>>Hey, everyone. Welcome to the AWS partner, showcase women in tech. I'm Lisa Martin from the cube. And today we're gonna be looking into the exciting evolution of women in the tech industry. I'm going to be joined by Danielle GShock, the ISP PSA director at AWS. And we have the privilege of speaking with some wicked smart women from Teradata NetApp. JFI a 10th revolution group, company and honeycomb.io. We're gonna look at some of the challenges and biases that women face in the tech industry, especially in leadership roles. We're also gonna be exploring how are these tech companies addressing diversity, equity and inclusion across their organizations? How can we get more young girls into stem earlier in their careers? So many questions. So let's go ahead and get started. This is the AWS partner showcase women in tech. Hey, everyone. Welcome to the AWS partner showcase. This is season one, episode three. And I'm your host, Lisa Martin. I've got two great guests here with me to talk about women in tech. Hillary Ashton joins us the chief product officer at Terry data. And Danielle Greshaw is back with us, the ISV PSA director at AWS ladies. It's great to have you on the program talking through such an important topic, Hillary, let's go ahead and start with you. Give us a little bit of an intro into you, your background, and a little bit about Teradata. >>Yeah, absolutely. So I'm Hillary Ashton. I head up the products organization. So that's our engineering product management office of the CTO team. Um, at Teradata I've been with Terra data for just about three years and really have spent the last several decades. If I can say that in the data and analytics space, um, I spent time, uh, really focused on the value of, of analytics at scale, and I'm super excited to be here at Teradata. I'm also a mom of two teenage boys. And so as we talk about women in tech, I think there's, um, uh, lots of different dimensions and angles of that. Um, at Teradata, we are partnered very deeply with AWS and happy to talk a little bit more about that, um, throughout this discussion as well. >>Excellent. A busy mom of two teen boys. My goodness. I don't know how you do it. Let's now look, Atter data's views of diversity, equity and inclusion. It's a, the, it's a topic that's important to everyone, but give us a snapshot into some of the initiatives that Terra data has there. >>Yeah, I have to say, I am super proud to be working at Teradata. We have gone through, uh, a series of transformations, but I think it starts with culture and we are deeply committed to diversity, equity and inclusion. It's really more than just a statement here. It's just how we live our lives. Um, and we use, uh, data to back that up. Um, in fact, we were named one of the world's most ethical companies for the 13th year in a row. Um, and all of our executive leadership team has taken an oath around D E and I that's available on LinkedIn as well. So, um, in fact, our leadership team reporting into the CEO is just about 50 50, um, men and women, which is the first time I've worked in a company where that has been the case. And I think as individuals, we can probably appreciate what a huge difference that makes in terms of not just being a representative, but truly being on a, on a diverse and equitable, uh, team. And I think it really, uh, improves the behaviors that we can bring, um, to our office. >>There's so much value in that. It's I impressive to see about a 50 50 at the leadership level. That's not something that we see very often. Tell me how you, Hillary, how did you get into tech? Were you an engineering person by computer science, or did you have more of a zigzaggy path to where you are now? >>I'm gonna pick door number two and say more zigzaggy. Um, I started off thinking, um, that I started off as a political science major or a government major. Um, and I was probably destined to go into, um, the law field, but actually took a summer course at Harvard. I did not go to Harvard, but I took a summer course there and learned a lot about multimedia and some programming. And that really set me on a trajectory of how, um, data and analytics can truly provide value and, and outcomes to our customers. Um, and I have been living that life ever since. Um, I graduated from college, so, um, I was very excited and privileged in my early career to, uh, work in a company where I found after my first year that I was managing, um, uh, kids, people who had graduated from Harvard business school and from MIT Sloan school. Um, and that was super crazy, cuz I did not go to either of those schools, but I sort of have always had a natural knack for how do you take technology and, and the really cool things that technology can do, but because I'm not a programmer by training, I'm really focused on the value that I'm able to help, um, organizations really extract value, um, from the technology that we can create, which I think is fantastic. >>I think there's so much value in having a zigzag path into tech. You bring Danielle, you and I have talked about this many times you bring such breadth and such a wide perspective. That really is such a value. Add to teams. Danielle, talk to us from AWS's perspective about what can be done to encourage more young women to get and under and underrepresented groups as well, to get into stem and stay. >>Yeah, and this is definitely a challenge as we're trying to grow our organization and kind of shift the numbers. And the reality is, especially with the more senior folks in our organization, unless you bring folks with a zigzag path, the likelihood is you won't be able to change the numbers that you have. Um, but for me, it's really been about, uh, looking at that, uh, the folks who are just graduating college, maybe in other roles where they are adjacent to technology and to try to spark their interest and show that yes, they can do it because oftentimes it's really about believing in themselves and, and realizing that we need folks with all sorts of different perspectives to kind of come in, to be able to help really, um, provide both products and services and solutions for all types of people inside of technology, which requires all sorts of perspectives. >>Yeah, the diverse perspectives. There's so much value and there's a lot of data that demonstrates how much value revenue impact organizations can make by having diversity, especially at the leadership level. Hillary, let's go back to you. We talked about your career path. You talked about some of the importance of the focus on de and I at Tarana, but what are, what do you think can be done to encourage, to sorry, to recruit more young women and under groups into tech, any, any carrot there that you think are really important that we need to be dangling more of? >>Yeah, absolutely. And I'll build on what Danielle just said. I think the, um, bringing in diverse understandings, um, of, of customer outcomes, I mean, I, the we've really moved from technology for technology's sake and I know AWS and entirety to have had a lot of conversations on how do we drive customer outcomes that are differentiated in the market and really being customer centric and technology is wonderful. You can do wonderful things with it. You can do not so wonderful things with it as well, but unless you're really focused on the outcomes and what customers are seeking, um, technology is not hugely valuable. And so I think bringing in people who understand, um, voice of customer who understand those outcomes, and those are not necessarily the, the, the folks who are PhD in mathematics or statistics, um, those can be people who understand a day in the life of a data scientist or a day in the life of a citizen data scientist. And so really working to bridge the high impact technology with the practical kind of usability, usefulness of data and analytics in our cases, I think is something that we need more of in tech and sort of demystifying tech and freeing technology so that everybody can use it and having a really wide range of people who understand not just the bits and bites and, and how to program, but also the value in outcomes that technology through data and analytics can drive. >>Yeah. You know, we often talk about the hard skills, but this, their soft skills are equally, if not more important that even just being curious, being willing to ask questions, being not afraid to be vulnerable, being able to show those sides of your personality. I think those are important for, for young women and underrepresented groups to understand that those are just as important as some of the harder technical skills that can be taught. >>That's right. >>What do you think about from a bias perspective, Hillary, what have you seen in the tech industry and how do you think we can leverage culture as you talked about to help dial down some of the biases that are going on? >>Yeah. I mean, I think first of all, and, and there's some interesting data out there that says that 90% of the population, which includes a lot of women have some inherent bias in their day, day behaviors when it comes to to women in particular. But I'm sure that that is true across all kinds of, of, um, diverse and underrepresented folks in, in the world. And so I think acknowledging that we have bias and actually really learning how, what that can look like, how that can show up. We might be sitting here and thinking, oh, of course I don't have any bias. And then you realize that, um, as you, as you learn more about, um, different types of bias, that actually you do need to kind of, um, account for that and change behaviors. And so I think learning is sort of a fundamental, um, uh, grounding for all of us to really know what bias looks like, know how it shows up in each of us. >>Um, if we're leaders know how it shows up in our teams and make sure that we are constantly getting better, we're, we're not gonna be perfect anytime soon. But I think being on a path to improvement to overcoming bias, um, is really, is really critical. And part of that is really starting the dialogue, having the conversations, holding ourselves and each other accountable, um, when things aren't going in, in a, in a Coptic way and being able to talk openly about that, that felt, um, like maybe there was some bias in that interaction and how do we, um, how do we make good on that? How do we change our, our behavior? Fundamentally of course, data and analytics can have some bias in it as well. And so I think as we look at the, the technology aspect of bias, um, looking at at ethical AI, I think is a, a really important, uh, additional area. And I'm sure we could spend another 20 minutes talking about that, but I, I would be remiss if I didn't talk more about sort of the bias, um, and the over the opportunity to overcome bias in data and analytics as well. >>Yeah. The opportunity to overcome it is definitely there you bring up a couple of really good points, Hillary. It, it starts with awareness. We need to be aware that there are inherent biases in data in thought. And also to your other point, hold people accountable ourselves, our teammates, that's critical to being able to, to dial that back down, Daniel, I wanna get your perspective on, on your view of women in leadership roles. Do you think that we have good representation or we still have work to do in there? >>I definitely think in both technical and product roles, we definitely have some work to do. And, you know, when I think about, um, our partnership with Teradata, part of the reason why it's so important is, you know, Teradata solution is really the brains of a lot of companies. Um, you know, the what, how, what they differentiate on how they figure out insights into their business. And it's, it's all about the product itself and the data and the same is true at AWS. And, you know, we really could do some work to have some more women in these technical roles, as well as in the product, shaping the products. Uh, just for all the reasons that we just kind of talked about over the last 10 minutes, um, in order to, you know, move bias out of our, um, out of our solutions and also to just build better products and have, uh, better, you know, outcomes for customers. So I think there's a bit of work to do still. >>I agree. There's definitely a bit of work to do, and it's all about delivering those better outcomes for customers at the end of the day, we need to figure out what the right ways are of doing that and working together in a community. Um, we've had obviously a lot had changed in the last couple of years, Hillary, what's your, what have you seen in terms of the impact that the pandemic has had on this status of women in tech? Has it been a pro is silver lining the opposite? What are you seeing? >>Yeah, I mean, certainly there's data out there that tells us factually that it has been, um, very difficult for women during COVID 19. Um, women have, uh, dropped out of the workforce for a wide range of, of reasons. Um, and, and that I think is going to set us back all of us, the, the Royal us or the Royal we back, um, years and years. Um, and, and it's very unfortunate because I think we we're at a time when we're making great progress and now to see COVID, um, setting us back in, in such a powerful way. I think there's work to be done to understand how do we bring people back into the workforce. Um, how do we do that? Understanding work life balance, better understanding virtual and remote, working better. I think in the technology sector, um, we've really embraced, um, hybrid virtual work and are, are empowering people to bring their whole selves to work. >>And I think if anything, these, these zoom calls have, um, both for the men and the women on my team. In fact, I would say much more. So for the men on my team, I'm seeing, I was seeing more kids in the background, more kind of split childcare duties, more ability to start talking about, um, other responsibilities that maybe they had, uh, especially in the early days of COVID where maybe daycares were shut down. And, um, you had, you know, maybe a parent was sick. And so we saw quite a lot of, um, people bringing their whole selves to the office, which I think was, was really wonderful. Um, uh, even our CEO saw some of that. And I think, um, that that really changes the dialogue, right? It changes it to maybe scheduling meetings at a time when, um, people can do it after daycare drop off. >>Um, and really allowing that both for men and for women makes it better for, for women overall. So I would like to think that this hybrid working, um, environment and that this, um, uh, whole view into somebody's life that COVID has really provided for probably for white collar workers, if I'm being honest for, um, people who are in a, at a better point of privilege, they don't necessarily have to go into the office every day. I would like to think that tech can lead the way in, um, you know, coming out of the, the old COVID. I don't know if we have a new COVID coming, but the old COVID and really leading the way for women and for people, um, to transform how we do work, um, leveraging data and analytics, but also, um, overcoming some of the, the disparities that exist for women in particular in the workforce. >>Yeah, I think there's, there's like we say, there's a lot of opportunity there and I like your point of hopefully tech can be that guiding light that shows us this can be done. We're all humans at the end of the day. And ultimately if we're able to have some sort of work life balance, everything benefits, our work or more productive, higher performing teams impacts customers, right? There's so much value that can be gleaned from, from that hybrid model and embracing for humans. We need to be able to, to work when we can, we've learned that you don't have to be, you know, in an office 24, 7 commuting, crazy hours flying all around the world. We can get a lot of things done in a ways that fit people's lives rather than taking command over it. Wanna get your advice, Hillary, if you were to talk to your younger self, what would be some of the key pieces of advice you would say? And Danielle and I have talked about this before, and sometimes we, we would both agree on like, ask more questions. Don't be afraid to raise your hand, but what advice would you give your younger self and that younger generation in terms of being inspired to get into tech >>Oh, inspired and being in tech? You know, I think looking at technology as, in some ways, I feel like we do a disservice to, um, inclusion when we talk about stem, cuz I think stem can be kind of daunting. It can be a little scary for people for younger people. When I, when I go and talk to folks at schools, I think stem is like, oh, all the super smart kids are over there. They're all like maybe they're all men. And so, um, it's, it's a little, uh, intimidating. Um, and stem is actually, you know, especially for, um, people joining the workforce today. It's actually how you've been living your life since you were born. I mean, you know, stem inside and out because you walk around with a phone and you know how to get your internet working and like that is technology right. >>Fundamentally. And so demystifying stem as something that is around how we, um, actually make our, our lives useful and, and, and how we can change outcomes. Um, through technology I think is maybe a different lens to put on it. So, and there's absolutely for, for hard sciences, there's absolutely a, a great place in the world for folks who wanna pursue that and men and women can do that. So I, I don't want to be, um, uh, setting the wrong expectations, but I, I think stem is, is very holistic in, um, in the change that's happening globally for us today across economies, across global warming, across all kinds of impactful issues. And so I think everybody who's interested in, in some of that world change can participate in stem. It just may be through a different, through a different lens than how we classically talk about stem. >>So I think there's great opportunity to demystify stem. I think also, um, what I would tell my younger self is choose your bosses wisely. And that sounds really funny. That sounds like inside out almost, but I think choose the person that you're gonna work for in your first five to seven years. And it might be more than one person, but be, be selective, maybe be a little less selective about the exact company or the exact title. I think picking somebody that, you know, we talk about mentors and we talk about sponsors and those are important. Um, but the person you're gonna spend in your early career, a lot of your day with a lot, who's gonna influence a lot of the outcomes for you. That is the person that you, I think want to be more selective about, um, because that person can set you up for success and give you opportunities and set you on course to be, um, a standout or that person can hold you back. >>And that person can put you in the corner and not invite you to the meetings and not give you those opportunities. And so we're in an economy today where you actually can, um, be a little bit picky about who you go and work for. And I would encourage my younger self. I actually, I just lucked out actually, but I think that, um, my first boss really set me, um, up for success, gave me a lot of feedback and coaching. Um, and some of it was really hard to hear, but it really set me up for, for, um, the, the path that I've been on ever since. So it, that would be my advice. >>I love that advice. I it's brilliant. I didn't think it choose your bosses wisely. Isn't something that we primarily think about. I think a lot of people think about the big name companies that they wanna go after and put on a resume, but you bring up a great point. And Danielle and I have talked about this with other guests about mentors and sponsors. I think that is brilliant advice and also more work to do to demystify stem. But luckily we have great family leaders like the two of you helping us to do that. Ladies, I wanna thank you so much for joining me on the program today and talking through what you're seeing in de and I, what your companies are doing and the opportunities that we have to move the needle. Appreciate your time. >>Thank you so much. Great to see you, Danielle. Thank you Lisa, to see you. >>My pleasure for my guests. I'm Lisa Martin. You're watching the AWS partner showcase season one, episode three. Hey everyone. Welcome to the AWS partner showcase. This is season one, episode three, with a focus on women in tech. I'm your host, Lisa Martin. I've got two guests here with me, Sue Peretti, the EVP of global AWS strategic alliances at Jefferson Frank, a 10th revolution group company, and Danielle brushoff. One of our cube alumni joins us ISV PSA director, ladies. It's great to have you on the program talking about a, a topic that is near and dear to my heart at women in tech. >>Thank you, Lisa. >>So let's go ahead and start with you. Give the audience an understanding of Jefferson Frank, what does the company do and about the partnership with AWS? >>Sure. Um, so let's just start, uh, Jefferson Frank is a 10th revolution group company. And if you look at it, it's really talent as a service. So Jefferson Frank provides talent solutions all over the world for AWS clients, partners and users, et cetera. And we have a sister company called revelent, which is a talent creation company within the AWS ecosystem. So we create talent and put it out in the ecosystem. Usually underrepresented groups over half of them are women. And then we also have, uh, a company called rubra, which is a delivery model around AWS technology. So all three companies fall under the 10th revolution group organization. >>Got it. Danielle, talk to me a little bit about from AWS's perspective and the focus on hiring more women in technology and about the partnership. >>Yes. I mean, this has definitely been a focus ever since I joined eight years ago, but also just especially in the last few years we've grown exponentially and our customer base has changed. You know, we wanna have, uh, an organization interacting with them that reflects our customers, right. And, uh, we know that we need to keep pace with that even with our growth. And so we've very much focused on early career talent, um, bringing more women and underrepresented minorities into the organization, sponsoring those folks, promoting them, uh, giving them paths to growth, to grow inside of the organization. I'm an example of that. Of course I benefit benefited from it, but also I try to bring that into my organization as well. And it's super important. >>Tell me a little bit about how you benefited from that, Danielle. >>Um, I just think that, um, you know, I I've been able to get, you know, a seat at the table. I think that, um, I feel as though I have folks supporting me, uh, very deeply and wanna see me succeed. And also they put me forth as, um, you know, a, represent a representative, uh, to bring more women into the organization as well. And I think, um, they give me a platform, uh, in order to do that, um, like this, um, but also many other, uh, spots as well. Um, and I'm happy to do it because I feel that, you know, if you always wanna feel that you're making a difference in your job, and that is definitely a place where I get that time and space in order to be that representative to, um, bring more, more women into benefiting from having careers in technology, which there's a lot of value there, >>A lot of value. Absolutely. So back over to you, what are some of the trends that you are seeing from a gender diversity perspective in tech? We know the, the numbers of women in technical positions, uh, right. There's so much data out there that shows when girls start dropping up, but what are some of the trends that you are seeing? >>So it's, that's a really interesting question. And, and Lisa, I had a whole bunch of data points that I wanted to share with you, but just two weeks ago, uh, I was in San Francisco with AWS at the, at the summit. And we were talking about this. We were talking about how we can collectively together attract more women, not only to, uh, AWS, not only to technology, but to the AWS ecosystem in particular. And it was fascinating because I was talking about, uh, the challenges that women have and how hard to believe, but about 5% of women who were in the ecosystem have left in the past few years, which was really, really, uh, something that shocked everyone when we, when we were talking about it, because all of the things that we've been asking for, for instance, uh, working from home, um, better pay, uh, more flexibility, uh, better maternity leave seems like those things are happening. >>So we're getting what we want, but people are leaving. And it seemed like the feedback that we got was that a lot of women still felt very underrepresented. The number one thing was that they, they couldn't be, you can't be what you can't see. So because they, we feel collectively women, uh, people who identify as women just don't see enough women in leadership, they don't see enough mentors. Um, I think I've had great mentors, but, but just not enough. I'm lucky enough to have a pres a president of our company, the president of our company, Zoe Morris is a woman and she does lead by example. So I'm very lucky for that. And Jefferson, Frank really quickly, we put out a hiring a salary and hiring guide a career and hiring guide every year and the data points. And that's about 65 pages long. No one else does it. Uh, it gives an abundance of information around, uh, everything about the AWS ecosystem that a hiring manager might need to know. But there is what, what I thought was really unbelievable was that only 7% of the people that responded to it were women. So my goal, uh, being that we have such a very big global platform is to get more women to respond to that survey so we can get as much information and take action. So >>Absolutely 7%. So a long way to go there. Danielle, talk to me about AWS's focus on women in tech. I was watching, um, Sue, I saw that you shared on LinkedIn, the Ted talk that the CEO and founder of girls and co did. And one of the things that she said was that there was a, a survey that HP did some years back that showed that, um, 60%, that, that men will apply for jobs if they only meet 60% of the list of requirements. Whereas with females, it's far, far less, we've all been in that imposter syndrome, um, conundrum before. But Danielle, talk to us about AWS, a specific focus here to get these numbers up. >>I think it speaks to what Susan was talking about, how, you know, I think we're approaching it top and bottom, right? We're looking out at what are the, who are the women who are currently in technical positions and how can we make AWS an attractive place for them to work? And that's all a lot of the changes that we've had around maternity leave and, and those types of things, but then also, um, more flexible working, uh, can, you know, uh, arrangements, but then also, um, early, how can we actually impact early, um, career women and actually women who are still in school. Um, and our training and certification team is doing amazing things to get, um, more girls exposed to AWS, to technology, um, and make it a less intimidating place and have them look at employees from AWS and say like, oh, I can see myself in those people. >>Um, and kind of actually growing the viable pool of candidates. I think, you know, we're, we're limited with the viable pool of candidates, um, when you're talking about mid to late career. Um, but how can we, you know, help retrain women who are coming back into the workplace after, you know, having a child and how can we help with military women who want to, uh, or underrepresented minorities who wanna move into AWS, we have a great military program, but then also just that early high school, uh, career, you know, getting them in, in that trajectory. >>Sue, is that something that Jefferson Frank is also able to help with is, you know, getting those younger girls before they start to feel there's