience is going to have to move from direct response clicking, to more experience, what tech is out there? >> Well, I think the biggest challenge has always been tracking and personalizing for a unique interaction. Just the sheer volume of data that's coming in, it's just too hard to consume. So I think the blend of AI and AI with the ability to tweak, adjust, look at multi-variate tests, and change the interaction as it goes, that's going to really massively affect the journeys for retailers, and I think the big benefit as brands move to the cloud, the cost of innovation, the cost of trying something and failing is so much less, and the pace of innovation is so much faster, I think we're seeing people try new things with the data they've got. Find out what works and what doesn't. >> Here's a question for you guys. We're talking to Jess Cahill, when this came up yesterday as well, Peter brought this up as part of the big data action going on with the AI and whatnot. Batch to real time is a shift, and this is clear here in the show that the batch is there, but still an older, but real time data in motion consumers in motion are out there, so the real time is now the key. Can you comment on that? >> I think it goes back to what Alistair was saying earlier about those micro-moments. I think transacting in new and unexpected places, ways, I think that's the key, and that's actually a huge challenge for our customers, because you have to be able to use that data in real time, because that customer is standing there with their phone, or in front of Alexa, or a speaker. >> John: It's an opportunity. >> It's a huge opportunity, and I think those opportunities are everywhere now. In a couple of years be the refrigerator, if you're re-ordering groceries, leveraging the screen, so I think that's going to be the challenge, but I think we've got time to help our customers figure out how to leverage that in real time. I think staying nimble and agile is going to be key and failing fast, and I guess a more positive way to say this-- >> The Agile Marketer, I think we had Roland Smart on yesterday, he literally wrote the book. But this is interesting, if you have the data, you can do these kinds of things. So the question is, certainly your point about the refrigerator and all these different things is going to create the omni-channel nightmare. It's not going to be, certainly multi-multi-omni. It's going to be too many challenges to deal with. >> Alistair: I think we prefer to see it as the omni-channel dream, than the nightmare. (group laughs) >> So many channels, there's no more channels, right? >> Well I think that's where things like Marketing Cloud, things like Integration Cloud help orchestrate that omni-channel journey, so that to your point on marketing and ad-spend, being able to analyze whether a benefit or promotion I showed during one micro-interaction affected something somewhere else, is so challenging but so important when you're moving this ad spend around. And I think where orchestrating and joining these micro-moments together, it's really where we're focusing a lot of our investment at the moment. >> One of the big things that's happening in the industry today is we're starting to develop techniques, and approaches, methods, for conceptualizing how a real thing is turned into a digital representation. IBM calls and not to mention them, or GE, perhaps more of a customer ... (group laughs) Yeah, I just did. >> That's all right. >> This notion of a digital twin. Commerce succeeds, where online electronic commerce succeeds as we are more successful at representing goods and services digitally. What's the relationship between IOT and some of these techniques for manifesting things digitally? And commerce, because commerce can expand its portfolio, things it can cover, as more of these things can be successfully digitally represented? >> I think that's key, and that's actually one of the predictions that we talked about in our keynote is how do you represent new ways of representing the physical store, the physical space with customers, so for me, I think something that probably Back to the Future or Judy Jetson, like a few years ago, augmented reality, or virtual reality, I think now we're going to see that more. We're starting to see it more with furniture sales, for example, you're on your iPad at home, and you can put the couch you've chosen in the space, right there with you, and see if it fits, but you're in your home, you don't have to go to the furniture store, and kind of guess with your tape measure whether the couch fits or not. And I think that's applicable in B to B as well, as 3D CAD drawings, you can kind of see them in VR, or AR. >> Amazon just announced Look, yesterday, which is the selfie tool that allows you to see what you're wearing. >> I think we're going to see a lot more of it in the coming years. >> Well, in many respects, it also, going back to this, we asked the question earlier about B to B, B to C, and the ability to represent that community. We're going to start seeing