>>I want to welcome back to the cubes coverage. We're here at another event in person I'm John furrier, host of the cube. We've got to CNCF coop con cloud native con for in-person 2021. And we're back. It's a hybrid event and we're streaming lives on all channels, as well as all the folks watching a great guest kicking off the show here from the co-chairs from cataract coast. Is that right? Danielle Cook. Who's the vice president at Fairwinds and John Foreman director at Accenture. Thanks for coming on your co-chair. Your third co-chair is not here, but you guys are here to talk about the cloud maturity model. Pretty mature funding is flowing tons of announcements. We're going to have a startup on $200 million. They're announcing in funding and observability of all of all hot spaces. Um, so the maturity is it's the journey in the cloud native space now is crossed over to mainstream. That's the we've been telling that story for a couple of years. Now, you guys have been working on this. Tell us about the cloud maturity model you guys worked on. >>So we got together earlier this year because we, um, four of us had been working on maturity models. So Simon Forester, who is one of the co-chairs, who isn't here, he had worked on a maturity model that looked at your legacy journey, all the way to cloud native, um, myself, I had been part of the Fairwinds team working on the Kubernetes maturity model. So, and then, um, we have Robbie, who's not here. And John Foreman, who we all got together, they had worked on a maturity model and we put it together and I've been working since February to go, what is cloud native maturity and what are the stages you need to go through to achieve maturity. So put this together and now we have this great model that people can use to take them from. I have no idea what cloud native is to the steps they can take to actually be a mature organization. >>And, you know, you've made it when you have a book here. So just hold that up to the camera real quick. So you can see it. It's very much in spirit of the community, but in all seriousness, it book's great, but this is a real need. What was the pain point? What was jumping out at you guys on the problem? Was it just where people like trying to get more cloud native, they want to go move faster. It was a confusing, what were the problems you solve in? >>Well, and if anything is, if we start at the beginning, right, there was during the cloud journey DevSecOps and the Kootenays being a thing that then there's journeys to DevSecOps tributaries as well. But everything is leading to cloud native. It's about the journey to cloud native. So everybody, you know, we're taught to go John, the ecosystem's an eyesore man. If I look at, you know, landscape, >>The whole map I >>Need, it's just like in trend map, it's just so confusing what we do. So every time we go to, I revert the wheel and I get them from zero to hero. So we just put together a model instead that we can re reuse yeah. As a good reference architecture. So from that is a primary, how we built because the native trademark you have with us today. So it's a five scale model from one to five what's twice today, or how to, to, you know, what our job is getting to a five where they could optimize a really rocket rolling. >>You know, it's interesting. I love these inflection points and, you know, being a student of history and the tech business there's moments where things are the new thing, and they're really truly new things like first-time operationalized dev ops. I mean the hardcore dev ops or early adopters we've been doing that, you know, we know that, but now mainstream, like, okay, this is a real disruption in a positive way. So the transformation is happening and it's new, new roles, new, new workflows, new, uh, team formations. So there's a, it's complicated in the sense of getting it up and running so I can see the need. How can you guys share your data on where people are? Because now you have more data coming in, you have more people doing dev ops, more cloud native development, and you mentioned security shepherds shifting left. Where's the data tell you, is it, as you said, people are more like a two or more. What's the, what's the data say? >>So we've had, so part of pulling this model together was your experience at Accenture, helping clients, the Fairwinds, um, experience, helping people manage Kubernetes. And so it's from out dozens of clusters that people have managed going, okay, where are people? And they don't even know where they are. So if we provide the guidelines from them, they can read it and go, oh, I am at about two. So the data is actually anecdotal from our experiences at our different companies. Um, but we, you know, we we've made it so that you can self identify, but we've also recognized that you might be at stage two for one application, but five for another application. So just because you're on this journey, doesn't mean everything is in, >>It's not boiler plate. It's really unique to every enterprise because they everyone's different >>Journey. Put you in journey with these things. A big part of this also torn apart one to five, your clients wants to in denial, you know? So, so Mr. CX level, you are level two. We are not, there's no way we would deal with this stuff for years. You've got to be a five. No, sorry. You're too. >>So >>There's use denial also about this. People think they do a cloud-native director rolling, and I'm looking at what they're doing and go, okay, do you do workups security? And they go, what's that? I go, exactly. So we really need to peel back the onion, start from seed year out and we need to be >>All right. So I want to ask more about the, um, the process and how that relates to the themes are involved. What are some of the themes around the maturity model that you guys can share that you see that people can look at and say, how do I self identify? What's the process will come to expect? >>Well, one of the things we did when we were putting it together was we realized that there were themes coming out amongst the maturity model itself. So we realized there's a whole people layer. There's a whole policy layer process and technology. So this maturity model does not just look at, Hey, this is the tech you need to do. It looks at how you introduce cloud native to your organization. How do you take the people along with it? What policies you need to put in place the process. So we did that first and foremost, but one of the things that was super important to all of us was that security was ever present throughout it. Because as everything is shifting left, you need to be looking at security from day one and considering how it's going to happen and roll out from your developers all the way to your compliance people. Um, it's super important. And one of the themes throughout. >>So, so it would be safe to say, then that security was a catalyst for the maturity models because you gotta be mature. I mean, security, you don't fool around security. >>About the last year when I created the program for, since I worked with Cheryl Holland, from CCF, we put together the community certification, her special program. I saw a need where security was a big gap in communities. Nobody knew anything about it. They wanted to use the old rack and stack ways of doing it. They wanted to use their tray micro tombs from yesteryear, and that doesn't work anymore. You need a new set of tools for Kubernetes. It's the upgrade system. It's different way of doing things. So that knowledge is critical. So I think you're part of this again, on this journey was getting certifications out there for people to understand how to do better. Now, the next phase of that now it's how do we put all these pieces together and built this roadmap? >>Well, it's a great group. You guys have the working groups hard to pronounce the name, but, uh, it's a great effort because one of the things I'm hearing and we've been reporting this one, the Cubans looking angle is the modern software developers want speed, and they don't want to wait for the old slow groups now and security, and it are viewed as blockers and like slow things down. And so you start to see a trend where those groups could provide policy and then start putting, feeding up, uh, data models that allow the developers in real time to do their coding, to shift left and to be efficient and move on and code not be waiting for weeks or days >>Comes to play. So today is the age of Caleb's right now, get up this emerging we're only to have now where everything is code policies, code, securities, code policies, cookie figures, code. That is the place for, and then again, walk a fusion more need for a cargo office. >>Okay. What's your thoughts on that? >>So I think what's really important is enabling service ownership, right? You need the developers to be able to do security, see policy, see it live and make sure that, you know, you're not your configuration, isn't stopping the build or getting into production. So, you know, we made sure that was part of the maturity model. Like you need to be looking continuous scanning throughout checking security checking policy. What is your process? Um, and we, you know, we made that ever present so that the developers are the ones who are making sure that you're getting to Kubernetes, you're getting to cloud native and you're doing it. >>Well, the folks watching, if you don't know the cloud native landscape slide, that ecosystem slide, it's getting bigger and bigger. There's more new things emerging. You see role of software abstractions coming in, automation and AI are coming in. So it makes it very challenging if you want to jump right in lifting and shifting to the clouds, really easy check, been there, done that, but companies want to refactor their applications, not just replatform refactoring means completely taking advantage of these higher level services. So, so it's going to be hard to navigate. So I guess with all that being said, what you guys advice to people who are saying, I need the navigation. I need to have the blueprint. What do I do? How do I get involved? And how do I leverage this? >>We want people to, you can go on to get hub and check out our group and read the maturity model. You can understand it, self identify where you're at, but we want people to get involved as well. So if they're seeing something that like, actually this needs to be adjusted slightly, please join the group. The cardiograph is group. Um, you can also get copies of our book available on the show. So if you, um, if you know, you can read it and it takes you line by line in a really playful way as to where you should be at in the maturity model. >>And on top of that, if you come Thursday was Sonia book. And of course, a lot of money, one day, I promise >>You guys are good. I gotta ask, you know, the final question is like more and more, just more personal commentary. If you don't mind, as teams start to change, this is obviously causing a lot of positive transformation if done, right? So the roles and the teams are starting to change. Hearing SRS are now not just the dev ops guys provisioning they're part of the, of the scale piece, the developers shifting left, new kind of workflows, the role of certain engineers and developers now, new team formations. Why were you guys seeing that evolve? Is there any trends that you see around how people are reconfiguring their team makeup? >>I think a lot of things is going to a single panic last tonight, where I'm taking dev and ops and putting them one panel where I can see everything going on in my environment, which is very critical. So right now we're seeing a pre-training where every client wants to be able to have the holy grail of a secret credit class to drive to that. But for you to get there, there's a lot of work you've got to do overnight that will not happen. And that's where this maturity model, I think again, will enhance that ability to do that. >>There's a cultural shift happening. I mean, people are changing there's new skillsets and you know, obviously there's a lot of people who don't have the skill. So it's super important that people work with Kubernetes, get certified, use the maturity model to help them know what skills they need. >>And it's a living document too. It's not, I mean, a book and I was living book. It's going to evolve. Uh, what areas you think are going to come next? So you guys have to predict if you had to see kind of where the pieces are going. Uh, obviously with cloud, everything's getting, you know, more Lego blocks to play with more coolness you have in the, in this world. What's coming next with Sue. Do you guys see any, any, uh, forecasts or >>We're working with each one of the tag groups within the CNCF to help us build it out and come up with what is next based on their expertise in the area. So we'll see lots more coming. Um, and we hope that the maturity grows and because of something that everybody relies on and that they can use alongside the landscape and the trail map. And, um, >>It's super valuable. I think you guys need a plug for any people want to, how they join. If I want to get involved, how do I, what do I do? >>Um, you can join the Carter Garfish group. You can check us out on, get hub and see all the information there. Um, we have a slack channel within the CNCF and we have calls every other Tuesday that people can see the pools. >>Awesome. Congratulations, we'll need it. And super important as people want to navigate and start building out, you know, you've got to edge right around the corner there it's happening real fast. Data's at the edge. You got cloud at the edge. Azure, AWS, Google. I mean, they're pushing really hardcore 5g, lot changes. >>Everybody wants to cloud today. Now one client is, one is more cloud. At least both the cloud is comfortable playing everywhere. One pump wife had DevOps. >>It's distributed computing back in the modern era. Thank you so much for coming on the keep appreciating. Okay. I'm Jennifer here for cube con cloud native con 2021 in person. It's a hybrid event. We're here live on the floor show floor, bringing you all the coverage. Thanks for watching station all day. Next three days here in Los Angeles. Thanks for watching. >>Thank you.