something wrong with me. I don't get this. Talk to us about how Jefferson Frank can help really drive up that in those younger girls. >>Uh, let me tell you one other thing to refer back to that summit that we did, uh, we had breakout sessions and that was one of the topics. What can cuz that's the goal, right? To make sure that, that there are ways to attract them. That's the goal? So some of the things that we talked about was mentoring programs, uh, from a very young age, some people said high school, but then we said even earlier, goes back to you. Can't be what you can't see. So, uh, getting mentoring programs, uh, established, uh, we also talked about some of the great ideas was being careful of how we speak to women using the right language to attract them. And some, there was a teachable moment for, for me there actually, it was really wonderful because, um, an African American woman said to me, Sue and I, I was talking about how you can't be what you can't see. >>And what she said was Sue, it's really different. Um, for me as an African American woman, uh, or she identified, uh, as nonbinary, but she was relating to African American women. She said, your white woman, your journey was very different than my journey. And I thought, this is how we're going to learn. I wasn't offended by her calling me out at all. It was a teachable moment. And I thought I understood that, but those are the things that we need to educate people on those, those moments where we think we're, we're saying and doing the right thing, but we really need to get that bias out there. So here at Jefferson, Frank, we're, we're trying really hard to get that careers and hiring guide out there. It's on our website to get more women, uh, to talk to it, but to make suggestions in partnership with AWS around how we can do this mentoring, we have a mentor me program. We go around the country and do things like this. We, we try to get the education out there in partnership with AWS. Uh, we have a, a women's group, a women's leadership group, uh, so much that, that we do, and we try to do it in partnership with AWS. >>Danielle, can you comment on the impact that AWS has made so far, um, regarding some of the trends and, and gender diversity that Sue was talking about? What's the impact that's been made so far with this partnership? >>Well, I mean, I think just being able to get more of the data and have awareness of leaders, uh, on how <laugh>, you know, it used to be a, a couple years back, I would feel like sometimes the, um, uh, solving to bring more women into the organization was kind of something that folks thought, oh, this is Danielle is gonna solve this. You know? And I think a lot of folks now realize, oh, this is something that we all need to solve for. And a lot of my colleagues who maybe a couple years ago, didn't have any awareness or didn't even have the tools to do what they needed to do in order to improve the statistics on their, or in their organizations. Now actually have those tools and are able to kind of work with, um, work with companies like Susan's work with Jefferson Frank in order to actually get the data and actually make good decisions and feel as though, you know, they, they often, these are not lived experiences for these folks, so they don't know what they don't know. And by providing data and providing awareness and providing tooling and then setting goals, I think all of those things have really turned, uh, things around in a very positive way. >>And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, to get those data points up, to get more women of, of all well, really underrepresented minorities to, to be able to provide that feedback so that you can, can have the data and gleamy insights from it to help companies like AWS on their strategic objectives. >>Right? So as I, when I go back to that higher that, uh, careers in hiring guide, that is my focus today, really because the more data that we have, I mean, the, and the data takes, uh, you know, we need people to participate in order to, to accurately, uh, get a hold of that data. So that's why we're asking, uh, we're taking the initiative to really expand our focus. We are a global organization with a very, very massive database all over the world, but if people don't take action, then we can't get the right. The, the, the data will not be as accurate as we'd like it to be. Therefore take better action. So what we're doing is we're asking people all over the, all over the world to participate on our website, Jefferson frank.com, the se the high, uh, in the survey. So we can learn as much as we can. >>7% is such a, you know, Danielle and I we're, we've got to partner on this just to sort of get that message out there, get more data so we can execute, uh, some of the other things that we're doing. We're, we're partnering in. As I mentioned, more of these events, uh, we're, we're doing around the summits, we're gonna be having more ed and I events and collecting more information from women. Um, like I said, internally, we do practice what we preach and we have our own programs that are, that are out there that are within our own company where the women who are talking to candidates and clients every single day are trying to get that message out there. So if I'm speaking to a client or one of our internal people are speaking to a client or a candidate, they're telling them, listen, you know, we really are trying to get these numbers up. >>We wanna attract as many people as we can. Would you mind going to this, uh, hiring guide and offering your own information? So we've gotta get that 7% up. We've gotta keep talking. We've gotta keep, uh, getting programs out there. One other thing I wanted to Danielle's point, she mentioned, uh, women in leadership, the number that we gathered was only 9% of women in leadership within the AWS ecosystem. We've gotta get that number up, uh, as well because, um, you know, I know for me, when I see people like Danielle or, or her peers, it inspires me. And I feel like, you know, I just wanna give back, make sure I send the elevator back to the first floor and bring more women in to this amazing ecosystem. >>Absolutely. That's not that metaphor I do too, but we, but to your point to get that those numbers up, not just at AWS, but everywhere else we need, it's a help me help use situation. So ladies underrepresented minorities, if you're watching go to the Jefferson Frank website, take the survey, help provide the data so that the woman here that are doing this amazing work, have it to help make decisions and have more of females and leadership roles or underrepresented minorities. So we can be what we can see. Ladies, thank you so much for joining me today and sharing what you guys are doing together to partner on this important. Cause >>Thank you for having me, Leah, Lisa, >>Thank you. My pleasure for my guests. I'm Lisa Martin. You're watching the cubes coverage of the AWS partner showcase. Thanks for your time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. We've got two female rock stars here with me next. Stephanie Curry joins us the worldwide head of sales and go to market strategy for AWS at NetApp and Danielle GShock is back one of our QM ISV PSA director at AWS. Looking forward to a great conversation, ladies, about a great topic, Stephanie, let's go ahead and start with you. Give us an overview of your story, how you got into tech and what inspired you. >>Thanks so much, Lisa and Danielle. It's great to be on this show with you. Um, thank you for that. Uh, my name's Stephanie cur, as Lisa mentioned, I'm the worldwide head of sales for, uh, AWS at NetApp and run a global team of sales people that sell all things AWS, um, going back 25 years now, uh, when I first started my career in tech, it was kind of by accident. Um, I come from a different background. I have a business background and a technical background from school, um, but had been in a different career and I had an opportunity to try something new. Um, I had an ally really that reached out to me and said, Hey, you'd be great for this role. And I thought, I'd take a chance. I was curious. Um, and, uh, it, it turned out to be a 25 year career, um, that I'm really, really excited about and, and, um, really thankful for that person, for introducing me to the, to the industry >>25 years in counting. I'm sure Danielle, we've talked about your background before. So what I wanna focus on with you is the importance of diversity for high performance. I know what a machine AWS is, and Stephanie'll come back to you with the same question, but talk about that, Danielle, from your perspective, that importance, um, for diversity to drive the performance. >>Yeah. Yeah. I truly believe that, you know, in order to have high performing teams, that you have to have people from all different types of backgrounds and experiences. And we do find that oftentimes being, you know, field facing, if we're not reflecting our customers and connecting with them deeply, um, on, on the levels that they're at, we, we end up missing them. And so for us, it's very important to bring people of lots of different technical backgrounds experiences. And of course, both men, women, and underrepresented minorities and put that forth to our customers, um, in order to make that connection and to end up with better outcomes. So >>Definitely it's all about outcomes, Stephanie, your perspective and NetApp's perspective on diversity for creating highly performant teams and organizations. >>I really aligned with Danielle on the comment she made. And in addition to that, you know, just from building teams in my, um, career know, we've had three times as many women on my team since we started a year ago and our results are really showing in that as well. Um, we find the teams are stronger, they're more collaborative and to Danielle's point really reflective, not only our partners, but our customers themselves. So this really creates connections, which are really, really important to scale our businesses and, and really, uh, meet the customer where they're at as well. So huge proponent of that ourselves, and really finding that we have to be intentional in our hiring and intentional in how we attract diversity to our teams. >>So Stephanie let's stay with you. So a three X increase in women on the team in a year, especially the kind of last year that we've had is really incredible. I, I like your, I, your thoughts on there needs to be a, there needs to be focus and, and thought in how teams are hired. Let's talk about attracting and retaining those women now, especially in sales roles, we all know the number, the percentages of women in technical roles, but what are some of the things that, that you do Stephanie, that NetApp does to attract and retain women in those sales roles? >>The, the attracting part's really interesting. And we find that, you know, you, you read the stats and I'd say in my experience, they're also true in the fact that, um, a lot of women would look at a job description and say, I can't do a hundred percent of that, that, so I'm not even going to apply with the women that we've attracted to our team. We've actually intentionally reached out and targeted those people in a good way, um, to say, Hey, we think you've got what it takes. Some of the feedback I've got from those women are, gosh, I didn't think I could ever get this role. I didn't think I had the skills to do that. And they've been hired and they are doing a phenomenal job. In addition to that, I think a lot of the feedback I've got from these hires are, Hey, it's an aggressive sales is aggressive. Sales is competitive. It's not an environment that I think I can be successful in. And what we're showing them is bring those softer skills around collaboration, around connection, around building teams. And they do, they do bring a lot of that to the team. Then they see others like them there and they know they can be successful cuz they see others like them on the team, >>The whole concept of we can't be what we can't see, but we can be what we can't see is so important. You said a couple things, Stephanie, that really stuck with me. And one of them was an interview on the Cub I was doing, I think a couple weeks ago, um, about women in tech. And the stat that we talked about was that women will apply will not apply for a job unless they meet 100% of the skills and the requirements that it's listed, but men will, if they only meet 60. And I, that just shocked me that I thought, you know, I, I can understand that imposter syndrome is real. It's a huge challenge, but the softer skills, as you mentioned, especially in the last two years, plus the ability to communicate, the ability to collaborate are incredibly important to, to drive that performance of any team of any business. >>Absolutely. >>Danielle, talk to me about your perspective and AWS as well for attracting and retaining talent. And, and, and particularly in some of those challenging roles like sales that as Stephanie said, can be known as aggressive. >>Yeah, for sure. I mean, my team is focused on the technical aspect of the field and we definitely have an uphill battle for sure. Um, two things we are focused on first and foremost is looking at early career women and that how we, how can we bring them into this role, whether in they're in support functions, uh, cl like answering the phone for support calls, et cetera, and how, how can we bring them into this organization, which is a bit more strategic, more proactive. Um, and then the other thing that as far as retention goes, you know, sometimes there will be women who they're on a team and there are no other women on that team. And, and for me, it's about building community inside of AWS and being part of, you know, we have women on solution architecture organizations. We have, uh, you know, I just personally connect people as well and to like, oh, you should meet this person. Oh, you should talk to that person. Because again, sometimes they can't see someone on their team like them and they just need to feel anchored, especially as we've all been, you know, kind of stuck at home, um, during the pandemic, just being able to make those connections with women like them has been super important and just being a, a long tenured Amazonian. Um, that's definitely one thing I'm able to, to bring to the table as well. >>That's so important and impactful and spreads across organizations in a good way. Daniel let's stick with you. Let's talk about some of the allies that you've had sponsors, mentors that have really made a difference. And I said that in past tense, but I also mean in present tense, who are some of those folks now that really inspire you? >>Yeah. I mean, I definitely would say that one of my mentors and someone who, uh, ha has been a sponsor of my career has, uh, Matt YK, who is one of our control tower GMs. He has really sponsored my career and definitely been a supporter of mine and pushed me in positive ways, which has been super helpful. And then other of my business partners, you know, Sabina Joseph, who's a cube alum as well. She definitely has been, was a fabulous partner to work with. Um, and you know, between the two of us for a period of time, we definitely felt like we could, you know, conquer the world. It's very great to go in with a, with another strong woman, um, you know, and, and get things done, um, inside of an organization like AWS. >>Absolutely. And S I've, I've agreed here several times. So Stephanie, same question for you. You talked a little bit about your kind of, one of your, uh, original early allies in the tech industry, but talk to me about allies sponsors, mentors who have, and continue to make a difference in your life. >>Yeah. And, you know, I think it's a great differentiation as well, right? Because I think that mentors teach us sponsors show us the way and allies make room for us at the table. And that is really, really key difference. I think also as women leaders, we need to make room for others at the table too, and not forget those softer skills that we bring to the table. Some of the things that Danielle mentioned as well about making those connections for others, right. And making room for them at the table. Um, some of my allies, a lot of them are men. Brian ABI was my first mentor. Uh, he actually is in the distribution, was in distribution, uh, with advent tech data no longer there. Um, Corey Hutchinson, who's now at Hashi Corp. He's also another ally of mine and remains an ally of mine, even though we're not at the same company any longer. Um, so a lot of these people transcend careers and transcend, um, um, different positions that I've held as well and make room for us. And I think that's just really critical when we're looking for allies and when allies are looking for us, >>I love how you described allies, mentors and sponsors Stephanie. And the difference. I didn't understand the difference between a mentor and a sponsor until a couple of years ago. Do you talk with some of those younger females on your team so that when they come into the organization and maybe they're fresh outta college, or maybe they've transitioned into tech so that they can also learn from you and understand the importance and the difference between the allies and the sponsors and the mentors? >>Absolutely. And I think that's really interesting because I do take, uh, an extra, uh, approach an extra time to really reach out to the women that have joined the team. One. I wanna make sure they stay right. I don't want them feeling, Hey, I'm alone here and I need to, I need to go do something else. Um, and they are located around the world, on my team. They're also different age groups, so early in career, as well as more senior people and really reaching out, making sure they know that I'm there. But also as Danielle had mentioned, connecting them to other people in the community that they can reach out to for those same opportunities and making room for them >>Make room at the table. It's so important. And it can, you never know what a massive difference and impact you can make on someone's life. And I, and I bet there's probably a lot of mentors and sponsors and allies of mine that would be surprised to know, uh, the massive influence they've had Daniel back over. Let's talk about some of the techniques that you employ, that AWS employees to make the work environment, a great place for women to really thrive and, and be retained as Stephanie was saying. Of course that's so important. >>Yeah. I mean, definitely I think that the community building, as well as we have a bit more programmatic mentorship, um, we're trying to get to the point of having a more programmatic sponsorship as well. Um, but I think just making sure that, um, you know, both everything from, uh, recruit to onboard to ever boarding that, uh, they they're the women who come into the organization, whether it's they're coming in on the software engineering side or the field side or the sales side that they feel as that they have someone, uh, working with them to help them drive their career. Those are the key things that were, I think from an organizational perspective are happening across the board. Um, for me personally, when I run my organization, I'm really trying to make sure that people feel that they can come to me at any time open door policy, make sure that they're surfacing any times in which they are feeling excluded or anything like that, any challenges, whether it be with a customer, a partner or with a colleague. Um, and then also of course, just making sure that I'm being a good sponsor, uh, to, to people on my team. Um, that is key. You can talk about it, but you have to start with yourself as well. >>That's a great point. You you've got to, to start with yourself and really reflect on that. Mm-hmm <affirmative> and look, am I, am I embodying what it is that I need? And not that I know they need that focused, thoughtful intention on that is so importants, let's talk about some of the techniques that you use that NetApp uses to make the work environment a great place for those women are marginalized, um, communities to really thrive. >>Yeah. And I appreciate it and much like Danielle, uh, and much like AWS, we have some of those more structured programs, right around sponsorship and around mentorship. Um, probably some growth there, opportunities for allies, because I think that's more of a newer concept in really an informal structure around the allies, but something that we're growing into at NetApp, um, on my team personally, I think, um, leading by example's really key. And unfortunately, a lot of the, um, life stuffs still lands on the women, whether we like it or not. Uh, I have a very, uh, active husband in our household, but I still carry when it push comes to shove it's on me. Um, and I wanna make sure that my team knows it's okay to take some time and do the things you need to do with your family. Um, I'm I show up as myself authentically and I encourage them to do the same. >>So it's okay to say, Hey, I need to take a personal day. I need to focus on some stuff that's happening in my personal life this week now, obviously to make sure your job's covered, but just allowing some of that softer vulnerability to come into the team as well, so that others, um, men and women can feel they can do the same thing. And that it's okay to say, I need to balance my life and I need to do some other things alongside. Um, so it's the formal programs, making sure people have awareness on them. Um, I think it's also softly calling people out on biases and saying, Hey, I'm not sure if you know, this landed that way, but I just wanted to make you aware. And usually the feedback is, oh my gosh, I didn't know. And could you coach me on something that I could do better next time? So all of this is driven through our NetApp formal programs, but then it's also how you manifest it on the teams that we're leading. >>Absolutely. And sometimes having that mirror to reflect into can be really eye-opening and, and allow you to, to see things in a completely different light, which is great. Um, you both talked about, um, kind of being what you, uh, can see, and, and I know both companies are upset customer obsessed in a good way. Talk to me a little bit, Danielle, go back over to you about the AWS NetApp partnership. Um, some of that maybe alignment on, on performance on obviously you guys are very well aligned, uh, in terms of that, but also it sounds like you're quite aligned on diversity and inclusion. >>Well, we definitely do. We have the best partnerships with companies in which we have these value alignments. So I think that is a positive thing, of course, but just from a, from a partnership perspective, you know, from my five now plus years of being a part of the APN, this is, you know, one of the most significant years with our launch of FSX for NetApp. Um, with that, uh, key key service, which we're making available natively on AWS. I, I can't think of a better Testament to the, to the, um, partnership than that. And that's doing incredibly well and it really resonates with our customers. And of course it started with customers and their need for NetApp. Uh, so, you know, that is a reflection, I think, of the success that we're having together. >>And Stephanie talk to, uh, about the partnership from your perspective, NetApp, AWS, what you guys are doing together, cultural alignment, but also your alignment on really bringing diversity into drive performance. >>Yeah, I think it's a, a great question. And I have to say it's just been a phenomenal year. Our relationship has, uh, started before our first party service with FSX N but definitely just, um, uh, the trajectory, um, between the two companies since the announcement about nine months ago has just taken off to a, a new level. Um, we feel like an extended part of the family. We worked together seamlessly. A lot of the people in my team often say we feel like Amazonians. Um, and we're really part of this transformation at NetApp from being that storage hardware company into being an ISV and a cloud company. And we could not do this without the partnership with AWS and without the, uh, first party service of Fs XM that we've recently released. Um, I think that those joint values that Danielle referred to are critical to our success, um, starting with customer obsession and always making sure that we are doing the right thing for the customer. >>We coach our team teams all the time on if you are doing the right thing for the customers, you cannot do anything wrong. Just always put the customer at the, in the center of your decisions. And I think that there is, um, a lot of best practice sharing and collaboration as we go through this change. And I think a lot of it is led by the diverse backgrounds that are on the team, um, female, male, um, race and so forth, and just to really, uh, have different perspectives and different experiences about how we approach this change. Um, so we definitely feel like a part of the family. Uh, we are absolutely loving, uh, working with the AWS team and our team knows that we are the right place, the right time with the right people. >>I love that last question for each of you. And I wanna stick with you Stephanie advice to your younger self, think back five years. What advice would you seen what you've accomplished and maybe the thet route that you've taken along the way, what would you advise your youngest Stephanie self. >>Uh, I would say keep being curious, right? Keep being curious, keep asking questions. And sometimes when you get a no, it's not a bad thing, it just means not right now and find out why and, and try to get feedback as to why maybe that wasn't the right opportunity for you. But, you know, just go for what you want. Continue to be curious, continue to ask questions and find a support network of people around you that wanna help you because they are there and they, they wanna see you be successful too. So never be shy about that stuff. >><laugh> absolutely. And I always say failure does not have to be an, a bad F word. A no can be the beginning of something. Amazing. Danielle, same question for you. Thinking back to when you first started in your career, what advice would you give your younger self? >>Yeah, I think the advice I'd give my younger self would be, don't be afraid to put yourself out there. Um, it's certainly, you know, coming from an engineering background, maybe you wanna stay behind the scenes, not, not do a presentation, not do a public speaking event, those types of things, but back to what the community really needs, this thing. Um, you know, I genuinely now, uh, took me a while to realize it, but I realized I needed to put myself out there in order to, um, you know, allow younger women to see what they could be. So that would be the advice I would give. Don't be afraid to put yourself out there. >>Absolutely. That advice that you both gave are, is so fantastic, so important and so applicable to everybody. Um, don't be afraid to put yourself out there, ask questions. Don't be afraid of a, no, that it's all gonna happen at some point or many points along the way. That can also be good. So thank you ladies. You inspired me. I appreciate you sharing what AWS and NetApp are doing together to strengthen diversity, to strengthen performance and the advice that you both shared for your younger selves was brilliant. Thank you. >>Thank you. >>Thank you >>For my guests. I'm Lisa Martin. You're watching the AWS partner showcase. See you next time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. I've got two female rock stars joining me. Next Vero Reynolds is here engineering manager, telemetry at honeycomb, and one of our cube alumni, Danielle Ock ISV PSA director at AWS. Join us as well. Ladies. It's great to have you talking about a very important topic today. >>Thanks for having us. >>Yeah, thanks for having me. Appreciate it. >>Of course, Vera, let's go ahead and start with you. Tell me about your background and tech. You're coming up on your 10th