more of a household approach, as to just a consumer approach, and I think you just mentioned a great one. When we are successfully, or when we are willing to start capturing more data about our physical house or what's going on inside, so that we can make more informed decisions, with others, about how we want to do things, has an enormous impact on the quality of the experience, and where people are going to go to make their purchases. >> Alistair: Definitely, and I think that as we try and merge those experiences between B to B and B to C, what we know about someone as a consumer also directly affects their buying decisions, as a B to B employee buying for their brand. And that just increases the sheer volume of data that people are trying to manage and test and orchestrate. I think we're seeing a shift not only in people being prepared to surrender some degree of privacy for a increased experience, but we're also seeing people trusting in that virtual experience being a reality when they buy. So people have a much higher trust level in AR, if I visualize a couch and then buy it, I've got a degree of faith that when it turns up, it'll be like the one I looked at. And I think that increased trust is really making virtual experiences, digital commerce, so much easier. >> I think that's an interesting point, we had CMO of Time Warner on yesterday, Kristen O'Hara, and she was, we asked her, "Oh yeah, these transformations", big use case, she's on stage, but I asked her, "How was it like the old way? "What would you do before Oracle?", she goes, "Well, there was no old way", they never did. The point is, she said, the point was we became a direct to consumer company, so B to B and B to C are completely merging. So now the B to B's have to be a B to C, inherently because of the direct connect to the consumer. Not saying that their business model's changing, just that's the way the consumer is impacting. >> Peter: Or is it data connection to a consumer? >> A data connection, and where there's gesture data, or interaction data coming in, so this makes, the B to Bs now have to bolt on more stuff, like loyalty, you mentioned loyalty, things of that nature. >> Yeah, if you're a B to B company, you're selling to other businesses, but who are the people on the other business? There are people who shop every day in consumer applications, so their expectations are, "I'm going to have a great personalized experience, "I'm going to be able to leverage the same tools "that I see in my consumer shopping experiences "for my B to B experience, why would it be different?" So I think that's something that B to B is really learning from B to C as well. >> True, but although there seems to be something of a counter-veiling trend, but an increasing number of people are now working at home. So in many respects, where we're going to, is we're talking about experience, not just being online. One of my little heroes, when I was actually trying to do development, a million years ago, was Christopher Alexander. The Timeless Way of Building, which was one of the basic texts that people use for a lot of this customer experience stuff, and the observation that he made was, you talk about spaces, you talk about people moving into spaces to do things in context. And increasingly, the spaces that we have to worry about are not just what's on the screen, but the physical space that people move in, and operate in, an the idea is, I'm going somewhere to do something, and I'm bringing physical space with me. So all of these, the ability to represent space, time and interests and wants and needs, are going to have an enormous impact on experience. Wouldn't you agree? >> Massively, and I think the challenge using that same approach is that people are co-existing in multiple spaces concurrently. They no longer do one thing at the same time. >> Peter: They may be in the same physical place, but have two different contexts associated with it. Like working my home office, I'm both a father, as well as an employee. >> Alistair: Yes. >> And those two sometimes conflict. (Katrina laughs) >> Yeah, absolutely, and you're a consumer and an employee, and as a father, you're potentially affecting the decisions that the rest of your household is making, as well as the decisions that your business is making, all in slightly different ways. But those two experiences with the B to B and B to C, overlap one another. >> Peter: In fact, switching contexts from consumer to father is one of the primary reasons why I lose where I am in the journey. So these are very powerful, and the ability to have the data and then go to your customers, and say, "We will be able to provide that end to end for you, "so that you can provide a consistent "and coherent experience for your customers" is really crucial. Is that kind of where you're taking us? >> Yeah, I mean we've always commerce isn't kind of a standalone little thing, it really connects and glues together so many other types of experiences, so it connects to marketing, it connects to service, you need all of