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

[Music] hi and welcome to this cube conversations in the cube in the cube Studios in Palo Alto California I'm your host Sonia - Gauri and today we're joined by Justin Warren the chief analyst and managing director for pivot 9 Justin welcome to the cube thanks for having me absolutely so tell us more about pivot 9 and more about your role yes so I found a pivot 9 back in 2011 and we help customers with their positioning in marketing and their messaging that's most of what we do these days we have a background in infrastructure enterprise consulting so we most of our clients tend to be focused on the enterprise and we also perform a bunch of analyst services basic research and understanding what the market is doing which helps us to to advise our clients on what makes a good position and message to take into the market that's great and you also founded this company so tell us about how you started this company and how you navigated funding well we're entirely so funded and have been profitable for for a while now it was kind of an accident in in the early days my background was in all traditional kind of consulting working with his clients on actually building infrastructure so I've done time in the trenches in in most of the different fields so I was once a DBA rapidly de-skilling and I got bored and decided that fairly company seemed like a good idea which was of course insane as anyone who is founded the company will gladly tell you but it has worked out okay for me in the end that's great and you're also you also do a couple other things you're a co-host on the cube or you're a host on the cube and you're also contributor of Forbes so tell us about how you got into hosting the cube and how that experience has been like for you host oh you can it was was kind of a happy accident I had known Stu for many years and an opportunity came up which I happened to be at a conference that he was he was at and said hey would you like to come on the cube and do a little bit of hosting and I will we said yes and have been doing a bit of it ever since every every now and again so yeah well it's when I happened to be at the same place and I do go to most of the major tech conferences it's it's always a pleasure to come on and guest host the Q but a little bit that's awesome and we love having you on the cube and you're also contributor on Forbes so tell us more about what articles you write what what topics in fields you mostly focus on yes oh uh mostly there I focus on enterprise and and cloud a little bit of networking and information security those are my interests and and it's my background so I know the enterprise technology field pretty well and now it's just interesting it gives me an opportunity to talk to a lot of different customers and find out or both customers and vendors and find out how they think about the market what what are they trying to build why are they trying to do that and whenever I'm talking to them I'm always trying to find a way that I can educate the audience about what what this means for them so it does dovetail nicely with the work we do through pivot nine but I just found it personally interesting and quite useful to be able to communicate what people are really doing and why it's why it's a good idea I think a lot of my readers value that that honesty and the insight that they get from that writing I certainly that's what they've told me so I like listening to customer feedback so if they tell me that I start to suck then I'll have to change what I do it but until when I'll keep doing it the way up and doing it that's awesome Justin thank you so much for being on the Kuban we really appreciate you have having you here no problem thank you so much absolutely thank you so much for watching the cube this has been a cube conversation at the cube studios and pellet [Music] you [Music]

[Music] hi and welcome to this cube conversation in the cube Studios in Palo Alto California I'm your host Sonia - Gauri and today we're joined by two guests Justin Warren who is the chief analyst and managing director of pivot 9 and John Troy the chief reckoner of tech reckoning John and Justin welcome to the cube Thanks thanks for having us great so Justin you're in Melbourne Australia John your local to California let's start with Justin Justin you work at pivot 9 tell us a little bit about your role and what you do so I'm the founder and chief analyst steered pivot know and so everything is my fault we we like to help customers with positioning and messaging that's what most of them come to us for so we we maintain a pretty good research focus on the market focus on enterprise infrastructure cloud and information security and our clients come to us for help with positioning into those markets that's awesome and John you're the chief reckoner at Tech reckoning so tell us more about tech reckoning and what you do sure in in a way my keep reckoner is just might know I guess I am also the bottle washer and analyst as well we work with companies that help them with their ecosystem of technologists we work community and influence and advocacy and Deverell is the term of art that people like right now but basically we work we help communities communicate with their their their the ecosystems of which that's great and you're both a host of the cube so let's go down the line John tell us how did you get into hosting the cube and how has that experience been like I was here at cube number one we we started to realize that video streaming was available in a reasonable way at events and I believe we worked we worked with John and Dave and some of the few boats who were Bill around now to bring them to VMworld over ten years ago I was also doing it home at myself with him disappear that we bought it electronic door I'm very quickly looking very welcome to have them take over a functionality for a lot of people and Justin how about you how's your experience been yeah it's been great it's a again happy accident as things started off I happen to nice to I've known him for a few years and they he was in need of submersed hosting spots at a conference that I I happen to be at anyway and I foolishly said yes and now I've done it more than once oh it's is it gets a lot easier after you've done it two or three time are there any tips and tricks you would give okay thank you so much for being on the cube and we will see you next time [Music] you [Music]

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Hi! And welcome to the Cube. I'm your host, Sonia, to guard. And we're live at Stanford University covering Woods Women in Data Science Conference The fifth annual one And joining us today is Daphne Koller, who is the co founder who sorry is the CEO and founder of In Citro that Daphne. Welcome to the Cube. >>Nice to be here, Sonia. Thank you for having me. So >>tell us a little bit about in Citro how you how you got founded and more about your >>role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey and that the data sets were quite small and limited. We're now in a different world where there's tools that are allowing us to create massive biological data sense that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem >>better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of >>drug discovery development in the last 50 to 70 years as being a bit of a glass half full glass, half empty. The glass half full is the fact that there's diseases that used to be a death sentence or of sentenced, a lifelong of pain and suffering that >>are now >>addressed by some of the modern day medicines. And I think that's absolutely amazing. The >>other side of >>it is that the cost of developing new drugs has been growing exponentially and what's come to be known as the Rooms law being the inverse of Moore's law, which is the one we're all familiar with because the number of drugs approved per 1,000,000,000 U. S. Dollars just keeps going down exponentially. So the question is, can we change that curve? >>And you talked in your keynote about the interdisciplinary culture to tell us more about that? I think in >>order to address some of the critical problems that we're facing. One needs to really build a culture of people who work together at from different disciplines, each bringing their own insights and their own ideas into the mix. So and in Citro, we actually have a company. That's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other Halford machine learning people in data scientists who are working on those. But it's not a handoff where one group produces that they then the other one consumes and interpreted. But really, they start from the very beginning to understand. What are the problems that one could solve together? How do you design the experiment? How do you build the model and how do you derive insights from that that can help us make better medicines for people? >>And, um, I also wanted to ask you the you co founded coursera, so tell us a little bit more about that platform. So I found that >>coursera as a result of work that I've been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, number of my colleagues as well. And at some point in the fall of 2011 there was an experiment of Let's take some of the content that we've been we've been developing within within Stanford and put it out there for people to just benefit from, and we didn't know what would happen. Would it be a few 1000 people, but within a matter of weeks with minimal advertising Other than one New York Times article that went viral, we had 100,000 people in each of those courses. And that was a moment in time where, you know, we looked at it at this and said, Can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking a what was supposed to be to really absence from Stanford to go and co found coursera, and I thought I'd go back after two years, but the But at the end of that two year period, the there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world. We just felt like this was something that I couldn't not dio. >>And how did you Why did you decide to go from an educational platform to then going into machine learning and biomedicine? >>So I've been doing Corsair for about five years in 2016 and the company was on a great trajectory. But it's primarily >>a >>a content company, and around me, machine learning was transforming the world, and I wanted to come back and be part of that. And when I looked around, I saw machine learning being applied to e commerce and the natural language and to self driving cars. But there really wasn't a lot of impact being made on the life science area. I wanted to be part of making that happen, partly because I felt like coming back to your earlier comment that in order to really have that impact, you need to have someone who speaks both languages. And while there's a new generation of researchers who are bilingual in biology and machine learning, there's still a small group in there, very few of those in kind of my age cohort and I thought that I would be able to have a real impact by bullying company in the space. >>So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in the similar field? Would you tell them they have to major in math? Or or do you think that maybe, like there's some other majors that may be influential as well? I think >>there is a lot of ways to get into data science. Math is one of them. But there's also statistics or physics. And I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand, and biology and health on the other one can, um, get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in, because that foundation is a really strong one. There is ah lot of people out there who are basically lightweight consumers of data science, and they don't really understand how the methods that they're deploying, how they work and that limits thumb in their ability to advance the field and come up with new methods that are