anniversary. Happy anniversary. >>Thank you. That's right. I can't believe it's been 10 years. Um, but yeah, I started in tech in 2012. Um, I was an engineer for most of that time. Uh, and just recently as a March, switched to engineering management here at honeycomb and, um, you know, throughout my career, I was very much interested in all the things, right. And it was a big FOMO as far as trying a few different, um, companies and products. And I've done things from web development to mobile to platforms. Um, it would be apt to call me a generalist. Um, and in the more recent years I was sort of gravitating more towards developer tool space. And for me that, uh, came in the form of cloud Foundry circle CI and now honeycomb. Um, I actually had my eye on honeycomb for a while before joining, I came across a blog post by charity majors. >>Who's one of our founders and she was actually talking about management and how to pursue that and whether or not it's right, uh, for your career. And so I was like, who is this person? I really like her, uh, found the company. They were pretty small at the time. So I was sort of keeping my eye on them. And then when the time came around for me to look again, I did a little bit more digging, uh, found a lot of talks about the product. And on the one hand they really spoke to me as the solution. They talked about developers owning their coding production and answering questions about what is happening, what are your users seeing? And I felt that pain, I got what they were trying to do. And also on the other hand, every talk I saw at the time was from, uh, an amazing woman <laugh>, which I haven't seen before. Uh, so I came across charity majors again, Christine Y our other founder, and then Liz Jones, who's our principal developer advocate. And that really sealed the deal for me as far as wanting to work here. >>Yeah. Honeycomb is interesting. This is a female founded company. You're two leaders. You mentioned that you like the technology, but you were also attracted because you saw females in the leadership position. Talk to me a little bit about what that's like working for a female led organization at honeycomb. >>Yeah. You know, historically, um, we have tried not to over index on that because there was this, uh, maybe fear awareness of, um, it taking away from our legitimacy as an engineering organization, from our success as a company. Um, but I'm seeing that, uh, rhetoric shift recently because we believe that with great responsibility, uh, with great power comes great responsibility, and we're trying to be more intentional as far as using that attribute of our company. Um, so I would say that for me, it was, um, a choice between a few offers, right. And that was a selling point for sure, because again, I've never experienced it and I've really seen how much they walk that walk. Um, even me being here and me moving into management, I think were both, um, ways in which they really put a lot of trust and support in me. And so, um, I it's been a great ride. >>Excellent. Sounds like it. Before we bring Danielle in to talk about the partnership. I do wanna have you there talk to the audience a little bit about honeycomb, what technology it's delivering and what are its differentiators. >>Yeah, absolutely. Um, so honeycomb is an observability tool, uh, that enables engineers to answer questions about the code that runs in production. And, um, we work with a number of various customers. Some of them are Vanguards, slack. Hello, fresh, just to name a couple, if you're not familiar with observability tooling, it's akin to traditional application performance monitoring, but we believe that observability is succeeding APM because, uh, APM tools were built at the time of monoliths and they just weren't designed to help us answer questions about complex distributed systems that we work with today, where things can go wrong anywhere in that chain. And you can't predict what you're gonna need to ask ahead of time. So some of the ways that we are different is our ability to store and query really rich data, which we believe is the key to understanding those complex systems. >>What I mean by rich data is, um, something that has a lot of attributes. So for example, when an error happens, knowing who it happened to, which user ID, which, um, I don't know, region, they were in, um, what, what, what they were doing at the time and what was happening at the rest of your system. And our ingest engine is really fast. You can do it in as little as three seconds and we call data like this. I said, kind of rich data, contextual data. We refer it as having high ality and high dimensionality, which are big words. But at the end of the day, what that means is we can store and we can query the data. We can do it really fast. And to give you an example of how that looks for our customers, let's say you have a developer team who are using comb to understand and observe their system. >>And they get a report that a user is experiencing a slowdown or something's wrong. They can go into comb and figure out that this only happens to users who are using a particular language pack with their app. And they operated their app last week, that it only happens when they are trying to upload a file. And so it's this level of granularity and being able to zoom in and out, um, under your data that allows you to understand what's happening, especially when you have an incident going on, right. Or your really important high profile customer is telling you that something's wrong. And we can do that. Even if everything else in your other tools looks fine, right? All of your dashboards are okay. You're not actually getting paged on it, but your customers are telling you that something's wrong. Uh, and we believe that's where we shine in helping you there. >>Excellent. It sounds like that's where you really shine that real time visibility is so critical these days. Danielle, Danielle, wanna bring you into the conversation. Talk to us a little bit about the honeycomb partnership from the AWS lens. >>Yeah. So excuse me, observability is obviously a very important, uh, segment in the cloud space, very important to AWS, um, because a lot of all of our customers, uh, as they build their systems distributed, they need to be able to see where, where things are happening in the complex systems that they're building. And so honeycomb is a, is an advanced technology partner. Um, they've been working with us for quite some time and they have a, uh, their solution is listed on the marketplace. Um, definitely something that we see a lot of demand with our customers and they have many integrations, uh, which, you know, we've seen is key to success. Um, being able to work seamlessly with the rest of the services inside of the AWS platform. And I know that they've done some, some great things with people who are trying to develop games on top of AWS, uh, things in that area as well. And so, uh, very important partner in the observa observability market that we have >>Back to you, let's kind of unpack the partnership, the significance that honeycomb ha is getting from being partners with an organization as potent and pivotal as AWS. >>Yeah, absolutely. Um, I know this predates me to some extent, but I know for a long time, AWS and honeycomb has really pushed the envelope together. And, um, I think it's a beneficial relationship for both ends. There's kind of two ways of looking at it. On the one side, there is our own infrastructure. So honeycomb runs on AWS and actually one of our critical workloads that supports that fast query engine that I mentioned uses Lambda. And it does so in a pretty Orthodox way. So we've had a longstanding conversation with the AWS team as far as drawing outside those lines and kind of figuring out how to use this technology in a way that works for us and hopefully will work for other customers of theirs as well. Um, that also allows us to ask for early access for certain features when they become available. >>And then that way we can be sort of the Guinea pigs and try things out, um, in a way that migrates our system and optimizes our own performance, but also allows again, other customers of AWS to follow in that path. And then the other side of that partnership is really supporting our customers who are both honeycomb users and AWS users, because it's, as you imagine, quite a big overlap, and there are certain ways in which we can allow our customers to more easily get their data from AWS to honeycomb. So for example, last year we built a tool, um, based on the new Lambda extension capability that allowed our users who run their applications in Lambdas to get that telemetry data out of their applications and into honeycomb. And it man was win, win. >>Excellent. So I'm hearing a lot of synergies from a technology perspective, you're sticking with you, and then Danielle will bring you in, let's talk about how honeycomb supports D and I across its organization. And how is that synergistic with AWS's approach? Yeah, >>Yeah, absolutely. So I sort of alluded to that hesitancy to over index on the women led aspect of ourselves. Um, but again, a lot of things are shifting, we're growing a lot. And so we are recognizing that we need to be more intentional with our DEI initiatives, and we also notice that we can do better and we should do better. And to that, and we're doing a few things differently, um, that are pretty recent initiatives. We are partnering with organizations that help us target specific communities that are underrepresented in tech. Um, some examples would be after tech hu Latinas in tech among, um, a number of others. And another initiative is DEI head start. That's something that is an internal, um, practice that we started that includes reaching out to underrepresented applicants before any new job for honeycomb becomes live. So before we posted to LinkedIn, before it's even live on our job speech, and the idea there is to kind of balance our pipeline of applicants, which the hope is will lead to more diverse hires in the long term. >>That's a great focus there. Danielle, I know we've talked about this before, but for the audience, in terms of the context of the honeycomb partnership, the focus at AWS for D E and I is really significant, unpack that a little bit for us. >>Well, let me just bring it back to just how we think about it, um, with the companies that we work with, but also in, in terms of, you know, what we want to be able to do, excuse me, it's very important for us to, you know, build products that reflect, uh, the customers that we have. And I think, you know, working with, uh, a company like honeycomb that is looking to differentiate in a space, um, by, by bringing in, you know, the experiences of many different types of people I genuinely believe. And I'm sure Vera also believes that by having those diverse perspectives, that we're able to then build better products for our customers. Um, and you know, it's one of, one of our leadership principles, uh, is, is rooted in this. I write a lot, it asks for us to seek out diverse perspectives. Uh, and you can't really do that if everybody kind of looks the same and thinks the same and has the same background. So I think that is where our de and I, um, you know, I thought process is rooted and, you know, companies like honeycomb that give customers choice and differentiate and help them, um, to do what they need to do in their unique, um, environments is super important. So >>The, the importance of thought diversity cannot be underscored enough. It's something that is, can be pivotal to organizations. And it's very nice to hear that that's so fundamental to both companies, Barry, I wanna go back to you for a second. You, I think you mentioned this, the DEI head start program, that's an internal program at honeycomb. Can you shed a little bit of light on that? >>Yeah, that's right. And I actually am in the process of hiring a first engineer for my team. So I'm learning a lot of these things firsthand, um, and how it works is we try to make sure to pre-load our pipeline of applicants for any new job opening we have with diverse candidates to the best of our abilities, and that can involve partnering with the organizations that I mentioned or reaching out to our internal network, um, and make sure that we give those applicants a head start, so to speak. >>Excellent. I like that. Danielle, before we close, I wanna get a little bit of, of your background. We've got various background in tag, she's celebrating her 10th anniversary. Give me a, a short kind of description of the journey that you've navigated through being a female in technology. >>Yeah, thanks so much. I really appreciate, uh, being able to share this. So I started as a software engineer, uh, back actually in the late nineties, uh, during the, the first.com bubble and, uh, have, have spent quite a long time actually as an individual contributor, um, probably working in software engineering teams up through 2014 at a minimum until I joined AWS, uh, as a customer facing solutions architect. Um, I do think spending a lot of time, hands on definitely helped me with some of the imposter syndrome, um, issues that folks suffer from not to say I don't at all, but it, it certainly helped with that. And I've been leading teams at AWS since 2015. Um, so it's really been a great ride. Um, and like I said, I'm very happy to see all of our engineering teams change, uh, as far as their composition. And I'm, I'm grateful to be part of it. >>It's pretty great to be able to witness that composition change for the better last question for each of you. And we're almost out of time and Danielle, I'm gonna stick with you. What's your advice, your recommendations for women who either are thinking about getting into tech or those who may be in tech, maybe they're in individual positions and they're not sure if they should apply for that senior leadership position. What do you advise them to do? >>I mean, definitely for the individual contributors, tech tech is a great career, uh, direction, um, and you will always be able to find women like you, you have to maybe just work a little bit harder, uh, to join, have community, uh, in that. But then as a leader, um, representation is very important and we can bring more women into tech by having more leaders. So that's my, you just have to take the lead, >>Take the lead, love that there. Same question for you. What's your advice and recommendations for those maybe future female leaders in tech? >>Yeah, absolutely. Um, Danielle mentioned imposter syndrome and I think we all struggle with it from time to time, no matter how many years it's been. And I think for me, for me, the advice would be if you're starting out, don't be afraid to ask, uh, questions and don't be afraid to kind of show a little bit of ignorance because we've all been there. And I think it's on all of us to remember what it's like to not know how things work. And on the flip side of that, if you are a more senior IC or, uh, in a leadership role, also being able to model just saying, I don't know how this works and going and figuring out answers together because that was a really powerful shift for me early in my career is just to feel like I can say that I don't know something. >>I totally agree. I've been in that same situation where just ask the question because you I'm guaranteed, there's a million outta people in the room that probably has the, have the same question and because of imposter syndrome, don't wanna admit, I don't understand that. Can we back up, but I agree with you. I think that is, um, one of the best things. Raise your hand, ask a question, ladies. Thank you so much for joining me talking about honeycomb and AWS, what you're doing together from a technology perspective and the focus efforts that each company has on D E and I, we appreciate your insights. Thank you so much for having us great talking to you. My pleasure, likewise for my guests, I'm Lisa Martin. You're watching the AWS partner showcase women in check. Welcome to the AWS partner showcase I'm Lisa Martin, your host. This is season one, episode three, and this is a great episode that focuses on women in tech. I'm pleased to be joined by Danielle Shaw, the ISV PSA director at AWS, and the sponsor of this fantastic program. Danielle, it's great to see you and talk about such an important topic. >>Yes. And I will tell you, all of these interviews have just been a blast for me to do. And I feel like there has been a lot of gold that we can glean from all of the, um, stories that we heard on these interviews and good advice that I myself would not have necessarily thought of. So >>I agree. And we're gonna get to set, cuz advice is one of the, the main things that our audience is gonna hear. We have Hillary Ashton, you'll see from TETA there, Reynolds joins us from honeycomb, Stephanie Curry from NetApp and Sue Paris from Jefferson Frank. And the topics that we dig into are first and foremost, diversity equity and inclusion. That is a topic that is incredibly important to every organization. And some of the things Danielle that our audiences shared were really interesting to me. One of the things that I saw from a thematic perspective over and over was that like D Reynolds was talking about the importance of companies and hiring managers and how they need to be intentional with de and I initiatives. And that intention was a, a, a common thing that we heard. I'm curious what your thoughts are about that, that we heard about being intentional working intentionally to deliver a more holistic pool of candidates where de I is concerned. What are your, what were some of the things that stuck out to you? >>Absolutely. I think each one of us is working inside of organizations where in the last, you know, five to 10 years, there's been a, you know, a strong push in this direction, mostly because we've really seen, um, first and foremost, by being intentional, that you can change the, uh, the way your organization looks. Um, but also just that, you know, without being intentional, um, there was just a lot of, you know, outcomes and situations that maybe weren't great for, um, you know, a healthy, um, and productive environment, uh, working environment. And so, you know, a lot of these companies have made a big investments and put forth big initiatives that I think all of us are involved in. And so we're really excited to get out here and talk about it and talk about, especially as these are all partnerships that we have, how, you know, these align with our values. So >>Yeah, that, that value alignment mm-hmm <affirmative> that you bring up is another thing that we heard consistently with each of the partners, there's a cultural alignment, there's a customer obsession alignment that they have with AWS. There's a D E and I alignment that they have. And I, I think everybody also kind of agreed Stephanie Curry talked about, you know, it's really important, um, for diversity on it, on, on impacting performance, highly performant teams are teams that are more diverse. I think we heard that kind of echoed throughout the women that we talked to in >>This. Absolutely. And I absolutely, and I definitely even feel that, uh, with their studies out there that tell you that you make better products, if you have all of the right input and you're getting all many different perspectives, but not just that, but I can, I can personally see it in the performing teams, not just my team, but also, you know, the teams that I work alongside. Um, arguably some of the other business folks have done a really great job of bringing more women into their organization, bringing more underrepresented minorities. Tech is a little bit behind, but we're trying really hard to bring that forward as well to in technical roles. Um, but you can just see the difference in the outcomes. Uh, at least I personally can just in the adjacent teams of mine. >>That's awesome. We talked also quite a bit during this episode about attracting women and underrepresented, um, groups and retaining them. That retention piece is really key. What were some of the things that stuck out to you that, um, you know, some of the guests talked about in terms of retention? >>Yeah. I think especially, uh, speaking with Hillary and hearing how, uh, Teradata is thinking about different ways to make hybrid work work for everybody. I think that is definitely when I talk to women interested in joining AWS, oftentimes that might be one of the first, uh, concerns that they have. Like, am I going to be able to, you know, go pick my kid up at four o'clock at the bus, or am I going to be able to, you know, be at my kids' conf you know, conference or even just, you know, have enough work life balance that I can, um, you know, do the things that I wanna do outside of work, uh, beyond children and family. So these are all very important, um, and questions that especially women come and ask, but also, um, you know, it kind of is a, is a bellwether for, is this gonna be a company that allows me to bring my whole self to work? And then I'm also gonna be able to have that balance that I need need. So I think that was something that is, uh, changing a lot. And many people are thinking about work a lot differently. >>Absolutely. The pandemic not only changed how we think about work, you know, initially it was, do I work from home or do I live at work? And that was legitimately a challenge that all of us faced for a long time period, but we're seeing the hybrid model. We're seeing more companies be open to embracing that and allowing people to have more of that balance, which at the end of the day, it's so much better for product development for the customers, as you talked about there's, it's a win-win. >>Absolutely. And, you know, definitely the first few months of it was very hard to find that separation to be able to put up boundaries. Um, but I think at least I personally have been able to find the way to do it. And I hope that, you know, everyone is getting that space to be able to put those boundaries up to effectively have a harmonious, you know, work life where you can still be at home most of the time, but also, um, you know, have that cutoff point of the day or at least have that separate space that you can feel that you're able to separate the two. >>Yeah, absolutely. And a lot of that from a work life balance perspective leads into one of the next topics that we covered in detail with, and that's mentors and sponsors the differences between them recommendations from, uh, the women on the panel about how to combat imposter syndrome, but also how to leverage mentors and sponsors throughout your career. One of the things that, that Hillary said that I thought was fantastic, advice were mentors and sponsors are concerned is, is be selective in picking your bosses. We often see people, especially younger folks, not necessarily younger folks. I shouldn't say that that are attracted to a company it's brand maybe, and think more about that than they do the boss or bosses that can help guide them along the way. But I thought that was really poignant advice that Hillary provided something that I'm gonna take into consideration myself. >>Yeah. And I honestly hadn't thought about that, but as I reflect through my own career, I can see how I've had particular managers who have had a major impact on helping me, um, with my career. But, you know, if you don't have the ability to do that, or maybe that's not a luxury that you have, I think even if you're able to, you know, find a mentor for a period of time or, um, you know, just, just enable for you to be able to get from say a point a to point B just for a temporary period. Um, just so you can grow into your next role, have a, have a particular outcome that you wanna drive, have a particular goal in mind find that person who's been there and done that and can really help you get through. If you don't have the luxury of picking your manager mentor, who can help you get to the next step. >>Exactly. That, that I thought that advice was brilliant and something that I hadn't really considered either. We also talked with several of the women about imposter syndrome. You know, that's something that everybody, I think, regardless of gender of your background, everybody feels that at some point. So I think one of the nice things that we do in this episode is sort of identify, yes, imposter syndrome is real. This is, this is how it happened to me. This is I navigated around or got over it. I think there's some great advice there for the audience to glean as well about how to dial down the imposter syndrome that they might be feeling. >>Absolutely. And I think the key there is just acknowledging it. Um, but also just hearing all the different techniques on, on how folks have dealt with it because everybody does, um, you know, even some of the smartest, most confident men I've, I've met in, uh, industry still talk to me about how they have it and I'm shocked by it oftentimes, but, um, it is very common and hopefully we, we talk about some good techniques to, to deal with that. >>I think we do, you know, one of the things that when we were asking the, our audience, our guests about advice, what would they tell their younger selves? What would they tell young women or underrepresented groups in terms of becoming interested in stem and in tech and everybody sort of agreed on me, don't be afraid to raise your hand and ask questions. Um, show vulnerabilities, not just as the employee, but even from a leadership perspective, show that as a leader, I, I don't have all the answers. There are questions that I have. I think that goes a long way to reducing the imposter syndrome that most of us have faced at some point in our lives. And that's just, don't be afraid to ask questions. You never know, oh, how can people have the same question sitting in the room? >>Well, and also, you know, for folks who've been in industry for 20, 25 years, I think we can just say that, you know, it's a, it's a marathon, it's not a sprint and you're always going to, um, have new things to learn and you can spend, you know, back to, we talked about the zing and zagging through careers, um, where, you know, we'll have different experiences. Um, all of that kind of comes through just, you know, being curious and wanting to continue to learn. So yes, asking questions and being vulnerable and being able to say, I don't know all the answers, but I wanna learn is a key thing, uh, especially culturally at AWS, but I'm sure with all of these companies as well, >>Definitely I think it sounded like it was really ingrained in their culture. And another thing too, that we also talked about is the word, no, doesn't always mean a dead end. It can often mean not right now or may, maybe this isn't the right opportunity at this time. I think that's another important thing that the audience is gonna learn is that, you know, failure is not necessarily a bad F word. If you turn it into opportunity, no isn't necessarily the end of the road. It can be an opener to a different door. And I, I thought that was a really positive message that our guests, um, had to share with the, the audience. >>Yeah, totally. I can, I can say I had a, a mentor of mine, um, a very, uh, strong woman who told me, you know, your career is going to have lots of ebbs and flows and that's natural. And you know that when you say that, not right now, um, that's a perfect example of maybe there's an ebb where it might not be the right time for you now, but something to consider in the future. But also don't be afraid to say yes, when you can. <laugh> >>Exactly. Danielle, it's been a pleasure filming this episode with you and the great female leaders that we have on. I'm excited for the audience to be able to learn from Hillary Vera, Stephanie Sue, and you so much valuable content in here. We hope you enjoy this partner showcase season one, episode three, Danielle, thanks so much for helping >>Us with it's been a blast. I really appreciate it >>All audience. We wanna enjoy this. Enjoy the episode.