that, to be able to make the experience work. So we're really focused on making sure that it's easy to connect those applications together, that its easy to manage them behind the scenes, and that it appears seamless to the customer on the front end. >> One other thought that I have is, and in many respects, increasingly, because we're going to be able to represent more things digitally, which means we'll be able to move more stuff through commerce platforms. This is where the CX is going to meet the customer road, is in the commerce platforms. Do you guys agree with that? You're going to measure things all over the place, but I'm just curious-- >> John: It's their products, yeah. >> What do you think? Is it going to be increasingly the basis for honest CX? >> Well we're already seeing it become the basis, so I wouldn't say it's a future thing, I think it's been a reality for quite some time, where commerce is the hub that kind of connects, in retail, the store to marketing experiences. >> John: It's bonafide data is what it is too. >> Yeah. >> That's good data. >> Katrina: It holds so much product information, transaction information, customer information, and it just connects and leverages. I don't know if you would agree? >> Alistair: I would agree completely, and I think you look at the fact that most companies ultimately are selling a product, so that's commerce, and I think the transition is that rather than going into the commerce site or the commerce space, you see a lot of brands over the last 12 months have got rid of their store.brand.com thing and just merged their commerce experience into everything else, you're always selling. And we've customers deploy commerce without the cart, but as a product and communication marketing model, to get this tracking data moving around. >> We were talking about Jack earlier, yesterday, Berkowitz, who was talking data, we were talking about data, good data, dirty data, clean data, and data quality in general. >> Katrina: It's a tough problem. >> In context to value, and he said a quote, he said, "Good data makes things happen, "great data makes amazing things happen". And to your point, retail, commerce data, you can't, it's undisputed, it's a transaction. It's a capture in time, and that can be used in context to help other data sets become more robust. >> Well, in many respects it's the most important first person data that you have in your business. >> Katrina: Yeah, and I think from an Oracle perspective, what we're doing with the adaptive intelligent applications for commerce, and for the other applications as well, and particular for commerce is combing that first hand information you have about your products and your customers as an online business, but then the immense amount of data that the data cloud has behind the scenes that augments and allows you to automatically personalize, when a customer comes to your storefront, because they're coming already with all the context that they have elsewhere out in the world, and you can combine that with your own data, and I think really enhance the experience. >> John: Yeah it's funny, we were joking yesterday, Oracle went to bed a software company, woke up a data company. >> Katrina: Yeah (laughs). >> So the data cloud is pretty impressive, what's happened there and what that's doing. >> Katrina: It's amazing, it's a huge differentiator for us. >> Huge differentiator. Okay, final word, I'd like both you guys to just quickly comment to end this segment, awesome segment on commerce and data, which we love. But your reaction to the show, what's the bottom line, what's exciting you this week? Share with the folks, each of you, a quick soundbite of what's happening here and the impact people should know about. >> Sure from a commerce perspective, this is the first year where we've got a 50/50 split in our customer base, so we're seeing a lot of our un-premise customers move to cloud, which is great, and we're really growing our commerce cloud customer base. I'm very excited about that. >> And you're trying to get 100% now, it's never going to be a hundred. >> Katrina: (laughs) Yeah, we need to work with customers and what's right for them, but yeah, it's very exciting right now. >> Alistair, your take? >> I think for me, it's just the sheer pace of innovation, we're seeing brands go from un-premised stories that would take 12, 15, 18 months to add new features, make changes to small nimble brands rolling out incredible innovative features in 12, 18 week time frames, and we're seeing more people having more discussions around the art of the possible. >> John: All right, Katrina, Alistair, great comment, great insight, great conversation about data and commerce, of course cloud, it's the marketing clouds, all cloud world, it's commerce cloud, it's data cloud, it's just the cloud (laughs). I'm John Furrier, Peter Burris, move live coverage here from Las Vegas, Oracle Modern CX after this short break. (electronic music) >> Host: Robert--