better suited, perhaps, of the problems of their tackling. So I think it's totally fine. And in fact, there's a lot of value to coming into data science from fields other than now third computer science. But I think taking courses in those fields, even while you're majoring in whatever field you're interested in, is going to make you a much better person who lives at that intersection. >>And how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies >>that are very strongly R and D, focused like like in Citro and others? Having a technical co founder is absolutely essential because it's fine to have and understanding of whatever the user needs and so on and come from the business side of it. And a lot of companies have a business co founder. But not understanding what the technology can actually do is highly limiting because you end up hallucinating. Oh, if we could only do this and that would be great. But you can't and people end up often times making ridiculous promises about what's technology will or will not do because they just don't understand where the land mines sit. And, um, and where you're going to hit reels, obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. >>And that being said, Where do you see in Teacher in the future? And how do you see it solving, Say, Nash, that you talked about in your keynote. >>So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up. In the old approach of that is very much a bespoke scientific um, analysis of the biology of different diseases and then going after targets are ways of dealing with the disease that are driven by human intuition. Where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to overtime create enough data and applying machine learning to address key bottlenecks in the drug discovery development process that we can bring better drugs to people, and we can do it faster and hopefully it much lower cost. >>That's great. And you also mention in your keynote that you think the 20 twenties is like a digital biology era, so tell us more about that. So I think if >>you look, if you take a historical perspective on science and think back, you realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things in the 18 seventies, that discipline was chemistry with the understanding of the periodic table, and that you actually couldn't turn lead into gold in the 19 hundreds. That was physics with understanding the connection between matter and energy in between space and time. In the 19 fifties that was computing where silicon chips were suddenly able to perform calculations that up until that point, only people have been able to >>dio. And then in 19 nineties, >>there was an interesting bifurcation. One was three era of data, which is related to computing but also involves elements, statistics and optimization of neuroscience. And the other one was quantitative biology. In which file do you move from a descriptive signs of taxonomy izing phenomenon to really probing and measuring biology in a very detailed on high throughput way, using techniques like micro arrays that measure the activity of 20,000 genes at once, or the human genome sequencing of the human genome and many others. But >>these two fields kind of >>evolved in parallel, and what I think is coming now, 30 years later, is the convergence of those two fields into one field that I like to think of a digital biology where we are able using the tools that have and continue to be developed, measure biology, an entirely new levels of detail, of fidelity of scale. We can use the techniques of machine learning and data signs to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do. And that will have implications and bio materials in energy and the environment in agriculture. And I think also in human health. And it's a incredibly exciting space toe to be in right now, because just so much is happening in the opportunities to make a difference and make the world a better place or just so large. >>That sounds awesome. Stephanie. Thank you for your insight. And thanks for being on the Cube. Thank you. I'm Sonia. Taqueria. Thanks for watching. Stay tuned for more. Okay? Great. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

>> Reporter: Live from Stanford University, it's theCUBE, covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE media. >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the fifth annual WiDs, Women in Data Science conference. Joining us today is Emily Glassberg Sands, the Head of Data Science at Coursera, Emily, welcome to theCUBE. >> Thanks, so great to be on. >> So, tell us a little bit more about what you do at Coursera. >> Yeah, absolutely, so Coursera is the world's largest platform for higher education. We partner with about 160 universities and 20 industry partners and we provide top learning content from data science to child nutrition to about 50 million learners around the world. I lead the end to end data team so spanning data engineering, data science and machine learning. >> Wow, and we just had Daphne Koller on earlier this morning who is the co-founder of Coursera and she's also the one who hired you. >> Yeah. >> So tell us more about that relationship. >> Well, I love Daphne, I think the world of her, as I will talk about shortly, she actually didn't hire me from the start. The first answer I got one from Coursera was a no, that the company wasn't quite ready for someone who wasn't a full blown coder. But I eventually talked to her into bringing me on board, and she's been an inspiration ever since. I think one of my first memories of Daphne was when she was painting the vision of what's possible with online education, and she said, "think about the first movie." The first movie was literally just filming a play on stage. You'll appreciate this, given your background in film, and then fast forward to today and think about what's possible in movies that could never be possible on the brick-and-mortar stage. And the analog she was creating was the first MOOC, the first Massive Open Online Course was very simply filming a professor in a classroom. But she was thinking forward to today and tomorrow and five years from now, and what's possible in terms of how data and technology can transform, how educators teach and how learners learn. >> That's very cool. So, how has Coursera changed from when she started it to now? >> So, it's evolved a lot. So, I've been at Coursera about six years, when I joined the company, it had less than 50 people. Today we're 10 times that size, we have 500. I think there have been obviously dramatic growth in the platform over all the three main changes to our business model. The first is we've moved from partnering exclusively with universities to recognizing that actually, a lot of the most important education for folks in the labor market is being taught within companies. So, Google is super incentivized to train people in Google Cloud, Amazon and AWS. Folks need to learn Tableau and a whole host of other software's. So, we've expanded to including education that's provided not just by top institutions like Stanford, but also by top institutions that are companies like Amazon and Google. The second big change is we've recognized that while for many learners and individual course or a MOOC is sufficient, some learners need access to full degree, a diploma bearing credential. So we've moved to the degree space we now have 14 degrees live on the platform masters in computer science and data science but also in business, accounting, and so on. And the third major changes, I think just sort of as the world has evolved to recognize that folks need to be learning throughout their lives. There's also general consensus that it's not just on the individuals to learn, but also on their companies to train them and governments as well, and so we launched Coursera enterprise, which is about providing learning content through employers and through governments so we can reach a wider swath of individuals who might not be able to afford it themselves. >> And how are you able to use data science to track individual, user preferences and user behavior? >> Yeah, that's a great question so you can imagine right? 50 million learners, they're from almost every country in the world from a range of different backgrounds have a bunch of different goals, And so I think what you're getting out is that so much of creating the right learning experience for each person is about personalizing that experience. And we personalized throughout the learner journey so in discovery up-front, when you first joined the platform, we ask you, what's your career goal? What role are you in today? And then we help you find the right content to close the gap. As you're moving through courses we predict whether or not you need some additional support. Whether it's a fully automated intervention like a behavioral nudge, emphasizing growth mindset, or a pedagogical nudge like recommending the right review material and provide it to you, and then we also do the same to accelerate support staff on campus. So, we identify for each individual what type of human touch might they need, and we serve up to support staff recommendations for who they should reach out to, whether it's a counselor reaching out to degree student who hasn't logged in for a while, or a TA reaching out to a degree student who's struggling with an assignment. So, data really powers all of that, understanding someone's goals, their backgrounds, the content that's going to close the gap, as well as understanding where they need additional support and what type of help we can provide. >> And how are you able to track this data, are you using AV testing? >> Yeah, great question, so the, we call it a venting level data, which basically tracks what every learner is doing as they're moving through the platform. And then we use AV testing to understand the influence of kind of our big feature. So, say we roll out a new search ranking algorithm or a new learning experience we would AV-Test that, yes to understand how learners in the new variant compared to learners in the old variant. But for many of our machine learn systems, we're actually doing more of a multi-armed bandit approach where on the margin, we're changing a little bit the experience people have to understand what effect that has on their downstream behavior, separate from this mass hold-in or hold-out AV-Test. >> And so today, you're giving a talk about Coursera's latest data products so give us a little insight about that. >> So, I'm covering three data products that we've launched over the last couple of years. The first two are oriented around really helping learners be successful in the learning experience. So the first is predicting when learners are going to need additional nudges and intervening in fully automated ways to get them back on track. The second is about identifying learners who need human support and serving up really easily interpretable insights to support staff so they can reach out to the right learner with the right help. And then the third is a little bit different. It's about once learners are out in the labor market, how can they credibly signal what they know, so that they can be rewarded for that learning on the job. And this is a product called skill scoring, where we're actually measuring what skills each learner has up to what level so I can for example, compare that to the skills required in my target career or show it to my employer so I can be rewarded for what I know. >> That can be really helpful when people are creating resumes, by ranking how much of a skill that they have. >> Absolutely. So, it's really interesting when you talk about resumes, so many of what, so much of what's shown on resumes are traditional credentials, things like What school did you go to? what did you major in? what jobs have you had? And as you and I both know, there's unequal access to the school you go to or the early jobs