(upbeat music) >> Hey everyone. Welcome to the AWS partner showcase. This is season one, episode three. And I'm your host, Lisa Martin. I've got two great guests here with me to talk about Women in Tech. Hillary Ashton joins us, the chief product officer at Teradata, and Danielle Greshock is back with us, the ISV PSA director at AWS Ladies. It's great to have you on the program talking through such an important topic. Hillary, let's go ahead and start with you. Give us a little bit of an intro into you, your background and a little bit about Teradata. >> Yeah, absolutely. So I'm Hillary Ashton. I head up the products organization. So that's our engineering, product management, officer of the CTO team at Teradata. I've been with Teradata for just about three years and really have spent the last several decades, if I can say that in the data and analytics space. I spent time really focused on the value of analytics at scale, and I'm super excited to be here at Teradata. I'm also a mom of two teenage boys. And so as we talk about women in tech, I think there's lots of different dimensions and angles of that. At Teradata we are partnered very deeply with AWS and happy to talk a little bit more about that throughout this discussion as well. >> Excellent. A busy mom of two teen boys. My goodness. I don't know how you do it. Let's now look at Teradata's views of diversity, equity and inclusion. It's a topic that's important to everyone but give us a snapshot into some of the initiatives that Teradata has there. >> Yeah, I have to say, I am super proud to be working at Teradata. We have gone through a series of transformations but I think it starts with culture and we are deeply committed to diversity, equity and inclusion. It's really more than just a statement here. It's just how we live our lives. And we use data to back that up. In fact, we were named one of the world's most ethical companies for the 13th year in a row. And all of our executive leadership team has taken an oath around DE&I, that's available on LinkedIn as well. So in fact, our leadership team reporting into the CEO is just about 50/50 men and women which is the first time I've worked in a company where that has been the case. And I think as individuals, we can probably appreciate what a huge difference that makes in terms of not just being a representative, but truly being on a diverse and equitable team. And I think it really improves the behaviors that we can bring to our office. >> There's so much value in that. It's I impressive to see about a 50/50 at the leadership level. That's not something that we see very often. Tell me how you, Hillary, how did you get into tech? Were you an engineering person by computer science or did you have more of a zigzaggy path to where you are now? >> I'm going to pick door number two and say more zigzaggy. I started off thinking that, I started off as a political science major or a government major and I was probably destined to go into the law field but actually took a summer course at Harvard, I did not go to Harvard, but I took a summer course there and learned a lot about multimedia and some programming. And that really set me on a trajectory of how data and analytics can truly provide value and outcomes to our customers. And I have been living that life ever since I graduated from college. So I was very excited and privileged in my early career to work in a company where I found after my first year that I was managing kids, people who had graduated from Harvard Business School and from MIT Sloan School. And that was super crazy 'cause I did not go to either of those schools but I sort of have always had a natural knack for how do you take technology and the really cool things that technology can do, but because I'm not a programmer by training, I'm really focused on the value that I'm able to help organizations really extract value from the technology that we can create, which I think is fantastic. >> I think there's so much value in having a zigzag path into tech. You bring... Danielle, you and I have talked about this many times, you bring such breadth and such a wide perspective that really is such a value add to teams. Danielle, talk to us from AWS's perspective about what can be done to encourage more young women to get, and underrepresented groups as well to get into STEM and stay. >> Yeah, and this is definitely a challenge as we're trying to grow our organization and kind of shift the numbers. And the reality is, especially with the more senior folks in our organization, unless you bring folks with a zigzag path, the likelihood is you won't be able to change the numbers that you have. But for me, it's really been about looking at that, the folks who are just graduating college, maybe in other roles where they are adjacent to technology and to try to spark their interest and show that, yes, they can do it because oftentimes it's really about believing in themselves and realizing that we need folks with all sorts of different perspectives to kind of come in to be able to help really provide both products and services and solutions for all types of people inside of technology which requires all sorts of perspectives. >> Yeah, the diverse perspectives. There's so much value and there's a lot of data that demonstrates how much value, revenue impact organizations can make by having diversity especially at the leadership level. Hillary, let's go back to you. We talked about your career path. You talked about some of the importance of the focus on DE&I at Teradata, but what do you think can be done to encourage, sorry, to recruit more young women and under represented groups into tech, any carrots there that you think are really important that we need to be dangling more of? >> Yeah, absolutely. And I'll build on what Danielle just said. I think the bringing in diverse understandings of customer outcomes, I mean, we've really moved from technology for technology's sake. And I know AWS and Entirety have had a lot of conversations on how do we drive customer outcomes that are differentiated in the market and really being customer-centric. And technology is wonderful. You can do wonderful things with it. You can do not so wonderful things with it as well but unless you're really focused on the outcomes and what customers are seeking technology is not hugely valuable. And so I think bringing in people who understand voice of customer, who understand those outcomes and those are not necessarily the folks who are PhD in mathematics or statistics, those can be people who understand a day in the life of a data scientist or a day in the life of a citizen data scientist. And so really working to bridge the high impact technology with the practical kind of usability, usefulness of data and analytics in our cases, I think is something that we need more of in tech and sort of demystifying tech and freeing technology so that everybody can use it and having a really wide range of people who understand not just the bits and bites and and how to program, but also the value and outcomes that technology through data and analytics can drive. >> Yeah. You know, we often talk about the hard skills but the soft skills are equally, if not more important that even just being curious, being willing to ask questions being not afraid to be vulnerable, being able to show those sides of your personality. I think those are important for young women and underrepresented groups to understand that those are just as important as some of the harder technical skills that can be taught. >> That's right. >> What do you think about from a bias perspective, Hillary, what have you seen in the tech industry and how do you think we can leverage culture as you talked about to help dial down some of the biases that are going on? >> Yeah. I mean, I think first of all, and there's some interesting data out there that says that 90% of the population, which includes a lot of women have some inherent bias in their day to day behaviors when it comes to women in particular. But I'm sure that that is true across all kinds of of diverse and underrepresented folks in the world. And so I think acknowledging that we have bias and actually really learning what that can look like, how that can show up, we might be sitting here and thinking, oh, of course I don't have any bias. And then you realize that as you learn more about different types of bias that actually you do need to kind of account for that and change behaviors. And so I think learning is sort of a fundamental grounding for all of us to really know what bias looks like, know how it shows up in each of us, if we're leaders, know how it shows up in our teams and make sure that we are constantly getting better. We're not going to be perfect anytime soon, but I think being on a path to improvement to overcoming bias is really critical. And part of that is really starting the dialogue, having the conversations, holding ourselves and each other accountable when things aren't going in a copesthetic way, and being able to talk openly about that felt like maybe there was some bias in that interaction and how do we make good on that? How do we change our behavior fundamentally. Of course, data and analytics can have some bias in it as well. And so I think as we look at the technology aspect of bias, looking at at ethical AI I think is a really important additional area. And I'm sure we could spend another 20 minutes talking about that, but I would be remiss if I didn't talk more about sort of the bias and the opportunity to overcome bias in data and analytics as well. >> Yeah. The opportunity to overcome it is definitely there, you bring up a couple of really good points, Hillary. It starts with awareness. We need to be aware that there are inherent biases in data in thought. And also to your other point, hold people accountable, ourselves, our teammates that's critical to being able to dial that back down. Danielle, I want to get your perspective on your view of women in leadership roles. Do you think that we have good representation or we still have work to do in there? >> I definitely think in both technical and product roles we definitely have some work to do. And when I think about our partnership with Teradata, part of the reason why it's so important is, Teradata solution is really the brains of a lot of companies, what they differentiate on, how they figure out insights into their business. And it's all about the product itself and the data, and the same is true at AWS. And we really could do some work to have some more women in these technical roles as well as in the product, shaping the products, just for all the reasons that we just kind of talked about over the last 10 minutes in order to move bias out of our solutions and also to just build better products and have better outcomes for customers. So I think there's a bit of work to do still. >> I agree. There's definitely a bit of work to do and it's all about delivering those better outcomes for customers at the end of the day. We need to figure out what the right ways are of doing that and working together in a community. We've had obviously a lot had changed in the last couple of years. Hillary, what have you seen in terms of the impact that the pandemic has had on this status of women in tech? Has it been a pro, is silver lining, the opposite? What are you seeing? >> Yeah, I mean, certainly there's data out there that tells us factually that it has been very difficult for women during COVID-19. Women have dropped out of the workforce for a wide range of reasons. And that I think is going to set us back all of us, the Royal us or the Royal we back years and years. And it's very unfortunate because I think we're at a time when we're making great progress and now to see COVID setting us back in such a powerful way I think there's work to be done to understand how do we bring people back into the workforce? How do we do that understanding work life balance better, understanding virtual and remote working better. I think in the technology sector we've really embraced hybrid virtual work and are empowering people to bring their whole selves to work. And I think if anything, these Zoom calls have, both for the men and the women on my team. In fact, I would say much more so for the men on my team, we're seeing more kids in the background, more kind of split childcare duties, more ability to start talking about other responsibilities that maybe they had, especially in the early days of COVID where maybe day cares were shut down and maybe a parent was sick. And so we saw quite a lot of people bringing their whole selves to the office which I think was really wonderful. Even our CEO saw some of that. And I think that that really changes the dialogue. It changes it to maybe scheduling meetings at a time when people can do it after daycare drop off and really allowing that both for men and for women, makes it better for women overall. So I would like to think that this hybrid working environment and that this whole view into somebody's life that COVID has really provided for, probably for white collar workers, if I'm being honest for people who are at a better point of privilege, they don't necessarily have to go into the office every day. I would like to think that tech can lead the way in coming out of the old COVID, I don't know if we have a new COVID coming, but the old COVID and really leading the way for women and for people to transform how we do work, leveraging data and analytics but also overcoming some of the disparities that exist for women in particular in the workforce. >> Yeah, I think there's, like we say, there's a lot of opportunity there and I like your point of hopefully tech can be that guiding light that shows us this can be done. We're all humans at the end of the day. And ultimately, if we're able to have some sort of work life balance, everything benefits. Our work, we're more productive, higher performing teams impacts customers. There's so much value that can be gleaned from that hybrid model and embracing for humans. We need to be able to work when we can. We've learned that you don't have to be in an office 24/7 commuting crazy hours, flying all around the world. We can get a lot of things done in ways that fit people's lives rather than taking command over it. I want to get your advice, Hillary, if you were to talk to your younger self, what would be some of the key pieces of advice you would say? And Danielle and I have talked about this before, and sometimes we would both agree on like, ask more questions, don't be afraid to raise your hand, but what advice would you give your younger self and that younger generation in terms of being inspired to get into tech? >> Oh, inspired in being in tech. I think looking at technology as, in some ways I feel like we do a disservice to inclusion when we talk about STEM, 'cause I think stem can be kind of daunting, it can be a little scary for people, for younger people. When I go and talk to folks at schools, I think STEM is like, oh, all the super smart kids are over there. They're all, like maybe they're all men. And so it's a little intimidating. And STEM is actually, especially for people joining the workforce today, it's actually how you've been living your life since you were born. I mean, you know STEM inside and out because you walk around with a phone and you know how to get your internet working and like that is technology fundamentally. And so demystifying STEM as something that is around how we actually make our our lives useful and how we can change outcomes through technology, I think is maybe a different lens to put on it. And there's absolutely, for hard scientists, there's absolutely a great place in the world for folks who want to pursue that, and men and women can do that. So I don't want to be setting the wrong expectations but I think STEM is very holistic in the change that's happening globally for us today across economies, across global warming, across all kinds of impactful issues. And so I think everybody who's interested in some of that world change can participate in STEM. It just may be through a different lens than how we classically talk about STEM. So I think there's great opportunity to demystify STEM. I think also what I would tell my younger self is choose your bosses wisely. And that sounds really funny. That sounds like inside out almost but I think choose the person that you're going to work for in your first five to seven years. And it might be more than one person, but be selective. Maybe be a little less selective about the exact company or the exact title. I think picking somebody that, we talk about mentors and we talk about sponsors and those are important, but the person you're going to spend in your early career, a lot of your day with, who's going to influence a lot of the outcomes for you. That is the person that you, I think want to be more selective about because that person can set you up for success and give you opportunities and set you on course to be a standout or that person can hold you back and that person can put you in the corner and not invite you to the meetings and not give you those opportunities. And so we're in an economy today where you actually can be a little bit picky about who you go and work for. And I would encourage my younger self, I just lucked out actually, but I think that my first boss really set me up for success, gave me a lot of feedback and coaching. And some of it was really hard to hear but it really set me up for the path that I've been on ever since. So that would be my advice. >> I love that advice. It's brilliant. And I think it, choose your bosses wisely, isn't something that we primarily think about. I think a lot of people think about the big name companies that they want to go after and put on a resume, but you bring up a great point. And Danielle and I have talked about this with other guests about mentors and sponsors. I think that is brilliant advice, and also more work to do to demystify STEM. But luckily we have great female leaders like the two of you helping us to do that. Ladies, I want to thank you so much for joining me on the program today and talking through what you're seeing in DE&I, what your companies are doing and the opportunities that we have to move the needle. Appreciate your time. >> Thank you so much. Great to see you, Danielle. Thank you, Lisa. >> Nice to see you. >> My pleasure. For my guests, I'm Lisa Martin. You're watching the AWS partner showcase season one, episode three. (upbeat music)

>>mhm Yes. Hello and welcome back to the cubes coverage of red hat summit 2021 virtual. I'm john for your host of the cube and cube coverage here with matt Hicks. Executive vice president of products and technologies at red hat cuba lum I've been on many times, knows the engineering side now running all the process of technologies matt. Great to see you. Thanks for coming on remote. I wish we were in real real life in person. I RL but doing it remote again. Thanks for coming on. >>Hey, thanks thanks for having me today. >>Hey, so what a year you know, um, I was just talking to a friend and another interview with the red hat colleagues. Chef on your team in 2019 I interviewed Arvin at IBM right before he bought red hat and you smile on his face and he wasn't even ceo then um, he is such a big fan of cloud native and you guys have been the engine underneath the hood if you will of IBM this transformation huge push now and with Covid and now with the visibility of the post Covid, you're seeing cloud Native at scale with modern applications just highly accelerated across the board In almost every industry, every vertical. This is a very key trend. You guys at the, at the center of it always have been, we've been covering you for many years, interesting time and so now you guys are really got the, got the formula at red hat, take us through the key transit you see on this wave for enterprises and how is red hat taking that, taking that through? >>Yeah, no, absolutely. It has been, it's been a great ride actually. I remember a couple years ago standing on stage with Arvin prior to the acquisition. So it's been uh, it's been a world one but I think if we look at Really would emerge in 2020, we've seen three trends that we hope we're gonna carry through in 2021 just in a better and better year for that. That the first is open hybrid cloud is really how customers are looking to adapt to change. They have to use what they have um assets they have today. On premise, we're seeing a lot of public cloud adoption that blend of being hybrid is just, it is a reality for how customers are having to deliver a edge computing I think is another area I would say uh the trend is really not going to be a fad or a new, you know, great texture. Um the capabilities of computing at the edge, whether that is automotive vehicles, radio access network capabilities to five G. It's pretty astounding at this point. So I think we're gonna see a lot of pushing edge computing for computing, getting closer to users. Uh but then also the choice aspect we're seeing with Ceos, we often talk about technology is choice, but I think the model of how they want to consume technology has been another really strong trend in 2020. Uh We look at this really is being able to deliver a cloud managed services in addition to technology that ceos around themselves. But those, those will probably be the three that stand out to me at least in 2020 we've seen, >>so matt take us through in your minds and red hats, perspective the workloads that are going to be highlighted in this cloud native surge that's happening. We're seeing it everywhere. You mentioned edge industrial edge to consumer Edge to lightweight, edge, massive new workloads. So take us through how you see kind of the existing workloads evolving and potentially new workloads that emerging. >>Yeah. So I think um you know first when you talk about edge workloads a big umbrella but if you look at data driven workloads, especially in the machine learning artificial intelligence spectrum of that, that's really critical. And a reason that those workloads are important is five G. Aside for now when you're running something at the edge you have to also be able to make decisions pretty well at the edge. And that that is that's where your data is being generated and the ability to act on that closely. Whether that's executing machine learning models or being able to do more than that with A I. That's going to be a really really critical workload. Uh huh. Coupled to that, we will see I think five G. Change that because you're going to see more blending in terms of what can you draw back to uh closer to your data center to augment that. So five G will shift how that's built but data driven workloads are going to be huge then I think another area will see is how you propagate that data through environment. Some Kafka has been a really popular technology will actually be launching a service in relation to that. But being able to get that data at the edge and bring it back to locations where you might do more traditional processing, that's going to be another really key space. Um and then we'll still have to be honest, there is still a tremendous amount of work loads out there that just aren't going to get rebuilt. And So being able to figure out how can you make them a little more cloud native? You know, the things your companies have run on for the last 20 years, being able to step them closer to cloud native, I think it's going to be another critical focus because he can't just rewrite them all in one phase and you can't leave them there as well. So being able to bridge shadow B T to >>what's interesting if folks following red hat, No, no, you guys certainly at the tech chops you guys have great product engineering staff been doing this for a long time. I mean the common Lennox platform that even the new generation probably have to leave it load limits on the server anymore. You guys have been doing this hybrid environment in I T for I T Sloan for decades. Okay. In the open, so, you know, it's servers, virtualization, you know, private, public cloud infrastructures and it's been around, we've been covering it in depth as you know, but that's been, that's a history. But as you go from a common Lennox platform into things with kubernetes as new technologies and this new abstraction layers, new control plane concept comes to the table. This need for a fully open platform seems to be a hot trend this year. >>How do you >>describe that? Can you take a minute to explain what this is, this is all about this new abstraction, this new control plane or this open hybrid cloud as you're calling? What is this about? What does it mean? >>Yeah, no, I'll do a little journey that she talked about. Yeah. This has been our approach for almost a decade at this point. And it started, if you look at our approach with Lennox and this was before public clouds use migrants existed. We still with Lennox tried to span bare metal and virtualized environments and then eventually private and public cloud infrastructure as well. And our goal there was you want to be able to invest in something, um, and in our world that's something that's also open as in Lennox but be able to run it anywhere. That's expanded quite a bit. That was good for a class of applications that really got it started. That's expanded now to kubernetes, for example, kubernetes is taking that from single machines to cluster wide deployments and it's really giving you that secure, flexible, fast innovation backbone for cloud native computing. And the balance there is just not for cloud native, we've got to be able to run traditional emerging workloads and our goal is let those things run wherever rail can. So you're really, you're based on open technologies, you can run them wherever you have resources to run. And then I think the third part of this for us is uh, having that choice and ability to run anywhere but not being able to manage. It can lead to chaos or sprawl and so our investments in our management portfolio and this is from insights the redhead advanced cluster management to our cluster security capabilities or answerable. Our focus has been securing, managing and monitoring those environments so you can have a lot of them, you can run where you want, but she just sort of treat it as one thing. So you are our vision, how we've executed up to this point has really been centered around that. I think going forward where you'll see us um really try to focus is, you know, first you heard paul announced earlier that we're donating more than half a billion dollars to open. I would cloud research and part of this reason is uh running services. Cloud native services is changing. And that research element of open source is incredibly powerful. We want to make sure that's continuing but we're also going to evolve our portfolio to support this same drive a couple areas. I would call out, we're launching redhead open shift platform plus and I talked about that combination from rail to open shift to being able to manage it. We're really putting that in one package. So you have the advanced management. So if you have a huge suites of cloud native real estate there, you can manage that. And it also pushes security earlier into the application, build workflows. This is tied to some of our technology is bolstered by the stack rocks acquisition that we did. Being able to bring that in one product offering I think is really key to address security and management side. Uh we've also expanded Redhead insights beyond Rehl to include open shift and answerable and this is really targeted it. How do we make this easier? How do we let customers lean on our expertise? Not just for Lennox as a service, but expand that to all of the things you'll use in a hybrid cloud. And then of course we're going to keep pushing Lennox innovation, you'll see this with the latest version of red hat enterprise, like so we're gonna push barriers, lower barriers to entry. Uh But we're also going to be the innovation catalyst for new directions include things like edge computing. So hopefully that sort of helps in terms of where, where we started when it was just Lennox and then all the other pieces were bringing to the table and why and some new areas. Uh We're launching our investment going forward. >>Yeah, great, that's great overview. Thanks for taking the time to do that. I think one of the areas I that's jumping out at me is the uh, advanced cluster management work you guys are doing saw that with the security peace and also red hat insights I think is is another key one and you get to read that edge. But on the inside you mentioned at the top of this interview, data workloads pretty much being, I mean that pretty much everything, much more of an emphasis on data. Um, data in general but also, you know, serve abilities a hot area. You know, you guys run operating system so you know, in operating systems you need to have the data, understand what's being instrumented. You gotta know that you've got to have things instrument and now more than ever having the data is critical. So take us through your vision of insights and how that translates. Because he said mentions in answerable you're seeing a lot more innovations because Okay I got provisions everything that's great. Cloud and hybrid clouds. Good. Okay thumbs up everyone check the box and then all of a sudden day too As they call day two operations stuff starts to, you know, Get getting hairy, they start to break. Maybe some things are happening. So day two is essentially the ongoing operational stability of cloud native. You need insights, you need the data. If you don't have the data, you don't even know what's going on. You can't apply machine learning. It's kind of you if you don't get that flywheel going, you could be in trouble. Take me through your vision of data driven insights. >>Yeah. So I think it's it's two aspects. If you go to these traditional traditional sport models, we don't have a lot of insight until there's an issue and I'm always amazed by what our teams can understand fix, get customers through those and I think that's a lot of the success red hats had at the same note, we want to make that better where if you look at real as an example, if we fixed an issue for any customer on the planet of which we fix a lot in the support area, we can know whether you're going to hit that same issue or not in a lot of cases and so that linkage to be able to understand environments better. We can be very proactive of not just hey apply all the updates but without this one update, you risk a kernel panic, we know your environment, we see it, this is going to keep you out of that area. The second challenge with this is when things go do break or um are failing the ability to get that data. We want that to be the cleanest handshake possible. We don't want to. Those are always stressful times anyway for customers being able to get logs, get access so that our engineering knowledge, we can fix it. That's another key part. Uh when you extend this to environments like open shift things are changing faster than humans can respond in it. And so those traditional flows can really start to get strained or broken broken down with it. So when we have connected open shift clusters, our engineering teams can not only proactively monitor those because we know cooper net is really well. We understand operators really well. Uh we can get ahead of those issues and then use our support teams and capabilities to keep things from breaking. That's really our goals. Finding that balance where uh we're using our expertise in building the software to help customers stay stable instead of just being in a response mode when things break >>awesome. I think it's totally right on the money and data is critical in all this. I think the trust of having that partnership to know that this pattern recognition is gonna be applied from the environment and that's been hurting the cybersecurity market people. That's the biggest discussion I had with my friends and cyber is they don't share the data when they do, things are pretty obvious. Um, so that's good stuff there and then obviously notifications proactive before there's a cause or failure. Uh great stuff. This brings up a point that paul come here, said earlier, I want to get your reaction to this. He said every C. I. O. Is now a cloud operator. >>That's a pretty bold >>statement. I mean, that's simply means that it's all cloud all the time. You know? Again, we've been saying this on the queue for many years, cloud first, whatever people want to call it, >>what does that actually >>mean? Cloud operator, does that just mean everything's hybrid? Everything's multiple. Cloud. Take me through an unpacked what that actually means? >>Yeah. So I think for the C I O for a lot of times it was largely a technology choice. So that was sort of a choice available to them. And especially if you look at what public clouds have introduced, it's not just technology choice. You're not just picking Kafka anymore. For example, you really get to make the choice of do I want to differentiate my business by running it myself or is this just technology I want to consume and I'm going to consume a cloud, native service and other challenges come with that. It's an infrastructure, not in your control, but when you think about a ceo of the the axes they're making decisions on, there are more capabilities now and I think this is really crucial to let the C i O hone in on where they want to specialist, what do they want to consume, what do they really want to understand, differentiate and Ron? Um and to support this actually, so we're in this vein, we're going to be launching three new managed cloud services and our our focus is always going to be hybrid in these uh but we understand the importance of having managed cloud services that red hat is running not the customers in this case. So one of those will be red hat open shift streams for Patrick Kafka. We've talked about that, that data connectivity and the importance of it and really being able to connect apps across clouds across data centers using Kafka without having to push developers to really specialize in running. It is critical because that is your hybrid data, it's going to be generated on prim, it's going to be generated the edge, you need to be able to get access to it. The next challenge for us is once you have that data, what do you do with it? And we're launching a red hat open shift data science cloud service and this is going to be optimized for understanding the data that's brought in by streams. This doesn't matter whether it's an Ai service or business intelligence process and in this case you're going to see us leverage our ecosystem quite a bit because that last mile of AI workloads or models will often be completed with partners. But this is a really foundational service for us to get data in and then bring that into a workflow where you can understand it and then the last one for us is that red hat open shift api management and you can think of this is really the overseer of how apps are going to talk to services and these environments are complex, their dynamic and being able to provide that oversight up. How should my apps be consuming all these a. P. S, how should they be talking? How do I want to control? Um and understand that is really critical. So we're launching these, these three and it fits in that cloud operator use, we want to give three options where you might want to use Kafka and three Scale technologies and open data hub, which was the basis of open shift data sides, but you might not want to specialize in running them so we can run those for you and give you as a C. I. O. That choice of where you want to invest in running versus just using it. >>All right, we're here with matt Hicks whose executive vice president prospect technology at red hat, matt, your leader at red hat now part of IBM and continues to operate um in the red hat spirit, uh innovating out in the open, people are wearing their red hat uh hoodies, which has been great to see. Um I ask every executive this question because I really want to get the industry perspective on this. Um you know, necessity is the mother of invention as the saying goes and, you know, this pandemic was a challenge for many In 2020. And then as we're in 2021, some say that even in the fall we're gonna start to see a light at the end of the tunnel and then maybe back to real life in 2022. This has opened up huge visibility for CSOS and leaders and business in the enterprise to say, Hey, what's working, what do we need? We didn't prepare for everyone to be working at home. These were great challenges in 2020. Um, and and these will fuel the next innovations and achievements going forward. Um again necessity is the mother of all invention. Some projects are gonna be renewed and double down on some probably won't be as hybrid clouds and as open source continues to power through this, there's lessons to be learned, share your view on what um leaders in in business can do coming out of the pandemic to have a growth strategy and what can we learn from this pandemic from innovation and and how open source can power through this adversity. >>Yeah. You know, I think For as many challenging events we had in 2020, I think for myself at least, it it also made me realize what companies including ourselves can accomplish if we're really focused on that if we don't constrain our thinking too much, we saw projects that were supposed to take customers 18 months that they were finishing in weeks on it because that was what was required to survive. So I think part of it is um, 2020 broke a lot of complacency for us. We have to innovate to be able to put ourselves in a growth position. I hope that carries into 2021 that drives that urgency. When we look at open source technologies. I think the flexibility that it provides has been something that a lot of companies have needed in this. And that's whether it could be they're having to contract or expand and really having that moment of did the architectural choices, technology choices, will they let me respond in the way I need? Uh, I'm biased. But first I think open models, open source development Is the best basis to build. That gives you that flexibility. Um, and honestly, I am an optimist, but I look at 2021, I'm like, I'm excited to see what customers build on sort of the next wave of open innovation. I think his life sort of gets back to normal and we keep that driving innovation and people are able to collaborate more. I hope we'll see a explosion of innovation that comes out and I hope customers see the benefit of doing that on a open hybrid cloud model. >>No better time now than before. All the things are really kind of teed up and lined up to provide that innovation. Uh, great to have you on the cube. Take a quick second to explain to the folks watching in the community What is red hat 2021 about this year? And red hat someone, I'll see. We're virtual and we're gonna be back in a real life soon for the next event. What's the big takeaway this year for the red hat community and the community at large for red hat in context of the market? >>You know, I think redhead, you'll keep seeing us push open source based innovation. There's some really exciting spaces, whether that is getting closer and closer towards edge, which opens up incredible opportunities or providing that choice, even down to consumption model like cloud managed services. And it's in that drive to let customers have the tools to build the next incredible innovations for him. So, And that's what summit 2021 is going to be about for us, >>awesome And congratulations to, to the entire team for the donation to the academic community, Open cloud initiative. And these things are doing to promote this next generation of SRS and large cloud scale operators and developers. So congratulations on that props. >>Thanks john. >>Okay. Matt Hicks, executive vice president of products and technology. That red hat here on the Cube Cube coverage of red hat 2021 virtual. I'm John Ferrier. Thanks for watching. Yeah.