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Professional BusinessWomen of California Conference. It's the 28th year, Jackie Speier started it a long time ago and now it's grown to 6,000 people. It's a pretty amazing conference, it crosses all indrustries and actually a lot more than California as well. And we're excited to actually have somebody to come talk to us about the conference itself. It's Deepti Srivastava, she's a Project Manager of Google Cloud from Google. Great to see you again, last we saw you, I looked it up was 2014 >> I know. >> at Topcoder Open. >> Indeed. >> And you were doing great work then, you were on a panel with a bunch of high school girls. I remember they'd bust in a couple of busloads of high school girls and you and a couple other mainly young professional women talkin' to 'em about the life of an engineer. So you're still doin' good things. >> I hope so. (laughs) >> Absolutely. >> I hope so, yeah, it's a passion of mine and I'm really happy to bring it to something like PBWC where I'm on the board. And we do a bunch of work across industries and across all levels. PBWC's mission is to work for gender equity and equal pay for women across all industries and in all professional settings. >> Right. >> That includes young professionals, as well as the pipeline of professionals coming in. >> That's terrific. So we could talk about your day job all day long. (Deepti laughs) Google Cloud's kickin' tail, you guys had your big conference a couple weeks back-- >> Here in fact. (chuckles) >> Here in Moscone West, right? >> Yeah. >> But in terms of what you're doing here with PBWC, give us a little bit of the history. So we know it was started by Jackie Speier, I think you said 1988. >> Yeah. >> That's just amazing. >> I know. >> Obviously it's much more than California. >> Yeah. >> But what is the top-level mission and how has the conference evolved over the last several years? >> So Professional BusinessWomen of California, as you said was started by Congresswoman Jackie Speier and Judy Bloom, who's a co-founder. And we still exist and we've been doing this for so long and we really care about our mission, which is to work for basically gender equity and equal pay as I said, for all professional settings for women. And in this particular case, this conference we are talking about inclusion. And we chose this theme because we really think it's pertinent to what's going on right now in the world and in our country. And we, PBWC, believe that the things that unite us, the potentials and aspirations that unite us are greater than our differences and things like that. So we want to make a statement and really address the inclusion work that we do, and the inclusion work that's required for all of us to really move forward as a country and as a people. And if you look at our lineup of speakers today, we really do walk the talk that we're talking about. We have amazing speakers today with Rosario Dawson to Taraji P. Henson and all the way to Secretary Clinton who's closing out our day today, we are so excited to have her. And there's nobody better to represent breaking the glass ceiling than she has so we're very excited to hear. >> And what a get, I think I heard that it's her first public speaking engagement post the election. >> Yeah, I know. And it's very exciting because again, I think we're all about coming together and rallying and being a force for good. The conferences, that's our aim ultimately as an organization. And having her here to give her speech, first public appearance after the election last year, very exciting I think. >> Right, right. >> And we're very excited to hear from her. I'm already inspired by the thought that she's going to be here. >> And really a big part of the theme was kind of the strategy work is done, everybody knows it's good. Now it's really time for the rubber to hit the road. It's about execution and about taking steps and measuring. And a lot of the real concrete, nuts-and-bolts activities that need to happen to really move this thing down the road. >> You mean like gender equity and-- >> Yeah, yeah. >> Yeah, absolutely. I think it's been a topic for awhile and I think, exactly, we need to have the rubber hit the road, we have to get together, we have to have actionable plans and that's what a bunch of our seminars today talk about. How to address those things in your, we really want to empower women and actually people of all backgrounds and ages and all sorts of people to take charge of their own lives. And especially, we are a professional women conference so that's kind of where we focus our messaging. But really we want women to take control of their own lives and we want to give them the tools, the networking opportunities, the inspirations to meet their aspirations in those fields. And so we want them to take charge and move forward by themselves, take away from here and go back to your job, to your work, to your home, to really bring your messaging forward. Take inspiration from here and bring it back to your life. >> Right, and I think Bev Crair, in the keynotes said, "Fill your well