you get. And so, part of the motivation behind skill scoring is to create more equitable or fair or accessible signals for the labor market. So, we're really excited about that direction. >> And do you think companies are taking that into consideration when they're hiring people who say have like a five out of five skills in computer science, but they didn't go to Stanford? >> Yeah. >> Think they're taking that >> Absolutely, I think companies are hungry to find more diverse talent and the biggest challenge is, when you look at people from diverse backgrounds, it's hard to know who has what skills. And so skill scoring provides a really valuable input, we're actually seeing it in use already by many of our enterprise customers who are using it to identify who have their internal employees is well positioned for new opportunities or new roles. For example, I may have a bunch of backend engineers, if I know who's good in math and machine learning and statistics, I can actually tap those folks to transition over to machine learning roles. And so it's used both as an external signal and external labor market, as well as an internal signal within companies. >> And just our last question here, what advice would you give to young women who are either out of college or just starting college who are interested in data science? Who maybe, don't haven't majored in a typical data science major? What advice would you give to them? >> So, I love that you asked you haven't made it, majored in a typical data science major. I'm actually an economist by training. And I think that's probably the reason why I was at first rejected from Coursera because an economist is a very strange background to go into data science. I think my primary advice to those young women would be to really not get too lost in the data science, in the math, in the algorithms and instead to remember that those are a means to an end, and the end is impact. So, think about the problems in the world that you care about. For me, it's education. For others, it's health care, or personal finance or a range of other issues. And remember that data science provides this vast set of tools that you can use to solve the problems you care about most. >> That's great, thank you so much for being on theCUBE. >> Thank you. I'm Sonia Tagare, thank you so much for watching theCUBE and stay tuned for more. (upbeat music)

>> Narrator: Live from Stanford University, it's theCUBE! Covering Stanford Women in Data Science 2020, brought to you by SiliconAngle Media. >> Hi, and welcome to the cube, I'm your host, Sonia Tagare. And we're live at Stanford University, covering the fifth annual WiDS, Women in Data Science Conference. Joining us today is Ya XU, the head of data science at LinkedIn. Ya Welcome to the cube. >> Thank you for having me. >> So tell us a little bit about your role and about LinkedIn. >> So LinkedIn is, first of all, the biggest professional social network, where we have a massive economic graph that we have been creating with millions actually close to 700 million members and millions of companies and jobs and of course, you know, with students of skills and also schools as well as part of it. And, and I lead the data science team at LinkedIn. And my team really spans across the global presence that LinkedIn offices have. And yeah really working on various different areas. That's both thinking about how we can iterate and understand and improve our products, that we deliver to our members and our customers. And also at the same time thinking about how we can make our infrast6ructure more efficient, and thinking about how we can make our sales and marketing more efficient as well, so we really span across. >> And how has the use of data science evolved to deliver a better user experience for users of LinkedIn? >> Yeah, so first of all, I think we LinkedIn in general, we truly believe that everybody can benefit from better data, better data access, in general. So we're certainly using data to continuously understand better of what our members are looking for. As a simple example, is that whenever we launch new feature, we're not just  blindly deciding ourselves what is the better feature for our members, but we actually understand how our users are reacting to it. Right? So we use data to understand that, and then certainly making decisions, and whether we should be eventually launching this feature to all members or not. So that's a very prominent way for us to use data. And obviously, we also use data to understand and just even before we build certain features. Is this sort of feature that's right feature to build. We do both survey and understand the survey data, but also at the same time understanding just user behavior data for us to be able to come up with better features for users. >> And do you use AB testing as well? >> Oh absolutely, Yeah. So we do a lot of AV experiments. That's what, I was not trying to use that word by that like that terminology, but this is what we use to have an understanding of user features that we are developing, that we are putting in front of our users. Is that what they enjoy as much as we think they will enjoy? >> Right, so you had a talk today about creating global economic opportunities with responsible data. So give us some highlights from your talk. >> So, first of all, at LinkedIn we we truly believe in the vision that we are working towards, which is really creating economic opportunity for every member of the global workforce. And if you're kind of starting from that, and thinking about that is our sort of the axiom that we're working towards, and then thinking about how you can do that, and obviously, the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us. Right, so how can we do that? We have some early effort in using and developing differential privacy as a technique for us to do a lot better. Always regarding preserving their privacy as we're leveraging the data, but also at the same time, it doesn't ends there, right? Because you're thinking about creating opportunity. It's not just about to preserve their privacy, but also, when we are leveraging the data, how can we leverage the data in a way that is able to create opportunity in a fair way? So here is also a lot of effort that we're having with regarding, how can we do that? And what does fairest mean? What are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop product, the way that we think about responsible design, and the way that we build our algorithms, the way that we measure in every single dimension. >> And and speaking about that bias, at the opening address, they mentioned that diversity is really great because it provides many perspectives, and also helps reduce this bias. So how have you at LinkedIn been able to create a more diverse team? >> So first of all, I think it's certain we all believe that diversity is certainly better as we building product. Thinking about if you have a diverse team that is really a representation of the customer and some members that you're serving, then definitely you're able to come up with better features that is able to serve the needs of the population of our members. But also at the same time, that's just the right thing to do as well. Right, thinking about we all have had experiences we may not you know, feel as much belonging when we walk into a room that we are the only person that we identify with to be in that room. And, we certainly wanted to be able to create that environment for all the employees as well. And and thinking about, I think there is also studies that has done as what makes a high performing team. Some of the studies has done I google with the psychological safety aspects of it, which is really there's a lot of brain science that says when you make people feel they belong, that they will actually be so much more creative and innovative and everything right. So we have that belief. But tactically, there are many things that we're doing from all the divs aspect, right? How can you bring diversity, inclusion and belonging? Starting from and hiring, right? So we certainly are very much emphasized how can we increase the diversity of individuals that we're bringing to LinkedIn? And when they are at LinkedIn, can we make them feel more belonging, and feel more included in every aspects? We have different inclusion groups, right? We have I mean, obviously, I'm very much involved in Women tech. At LinkedIn we have both money efforts that we do to help women at LinkedIn in engineering, and in other groups as well to feel they belong to this community. At the same time, there is concrete actions that we're taking too. Right, that we are helping women to have a much better understanding, and aware of some of the ways that we operate that is slightly different from maybe our male colleagues will operate, right? There are certain things that we're doing to change the current processes, hiring processes, promotion process, that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity. >> Right, that's great. And what advice would you give to women who are just starting college or who are just out of college who are interested in going into data science. >> So I want to say the biggest learning for me, is just have that can do attitude. I, you know, the woman biologically and all just like in every way, we're not any less than men. And that you certainly have seen many strong and very talented women that we have in the field. So don't let people's perceptions or biases around you to bring you down. And then thinking about what you wanted, and then just go for it, and then go for the the advice that you can get from people. And then there are so many as you can see in the conference today, so many talented women that you can reach out to who are winning and very willing to help you as well. >> And in this age of AI and ML, where do you see data science going in the future? >> That's a really interesting question. So in the way that, you know, data science I want to say is a field that is really broad, right? So if you're thinking about things that I would consider to be part of data science may not necessarily part of AI, but some of the course of influence that is extremely popular and important. And then I think the fields will continue to evolve, there are going to be and then the fields are continually overlapping with each other as well. You cannot do data science without understanding or have a strong skill in AI and machine learning. And you also can't do great machine learning without understanding the data science either. Right? So thinking about some of the talk that definitely colder earlier was sharing, as in you know, you can blind in the wrong algorithm and without realizing the bias. That all the algorithm is really just detecting the machines that's using the images versus you know, actually detecting the difference between broken bones or not right, like so. So I think having, I do see there is a continuously big overlap and I think the individuals who are involved in both communities should continue to be very comfortable being in that way too. >> Right, great. Thank you so much for being on theCUBE and thank you for your insight. >> Of course, thank you for having me. >> I'm your host, Sonia Takari. Thank you for watching theCUBE and stay tuned for more. (Upbeat music)