>> live from Miami Beach, Florida It's the que covering demon 2019. Brought to you, by the way, >> Welcome back to Vima on 2019 in Miami. Everybody, we're here at the Fountain Blue Hotel. This is Day two of our coverage of the Cube, the leader in live Tech. And I'm David Dante with Peter Bors. Pippen. Jay Raj is here. He's the vice president and CEO of Make A Wish America. Just that awesome foundation nonprofit people. Thanks for coming on the Cube. >> Thank you for having me appreciate it. >> So make a wish. Children with wishes and have terminal illnesses. You guys make them come true. It's just a great organizations. Been around for a long time, I think, since the early eighties, right, >> 39 years and going >> years and hundreds of thousands of wishes made. So just how did you get Teo make a wish that all come about >> it? It wasn't interesting journey. I was consulting in I t for multiple big companies. And, you know, two years back, it was through a recruiting channel that I got an opportunity to start some conversations as the CIA and make a wish. Uh, the thing that got me in the opportunity was predominately about enterprises and just to give you a little bit off, make official operations. Make a Wish was Founded and Phoenix, Arizona. And but we also operate a 60 chapters across the United States that it is 60 chapters each of the chapter there 501 C three companies themselves with the CEO and abort. Essentially, it is 60 plus one. The national team kind of managing. All of the chapters are helping the chapters. National does not do any wish. Granting all the wish planning happens to the chapters. But National helps the chapters with the distribution of funding models brand. And thanks for That's a couple of years back in the national board talked about in our dream and mission, which is granting every eligible child the notion ofthe enterprise. You know, working as an enterprise came into four and it being a great piece off providing shared services and thanks for that. So I was brought on board and we took on I would call as the leader today said and dashes dream off. Bringing together all the 60 chapters and the city chapter's essentially are split across 120 locations. So Wade took on a project off. You know, combining our integrating all of their infrastructure needs into one place. And Phoenix without ada, sent a provider. You know, we worked with a partner. Phoenix. Now fantastic partners >> there. We had them on the other day. >> Yep, yep. Yeah, MacLaren. I mean, and the team, they did a great job. And, you know, when we had to move all of the data, everything from the 60 chapters applications everything into a centralized data center, locations that we managed right now from Make a Wish National office and provide a service back to the chapters That gives you a little bit off. You know, from behind the scenes. What happened? >> You provide the technical overview framework for all the 60 chapters. >> It almost sounds like a franchise model. >> It's what we call a Federated model back in the nonprofit. >> But but but but because make a wish is so driven by information. Yep. Both in the application as well as the programs to deliver thie brand promise. And the brand execution has got to be very, very closely tied to the quality of a shared services you provide >> exactly. Exactly. And like I said, the reason I talked about them being a separate companies themselves is you know, as I always say to my 60 CEOs, Ah, I should be able to provide the services because they wanted, because they have a choice to go outside and have their own partner. Another thing for that which they can. But they would want to work with the national team and get my, you know, work through our services rather than having have to because of the very it's A. It's a big difference when it comes to, but I've been lucky on privileged to you have these conversations with the CEO's. When I start talking to them about the need for centralization, the enterprise society assed much, there are questions when he start leading with the mission and the business notion of why we need to do that, it's It's fantastic. Everybody is in line with that. I mean, there's no question, then, as toe Hey, guys, uh, let me do all the Operation Manisha fight and leave it to me and I'll in a handler for you, and I let you guys go to what you do best. which is granting wishes. So then it becomes it doesn't become a question off, you know, should be a shouldn't way. And of course, to back that up. But I was talking to the dean, folks, It just solutions. Like VMware, Veeam. It makes it much simpler even from a cost prospect. You not for me to manage a bigger team s so that I can take those dollars and give it back to the business to grant another wish. So it's it's pretty exciting that >> way. So you set the standards. Okay, here's what you know, we recommend and then you're you're saying that adoption has been quite strong. Yeah, I remember Peter. Don't say easy. I used to run Kitty Sports in my local town in which is small town. And there was, you know, a lot of five or six or seven sports, and I was the sort of central organization I couldn't get six sports to agree that high man is 60 different CEO's. But that's okay. So not easy. But so how were you able to talk leadership or leading as we heard from Gino Speaker today? How were you able to get those guys, you know, aligned with your vision. >> Uh, it's it's been fantastic. I've had a lot ofthe good support from our executive came from a leadership team because leadership is always very important to these big initiatives are National board, which comprises off some of the that stuff best leaders in America and I have the fortune toe be mentored by Randy Sloan, who used to be the CEO of Southwest. And before that, you see a global CEO for, uh, you know, Popsicle. You know, he always told me, but but I mean CIA job. One thing is to no the technology, but completely another thing. Toe building relationships and lead with the business conversation. And so a typical conversation with the CEO about Hey, I need to take the data that you have all the I t things that you have and then me doing it. And then there are questions about what about my staff and the's conversations. Because you know, it's a nonprofit is a very noble, nice feeling, and you wouldn't want the conversations about, you know, being rift and things like that are being reduced producing the staff and thinks of that. But you know as he walked through that and show the benefits of why we doing it. They get it. And they've been able to repurpose many off the I. D functions back in tow, revenue generation model or ofhis granting in our team. And in many cases, I've been ableto absolve some off their folks from different places, which has worked out fine for me, too, because now I have kind of a power user model across the United States through which I can manage all these 120 locations. It's very interesting, >> you know, site Reliable and Engineering Dev Ops talks about thie error budget or which is this notion of doo. You're going tohave errors. You're going to have challenges. Do you want it in the infrastructure you wanted the functions actually generating value for the business? I don't know much about Make a wish. I presume, however, that the mission of helping really sick kids achieve make achieve a wish is both very rewarding, very stressful. He's gotta be in a very emotional undertaking, and I imagine it part of your message them has got to be let's have the stress or that emotional budget be dedicated to the kids and not to the technology >> completely agree. That's that. That's been one of my subjects, as you asked about How is it going about? It's about having the conversation within the context of what we talked about business and true business. Availability of data. You know, before this enterprise project data was probably not secure enough, which is a big undertaking that we're going down the path with cyber security. And you know, that is a big notion, misplaced notion out there that in a non profits are less vulnerable. Nobody. But that's completely untrue, because people have found out that nonprofits do not probably have the securing of walls and were much more weight being targeted nonprofits as a whole, targeted for cyber security crimes and so on and so forth. So some of these that I used to, you know, quote unquote help or help the business leaders understand it, And once they understand they get it, they ableto, you know, appreciate why we doing it and it becomes the conversation gets much more easier. Other What's >> the scope of the size of the chapters is that is a highly variable or there is. >> It is highly variable, and I should probably said, That's Thesixty chapters. We look at it as four categories, so the cat ones are what we call the Big Ice, the Metro New Yorkers and Francisco Bay Area. They're called Category one chapters anywhere between 4 1 60 to 70 staff. Grant's close to around 700 wishes you so as Make a Wish America, we ran close toe 15,600 wishes a year, and cat ones do kind of close to 700 15,600 400 to 700. And then you get into care to scare threes and cat for scat force are anywhere between, you know, given example Puerto Rico or Guam territory there. Cat Force New Mexico is a cat for three staff members Gammas operated by two staff members and 20 volunteers. They grant about 3 2 20 12 to 15 which is a year, so it's kind of highly variable. And then, you know, we talk about Hawaii chapter. It's a great example. They cat once predominate because of the fact that you know, they they do. There's not a lot ofthe wishes getting originated from how I but you know, Florida, California and how your three big chapters with a grand are a vicious ist with a lot of grant, you know, wish granting. So there's a lot off, you know, traffic through those chapters >> so so very distributed on diverse. What's the relationship between data and the granting of wishes? Talk about the role of data. >> Should I? I was say this that in a and I probably race a lot of fibrosis and my first introductory session a couple of years back when I John make a wish with the CEO's uh, when we had the CEO meeting and talk to them about I leaders the days off making decisions based on guts are gone. It has to be a data driven decision because that's where the world is leading to be. Take anything for that matter. So when we talk about that, it was very imperative going back to my project that the hall we had all of the data in one place or a semblance off one single place, as opposed to 60 different places to make decisions based on wish forecast, for example, how many wishes are we going to do? How many wishes are coming in? How's the demand? Was the supply matching up one of the things that we need to do. Budget purposes, going after revenue. And thanks for that. So data becomes very important for us. The other thing, we use data for the wish journeys. Essentially, that's a storytelling. You know, when I you know, it was my first foray into for profit Sorry, nonprofit. And me coming from a full profit is definitely a big culture shock. And one of the things they ask us, what are we selling? Its emotions and story. And that's our data. That is what you know. That's huge for us if we use it for branding and marketing purposes. So having a good semblance off data being ableto access it quickly and being available all the time is huge for us. >> Yeah, and you've got videos on the site, and that's another form of data. Obviously, as we as we know here, okay. And then, from a data protection standpoint, how do you approach that? Presume you're trying to standardize on V maybe is way >> are actually invested in veeam with them for a couple of years right now, as we did the consolidation of infrastructure pieces Veeam supporters with all of the backup and stories replication models. Uh, we're thinking, like Ratmir talked about act one wi be a part of the journey right now, and we're looking at active. What that brings to us. One of the things that you know, dream does for us is we have close to 60 terabytes of data in production and close to another 400 terabytes in the back of things. And, uh, it's interesting when they look about look at me equation, you think about disaster recovery back up. Why do you need it? What? The business use cases case in point. This classic case where we recently celebrated the 10th anniversary ofthe back wish bad kid in San Francisco, we have to go back and get all the archives you know, in a quick fashion, because they're always often requests from the media folks to access some of those. They don't necessarily come in a planned manner. We do a lot of things, a lot of planning around it, but still there are, you know, how How did that come about? What's the story behind? So you know, there are times we have to quickly go back. That's one second thing is having having to replicate our data immediately. Another classic case was in Puerto Rico. There was a natural disaster happened completely. Shut off. All the officers work down. We had to replicate everything what they had into a completely different place so that they could in a vpn, into an access that other chapters and our pulled in to help. They were close to 10 wish families close to 10 which families were stranded because of that. So, you know, gaining that data knowledge of where the family is because the minute of his journey starts. Everything is on us till the witch's journey ends. So we need to make sure everything is proper. Everything goes so data becomes very crucial from those pants >> you're tracking us. I mean, if you haven't been on the make a Wish site is some amazing stories. There I went on the other day. There's a story of ah, of 13 year old girl who's got a heart condition. Who wanted to be a ballerina. A kid with leukemia five years old wants to be a You want to be a chef. My two favorites, I'll share What? It was this kid Brandon a 15 year old with cystic fibrosis. I wanted to be a Navy seal. You guys made that happen. And then there was this child. Colby was 12 years old and a spinal muscular issue. You want to be a secret agent so very creative, you know, wishes that you ran >> way had another wish a couple of years last year in Georgia, where they wish kid wanted to go to Saturn. Yes, yes, it was huge. I mean, and you know the best part about us once we start creating those ideas, it's amazing how much public support we get. The community comes together to make them wish granting process. Great. Now. So I got involved in that. They gave the wish Kato training sessions to make sure that he is equipped when he goes into. And we had a bushel reality company create the entire scene. It was fabulous. So, you know, the way you talk about data and the technology is now some of the things I'm very excited about us usage off thes next Gen technology is like our winter reality to grant a wish. I mean, how cool would that be for granting a wish kid who is not able to get out of the bed. But having able to experience a the Hawaii is swimming. Are being in Disney World enough a couple of days? That's That's another use case that we talked about. That other one is to put the donors who pay the money in that moment off granting, you know, they are big major gift, uh, donors for make a wish. Sometimes we were not able to be part of a fish, but that would be pretty cool if you can bring the technology back to them and you know not going for them. You know pretty much everybody and make the ass through that rather than a PowerPoint or a storytelling, when the storytelling has to evolve to incorporate all of that so pretty excited >> and potentially make a participatory like, say, the virtual reality and then even getting in more into the senses and the that the smells. And I mean this is the world that we're entering the machine intelligence, >> which you still have to have, But you still have to be a functioning, competent, operationally sound organization. There've been a number of charities, make a wish is often at the top of the list of good charities. But there were a number of charities where the amount of money that's dedicated to the mission is a lot less an amount of money, dedicated administration of fundraising, and they always blame it. Systems were not being able to track things. So no, it's become part of the mission to stay on top of how information's flowing because it's not your normal business model. But the services you provide is really useful. Important. >> Sure, let me percent you the business conundrum that I have personally as a 90 leader. It takes close to $10,400 on an average to grant a wish. Uh, and, uh, partly because of me. But being part of the mission, plus me as a 90 leader wanting to understand the business more, I signed up. I'm a volunteer at the local Arizona chapter. I've done couple of expanding myself, and, uh, the condom is, if asked, if you want to go, uh, you know, do the latest and greatest network upgrade for $10,400 are what do you want to, uh, you know and make the network more resilient cyber security and all that stuff. What do you want to go grant? Another wish as a 90 leader probably picked the former. But as a volunteer, I would be like, No, it needs to go to the kid. It's Ah, it's It's an interesting kind of number, you know? You have to find the right balance. I mean, you cannot be left behind in that journey because at many points of time s I talked about it being a cost center. It being a back office. I think those days have clearly gone. I mean, we we evolved to the point where it is making you steps to be a participant b A b a enabler for the top line to bring in more revenues, tow no augment solutions for revenue and things. For that sofa >> rattles the experience or exact role citizens. And in your case, it's the experience is what's being delivered to the degree that you can improve the experience administratively field by making operations cheaper. Great. But as you said, new digital technologies, they're going to make it possible to do things with the experience that we could even conceive of. Five >> wears a classic example. Williams and Beam. I couldn't have taken the data from 60 chapters 120 locations into one single location manageable, and it reduced the cost literally reduce the cost of the 60 instances in one place without technology is like, you know what Sharia virtual machines. And and then to have a backup robust backup solution in a replication off it. It's fantastic. It's amazing >> there. And that's against here. You could give back to the dash chapters and backing, But thanks so much for sharing your story. You Thank you. Thank you. You're welcome. Alright, keep it right there. Buddy. Peter and I were back with our next guest. You watching the Cube live from V mon from Miami? 2019. We're right back. Thank you.