today." >> Yeah. >> 'Cause as soon as you leave here it's back to the grind and you're going to need that energy. So while you're here surrounded by this energy and your peers, take it all in and load up. >> Absolutely. And I also want to say that we started out as a conference, an annual conference, and that's definitely our marquee thing that we do every year. But we actually have a lot more offerings that people can continue to engage over the year. So we have webinars and seminars that people can attend, there's community events that happen here. And you can go to the PBWC website and see what all offerings we have. But we want people to engage and we want to be able to provide them with the means to engage throughout the year, not just here but take this, everything you get today and then take it along the rest of the year and recharge yourself. >> It's kind of this whole 365 concept which we talk about on theCUBE a lot too, 'cause we go to so many shows. And there's a huge investment of time and energy and money on those two or three days, but how do you extend that out beyond the show? How do you build the excitement leading into the show so it's not just a one time kind of a shot, then everything goes back to normal? >> Yeah exactly, I think that's exactly the point, that this is not just a one day, you go there, you get inspired and then what next, right? >> Right. >> There's something you can go back to with our various offerings and continue your learning journey if that's what you want, or networking journey if that's what you want to do. Wherever you are in your career, we actually have a Young Women's Professional Summit that I have the honor of chairing, that we have every year and it's meant to help young professional women navigate their way from being in college and high school and those entering a professional life so as I said, we want to cater to all levels and all ages and all sorts of challenges that people face as they're going through their professional careers. >> So that's a separate event? >> It is, it is an annual conference. >> And when is that? Give a plug. Or do you have a date? (Deepti chuckles) >> Yeah, we don't have a date yet but it's going to be in the summer. >> In the summer, okay great. Well I think when we met last, I thought that was such an important piece of that Topcoder Open because it wasn't the Sheryl Sandbergs or the Hillary Clintons or these super mega top-of-the-pyramid people. It was a bunch of young professionals, one of the gals was still in school, hadn't finished graduating, to make it so much real for those high schoolers. They didn't have to look so far to say, "I could see myself, I kind of look like that person, "I kind of see things touch." >> And I think that's very important, Jeff. Exactly. It's very important and that's what we try to do here at PBWC as well. We want to go from catering to the Millennials and how we interact with them and all the way up to C-suite, we had a Senior Leadership Summit yesterday leading up to the conference today where we have a bunch of C-suites and CDOs, Chief Diversity Officers, come together and talk about trending topics and how to solve them. So we really are trying to move the needle forward on many fronts here, but our aim is all of that to culminate into moving women and people of all backgrounds forward. >> Right. And then there's this whole entrepreneurial bit which you can't see behind the camera, but there's booths all over for Intel and LinkedIn and Microsoft and the names that you would expect, Google of course, but there's also all the little boutiques, clothing stores and jewelry stores and crafty things. There's even of course women-focused snacks with the Luna Bars and I forget the other one. (chuckles) So it's kind of a cool entrepreneurial spirit kind of on top of everything else. >> Absolutely. And you know Jackie Speier, Congresswoman, started this conference to help women who were in the SMB, sort of SME market, basically women who ran small businesses. And we want to continue to do that as well but now of course the world is changing and we have a much more of a corporate presence and we want to help there too. But yeah, we pay homage to that by having women who are women entrepreneurs running women-focused businesses, and we have them here in the expo area if you can get a shot of that later. >> Right. >> The energy is palpable, the excitement is there and it's so great to be here and harness that, and take it back, I mean the first time I was here many years ago when I was not even on the board, I was just like, oh my gosh, there's so many women here who are like me or who are, they're people I could look up to all the way up to the C-suite who are making their presence felt here. And also all the people around me and like-minded, like me. So it's a really inspiring event. And I've been here for many years but I'm still inspired by it. So I'm so excited that we do this and continue to do this. >> So, little harder to question. So, and you've been doing this for awhile, what surprises