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. Yeah. >>Hi. And welcome to the Cube. I'm your host Sonia Category. And we're live at Stanford University for the fifth annual Woods Women in Data Science Conference. Joining us today is none. Ho, the director of data Science at Intuit None. Welcome to the Cube. >>Thank you for having me here, so yeah, >>so tell us a little bit about your role at Intuit. So I leave the >>applied Machine Learning teams for our QuickBooks product lines and also for our customer success organization within my team. We do applied machine learning. So what? We specialize in building machine learning products and delivering them into our products for >>our users. Great. Today. Today you're giving a talk. You talked about how organizations want to achieve greater flexibility, speed and cost efficiencies on. And you're giving it a technical vision. Talk today about data science in the cloud world. So what should data scientists know about data science in a cloud world? >>Well, I'll just give you a little bit of a preview into my talk later because I don't want to spoil anything. Yeah, but I think one of the most important things being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of a start on our laptops or a server and do our work. But when you move to the cloud, it's like all bets are off. All the limiters are off. And so how do you fully take advantage of that? How do you change your workflow? What are some of the things that are available to you that you may not know about? And in addition to that, some some things that you have to rewire in your brain to operate in this new environment. And I'm going to share some experiences that I learned firsthand and also from my team in into its cloud migration over the past six years. >>That's great. Excited to hear that on DSO you were getting into it into it has sponsored Woods for many years now. Last year we spoke with could be the San Juan from Intuit. So tell us about this Intuit's sponsorship. Yeah, >>so into it. We are a champion of gender diversity and also all sorts of diversity. And when we first learned about which we said, We need to be a champion of the women in data science conference because for me personally, often times when I'm in a room, um, going over technical details I'm often the only woman and not just I'm often the only woman executive and so part of the sponsorship is to create this community of women, very technical women in this field, to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science. >>And so Intuit has always been really great for embracing diversity. Tell us a little bit about about bad experience, about being part of Intuit and also about the tech women part. Yeah, >>so one of the things that into it that I really appreciate is we have employees groups around specific interests, and one of those employees groups is tech women at Intuit and Tech women at Intuit. The goal is to create a community of women who can provide coaching, mentorship, technical development, leadership development and I think one of the unique things about it is that it's not just focused on the technical development side, but on helping women develop into leadership positions. For me, When I first started out, there were very few women in executive positions in our field and data science is a brand new field, and so it takes time to get there. Now that I'm on the other side, one of the things that I want to do is be able to give back and coach the next generation. And so the tech women at Intuit Group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that I can provide that coaching in that leadership development >>and speaking about like diversity. In the opening address, we heard that diversity creates perspectives, and it also takes away bias. So why gender diversity is so important into it, and how does it help take away that bias? Yeah, >>so one of the important things that I think a lot of people don't realize is when you go and you build your products, you bring in a lot of biases and how you build the product and ultimately the people who use your products are the general population for us. We serve consumer, small businesses and self employed. And if you take a look at the diversity of our customers, it mirrors the general population. And so when you think about building products, you need to bring in those diverse perspectives so you could build the best products possible because of people who are using those products come from a diverse background as well, >>right? And so now at Intuit like instead of going from a desktop based application, we're at a cloud based application, which is a big part of your talk. How do you use data Teoh for a B testing and why is it important? >>Yeah, a B testing That is a personal passion of mine, actually, because as a scientist, what we like to do is run a lot of experiments and say, Okay, what is the best thing out there so that ultimately, when you ship a new product or feature, you send the best thing possible that's verified by data, and you know exactly how users are going to react to it. When we were on desktop, they made it incredibly difficult because those were back in the days. And I don't know if you remember those put back in the days when you had a floppy disk, right or even a CD ROM's. That's how we shipped our products. And so all the changes that you wanted to make had to be contained. In the end, you really only ship it once per year. So if there's any type of testing that we did, we're bringing our users and have them use our products a little bit and then say Okay, we know exactly what we need to dio ship that out. So you only get one chance now that we're in the cloud. What that allows us to do is to test continuously via a B, testing every new feature that comes out. We have a champion Challenger model, and we can say Okay, the new version that we're shipping out is this much better than the previous one. We know it performs in this way, and then we got to make the decision. Is this the best thing to do for a customer? And so you turn what was once a one time process, a one time change management process. So one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers. >>That's awesome. Um, so, um, what advice would you give to the next generation of women who are interested in stem but maybe feel like, Oh, I might be the only woman. I don't know if I should do this. Yeah, I think that the biggest >>thing for me was finding men's ownership, and initially, when I was very early career and even when I was doing my graduate studies for me, a mentor with someone who was in my field. But when I first joined into it, an executive in another group who is a female, said, Hey, I'd like to take your side, provide you some feedback, and this is some coaching I want to give you, And that was when I realized you don't actually need to have that person be in your field to actually guide you through to the next up. And so, for women who are going through their journey and early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field there in, because everybody has diverse perspectives and things that they can teach you as you go along. >>And how do you think Woods is helping women feel like they can do data science and be a part of the community? Yeah, I think >>what you'll see in the program today is a huge diversity of our speakers, our Panelists through all different stages of their career and all different fields. And so what we get to see is not only the time baseline of women who are in their PhDs all the way to very, very well established women. The provost of Stanford University was here today, which is amazing to see someone at the very top of the career who's been around the block. But the other thing is also the diversity and fields. When you think about data science, a lot of us think about just the tech industry. But you see it in healthcare. You see it in academia and there's a scene that wide diversity of where data science and where women who are practicing data science come from. I think it's really empowering because you can see yourself in the representation does matter quite a bit. >>Absolutely. And where do you see data science going forward? >>Oh, that is a, uh, tough and interesting question, actually. And I think that in the current environment today, we could talk about where it could go wrong or where it could actually open the doors. And for me, I'm an eternal optimist on one of the things that I think is really, really exciting for the future is we're getting to a stage where we're building models, not just for the general population. We have enough data and we have enough compute where we can build a model. Taylor just for you, for all of your life's on for me. I think that that is really, really powerful because we can build exactly the right solution to help our customers and our users succeed. Specifically, me working in the personal friend, Small business finance lease. That means I can hope that cupcake shop owner actually manage her cash flow and help her succeed to me that I think that's really powerful. And that's where data science is headed. >>None. Thank you so much for being on the Cube and thank you for your insight. Thank you so much. I'm so sorry. Thanks for watching the Cube. Stay tuned for more. Yeah, Yeah, yeah, yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

>> Announcer: Live from Stanford University. It's theCUBE, covering Stanford Women in Data Science 2020, brought to you by SiliconANGLE Media. (upbeat music) >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare. And we're live at Stanford University covering the fifth annual WiDS Women in Data Science Conference. Joining us today is Lucy Bernholz, who is the Senior Research Scholar at Stanford University. Lucy, welcome to theCUBE. >> Thanks for having me. >> So you've led the Digital Civil Society Lab at Stanford for the past 11 years. So tell us more about that. >> Sure, so the Digital Civil Society Lab actually exists because we don't think digital civil society exists. So let me take that apart for you. Civil society is that weird third space outside of markets and outside of government. So it's where we associate together, it's where we as people get together and do things that help other people could be the nonprofit sector, it might be political action, it might be the eight of us just getting together and cleaning up a park or protesting something we don't like. So that's civil society. But what's happened over the last 30 years really is that everything we use to do that work has become dependent on digital systems and those digital systems, some tier, I'm talking gadgets, from our phones, to the infrastructure over which data is exchanged. That entire digital system is built by companies and surveilled by governments. So where do we as people get to go digitally? Where we could have a private conversation to say, "Hey, let's go meet downtown and protest x and y, or let's get together and create an alternative educational opportunity 'cause we feel our kids are being overlooked, whatever." All of that information that get exchanged, all of that associating that we might do in the digital world, it's all being watched. It's all being captured (laughs). And that's a problem because both history and political science, history and democracy theory show us that when there's no space for people to get together voluntarily, take collective action, and do that kind of thinking and planning and communicating it just between the people they want involved in that when that space no longer exists, democracies fall. So the lab exists to try to recreate that space. And in order to do that, we have to first of all recognize that it's being closed in. Secondly, we have to make real technological process, we need a whole set of different kind of different digital devices and norms. We need different kinds of organizations, and we need different laws. So that's what the lab does. >> And how does ethics play into that. >> It's all about ethics. And it's a word I try to avoid actually, because especially in the tech industry, I'll be completely blunt here. It's an empty term. It means nothing the companies are using it to avoid being regulated. People are trying to talk about ethics, but they don't want to talk about values. But you can't do that. Ethics is a code of practice built on a set of articulated values. And if you don't want to talk about values, you don't really having conversation about ethics, you're not having a conversation