>> Live from Times Square in New York City, it's theCUBE. Covering IBMs Change the Game, Winning With AI. Brought to you by, IBM. >> Hi, everybody, we're back. My name is Dave Vellante and you're watching, theCUBE. The leader in live tech coverage. We're here with Scott Hebner who's the VP of marketing for IBM analytics and AI. Scott, it's good to see you again, thanks for coming back on theCUBE. >> It's always great to be here, I love doing these. >> So one of the things we've been talking about for quite some time on theCUBE now, we've been following the whole big data movement since the early Hadoop days. And now AI is the big trend and we always ask is this old wine, new bottle? Or is it something substantive? And the consensus is, it's real, it's real innovation because of the data. What's your perspective? >> I do think it's another one of these major waves, and if you kind of go back through time, there's been a series of them, right? We went from, sort of centralized computing into client server, and then we went from client server into the whole world of e-business in the internet, back around 2000 time frame or so. Then we went from internet computing to, cloud. Right? And I think the next major wave here is that next step is AI. And machine learning, and applying all this intelligent automation to the entire system. So I think, and it's not just a evolution, it's a pretty big change that's occurring here. Particularly the value that it can provide businesses is pretty profound. >> Well it seems like that's the innovation engine for at least the next decade. It's not Moore's Law anymore, it's applying machine intelligence and AI to the data and then being able to actually operationalize that at scale. With the cloud-like model, whether its OnPrem or Offprem, your thoughts on that? >> Yeah, I mean I think that's right on 'cause, if you kind of think about what AI's going to do, in the end it's going to be about just making much better decisions. Evidence based decisions, your ability to get to data that is previously unattainable, right? 'Cause it can discover things in real time. So it's about decision making and it's about fueling better, and more intelligent business processing. Right? But I think, what's really driving, sort of under the covers of that, is this idea that, are clients really getting what they need from their data? 'Cause we all know that the data's exploding in terms of growth. And what we know from our clients and from studies is only about 15% of what business leaders believe that they're getting what they need from their data. Yet most businesses are sitting on about 80% of their data, that's either inaccessible, un-analyzed, or un-trusted, right? So, what they're asking themselves is how do we first unlock the value of all this data. And they knew they have to do it in new ways, and I think the new ways starts to talk about cloud native architectures, containerization, things of that nature. Plus, artificial intelligence. So, I think what the market is starting to tell us is, AI is the way to unlock the value of all this data. And it's time to really do something significant with it otherwise, it's just going to be marginal progress over time. They need to make big progress. >> But data is plentiful, insights aren't. And part of your strategy is always been to bring insights out of that dividend and obviously focused on clients outcomes. But, a big part of your role is not only communicating IBMs analytic and AI strategy, but also helping shape that strategy. How do you, sort of summarize that strategy? >> Well we talk about the ladder to AI, 'cause one thing when you look at the actual clients that are ahead of the game here, and the challenges that they've faced to get to the value of AI, what we've learned, very, very clearly, is that the hardest part of AI is actually making your data ready for AI. It's about the data. It's sort of this notion that there's no AI without a information architecture, right? You have to build that architecture to make your data ready, 'cause bad data will be paralyzing to AI. And actually there was a great MIT Sloan study that they did earlier in the year that really dives into all these challenges and if I remember correctly, about 81% of them said that the number one challenge they had is, their data. Is their data ready? Do they know what data to get to? And that's really where it all starts. So we have this notion of the ladder to AI, it's several, very prescriptive steps, that we believe through best practices, you need to actually take to get to AI. And once you get to AI then it becomes about how you operationalize it in the way that it scales, that you have explainability, you have transparency, you have trust in what the model is. But it really much is a systematical approach here that we believe clients are going to get there in a much faster way. >> So the picture of the ladder here it starts with collect, and that's kind of what we did with, Hadoop, we collected a lot of data 'cause it was inexpensive and then organizing it, it says, create a trusted analytics foundation. Still building that sort of framework and then analyze and actually start getting insights on demand. And then automation, that seems to be the big theme now. Is, how do I get automation? Whether it's through machine learning, infusing AI everywhere. Be a blockchain is part of that automation, obviously. And it ultimately getting to the outcome, you call it trust, achieving trust and transparency, that's the outcome that we want here, right? >> I mean I think it all really starts with making your data simple and accessible. Which is about collecting the data. And doing it in a way you can tap into all types of data, regardless of where it lives. So the days of trying to move data around all over the place or, heavy duty replication and integration, let it sit where it is, but be able to virtualize it and collect it and containerize it, so it can be more accessible and usable. And that kind of goes to the point that 80% of the enterprised data, is inaccessible, right? So it all starts first with, are you getting all the data collected appropriately, and getting it into a way that you can use it. And then we start feeding things in like, IOT data, and sensors, and it becomes real time data that you have to do this against, right? So, notions of replicating and integrating and moving data around becomes not very practical. So that's step one. Step two is, once you collect all the data doesn't necessarily mean you trust it, right? So when we say, trust, we're talking about business ready data. Do people know what the data is? Are there business entities associated with it? Has it been cleansed, right? Has it been take out all the duplicate data? What do you when a situation with data, you know you have sources of data that are telling you different things. Like, I think we've all been on a treadmill where the phone, the watch, and the treadmill will actually tell you different distances, I mean what's the truth? The whole notion of organizing is getting it ready to be used by the business, in applying the policies, the compliance, and all the protections that you need for that data. Step three is, the ability to build out all this, ability to analyze it. To do it on scale, right, and to do it in a way that everyone can leverage the data. So not just the business analysts, but you need to enable everyone through self-service. And that's the advancements that we're getting in new analytics capabilities that make mere mortals able to get to that data and do their analysis. >> And if I could inject, the challenge with the sort of traditional decision support world is you had maybe two, or three people that were like, the data gods. You had to go through them, and they would get the analysis. And it's just, the agility wasn't there. >> Right. >> So you're trying to, democratizing that, putting it in the hands. >> Absolutely. >> Maybe the business user's not as much of an expert as the person who can build theCUBE, but they could find new use cases, and drive more value, right? >> Actually, from a developer, that needs to get access, and analytics infused into their applications, to the other end of the spectrum which could be, a marketing leader, a finance planner, someone who's planning budgets, supply chain planner. Right, so it's that whole spectrum, not only allowing them to tap into, and analyze the data and gain insights from it, but allow them to customize how they do it and do it in a more self-service. So that's the notion of scale on demand insights. It's really a cultural thing enabled through the technology. With that foundation, then you have the ability to start infuse, where I think the real power starts to kick in here. So I mean, all that's kind of making your data ready for AI, right? Then you start to infuse machine learning, everywhere. And that's when you start to build these models that are self-learning, that start to automate the ability to get to these insights, and to the data. And uncover what has previously been unattainable, right? And that's where the whole thing starts to become automated and more real time and more intelligent. And that's where those models then allow you to do things you couldn't do before. With the data, they're saying they're not getting access to. And then of course, once you get the models, just because you have good models doesn't mean that they've been operationalized, that they've been embedded in applications, embedded in business process. That you have trust and transparency and explainability of what it's telling you. And that's that top tier of the ladder, is really about embedding it, right, so that into your business process in a way that you trust it. So, we have a systematic set of approaches to that, best practices. And of course we have the portfolio that would help you step up that ladder. >> So the fat middle of this bell curve is, something kind of this maturity curve, is kind of the organize and analyze phase, that's probably where most people are today. And what's the big challenge of getting up that ladder, is it the algorithms, what is it? >> Well I think it, it clearly with most movements like this, starts with culture and skills, right? And the ability to just change the game within an organization. But putting that aside, I think what's really needed here is an information architecture that's based in the agility of a cloud native platform, that gives you the productivity, and truly allows you to leverage your data, wherever it resides. So whether it's in the private cloud, the public cloud, on premise, dedicated no matter where it sits, you want to be able to tap into all that data. 'Cause remember, the challenge with data is it's always changing. I don't mean the sources, but the actual data. So you need an architecture that can handle all that. Once you stabilize that, then you can start to apply better analytics to it. And so yeah, I think you're right. That is sort of the bell curve here. And with that foundation that's when the power of infusing machine learning and deep learning and neuronetworks, I mean those kind of AI technologies and models into it all, just takes it to a whole new level. But you can't do those models until you have those bottom tiers under control. >> Right, setting that foundation. Building that framework. >> Exactly. >> And then applying. >> What developers of AI applications, particularly those that have been successful, have told us pretty clearly, is that building the actual algorithms, is not necessarily the hard part. The hard part is making all the data ready for that. And in fact I was reading a survey the other day of actual data scientists and AI developers and 60% of them said the thing they hate the most, is all the data collection, data prep. 'Cause it's so hard. And so, a big part of our strategy is just to simplify that. Make it simple and accessible so that you can really focus on what you want to do and where the value is, which is building the algorithms and the models, and getting those deployed. >> Big challenge and hugely important, I mean IBM is a 100 year old company that's going through it's own digital transformation. You know, we've had Inderpal Bhandari on talking about how to essentially put data at the core of the company, it's a real hard problem for a lot of companies who were not born, you know, five or, seven years ago. And so, putting data at that core and putting human expertise around it as opposed to maybe, having whatever as the core. Humans or the plant or the manufacturing facility, that's a big change for a lot of organizations. Now at the end of the day IBM, and IBM sells strategy but the analytics group, you're in the software business so, what offerings do you have, to help people get there? >> Well in the collect step, it's essentially our hybrid data management portfolio. So think DB2, DB2 warehouse, DB2 event store, which is about IOT data. So there's a set of, and that's where big data in Hadoop and all that with Wentworth's, that's where that all fits in. So building the ability to access all this data, virtualize it, do things like Queryplex, things of that nature, is where that all sits. >> Queryplex being that to the data, virtualization capability. >> Yeah. >> Get to the data no matter where it is. >> To find a queary and don't worry about where it resides, we'll figure that out for you, kind of thought, right? In the organize, that is infosphere, so that's basically our unified governance and integration part of our portfolio. So again, that is collecting all this, taking the collected data and organizing it, and making sure you're compliant with whatever policies. And making it, you know, business ready, right? And so infosphere's where you should look to understand that portfolio better. When you get into scale and analytics on demand, that's Cognos analytics, it is our planning analytics portfolio. And that's essentially our business analytics part of all this. And some data science tools like, SPSS, we're doing statistical analysis and SPSS modeler, if we're doing statistical modeling, things of that nature, right? When you get into the automate and the ML, everywhere, that's Watson Studio which is the integrated development environment, right? Not just for IBM Watson, but all, has a huge array of open technologies in it like, TensorFlow and Python, and all those kind of things. So that's the development environment that Watson machine learning is the runtime that will allow you to run those models anywhere. So those are the two big pieces of that. And then from there you'll see IBM building out more and more of what we already have. But we have Watson applications. Like Watson Assistant, Watson Discovery. We have a huge portfolio of Watson APIs for everything from tone to speech, things of that nature. And then the ability to infuse that all into the business processes. Sort of where you're going to see IBM heading in the future here. >> I love how you brought that home, and we talked about the ladder and it's more than just a PowerPoint slide. It actually is fundamental to your strategy, it maps with your offerings. So you can get the heads nodding, with the customers. Where are you on this maturity curve, here's how we can help with products and services. And then the other thing I'll mention, you know, we kind of learned when we spoke to some others this week, and we saw some of your announcements previously, the Red Hat component which allows you to bring that cloud experience no matter where you are, and you've got technologies to do that, obviously, you know, Red Hat, you guys have been sort of birds of a feather, an open source. Because, your data is going to live wherever it lives, whether it's on Prem, whether it's in the cloud, whether it's in the Edge, and you want to bring sort of a common model. Whether it's, containers, kubernetes, being able to, bring that cloud experience to the data, your thoughts on that? >> And this is where the big deal comes in, is for each one of those tiers, so, the DB2 family, infosphere, business analytics, Cognos and all that, and Watson Studio, you can get started, purchase those technologies and start to use them, right, as individual products or softwares that service. What we're also doing is, this is the more important step into the future, is we're building all those capabilities into one integrated unified cloud platform. That's called, IBM Cloud Private for data. Think of that as a unified, collaborative team environment for AI and data science. Completely built on a cloud native architecture of containers and micro services. That will support a multi cloud environment. So, IBM cloud, other clouds, you mention Red Hat with Openshift, so, over time by adopting IBM Cloud Private for data, you'll get those steps of the ladder all integrated to one unified environment. So you have the ability to buy the unified environment, get involved in that, and it all integrated, no assembly required kind of thought. Or, you could assemble it by buying the individual components, or some combination of both. So a big part of the strategy is, a great deal of flexibility on how you acquire these capabilities and deploy them in your enterprise. There's no one size fits all. We give you a lot of flexibility to do that. >> And that's a true hybrid vision, I don't have to have just IBM and IBM cloud, you're recognizing other clouds out there, you're not exclusive like some companies, but that's really important. >> It's a multi cloud strategy, it really is, it's a multi cloud strategy. And that's exactly what we need, we recognize that most businesses, there's very few that have standardized on only one cloud provider, right? Most of them have multiples clouds, and then it breaks up of dedicated, private, public. And so our strategy is to enable this capability, think of it as a cloud data platform for AI, across all these clouds, regardless of what you have. >> All right, Scott, thanks for taking us through the strategies. I've always loved talking to you 'cause you're a clear thinker, and you explain things really well in simple terms, a lot of complexity here but, it is really important as the next wave sets up. So thanks very much for your time. >> Great, always great to be here, thank you. >> All right, good to see you. All right, thanks for watching everybody. We are now going to bring it back to CubeNYC so, thanks for watching and we will see you in the afternoon. We've got the panel, the influencer panel, that I'll be running with Peter Burris and John Furrier. So, keep it right there, we'll be right back. (upbeat music)

>> Announcer: Live, from Las Vegas, It's theCUBE, covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018 from Mandalay Bay in Las Vegas. My name is Dave Vellante. I'm here with Peter Burris, my co-host. You're watching theCUBE, the leader in live tech coverage. Scott Hebner's here as the Vice President of Marketing for IBM Analytics. Scott, welcome back, good to see you. >> Thank you, glad to be back again. >> So you heard Jenny this morning, a very inspiring speech. I love her talks. She's really good in front of an audience and one-on-one. What were your takeaways, specifically as it relates to your group? >> Well I think the theme of this whole conference is a lot of these technologies over the years that have been purchased separately and are thought of separate, quote-unquote, segments, are really all starting to fuse together. They're becoming different facets of the same challenge that a large majority of our clients have. And that is really this evolution towards a more AI based set of business models, right? There's a stack of things that need to be done to make that successful. You've got to move to the cloud for the agility of it, the economics of it. You got to get more value out of your data, and make your data ready for AI. Then you can start to more effectively train your AI models and allow them to continue to learn and everything. So it all really comes together, and I thought that's what she was framing, of what IBM's trying to do uniquely. >> Yeah, and I think it came across that way. Obviously, this conference is about bringing together all the separate... And your organization is evolving. I mean, when you think about IBM... Go back, Peter, to even the Gerstner days, and he said, "No, we're not going to split up "into a million companies. "We're going to have one face to the customer." And then, obviously, IBM was very successful there. You now had some major changes in the marketplace and you're responding to those. >> Yeah, and I think that's exactly right. We're being very customer-driven. One of the great advantages of IBM is that we have so many customers, right? A mix of new ones, a mix of ones we've had for a long time. We have so many people that engage. If you think about the size of IBM and how many are engaged with customers every single day at all levels, from the very most technical to the people that manage relationships, we learn a lot collectively. With all the new technologies, particularly around digital, net promoter score, all these things, we learn a lot about what they're trying to do. And that's what's driving us to fuse these strategies together into a more wholistic one. And that's what you heard this morning from Jenny. >> So, I also really enjoyed what I heard this morning from Jenny. It takes me reminded me, though, of one of those television shows where people bring in their old family artifacts, and then people price them. I imagine enterprises today literally looking at their data, the 80% that nobody has visibility to, and finding Grandpa's letter from Abraham Lincoln. >> Yeah. >> And using and discovering that this is a source of value that they've never envisioned before. Is that kind of the mentality, the conversation, that we're having today? >> No, that's exactly right. A large, large majority of CEOs have declared their data to be a strategic asset, but only about 10% of them believe their company treats it that way. And it leads to the statistic that you just referenced, which is 80% of data is either unanalyzed, untrusted, or inaccessible. So they're sitting on a gold mine of data, right? It's not just empirical customer records, but it's increasingly IOT and sensor data. It's behavioral data. There's a gold mine there. Step one is how do you take advantage of that and get more value out of it, right? Just in today's world, right? And then it really becomes fundamental to being successful with artificial intelligence. You have to have an information architecture. We kind of say if there's no IA, there's no AI. You have to have that information architecture to be successful, and that's really where we're focused on at this conference today, is getting that data ready for AI. >> So getting the data ready for AI, there's a lot that goes into that. But when you consider the notion of data as an asset, and what we heard from Jenny this morning, it seems as so, in many respects, there's kind of two models happening in the industry. You can see if I got this right. Companies that make money off of your data and companies that aren't going to make money off of your data. >> Right. >> Would you agree... I mean, is that kind of how the split is starting to happen in the industry right now? >> Yeah, no, I think that's right. I mean, I think a large majority of our clients are using their data within their firewall to operate their businesses better, better understand their customers. >> No, I learned something different. Yeah, sorry, I apologize. Companies that are going to make money off their customers' data-- >> Yes. >> And companies that are not going to make money off their customers' data. >> Yeah. >> Right? >> Yeah. What I'm saying is... No, I get the question. Different companies have different business models with what they're going to do with their data. Some see it as an asset to run their business more effectively. Others see it at as a direct asset that they sell and resell and resell, right? What I'm saying is the majority of the customers we deal with are looking at their data as an asset to run their business better. >> And that's the basis for the argument that the incumbency, that we're entering back into the area of the incumbency because of all these rich assets that aren't currently being utilized. Is that right? >> That's right. >> Great. >> It all starts with the fact that the data is fragmented everywhere. Business partner networks across different databases. Step one is to make that data simple and accessible. But once you do that, that's not the end of it because you need to make sure that the data that people are using is trusted. You have to have that trusted analytics foundation. So you got to integrate it, replicate it, catalog it, cleanse it, manage its lifecycle. You need to have one version of the truth, right, that everyone works off of, which is a major problem, by the way. It's the whole notion of governance and that falls into other categories like privacy and all the compliance challenges that customers have. Then from there, you have that foundation where you can start to drive more insights out of it through things like machine learning and pattern recognition. As you start to build those skills around data science, it starts to get you really ready for that next step on that ladder to AI. That's where a lot of these customers are figuring out how do I get on this roadmap to AI. And 85% or so say they're going to get there in the next five years. There's a great study from MIT Sloan that came out last year of 3,000 customers and was very clear. The difference between the pioneers that are having success, and those that aren't, is the pioneers have figured out how to make their data ready for AI. It all really starts there. That's really what we're focused on here at the show. >> Let's talk about that incumbent theme. It was part of Jenny's talk this morning. >> Scott: Yup. >> And you're right, the incumbents, their data exists in silos, even though they're maybe data companies, like a bank. >> Scott: Yeah. >> They're organized, perhaps, around their products. Or a manufacturer might be organized around the bottling plant, as you say. Whereas those companies that are AI driven have data at their core. So it's a challenge for the incumbent. >> Huge. >> How are you helping them close that gap, that AI gap, if you will? >> Right, and that's exactly what I was just saying before, is that the data is incredibly dynamic and growing at exponential rates. Not only through what you just mentioned, but there's acquisitions. There's different business partners that evolve through your networks, your client data, things of that nature. >> Dave: And data sources, yeah. >> Data sources are changing. And then you get into the technical layer of all different types of data, from images to empirical data. And then you get into different databases. It becomes a very heterogenous mess. Step one is to make it simple and accessible. And doing that though big data and being able to view through a single layer all the data as it changes, right? Because if you don't have access to your data, then what are you going to be training your AI algorithms on? And again, from there, you've got to govern it in a way that it's trusted data. This is a huge challenge for customers, because they get different versions of data that tell them different things. Which is the single version of the truth? It's kind of like if you've ever been on a... When you get on a treadmill, your watch says this many steps, your phone says another number of steps, the treadmill says the third number of steps. You're like, how many steps did I really take? They have that challenge every day. When you get that foundation and information architecture together, then you're ready for AI. What this MIT Sloan study showed was that bad data is paralyzing to AI. No matter how sophisticated your algorithmic AI capabilities are, bad data is simply paralyzing. So that's really where it needs to start. To circle back to your point about 80% of data, untrusted, unanalyzed, and inaccessible, that's got to be step one on that ladder to AI. >> So how are we going to use ML, machine learning AI, to help us get our data ready for machine learning AI? >> Well, that's exactly what we're doing in the IBM portfolio of data and analytics products, is we have this theme called Machine Learning Everywhere. So it actually is in almost every part of our platform. Hybrid data management uses machine learning to help do a much quicker assessment of how you bring data together and analyze it and things of that nature. We use it in the governance. In fact, we have a technology prototype that we've been working with some customers on, that will do the work for GDPR, the European Compliance Guidelines, in probably a few days to a week versus months and months and months. 'Cause we will go in and do all the entity associations for all your data. Help you organize it in a way that you can actually manage what to do with the compliance. And then, obviously, machine language is fundamental to just business analytics in general, right, pattern recognition. The traditional analytics tools will help you understand the data as it's presented, based on what you are trying to get out of it. Often, you don't know what you're trying to get out of it. Machine learning gives that data science method of actually uncovering patterns, which you can't really see. >> Peter: Creating models. >> Yeah, creating models and then you add the neuro-networks to it in deep learning. It's really literally a ladder that you're building that when you get to AI, you're going to be a lot more successful because you've built that trusted foundation underneath it. And I think Jenny was touching on that to some degree this morning. That's what we're majoring on, is that that data is really the key element of AI. >> Scott, who are the roles that you see developing this information architecture, getting ready for AI? CDO, CIO, Chief Digital Officer, where do they all fit? >> Yeah, I think it leads under the CDO. And actually both CDOs, the chief digital officer and the chief data officer, and their collection of data engineers, data stewards, things of that nature. 'Cause, again, you got to start by getting that information architecture in place. It also involves sort of a new generation of data developers that are building cloud-based data intensive applications, particularly of event-space data, which is a little bit different that customer data from sensors and all that, where you need that massive ingest speeds. It's those data-driven applications from the cloud that are really starting to incorporate machine learning. So they become really key. Then from there, if you think of it as a collaborative lifecycle, you get into the data scientists that are applying analytics. They're applying a more sophisticated version of mathematical programming and data science. Then there's a new, sort of subset of them, which are the AI developers. It's really from the data engineer right through business analysts. There's a lifecycle of people that are part of that team. They all have to work off a common platform, a common set of trusted data, to be successful. 'Cause you can no longer segment it. >> Is your strategy to build tooling that allows all of those roles to collaborate, maybe not the chief digital and chief data officer, but the data engineer, the data quality engineer, the application developer, the data scientist, right. Is that correct? >> That is absolutely correct and the CDOs. Actually, what we're announcing at the show is a new offer called IBM Cloud Private For Data. >> Dave: Right. >> So if you're familiar with IBM Cloud Private, it's our private, behind the firewall, cloud platform. We're coming out with a new offering that plugs into it. It's based on Coubanettis, so it runs on IBM Cloud Private For Data, and will run on other Coubanettis-based platforms. It is a fully integrated data and analytics platform, where no assembly is required. It will provision in minutes a pre-assembled, customized experience for you, based on what your role is. So if you're the CDO and you're the data scientist, and I'm the data engineer, we're all going to have a different set of requirements of what we want to get out of the data and what we're looking to do. It will pre-provision that for you very, very quickly. And you're all working off a common platform. It's collaborative in nature, with dynamic dashboards so you can see what's going on. It's really taking the building blocks that you need to move up that ladder and integrating to microservices in to a cloud platform that is just lightning fast in terms of, not only its ingest speeds of data but, more importantly, the ability to provision new users. So it's a major step forward in making it so much easier, so much more simple to get more out of your data and to get your data ready for AI. >> So, last question. You have this giant portfolio. We just finished our Big Data report. You guys, IBM, came up number one. Well, that was services, but still, you got a lot of software in there as well. >> Scott: Yes, we do. >> You've been working hard to pull those pieces together so the clients, it's simplify data. >> Scott: Yup. >> Okay, here's where are are, 2018, where do you want to take this thing? >> Well, I think, again, I think step one is this unified experiences. Because, again, we were kind of majoring on this conversation about the desegmentation of how people work in a business, what technology, what data they use. 'Cause with AI, it really does need to come together, right? So we're trying to do the same thing for the users, which is provision-based, almost on-demand, what you need based on what you're looking to do. And I think what's going to change as we go through time is it becomes more and more machine learning based, pattern recognition. It's more automated and customized and personalized, based on what you're trying to do. That's going to allow businesses to move at a much more rapid pace. And, again, I think the overriding theme when you look over a five year horizon is, is your data ready for AI? And that's where we're moving this whole thing. It's about the data. It's about the people and their skills. And it's the ability to move quickly. That's where the linkage with cloud comes in. >> Getting to pervasive AI, but you got to get your data house in order first. >> You got it. >> Scott Hebner, thanks very much for coming on theCUBE. >> Thank you. >> Great to see you again. >> Great meeting you. >> All right, keep right there everybody. We'll be back with our next guest. You're watching theCUBE at IBM Think 2018.