you on the negative that still you know, you're still fighting that battle that you wouldn't have expected to still be doing? And then conversely what has surprised you on the positive, in terms of what's moved maybe further than you might've thought or faster than you might've thought? >> That's a good question. I think you already nailed it, right. The fact that we are still here talking about this is interesting to me, and as I got more involved in this kind of work I realized that people have been doing this for a long time. Congresswoman herself has been doing this for so long and a fearless advocate for women's rights and equal pay and diversity and inclusion. And the fact that we are still here, it is indicative of the fact that we need to have a groundswell movement in order to change policy. We can talk about it all we want but unless there's actionable things you can take away and really have that grassroots-level work to push the envelope forward, it's not going to happen. I think the positive is, as I've seen this conference over the years, it's grown. And it's gotten a lot more young people involved and it's not just the senior leadership that is trying to pull people forward, it's the people starting out early in their careers or mid-level in their careers that are looking at taking charge of their own destiny and pushing their agenda forward in this sense. They want, they're asking for equal pay. They're really engaged and aware. And conferences like PBWC actually help with that, getting those minds together and making things move forward. So I think from a positive side I'm really excited to see so many more people engaged in this fight. And the more people we have, the more we can actually make real progress and real inroads. >> And if you look back, as someone who's never been here and then they see this interview and they say, "This looks awesome, I'm going to sign up," what do you think the biggest surprise when they come for the first-timer? >> I'll tell you what I was surprised by, is seeing so many women together across industries, across ages, across backgrounds. Everybody together, really wanting to move forward. They're really wanting to engage, to connect with each other and to actually make a difference. People are here to make a difference, right? >> Right, right. >> And that's, to say that 6,000 people come together and really all of them have that same sort of mentality of like yes, I'm empowered to make a difference, is electrifying. >> Deepti, I love the energy. >> (laughs) Thank you. >> I love the energy, absolutely. >> It's all these people. >> It is. >> Trust me, I'm sleep deprived (Jeff laughs) with my very young son. So yeah, this is all the energy that I need to feed off of. >> No, it's good. And there is something special here. >> Mm-hmm. >> And you can feel it. 'Cause we go to a lot of shows, you go to a lot of shows. And again, it's not an exclusive tech show which is kind of nice 'cause we cross a lot of industries. But there's definitely, there's an energy, there's a vibe that comes from the little entrepreneurial outlets, it just comes from the, that room was packed. The keynote room was... >> I know. >> Was not fire marshal friendly. (Deepti laughs) Hopefully the fire marshal was not close by-- >> Yes, we had some discussion on that too. But to your point, this is one of the conferences that I've seen where we really, perhaps the only conference I've seen where we really cut across all industries. Because there's tech-focused, there's business-focused, there's all sorts of focused conferences trying to do either their professional work on technology or whatnot, or they're trying to solve the problem on the gender and diversity and inclusion piece in their own silos. And we try to cut across so that we can actually have a coming together of all of these various industries and their leaders, thought leaders, sharing ideas and sharing best practices so that we can actually all move forward together, I think that's again our Senior Leadership Summit which happened last night and the VIP reception which happened last night is all about getting those thought leaders together and getting them to share their best practices and ideas so that again, they can take it back to their companies and really move forward with DNI initiatives. >> It's action right, it's all about the action. >> Absolutely. >> So I promise next time that we talk, we'll talk about Google Cloud. >> Oh, sure. >> 'Cause that's hoppin'. (Deepti laughs) But it was great to see you and congratulations on all your work with the board and with your event >> Thank you. >> in the summer. People should go to the website, keep an eye out. >> Absolutely. >> It'll be comin' out. >> Yeah. >> So thank you. >> Thank you so much, it was great to see you too, Jeff. >> Absolutely. Alright she's Deepti, I'm Jeff, you're watching theCUBE. We're at the Professional BusinessWomen of California Conference. The 28th year, pretty amazing, 6,000 people. Here at Moscone West, thanks for watchin'. (upbeat techno music)