about the choices you're going to make in a difficult situation. You're not having a conversation over whether one life is worth 5000 lives or everybody's lives are equal. Or if you should shift the playing field to account for the millennia of systemic and structural biases that have been built into our system. There's no conversation about ethics, if you're not talking about that thing and those things. As long as we're just talking about ethics, we're not talking about anything. >> And you were actually on the ethics panel just now. So tell us a little bit about what you guys talked about and what were some highlights. >> So I think one of the key things about the ethics panel here at WiDS this morning was that first of all started the day, which is a good sign. It shouldn't be a separate topic of discussion. We need this conversation about values about what we're trying to build for, who we're trying to protect, how we're trying to recognize individual human agency that has to be built in throughout data science. So it's a good start to have a panel about it, the beginning of the conference, but I'm hopeful that the rest of the conversation will not leave it behind. We talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control. Data scientists are building data sets and algorithmic forms of analysis, that are both of those two things are just coated sets of values. And if you try to have a conversation about that, at just the math level, you're going to miss the social level, you're going to miss the fact that that's humanity you're talking about. So it needs to really be integrated throughout the process. Talking about the values of what you're manipulating, and the values of the world that you're releasing these tools into. >> And what are some key issues today regarding ethics and data science? And what are some solutions? >> So I mean, this is the Women and Data Science Conference that happens because five years ago or whenever it was, the organizers realize, "Hey, women are really underrepresented in data science and maybe we should do something about that." That's true across the board. It's great to see hundreds of women here and around the world participating in the live stream, right? But as women, we need to make sure that as you're thinking about, again, the data and the algorithm, the data and the analysis that we're thinking about all of the people, all of the different kinds of people, all of the different kinds of languages, all of the different abilities, all of the different races, languages, ages, you name it that are represented in that data set and understand those people in context. In your data set, they may look like they're just two different points of data. But in the world writ large, we know perfectly well that women of color face a different environment than white men, right? They don't work, walk through the world in the same way. And it's ridiculous to assume that your shopping algorithm isn't going to affect that difference that they experience to the real world that isn't going to affect that in some way. It's fantasy, to imagine that is not going to work that way. So we need different kinds of people involved in creating the algorithms, different kinds of people in power in the companies who can say we shouldn't build that, we shouldn't use it. We need a different set of teaching mechanisms where people are actually trained to consider from the beginning, what's the intended positive, what's the intended negative, and what is some likely negatives, and then decide how far they go down that path? >> Right and we actually had on Dr. Rumman Chowdhury, from Accenture. And she's really big in data ethics. And she brought up the idea that just because we can doesn't mean that we should. So can you elaborate more on that? >> Yeah well, just because we can analyze massive datasets and possibly make some kind of mathematical model that based on a set of value statements might say, this person is more likely to get this disease or this person is more likely to excel in school in this dynamic or this person's more likely to commit a crime. Those are human experiences. And while analyzing large data sets, that in the best scenario might actually take into account the societal creation that those actual people are living in. Trying to extract that kind of analysis from that social setting, first of all is absurd. Second of all, it's going to accelerate the existing systemic problems. So you've got to use that kind of calculation over just because we could maybe do some things faster or with larger numbers, are the externalities that are going to be caused by doing it that way, the actual harm to living human beings? Or should those just be ignored, just so you can meet your shipping deadline? Because if we expanded our time horizon a little bit, if you expand your time horizon and look at some of the big companies out there now, they're now facing those externalities, and they're doing everything they possibly can to pretend that they didn't create them. And that loop needs to be shortened, so that you can actually sit down at some way through the process before you release some of these things and say, in the short term, it might look like we'd make x profit, but spread out that time horizon I don't know two x. And you face an election and the world's largest, longest lasting, stable democracy that people are losing faith in. Set up the right price to pay for a single company to meet its quarterly profit goals? I don't think so. So we need to reconnect those externalities back to the processes and the organizations that are causing those larger problems. >> Because essentially, having externalities just means that your data is biased. >> Data are biased, data about people are biased because people collect the data. There's this idea that there's some magic debias data set is science fiction. It doesn't exist. It certainly doesn't exist for more than two purposes, right? If we could, and I don't think we can debias a data set to then create an algorithm to do A, that same data set is not going to be debiased for creating algorithm B. Humans are biased. Let's get past this idea that we can strip that bias out of human created tools. What we're doing is we're embedding them in systems that accelerate them and expand them, they make them worse (laughs) right? They make them worse. So I'd spend a whole lot of time figuring out how to improve the systems and structures that we've already encoded with those biases. And using that then to try to inform the data science we're going about, in my opinion, we're going about this backwards. We're building the biases into the data science, and then exporting those tools into bias systems. And guess what problems are getting worse. That so let's stop doing that (laughs). >> Thank you so much for your insight Lucy. Thank you for being on theCUBE. >> Oh, thanks for having me. >> I'm Sonia Tagare, thanks for watching theCUBE. Stay tuned for more. (upbeat music)

>>live from Stanford University. It's the queue covering Stanford women in data Science 2020. Brought to you by Silicon Angle Media. >>Hi, and welcome to the Cube. I'm your host, Sonia today, Ari. And we're live at Stanford University covering wigs, Women in Data Science Conference 2020 And this is the fifth annual one. Joining us today is John Hoegger, who is the principal data scientist manager at Microsoft. John. Welcome to the Cube. Thanks. So tell us a little bit about your role at Microsoft. >>I manage a central data science team for myself. 3 65 >>And tell us more about what you do on a daily basis. >>Yeah, so we look at it across all the different myself. 365 products Office Windows security products has really try and drive growth, whether it's trying to provide recommendations to customers to end uses to drive more engagement with the products that they use every day. >>And you're also on the Weeds Conference Planning Committee. So tell us about how you joined and how that experience has been like, >>Yeah, actually, I was at Stanford about a week after the very first conference on. I got talking to Karen, one of this co organizers of that that conference and I found out there was only one sponsor very first year, which was WalMart Labs >>on. >>The more that she talked about it, the more that I wanted to be involved on. I thought that makes it really should be a sponsor, this initiative. And so I got details. I went back and my assessment sponsor. Ever since I've been on the committee trying it help with. I didn't find speakers on and review and the different speakers that we have each year. And it's it's amazing just to see how this event has grown over the four years. >>Yeah, that's awesome. So when you first started, how many people attended in the beginning? >>So it started off as we're in this conference with 400 people and just a few other regional events, and so was live streamed but just ready to a few universities. And ever since then it's gone with the words ambassadors and people around the world. >>Yes, and outwits has is over 60 countries on every continent except Antarctica has told them in the Kino a swell as has 400 plus attendees here and his life stream. So how do you think would has evolved over the years? >>Uh, it's it's term from just a conference to a movement. Now it's Ah, there's all these new Our regional events have been set up every year and just people coming together, I'm working together. So, Mike, self hosting different events. We had events in Redmond. I had office and also in New York and Boston and other places as well. >>So as a as a data scientist manager for many years at Microsoft, I'm I'm sure you've seen it increase in women taking technical roles. Tell us a little bit about that. >>Yeah, And for any sort of company you have to try and provide that environment. And part of that is even from recruiting and ensuring that you've got a diverse into s. So we make sure that we have women on every set of interviews to be able to really answer the question. What's it like to be a woman on this team and your old men contents of that question on? So you know that helps as faras we try, encourage more were parented some of these things demos on. I've now got a team of 30 data scientists, and half of them are women, which is great. >>That's also, um So, uh, um, what advice would you give to young professional women who are just coming out of college or who just starting college or interested in a stem field? But maybe think, Oh, I don't know if they'll be anyone like me in the room. >>Uh, you ask the questions when you interview I go for those interviews and asked, like Like, say, What's it like to be a woman on the team? All right. You're really ensuring that the teams that you're joining the companies you joined in a inclusive on and really value diversity in the workforce >>and talking about that as we heard in the opening address that diversity brings more perspectives, and it also helps take away bias from data science. How have you noticed that that bias becoming more fair, especially at your time at Microsoft? >>Yeah, and that's what the rest is about. Is just having those diverse set of perspectives on opinions in heaven. More people just looking like a data and thinking through your holiday to come. Views on and ensure has been used in the right way. >>Right. Um and so, um, what do you going forward? Do you plan to still be on the woods committee? What do you see with is going how DC woods in five years? >>Ah, yeah. I live in for this conference I've been on the committee on. I just expected to continue to grow. I think it's just going right beyond a conference. Dossevi in the podcasts on all the other initiatives that occurring from that. >>Great. >>John, Thank you so much for being on the Cube. It was great having >>you here. Thank you. >>Thanks for watching the Cube. I'm your host, Sonia, to worry and stay tuned for more. Yeah.