(upbeat symphonic orchestra) >> Hello and welcome to this Cube Conversation here in Palo Alto, California, the Cube Headquarters. I'm John Furrier, the co-founder of SiliconANGLE Media for a special Cube Conversation with Tami Zhu, who is the General Manager of Kika Tech Headquarters in San Jose. She's a friend of the Cube, I've known Tami since almost about 15 years ago from the Web 2.0 era. Dual degree in Computer Science, undergraduate and a Master's as well as an M.B.A. from M.I.T., Sloan. Great to see you. >> Thank you, John, for having me here. >> Great to see you. So we've kind of been through Web 2.0. I think you were at AOL Ventures then, and riding other careers. You've been in the trenches, certainly in the front lines in tech. You've seen a lot of waves. So where are you now? Give us an update on what you're doing now, lot of great things happening. >> Yes, since we last saw each other 15 years ago. Most recently, I joined the company called Kika Tech and we're headquartered in San Jose. As a matter of fact, the reason the company recruited me to join the company is for two things. One is to develop our A.I. effort and product, and secondly is to move the headquarters from China to San Jose because a large percentage of our consumers are U.S. based. >> We love the China connection. We've been covering China recently for SiliconANGLE and the Cube. We just did Hangzhao for Alibaba but this really speaks to- I don't want to say the Chinese invasion of North America, but that's certainly happening, but also the rest of the world is going to China. Tons of users out there. It's exploded with mobile usage, really setting the trends. So the globalization of the internet is happening. The software on mobile is just getting better and better. You're doing some A.I. work with Kika. What's going on with A.I. and Kika? You guys have spectacular performance. What, 400 million downloads? What is it all about? What is the big trend that you're riding? >> Yeah, so the mission of Kika is to revolutionize communication with A.I. If you were to look at the purposes of human communication, we categorize into three categories. Number one is by sharing information, and number two is about initiating requests and having your requests fulfilled. Number three is about sharing your emotion. A lot of companies out there are addressing one of the three challenges and purposes where at Kika, we're taking on the challenges, addressing all three purposes in communication. >> Well congratulations on all your successes as General Manager and expanding out in North America from the Chinese base company. You've got a big challenge ahead of you, but I've got to ask you on a personal level, I've always seen you in a male-dominated culture in the Web 2.0 era. You've been very successful as a woman in tech, and... what got you into technology? You've kind of a nerd like me and you love to get in there and look at the technology. You're not afraid to get your hands dirty in the tech. How did you get into the technology business? >> I'm probably nerdier than you. (laughs) As a starter. So I grew up in a very academic family. My parents are both engineering professors. They encouraged me to excel in academics at school. I was very competitive and I always wanted to be number one, I was always number one as a matter of fact throughout the entire school and academic career. When I was 12 years old, my dad was a visiting professor here in the United States, and he told me a lot about Stanford and the Silicon Valley. At that time, I decided I was going to come to the Silicon Valley when I grew up and participate in technological innovation. I just thought that was so cool. >> And you did? >> Tami: Yes, absolutely. This is something that I'm passionate about and that I love to do. >> You're certainly an inspiration. I've always enjoyed the work you've done and just the energy you bring to the table. This is something we need more of. You're out there... what do you say to people? "Hey, I've been around the block a few times." There's a lot of people trying to figure out the whole women in tech thing. There's been such negative things going on in the business. You're a positive light. What would you like to share for folks around just your thoughts on this whole... women in tech, should they be special? The pipelining issues, all these issues and conversations. What's your perspective? How would you take it perspectively? >> Right. I say we take advantage of our individual strengths and a number of things I continue to emphasize to my colleagues at work. Number one is every day you check in and ask yourself, "do I love this work? Is this something I'm passionate about?" If you are, it's more likely you're going to be successful in the business with some perseverance, right? The second thing that I emphasize is don't be afraid of experimenting and try to make mistakes, that's okay. Completely okay. Try to make mistakes early and frequent as long as you don't make the same mistakes again and learn from that. The third thing I continue to emphasize, a matter of fact, I lead by example, is never procrastinate. We have dreams and hopes and we talk about that, that's great. But we need to execute on that now. >> I love your competitive spirit. I think you're an inspiration. But also, you said you like to be number one, and you were in school. I think you might be a little bit nerdier than me, but we can talk about it after. When you're number one, you're going fast, you're moving fast and you're learning, you're not going to go without a few interactions that are unfavorable. So how do you talk to other women when you're out in the field? When you're hard-charging like that and you're smart, you've got to deal with a lot of bad actors. It could be men, it could be harassment, it could be sexual, whatever it is, you know you've got to break through it. If you want to be number one, you've got to deal with this. >> Sure. >> I've talked to a lot of women who have said they've had their fair share of interactions that were unpleasant, but I moved past it. How do you deal with it? I'm sure you have stories and can share a perspective on how you deal with unwanted advances to just bad behavior. >> Right. I think I'm luckier, probably, than some of the... average population in that I've not really dealt with much bad behavior. Certain behaviors, I'd say, look way beyond that. Don't play the same game. Don't play the game at all. Don't entertain any of the bad behaviors. Believe in yourself and perseverance will get you far and apart. Never give up. >> Awesome. On the inspiration side, how do you inspire other women? I'm seeing some really good things happening. One thing is, I'm seeing a lot of conversations. A lot of people coming together. A lot of young women are looking up for leaders and looking to folks who have been through, climbing the mountain, close to the top or at the top. You have this new really cool vibe going on where the women are coming together at all ages for sharing. How do you do it? >> As a matter of fact, compared to 15 years ago when we met doing Web 2.0 I think there were a lot fewer women in tech. Nowadays with a new generation of technology and social media, we're actually seeing women in computer science taking the lead. Just taking the time, be patient, and I think one of the things as human being, we often worry about compensation and how much we're being paid now, how much we're worth, and what exactly the title is, right? I say don't even worry about that. Focus on what you're passionate about. It will take some time. Be patient and it will get there. >> We always say, "respect for the individual," but just be a good person. Don't deal with the nonsense, just move past it and don't play the games. Alright got to get back into the tech since we're going to geek out here. So A.I. I think is the hottest thing on the planet right now. Obviously I.O.T. is super important. We cover it heavily on the Cube. No one wakes up in the morning and says, "I can't wait to talk about I.O.T with my friend!" They all love A.I. because it's got a cooler vibe to it, but we're talking about software. We're talking about really cool software and a Renaissance of software development. So A.I. is super hot, you guys are doing a lot of A.I. at Kika. What is the coolness, for male and female, for anyone to get involved - What is the hot A.I. trend? Is it the machine learning, is it the deep learning? Is it the user experience, is it making it easier? What are some of the advances that you're excited about in A.I.? >> So depending on the timing and the year, say 15 years ago, or 20 years ago... Let's say 20 years ago, at the time, A.I. actually, there was a small boom that very quickly went into an ice age. A cold winter. Matter of fact, during that time, I was in undergrad and my undergrad thesis was natural language processing in Chinese languages. With that expert system at that time, the framework never got anywhere. They were really limited because of the knowledge from experts. So now fast-forward to two, three years ago when Amazon Echo first launched. I think there was a lot of doubt. In academia and the amount of people in the industry were thinking pretty cynically. Saying, "well that's just another boom. I doubt that." Echo really paved the way and brought artificial intelligence into the homes of consumers. Two, three years ago it was very cutting edge in terms of voice recognition. You hear a lot about far field, noise cancellation, but nowadays, the voice recognition is becoming far more mature, right? For someone who wants to work on the most cutting edge thing, from my point of view, voice may be a little bit to the point where it's mature and people understand the problems. So this year, only recently, Apple announced an emoji. So this is the starting point of computer vision in consumers' lives. Say if I were an engineer, I would want to get into computer vision, because there's so many more things you could potentially create with that. >> John: It's the next level U.I. in the interaction, I mean, I think NLP, National Language Processing, has always been kind of fun. I remember back when I was getting my C.S. degree, entologies were big. That kind of stalled, the nuclear winter, or the cold winter. But now with cloud computing, and mobile being so powerful, you now have so much at your disposal. With all these libraries and open source developing, it's a dream for a developer because now you can create new experiences. Not the old way, browser, or just typing on a phone. You guys have got a really cool app that you can download Kika Technologies. You got huge opportunities that reimagine the interface and the interactions. I think A.I. has put a picture in the mind of the user, the consumer, and the developer. Self-driving cars, Teslas. This is a new coolness. What are some other examples of this new coolness that you can share that are happening whether it's computer vision, Teslas, or voice interaction? What are some examples of the coolness? >> So I've been very limited in that. I've been so focused on work. We have something really cool coming up in 2018. Matter of fact, we're kicking off 2018 with launching a brand new product that's taking our existing input method keyboard to the whole next level. The whole I.O.T., you were just mentioning, "who cares about I.O.T.?" (laughs) >> Well it's one of the fastest growing areas, but I.O.T. is A.I will become an edge of the network. Now on this launch, is this going to happen at C.E.S? >> Yes, we're going to launch at C.E.S. >> So we'll look for the news at C.E.S. >> Yes. It'll be very exciting, matter of fact. >> I'll have to dig some information out of Tami after this interview is over. Find out more. We'll be at C.E.S. Okay, final question. In general, just your thoughts on the tech cycle right now. You've ridden many waves, you've seen a lot, you know the tech under the covers. What's the big movement that young people should be jumping on? The new Renaissance in software development is happening. We see the cloud there. It's clear from Amazon success of the new models here, you're seeing some successes. How would you describe this new era, this new guard of technology providers and software? >> From a talent point of view, 10 or 15 years ago, if you got a P.H.D. in computer science, you could hardly find a job other than finding a professorship somewhere. Nowadays, if you're to look at Facebook or Google as a P.H.D. in computer science, then you are worth a lot more- >> Some say Google is turning into academia, but that's a whole other conversation. But okay, if you can get a P.H.D., neural nets are hot still. Neural networks, things of that nature. P.H.D., there's a lot of work there. Anything else? >> Yes. A.I. will continue to develop, and now A.I. is the real thing compared to 15 or 20 years ago, right? It was very limited to academia. That's going to continue to develop, and you'll look at other areas. For example, digital advertising. In the past four or five years, it was programmatic advertising. How do you accurately target the audience and then maximize the CPA or CPM per audience. Then the next level is about how to build an advertising network that's effective and targeting the audience, not only maximizing the revenue, but also how do you keep the audience and continue to grow the audience. So these are- >> In the role of data, just one final thought on the data, the role of data in all of this is the center of all this. Your thoughts on the role of data and how that's going to shape- because those experiences of targeting might shift around with the users who are now driving the data. >> Matter of fact, the data is key. At Kika, our number one differentiation is a large volume of training data, so with that data, we can train our deep learning algorithm. Make our algorithm, find patterns and predict contacts and text. That's the number one thing. The number two thing is because you have the data, there are a lot of privacy policies that you need to watch out and make sure there's no data leaking or security leak that could potentially create that press. Also it's not safe for the consumers. So we're talking about data. Data really is the competitive advantage. >> If you're a data geek out there, you have no problem getting a job. We're here with Tami Zhu who is the general manager of Kika Tech headquarters in San Jose here inside the Palo Alto Cube studios for Cube Conversation, I'm John Furrier, thanks for watching. (upbeat electro)

(upbeat techno music) >> Announcer: Live, from Orlando, Florida. It's The Cube. Covering ServiceNow Knowledge17. Brought to you by ServiceNow. (upbeat techno music) >> We're back at Knowledge17. Dave Vellante with Jeff Frick. Josh Gluck is here, he's the deputy CIO of Weill Cornell Medical College in the big apple. Thanks for coming to The Cube. >> Thanks very much for having me. >> Tell us about Weill Cornell, It's a collaboration with Sloan Kettering, originally, and ... >> Yeah, we're a three part, mission-oriented institution. Patient care, being first. Our physician organization delivers patient care in New York City. We're partnered with New York Presbyterian Hospital, Memorial Sloan Kettering Cancer Center, and also the hospital for special surgery. >> So, let's get right into it. CIO, you were probably doing some of the CIO activities here, this week. Love to hear about that. But let's get right into how you're, you know, using automation, how you're using the ServiceNow platform. Let's talk in the context of IT transformation. >> Yeah. So we've been a ServiceNow customer since 2012. We actually went live on 12/12/12. Everybody thought that was a joke, but it turned out to be the real "go live" date. You know, and as the platform's matured, and as our organization's matured, you know, we started focused on ITSM, strictly. Over the last few years though, we've found that, you know, our focus for ServiceNow should be the equivalent of building a 3-1-1 platform for the administrative departments. So we've onboarded folks in HR. We're doing case management now with ServiceNow. Obviously all the ITSM, ITIL-based processes. We've worked with our Department of Environmental Health and Safety. To help them with some of the regulatory compliance, about workflows that they need to have in place. We've also built out Project and Portfolio Management in ServiceNow, and we've been doing it, actually, since the beginning. We worked with ServiceNow pretty intimately to build out those functions. And now, we're actually at the point where, the platform has surpassed what we custom developed back in the early days. And we're really focused on understanding where we can unwrap some of those customizations, and just go to the native portfolio. >> Yeah, I wanted to ask you about that. >> Yeah. >> So, that's not an uncommon story and how complicated is it to unwrap that stuff? 'Cause obviously, you don't want the custom mods there if you don't have to have them. >> Yeah, well you know we spent, what, five, six years now, focused on developing the platform to meet our needs, meet our process. You know, we're academics at heart. Right, being part of Cornell University. So, I think we have a habit of sometimes overthinking solutions. So, our customizations are pretty complex. We also though, understand that it's a heavy lift for us to keep it up. So, we partner with ServiceNow, we've had them come in and help us to an evaluation of what really could be done with a slight change to our process. Or, even just direct support for our process, straight out of the box. We're really excited about the stuff that's coming out of Jakarta. >> Okay, so it's fair to say, I mean, we've all been there. Where you have software development problems, and you go "ah, jeez, I wish I had done it differently." But, when we talk to folks like you, that are unwrapping, unraveling, custom mods, there's no regrets. You got a lot of value >> Josh: Yeah, no. >> out of 'em. And now you're moving forward, right? >> Josh: Yep. Yeah we >> That's interesting. >> Josh: Definitely did the right thing, at the right time. You know, we went through an evolution, in the way that we did Project and Portfolio Management internally at Weill Cornell. And we're focused on some of the high-level problems, high-order problems today, that some organizations may not get to. Right, we're doing resource management, proactive scheduling, and you know, for us to get to the next level, the enhancements that are available in Jakarta are around time-carding and resource management, are really going to help us, I think, not overthink the problem. And come to some standard that the rest of the industry, or other verticals are using, in how they do their resource management. >> And Josh, the 3-1-1 concept is interesting. When did you go from "this is our an ITSM tool, that's going to be pretty cool." >> Yeah. >> To "this is a platform, that we can now take this kind of 3-1-1 approach, and use that as kind of an overarching mission, >> Yeah. >> for that which you're trying to accomplish"? >> I think the concept ... I think when we first went into partnership with ServiceNow, we knew that we wanted it to be more than just a replacement for heat, right? I've actually been with two different organizations. New York Presbyterian Hospital and Weill Cornell, who have come from other ITIL platforms, ITSM platforms, and moved to ServiceNow. I was a BMC Remedy customer for a long time at New York Presbyterian. We were a heat customer at Weill Cornell, prior to going to ServiceNow. So, I think we were all familiar with the fact that it doesn't make sense to buy these point products, to do all of these different workflows. Let's buy a platform. ServiceNow represented that platform. Even in its early stages, we knew that we wanted to do more with it. We had conversations about process users. And I know you guys were talking a little bit before about changes to the license model that are happening. >> Dave: Yep. >> But we really wanted it to be something we could develop further. Our first project just happened to be, in both cases "we have an ITSM platform that isn't working." Remedy at NYP, heat at Weill Cornell. "Let's get off of it, and get onto ServiceNow." But I think, we didn't start calling it the 3-1-1 until maybe a year or two ago. >> Okay. >> And it really started with Case Management. I think that was a big deal. >> It's a good little marketing, CIO selling. >> Josh: Yeah. >> You know, Daniel Pink. How large of an organization ... >> Josh: Is, IT, or Weill Cornell itself? >> Weill Cornell. >> We're between ... We're about five-thousand and change. >> Okay, so not enormous. But, the reason for the question is, at what point does it make sense to bring in a ServiceNow? You know, our little fifty-person company. You know, we're trying ... >> Josh: Yeah. But it's still not there yet. Is it size of company? Is it size of problem? What is your advice there? >> You know, I think it's actually a good idea for most mid-level companies to talk to ServiceNow. And I think there's even a play for some small businesses. It depends on what you want to get out of the tool. Right? I mean, if you're going to use it as just a simple incident-response system, which isn't really the value that ServiceNow provides, it might be a hard sell. But, because it's a hosted system, because there is such a wealth of partners in the community now, and such a following for ServiceNow, I don't know. If you were a ten-person organization and you were customer focused, and you wanted to use it to do ... >> Jeff: Yep, yeah, that makes sense. A couple of different business processes, it could actually make sense for you. >> Josh, really tight schedule today, we'll give you the last word on Knowledge17, some of the things that have excited you, what's the bumper sticker on K17 for you? >> I think the keynotes have been great. I think you guys at The Cube have been doing a great job, of also, >> Dave: Thank you very much, appreciate that. >> you know, getting people up here and asking 'em tough questions and stuff. I appreciate you going easy on me. Than you. But, it's been great. It's been a really good show. >> Well come back again, and we'll really go at it. So, thanks very much Josh, >> Josh: Thank you. appreciate your time. Alright, keep it right there everybody. We'll be back with our next guest, right after this short break. (upbeat techno music)

(electronic music) >> Narrator: Live from Boston, Massachusetts it's The Cube covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, RedHat and additional ecosystem support. >> Welcome back, I'm Stu Miniman joined by my cohost John Troyer. We always want to give the community what they want. and I think from the early returns on day one, we brought back Mark Shuttleworth. So Mark, founder of Canonical, had you on yesterday. A lot of feedback from the communities, so welcome back. >> Thank you, great to be here and looking forward to questions from the community and you. >> Yeah, so let's start with, we love at the show you get some of these users up on stage and they get to talk about what they're doing. We were actually, John and I, were catching up with a friend of ours that talked about how a private cloud, the next revision is going to use OpenStack, so really, OpenStack's been a little under the covers in many ways. The composability of OpenStack now, we're going to see pieces of it show up a lot of places. We've heard a lot about the Telco places, maybe talk about some of the emerging areas, enterprise customers, that you find for Ubuntu and OpenStack specifically? >> Sure. Well it seems as if every industry has a different name for the same phenomenon, right. So, for some it's "digital", for other's it's essentially a transformation of some aspect of what they're doing. The Telcos call it NFV, in media you have OTT as a sort of emerging threat and the response, in every case, is really to empower developers. That's why it's such a fun time to be a software developer, because the established guys realize that if they aren't already competing with Silicon Valley, they're going to be competing with Silicon Valley. So in each industry there's a sort of challenges or labels that they give this process of kind of unleashing developers and it's fun for us, because we get to be part of that in many cases. I think the big drivers under the hood, other than the operational and economic dynamics of cloudification, I think the really big changes are going to be machine learning, which seems to be moving very quickly into every industry. Retailers are using it for predictive analytics on what to put in store or what to recommend online. It just has this huge effect on almost any business when you figure out how to use your data in that way. All of that is developer driven, all of that needs this kind of underlying infrastructure to power it and it's kind of relevant to every industry. For us media is a key prospect, you know that we've done very, very well in Telco. Media is now a sort-of critical focus. Companies like Bloomberg for example us Ubuntu as an elastic platform for agility for the developers. They're a pretty astonishing operation; media company, but very tech-centric, very tech-savvy. I don't know if you've had them on the show. In retail, Ebay, PayPal it's kind of a crossover finance. They're all using Ubuntu in that sort of way. They may now see the major financials who are looking at the intersection of machine learning and transactions systems effectively as the driver for that kind of change. >> Stu: So in our last interview we talked about are companies making money in OpenStack and your answer, resoundingly, was yes. >> Mark: For us, certainly, yeah. >> One of the things we always look at is kind of the open source model itself. I was at DockerCon a few weeks ago, it's like everybody's using Docker. How do they make money? The question I get from a number of people in the community is, everybody I talk to knows Ubuntu, uses Ubuntu, when do they transition to paying for some of the products? >> Well so one of our key tenants is that we want to put no friction in front of developers. So many of the people that you'll meet here or that you'll meet at other developer-centric summits, they're developer-oriented. They're creatives, effectively. So our products, our commercial products aren't really designed to tax developers effectively. What we want is developers to have the latest and greatest platforms, to have that absolutely free, to be able to have confidence in the fact that it can go into production. When applications get into production, a whole different set of people get involved. For example the security guys will say, does this comply with FIPS security? And that's a commercial capability that customers get from Canonical if they wanted so we're now getting a set of security certifications that enable people to take apps on Ubuntu into production inside defense industries or other high security industries. Similarly if you look at the support life cycle, our standard public free support maintenance window is five years, which is a long time, but for certain applications it turns out the app needs to be in production for 10 years and again that's a driver for a different set of people. Not the developers, but for compilers and system administration operation types to engage with Canonical commercially. Sometimes we would walk through the building and the developers love us as everything's free and then the ops guys love us because we will support them for longer than we would support the