live from Stanford University it's the hue covering Stanford women in data science 2020 brought to you by Silicon angle media hi and welcome to the cube I'm your host Sonia - Garrett and we're live at Stanford University covering wigs women in data science conference the fifth annual one and joining us today is Daphne Koller who is the co-founder who sari is the CEO and founder of in seat row that Daphne welcome to the cube nice to be here Sonia thank you for having me so tell us a little bit about in seat row how you how it you got it founded and more about your role so I've been working in the intersection of machine learning and biology and health for quite a while and it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and one of those problems that I think is really important is drug discovery development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half-full glass half-empty the glass half-full is the fact that there's diseases that used to be a death sentence or of the sentence still a life long of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Arun was law being the inverse of Moore's Law which is the one we're all familiar with because the number of drugs approved per billion u.s. dollars just keeps going down exponentially so the question is can we change that curve and you talked in your keynote about the interdisciplinary cold to tell us more about that I think in order to address some of the critical problems that were facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in seat row we actually have a company that's half-life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interpreted but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I'd been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a teary leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a Content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to ecommerce and the natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building and company in the space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in a similar field would you tell them they have to major in math or or do you think that maybe like there are some other majors that may be influential as well I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that found the is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than a third computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly Rd focused like like in C tro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what technology will or will not do because they just don't understand where the land mines sit and and where you're gonna hit real obstacles and in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see an teacher in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in seat row we'll be a fully integrated drug discovery and development company that is based on a slightly different foundation than a traditional pharma company where they grew up in the old approach of that is very much bespoke scientific analysis of the biology of different diseases and then going after targets or our ways of dealing with the disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that the cord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think that 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry was the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicon chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization of neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of techsan amaizing phenomena to really probing and measuring biology in a very detailed and a high-throughput way using techniques like microarrays that measure the activity of 20,000 genes at once Oh the human genome sequencing of the human genome and many others but these two feels kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials in energy in the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on cute thank you I'm so neat agario thanks for watching stay tuned for more great

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

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

>>from Menlo Park, California In the heart of Silicon Valley, it's the Cube covering cloud now. Awards 2020 Brought to you by Silicon Angle Media. Now here's Sonia category. >>Hi, and welcome to the Cube. I'm your host Sonia category, and we're on the ground at Facebook headquarters in Menlo Park, California covering Cloud now's top women entrepreneurs in Cloud Innovation Awards. >>Joining us today is Melon Yen, founder and partner of operator Collective Madeleine, Welcome to the Cube. Thank you so much. So tell us a little bit about your background. >>So Operator Collective is actually my fourth organization that been apart of starting, and all of them have had an aspect of it that had a strong community to it. And so that was one of the reasons why, um, as you hear about in a second, I could put together this kind of crazy idea for a fund that looks like no other. >>Um, So what inspired you to start this company? And how did you navigate getting funding? >>Sure. So? So, because that operator collective is my fourth company. The 1st 1 was actually a nonprofit. The 2nd 1 was a venture backed company that we took from 0 to 100 million in public in less than three years, and the 3rd 1 was something called Faster, which is the world's largest B two b B two b community for SAS Softwares of service, the company that was a venture backed startup that we took from 0 to 100 million in public in less than three years. Even though I helped launch it, I didn't actually officially joined as an employee until about 18 months in, and by that time it's employees 65 I noticed a number of things, which is there were largely homogenous group of people who were there before me, all really great people. But you tend to know people like you and the hyper growth stages of startups. You tend to turn around and say, Who can I get? And so you and you turn to the people that you know, And so you end up with companies that look like yourself and so spent a lot of time looking at what was going on in the venture world, which is that in the area that I focus on, which is enterprise and software enterprise software. It is over 90% male in terms of veces as well as founders and the world revolves around in the venture world revolves around veces and founders. And so I looked around and said, Well, where the operators, the people who build and grow and scale up these companies, they're largely not. They're not efficiently and effectively part of this ecosystem and then second, where the women and people of color And so but as I started to dig in more and talk to people, what I realized was that the VCs and founders actually wanted to bring in the operators. They wanted to bring in the people with different backgrounds, but the network's didn't naturally overlap. And so I thought, there's got to be a way to bring them in, because I know the operators and the operators also want to participate. But the system isn't optimized to make it efficient or friendly are comfortable for them to be able to participate. So that's why I decided to put operator collective together. >>Wow, So you are key noting today for cloud. Now, um, what has this experience been like? And what is the main message you want to give to the award winners and to the cloud now community. >>So it's incredibly inspiring to be with all of the women who are being honored tonight as well as, frankly, the organizers. The organization itself Cloud now is incredibly impactful. And so one of the reasons I was so excited to be asked is a number of the women who were being honored. I either know or have heard of. And the recognition is something that is very important because we need to tell the stories and recognize these people who are not. Maybe the usual suspects, the ones who maybe not our everyday names. And so I was super excited to be here. >>So you were talking about how it's about 90% male in the VC and founder community, Um, in one of your articles, which are amazing, by the way you said, Don't let the excuse of cultural fit be a vehicle for perpetuating sameness, and I thought that was so profound. So, um, are you still seeing this notion of cultural fit being a huge issue and if so, what can be done? Teoh mitigate it? Yeah, I think there's >>more awareness now of the fact that if you hire for cultural fit, you'll end up with 65 people who are exactly like you. And that's not optimizing for a successful company because right there studies that show that diverse teams outperform out innovate, homogeneous teams. But what's also interesting is the same study says that, but homogeneous teams are more certain that they've gotten to the right answer, even if they've got into the answer less less often than the diverse teams. And so when you have people who are just like you, then everyone agrees with each other than you don't realize that. Maybe there's another way of looking at something and so cultural fit is is a warning sign. I think to say that. Okay, well, there just like me, I'm very comfortable sometimes. Being uncomfortable is good. >>That's a great message. I think it's really hard to to say like, Oh, I'm okay with being comfortable. Um, so in, in in in one of your other articles, you bring up this idea of, um, don't check all the boxes, but rather fill in the gaps. So can you explain more about that? >>Yeah. So the idea behind that is, if you look for only the typical candidates. The ones who maybe think of a startup founder went to Stanford. Where's the hoodie? Right? Did computer science then that's fine. There are plenty of those people who have been successful, but you're ignoring all the people who didn't. And so, in fact, I'm the beneficiary of people who were willing to not just check all the boxes because I >>didn't >>check any of the boxes. If you look at, if you look at my background, I should not have been able to raise. Is the first time fund and a first time fund manager to be able to raise a $50 million fund because I'm a um Ah, let's see, I'm a solo GP, right? So, General partner who hasn't been a VC before with the first time fund, I don't have the traditional venture background. My previous background was I was an intellectual property attorney. Um, then help start a company as a result of that and then and then also when you check the boxes, 40% of the seas went to Stanford or Harvard, and when you look at the numbers, I didn't check all the boxes, but precisely because I didn't check all the boxes, I was able to actually look at this differently and say, Hey, that's not the model that that I want to build. And frankly, if I tried to build the same model that everyone else did, my background so doesn't look like anything. I wouldn't have been successful. And by taking it and saying, Look, I'm gonna build a model that's totally different from the ground up that allowed me to build a platform in a community that looked like no one else is as a result of that was able to raise money from institutional investors, for instance, which very rarely back first time funds. And so, by not checking all the boxes, um, I was able to build a model, but by other people also saying, Look, she doesn't check any of our typical boxes. But we >>would like this >>idea because it's so different than everyone else is. We will. We are now, you know, part of the fund >>and sometimes different is good, and it's what's what's needed? Absolutely. Um, so speaking of that, um, in terms of operator collective, what workplace environment are you trying to strive for. >>So what we say is we seek to back founders from all backgrounds who believe you share are believed that culture, diversity and operational excellence are a key part of building truly great companies. So we strive to be inclusive way. We strive to have a variety of backgrounds. We use a lot of the tools that of the companies, because we focus only on enterprise and B two B software and technology and infrastructure. And so we also try to use a lot of those tools. So we are mostly women team and we are distributed team. We largely work out of our homes and we work a lot on Zoom and we all a lot of us have kids too, and so what we do is we adjust the schedule so we can do drop off in the morning. We work like crazy, right? We work long hours, but we also do it so that people can can take their kids to doctor's appointments or pick up their kids at the end of the day. But we what was important to me was that we created environment that worked with our busy lives, and it wasn't that we were trying to take, take take these incredibly talented women and make it fit into just the corporate norm. Because you can have an incredibly successful work relationship. I mean, you can have an incredibly successful, um career if you don't have to sacrifice everything else in your life for it, >>right? Right. And that balance is so important. Um, so what advice would you give to aspiring female entrepreneurs who maybe have, ah, not so technical background or who are struggling to navigate in this male dominated industry. >>So one of the things >>I talked about in my keynote today was was that you never get this right. You're never going to raise a fund. If if you do this, you're never gonna raise a fund. And so when you're starting a company, you will go when you talk to a lot of people as you should, because you will get lots of great information. Ah, lot of people are going to say, Well, you're never gonna have a You're never going to start a company if you don't have a technical co founder never going to start a company. If you're gonna try to do X and So while you some might say, Well, you should just ignore those people actually say, Don't ignore those people because they are saying that other people are going to think that too. But think of a way to counter that. And that actually help make the operator collective business model stronger. Because we said Okay, we know that's gonna be the mindset. Let's turn it around and actually make this a strength. And so, for female founders or any founders, what I would say is listen to a lot of people talk to a lot of people here what they have to say. Ultimately, trust your instinct. Trust your gut. And because you know what's best for the company that you're trying to build. >>Great words of advice. Melon. Thank you so much for being on the Cube. Thank you >>so much for having me. Absolutely. >>I'm Sonita Gari. Thanks for watching the Cube. Stay tuned for more. >>Yeah, yeah, yeah.

>>Trump and low park California in the heart of Silicon Valley. It's the cube covering cloud now. Awards 2020 brought to you by Silicon angle media. Now here's Sonya to garden. >>Hi and welcome to the cube. I'm your host Sonia to Gary. And we're on the ground at Facebook headquarters in Menlo park, California covering cloud now's top women entrepreneurs in cloud innovation awards. Joining us today is modest to get day, the cofounder of branch motto. Welcome to the cube. Thank you so much for having me. So you're receiving an award today for being a top female entrepreneur in cloud innovation. How does that feel? >>It feels awesome. I'm humbled to be in such amazing company with some great ladies that have started really great companies, so pretty excited to be here. >>Great. So just give us a brief overview of your background. >>Sure. Uh, my background, well, I probably don't have the regular Silicon Valley background. I was born and raised in communist Romania, uh, in a pretty small town called Barco, uh, in the Rijo Romania called Moldavia. I was very good at math. Um, and my parents, uh, pushed me to explore applying to schools in the United States, which I did. Um, and I applied to 23 colleges and the DOB, uh, getting a full scholarship from Cornell where I studied computer engineering. Um, I dreamt of working for big companies, which I did for a while, uh, until one day when I remember I was doing a master's to Stanford and one professor told me I was, I told him, I was like, I don't think I could ever start a company. And he was like, what if you don't? Like, who do you think? Well, so I was like, Oh, I never thought about it that way. Um, and that's when I think my entrepreneurial dream started. And a few years later I started, um, phone co-founders and started a few different companies that eventually ended up being branch. That's a long answer to your question. >>No, that's perfect. So what inspired you to start branch and how did you navigate getting funding? >>Um, it's a, it's an interesting story. I think we came together, my cofounders and I were in business school, Stanford, we all want to start a company and we did what all business school students do. We just started something that sounded cool but maybe it didn't have such a big market. Um, and uh, then pivoted and ended up building an app. So we worked on an app or the mobile photo printing app called kindred. We worked on the Apple for quite some time. It was, um, over a year we sold over 10,000 photo books. I've seen a lot of images of babies and pets and we reviewed manually every single book and we had a really hard time growing. So if you think about the mobile ecosystem today, and if you compare it to the web on the web, the web is a pretty democratic system. >>You, um, you have the HTTP protocol and you are able to put together a website and make sure that the website gets found through social media to research to all this other platforms. Apps are much harder to discover. Um, the app ecosystem is owned by the platforms. And we had a really hard time applying. I was coming from the web world and all the things I had done to market websites just in the work with the apps. And it was hard. Uh, you know, you could only Mark at the top and how out all the content inside the app. That's a lot more interesting than the app itself. So we, we felt that we were like really, really struggling and we would need it to kind of shut the company down. And then we realized that one of the things that we were trying to build for us to a disability to allow people to share and get to content within the app, which is in our case was photo books was actually something that everyone in the ecosystem needed. >>So we, we asked a lot of people and it seemed like this was a much bigger need. Uh, then, you know, the photo books. And, uh, we had started to already build it to solve our own problem. So we started building a linking and attribution platform, um, to help other app. And mobile companies grow and understand their user journey and help build like interesting connections for the user. So, you know, our mission is to, um, to help people discover content within apps, uh, through links that always work. Uh, and it's been a wonderful, like an F pretty exciting journey ever since. That's really inspiring and, and solving a real world problem, a real world problem. >> So it's interesting when you ask about fundraising. Uh, it was so hard to raise money for the photo book app. And we raised actually from, uh, uh, pay our ventures and they actually, even now I remember, uh, the guy patch man sat us down in a very Silicon Valley fashion at the rosewoods and was a very hot day and there was like Persian tea being served and he gave us money and he said, you know, I just want to do something. >>I am not investing in the idea. I'm investing in you as a team. Uh, and if you pivot away from photo books, you know, uh, which we did and I think we pivoted the way because we ended up finding a much, much bigger problem. And we felt that, you know, we could actually make a, an actual change into the mobile cloud ecosystem. And that's how, that's how it all started. Uh, and it wasn't actually was easier to raise money after we had a really big problem. We had a good team that had been working together for almost two years. We had product market fit. >> So, uh, so yeah. So what are some things that have influenced you in your journey to become an entrepreneur? Um, some things interesting. Um, well I would say the Stanford design school. Um, I think I came from working for Siemens, which is a giant company. >>And I started doing this project and I remember one of the projects was we built, um, an, uh, a toolbar we were supposed to where we're doing a project for, um, Firefox, which, you know, Mozilla was utilize browser, uh, which was in some ways the precursor to Chrome. And we're trying to help it grow. And we didn't know. And one of the ideas was we, we built this toolbar for eBay and eBay hadn't had a toolbar for Firefox. And we, you know, we were some students for two weeks. We build this toolbar bar and then someone bought the car to our toolbar. And I was like, wow. Like how incredible is it that you can just kind of put your thoughts on something and just get something done and make an actual impact someone's life. And I think that's when the spark of the entrepreneurial spark, it was during that time that, um, Michael Dearing course, a professor and one of my D school courses also told me the thing that if I don't do it, who will? >>And I think that's when, that's when it all started. I think the things that have helped me along the way, I mean, my cofounders, I think I've been incredibly lucky to find cofounders that are incredibly eager to be good at what they do and also very different from me. So I think if you think about why many companies implode, it's usually because of the founding team. We've been together for almost seven years now. Uh, and it's been an interesting way to find balance through so many failed companies. So many stages of growth branches over 400 people now. So you know, our roles have shifted over time and it's been like, uh, an interesting journey and I think recently more in the past few years, I think one of the things that has helped me find balance has been having a group of female founder friends. Um, it's really interesting to have a peer group that you can talk about things with and be vulnerable with. >>And I didn't have that in the first few years and I wish I did. My cofounders are amazing, but I think in some ways we are also coworkers. So having an external group has been incredibly helpful in helping me find balance in my life. So I think a lot of women feel that way. They feel that it's really difficult to navigate in this male dominated workspace. So what advice would you give to female entrepreneurs in this space? Yeah, I mean it is really hard and I think confidence is something that I've noticed with myself, my peers, the women that I've invested in. I do investing on the side. Uh, I would say believe that you can do it. Uh, believe that the only, the sky's the limit believe that, um, you can do more than you think you can do. I think sometimes, uh, you know, our, our background and the society around us, um, doesn't necessarily believe that we can do the things that we can do as women. >>So I think believing in ourselves is incredibly important. I think the second part is making sure that we build networks around us. They can tell us that they believe in us. They can push us beyond what we think is possible. And I think those networks can be peers. Like my funeral founder group, we call each other for ministers or, uh, I think investors. Um, I think it can be mentors. And I've had, I've been lucky enough to have amazing women investors, uh, women mentors. Um, and I, it's been a really incredible to see how much they helped me grow. So I think the interesting thing is when I was just getting started, I didn't look for those communities. I didn't look for a guy. I just kinda felt, Oh, I can do it. But I didn't actually realize that being part of a community, being vulnerable, asking questions can actually go help me go so much further. Um, so the advice would be to start early and find a small group of people that you can actually rely on, and that can be your advocates and your champions. So, yeah. Well, thank you so much for those words of wisdom. Thanks for having me. Thank you for being on the cube. I'm your host, Sonia to Gary. Thanks for watching the cube. Stay tuned for more.