developers. >> Can we talk about Open Source as a component of business models in general maybe, and how you would like to see the ecosystem growing, and even Canonical's business model. In the course of the last decade in the industry itself, right, a lot of people sniping at each other; "Well, you know open core is the way to go, open source is not a business model" there's a lot of yelling. You've been around, you know what works. How do you a set of healthy companies that use open source develop in our ecosystem? >> So this is a really, really interesting topic and I'll start at the high end. If you think of the Googles, and the Facebooks, and the Amazons, and the Microsofts, and the Oracles, I think for them open source is now a weapon. It's a way to commoditize something that somebody else attaches value to and in the game of love and war, or Go, or chess, or however you want to think of it, between those giants open source very much has become a kind of root to market in order to establish standards for the next wave. Right now in machine learning for example we see all of these major guys pushing stuff out as open source. People wouldn't really ask "what's the business model" there 'cause they understand that this is these huge organizations essentially trying to establish standards for the next wave through open source. Okay, so that's one approach. On the startup side it's a lot more challenging and there I think we need to do two things. So right now I would say, if you're a single app startup it's very difficult with open source. If you've got a brilliant idea for a database, if you've got a brilliant idea for a messaging system, it's very, very difficult to do that with open source and I think you've seen the consequences of that over the years. That's actually not a great result for us in open source. At the end of the day, what drives brilliant folks to invest 20 hours a day for three years of their life to create something new, part of it is the sense they'll get a return on that and so, actually, we want that innovation. Not just from the Googles, and the Oracles, and the Microsofts, but we want innovation from real startups in open source. So one of the things I'd like to see is that I'd like to see the open source community being more generous of spirit to the startups who are doing that. That's not Canonical, particularly, but it is the Dockers of the world, it is the RethinkDBs, as a recent example. Those are great guys who had really good ideas and we should caution open source folks when they basically piss on the parade of the startup. It's a very short-sighted approach. The other thing that I do need to do is we need to figure out the monetization strategy. Selling software the old way is really terrible. There's a lot of friction associated with it. So one of the things that I'm passionate about is hacking Ubuntu to enable startups to innovate as open source if they want to, but then deliver their software to the enterprise market. Everywhere where you can find Ubuntu, and you know now that's everywhere right? Every Global 2000 company is running Ubuntu. Whether we can call them a customer or not is another question. But how can we enable all those innovators and startups to deliver their stuff to all of those companies and make money doing it? That's really good for those companies, and it's really good for the startups, and that's something I'm very passionate about. >> We've seen such a big transformation. I mean, the era of the shrink wrapped software is gone. An era that I want to get your long term perspective on is, when it comes to internet security. Back to your first company, we had Edward Snowden and the keynote this morning talking about security, and he bashed the public cloud guys and said "We need private cloud, and you need to control a lot more there" any comments on his stuff, the public/private era and internet security in general today? Are we safer today than we were back in '99? >> We certainly are safer in part because of Edward Snowden. Awareness is the only way to start the process of getting stuff better. I don't think it's simplistically that you can bash the public clouds. For example Google does incredible work around security and there's a huge amount of stuff in the Linux stack today around security specifically that we have Google to thank for. Amazon and others are also starting to invest in those areas. So I think the really interesting question is, how do we make security easy in the field and still make it meaningful? That's something we can have a big impact on because security when you touch it it can often feel like friction. So for example we use AppArmor. Now AppArmor is a more modern of the SC Linux ideas that is just super easy to use which means people don't even know that they're using it. Every copy of Ubuntu out there is actually effectively as secure as if you've turned on SC Linux, but administrators don't ever have to worry about that because the way AppArmor works is designed to be really, really easy to just integrate and that allows each piece of the ecosystem, the upstreams, the developers, the end users to essentially upgrade their security without really have to think about that as a budget item or a work ticket item, or something that's friction. >> Mark, any conversations on the show surprise you? Excite you? There's always such a great collection of some really smart and engaged people at this show. I'm curious what your experience has been so far. >> Sure. I think it's interesting. Open Stack moved so quickly from idea to superstar. I guess it's like a child prodigy, you know, a child TV star. The late teens can be a little rocky, right? (Mark laughs) I think it will emerge from all of that as quite a thoughtful community. There were a ton of people who came to these shows who were just stuffed, effectively, there by corporates who just wanted to do something in cloud. Now I think the conversation is much more measured. You've got folks here who really want these pieces to fit together and be useful. Our particular focus is the consumption of OpenStack in a way that is really economically impactful for enterprises. But the people who I see continuing to make meaningful contributions here are people who really want something to work. Whether that's networking, or storage, or compute, or operations as in our case but they're the folks who care about that infrastructure really working rather than the flash in the pan types and I think that's a good transition for the community to be making. >> Can you say a little more about the future of OpenStack and the direction you see the community going. I don't know. If you had a magic wand and you look forward a couple of years. We talked a lot about operability and maintainability, upgradeability, ease of use. That seems to be one of the places that you're trying to drive the ecosystem. >> One of the things that I think the community is starting to realize is that if you try to please everybody, you'll end up with something nobody can really relate to. I think if you take the mission of OpenStack as to say, look, open source is going to do lots of complicated things but if we can essentially just deliver virtualized infrastructure in a super automated way so that nobody has to think about it, the virtual machines, virtual disks, virtual networks on demand. That's an awesome contribution to the innovation stack. There are a ton of other super shiny things that could happen on any given culture and ODS but if we just get that piece right, we've made a huge contribution and I think for a while OpenStack was trying to do everything for everybody. Lots of reasons why that might be the case but now I think there's a stronger sense of "This is the mission" and it will deliver on that mission, I have great confidence. It was contrarian then to say we shouldn't be doing everything, it's contrarian now to say "actually, we're fine". We're learning what we need to be. >> The ebb and flows of this community have been really interesting. NASA helped start it. NASA went to Amazon, NASA went back to OpenStack. >> Think about the economics of cars, right. It's kind of incredible that I can sit outside the building and pull up the app, and I have a car. It's also quite nice to own a car. People do both. The economics of ownership and the economics of renting, they're pretty well understood and most institutions or most people can figure out that sometimes they'll do a bit of either. What we have to do is, at the moment we have a situation where if you want to own your infrastructure the operations are unpredictable. Whereas if you rent it it's super predictable. If we can just put predictability of price and performance into OpenStack, which is, for example what the manage services, what BootStack does. Also what JUJU and MAAS do. They allow you to say, I can do that. I can do that quickly, and I don't have to go and open a textbook to do that or hire 50 people to do it. That essentially allows people now to make the choice between owning and renting in a very natural way, and I think once people understand that that's what this is all about it'll give them a sense of confidence again. >> Curious your viewpoint on the future of jobs in tech. We talked a little bit before about autonomous vehicles. It has the opportunity to be a great boon from a technology standpoint but could hollow out this massive amount of jobs globally. Is technology an enabler of some of these things? Do we race with the machines? We interviewed Erik Brynjolfsson and Andy McAfee from the MIT Sloan School. Did you personally have some thoughts on that? In places where Canonical looks about our future workforce, do we end up with "coding becomes the new blue collar job"? >> I don't know if I can speak to a single career but I think the simple fact is there's nothing magical about the brain. The brain is a mesh network competing flows and it makes decisions, and I think we will simulate that pretty soon and we'll suddenly realize there's nothing magical about the brain but there is something magical about humans and so, what is a job? A job is kind of how we figure out what we want to do most of the day and how we want to define ourselves in some sense. That's never going to go away. I think it's highly likely that humans are obsolete as decision makers and surprisingly soon. Simply because there's nothing magic about the brain and we'll build bigger and better brains for any kind of decision you can imagine. But the art of being human? That's kind of magical, and humans will find a way to evolve into that time. I'm not too worried about it. >> Okay. Last thing I want to ask is, what's exciting you these days? We've talked about space exploration a few times. Happy to comment on it. I mean, the last 12 months has been amazing to watch for those of us. I grew up studying engineering. You always look up to the stars. What's exciting you these days? >> Well the commercialization of space, the commercial access to space is just fantastic to see, sure, really dawning and credit to the Bezoses and the Musks who are kind of shaking up the status quo in those industries. We will be amongst the stars. I have no doubt about it. It will be part of the human experience. For me personally, I expect I'll go back to space and do something interesting there. It'll get easier and easier and so I can pack my walking stick and go to the moon, maybe. But right now from a love of technology and business point of view, IoT is such rich pickings. You can't swing a cat but find something that can be improved in a very physical way. It's great to see that intersection of entrepreneurship and tinkering suddenly come alive again. You don't have to be a giant institution to go and compete with the giant institutions that are driving the giant clouds. You just have to be able to spot a business opportunity in real life around you and how the right piece of software in the right place with the right data can suddenly make things better and so it's just delicious the sort of things people are doing. Ubuntu again is a great platform for innovating around that. It's just great fun for me to see really smart people who three years ago would say, do I really want to go work at a giant organization in Silicon Valley? Or can I have fun with something for a while that's really mine and whether that's worth 12 bucks or 12 billion who knows? But it just feels fun and I'm enjoying that very much, seeing people find interesting things to do at the edge. >> Mark Shuttleworth, appreciate being able to dig into a lot more topics with you today and we'll be right back with lots more coverage here from OpenStack 2017 in Boston. You're watching the cube. (electronic music)

>> Welcome to the latest edition of the MIT Sloan Expert Series. I'm your host, Rebecca Knight. Our topic today is racial bias in the sharing economy, how Uber and Lyft are failing black passengers, and what to do about it. Here to talk about that is Chris Knittel. He is a professor of Applied Economics here at MIT Sloan, and he's also the co-author of a study that shows how Uber and Lyft drivers discriminate based on a passenger's skin color. Thanks so much for joining us. >> Oh, it's great to be here. >> Before we begin, I want to remind our viewers that we will be taking your questions live on social media. Please use the hashtag MITSloanExpert to pose your questions on Twitter. Chris, let's get started. >> Chris: Sure. So there is a lot of research that shows how difficult it is to hail a cab, particularly for black people. Uber and Lyft were supposed to represent a more egalitarian travel option, but you didn't find that. >> That's right, so what we found in two experiments that we ran, and one in Seattle, and one in Boston, is that Uber and Lyft drivers were discriminating based on race. >> Rebecca: We've already seen, actually some evidence of racial discrimination in the sharing economy, not just with ride sharing apps. >> Sure, so there's evidence for Airbnb. And what's interesting about Airbnb actually, is that discrimination is two-sided. So not only do white renters of properties not want to rent to black rentees, but white renters do not stay at a home of a black home owner. >> Did your findings and the findings of that other research you just talked about, does it make you discouraged? >> Partly, I was an optimist. We went into this, at least I went into this hoping that we wouldn't find discrimination, but one thing that has helped, or at least shined a more positive light, is that there are ways that we can do better in this sector. >> You've talked about this study, which you undertook with colleagues from the University of Washington and Stanford, shows the power of the experiment. Can you talk a little bit about what you mean by that? >> Sure, what we did was actually run two randomized control trials. Just like you would test whether a blood pressure medication works, so you would have a control group that wouldn't get the medication, and a treatment group that would. We did the same thing where we sent out in Seattle both black and white RAs that hailed Uber and Lyft rides, and we randomized whether or not it was a black RA calling the ride or a white RA that particular time, and they all drove the same exact route at the same exact times of the day. >> So what did you find? Let's talk about first, what you found in Seattle. >> Sure, so in Seattle, we measured how long it took for a ride to be accepted, and also, how long it took, once it was accepted, for the driver to show up and pick up the passenger. And what we found is, if you're a black research assistant, that in hailing an Uber ride, it took 30 percent longer for a ride to be accepted, and also 30 percent longer for the driver to show up and pick you up. >> 30 percent seems substantial. >> Well, for the time it takes to accept the ride, we're talking seconds, but for the time it takes for a passenger to actually be picked up, it's over a minute longer. And I'll mention also for Lyft, we found a 30 percent increase in the amount of time it took to be accepted, but there was no statistically significant impact on how long it took for the driver to actually show up. >> So, the thing about the minute difference, that can be material, particularly if you're trying to catch a cab, an Uber or a Lyft for a job interview or to get to the airport. >> Yeah, this is introspection, but I always seem to be late, so even a minute can be very costly. >> I hear you, I hear you. So why do you think there was the difference between Lyft and Uber? >> What's interesting, and we learned this while we were doing the experiment, a Lyft driver sees the name of the passenger before they accept the ride, whereas an Uber driver only sees the name after they've accepted. So in order for an Uber driver to discriminate, they have to first accept the ride, and then see the name and then cancel, whereas a Lyft driver can just pass it up right away. So it turns out because of that, the Lyft platform is more easily capable of handling discrimination because it pushed it to another driver faster than the Uber platform. >> I want to come back to that, but I want to say also, that difference caused you to change the way you did the experiment in Boston. >> In Boston, a couple differences. One is that we sent out RAs with two cell phones actually. So each RA had an Uber and Lyft account under a stereotypically white sounding name, and then also an Uber and Lyft account under a stereotypically black sounding name. That was one difference, and then also, what we measured in Boston that we didn't measure in Seattle, is cancellations. So an Uber driver accepts the ride, and then cancels on the RA. >> Let's go back to the stereotypically black sounding name verses white sounding name. You're an economist, how did you determine what those names are? >> We relied on another published paper that actually looked at birth records from the 1970s in Boston, and the birth records tell you not only the name, but also the race of the baby. So they found names that actually 100 percent of the time were African American or 100 percent of the time were not African American. So we relied on those names. >> And the names were... >> So you could imagine Jamal for example, compared to Jerry. >> Alright, Ayisha and Alison. >> Chris: Sure. >> So what was your headline finding in Boston? >> In Boston, what we found is, if you were a black male calling an Uber ride, that you were canceled upon more than twice as often as if you were a white male. >> And what about Lyft? >> For Lyft, there is no cancellation effect, and that's not because there's no discrimination, it's just that they don't have to accept and then cancel the ride, they can just pass up the ride completely. It's actually a nice little experiment within the experiment, we shouldn't find an effect of names on cancellations for Lyft and in fact, we don't. >> And also, the driver network is much thicker in Boston than in Seattle. >> So in Boston, although we found this cancellation effect, we didn't find that it has a measurable impact on how long you wait. And this is somewhat speculation, but we speculate that that's because the driver network is so much more dense in Boston that, although you were canceled upon, there's so many only drivers nearby, that it doesn't lead to a longer wait time. >> How do you think what you found compares to hailing traditional cabs? We started our conversation talking about the vast body of research that shows how difficult it is for black people to hail cabs. >> Yeah, we are quick to point out that we are not at all saying that Uber and Lyft are worse than traditional, status quo system, and we want to definitely make that clear. In fact, in Seattle, we had our same research assistants stand at the busiest corners and hail cabs. What we found there is, if you were a black research assistant, the first cab passed you 80 percent of the time. But if you were a white research assistant, it only passed you 20 percent of the time. So just like the previous literature has found, we found discrimination with the status quo system as well. >> You've talked to the companies about you findings, what has the response been? >> That's been actually heartening. Both companies reached out to us very quickly, and we've had continued conversations with them, and we're actually trying to design followup studies to minimize the amount of discrimination that's occurring for both Uber and Lyft. >> But those are off the record and... >> Right, we're not talking specifics, but what I can say is that the companies understand this research and they definitely want to do better. >> In fact, the companies both have issued statements about this, the first one is from Lyft, "we are extremely proud of the positive impact..." Uber has also responded. So let's talk about solutions to this. What do you and your colleagues who undertook this research suggest? >> We've been brainstorming, we don't know for sure if we have the silver bullet, but a few things could change, for example, you could imagine Uber and Lyft getting rid of names completely. We realize that has a trade off in the sense that it's nice to know the name of the driver... >> Rebecca: Sure, you can strike up a conversation... >> It makes it more social, it makes it more personal, more peer to peer if you will. But it would eliminate the type of discrimination that we uncovered. Another potential change is to delay when you give the name to the driver, so that the driver has to commit more to the ride than he or she previously had to. And that may increase the costs of discrimination. >> So that would be changing the software? >> Right, so you could imagine now, like I said, with Lyft that you see the name right away. Maybe you wait until they're 30 seconds away from the passenger before you give them the name. >> What about the dawn of the age of autonomous vehicles? Might that have an impact? We already know that Uber is experimenting with driverless cars in Pittsburgh and Arizona. >> That would obviously solve it, so that would take the human element out of things, and it's important to point out that these are the drivers that are deciding to discriminate. So provided you didn't write the autonomous vehicle software to discriminate, you would know for sure that that car is not going to discriminate. >> What about a driver education campaign? Do you think that would make a difference? I'm reminded of an essay written by Doug Glanville, who is an ESPN commentator and former pro ball player. He writes, on talking about his experience being denied service by an Uber driver, "the driver had concluded I was a threat, "either because I was dangerous myself, "or because I would direct him to a bad neighborhood, "or give him a lower tip, either way, "given the circumstances, it was hard "to attribute his refusal to anything other than my race. "Shortly after we walked away, I saw the driver assisting "another passenger who was white." >> We all hope that information helps, and eliminates discrimination. It's certainly possible that Uber and Lyft could have a full information campaign, where they show the tip rates for different ethnicities, they show the bad ride probabilities for different ethnicities, and my hope is that once the drivers learn that there aren't differences across ethnicities, that the drivers would internalize that, and stop discriminating. >> Policy, Senator Al Franken has weighed in on this, urging Uber and Lyft to address your research. Do you think that there could be policies too? Does government have a role to play? >> Potentially, but what I'll say again is, that Uber and Lyft, I think have all the incentive in the world to fix this, and that they seem to be taking active steps to fixing this. So what could policy makers do? They can, obviously it's already outlawed. They could come down and potentially fine the companies if there's more evidence of discrimination. But I would at least allow the companies some time to internalize this research, and respond to it, and see how effective they can be. >> Many, many think tanks and government advocacy groups have weighed in too. The MIT Sloan Expert Series recently sat down with Eva Millona of the Massachusetts Immigrant and Refugee Coalition. She will talk about this research in the context of immigration, let's roll that clip. >> We're an advocacy organization, and we represent the interest of foreign born, and our mission is to promote and enhance immigrant and refugee integration. Anecdotally, yes, I would say that the research, and given the impressive sample of the research really leads to a sad belief that discrimination is still out there, and there is a lot that needs to be done across sectors to really address these issues. We are really privileged to live in such a fantastic commonwealth with the right leadership and all sectors together, really making our commonwealth a welcoming place. And I do want to highlight the fantastic role of our Attorney General for standing up for our values, but Massachusetts is one state, and it could be an example, but the concern is nation wide. Given a very divisive campaign, and also not just a campaign, but also, what is currently happening at the national level that the current administration is really rejecting this welcoming effort, and the values of our country as a country, who welcomes immigrants. All sectors need to be involved in an effort to really make our society a better one for everyone. And it's going to take political leadership to really set the right tone, send the right message, and really look into the integration, and the welcoming of the newcomers as an investment in our future of our nation. Uber and Lyft have an opportunity here to provide leadership and come up with promotion of policies that integrate the newcomers, or that are welcoming to the newcomers, provide education and training, and train their people. And as troubling as the result of this research are, we like to believe that this is the attitude of the drivers, but not really what the corporate represents, so we see an opportunity for the corporate to really step in and work and promote policies of integration, policies of improvement and betterment for the whole of society and provide an example. Let me say thank you to Professor Knittle for his leadership and MIT for always being a leader, and looking into these issues. But if we can go deeper into A, the size, B, the geography, but also looking into a wider range of all communities that are represented. Looking into the Latino community, looking into the Arab communities in other parts of the nation in a more rigorous, more deep and larger size of research will be very helpful in terms of promoting better policies and integration for everybody who chooses America to be their home. >> That was Eva Millona of the Massechusetts Immigrant and Refugee Advocacy Coalition. Chris, are you confident this problem can in fact be remedied? >> I think we can do better, for sure. And I would say we need more studies like what we just preformed to see how widespread it is. We only studied two cities, we also haven't looked at all at how the driver's race impacts the discrimination. >> Now we're going to turn to you, questions from our viewers. Questions have already been coming in this morning and overnight, lots of great ones. Please use the hashtag MITSloanExpert to pose your question. The first one comes from Justin Wang, who is an MIT Sloan MBA student. He asks, "what policies can sharing economy startups "implement to reduce racial bias?" >> Well, I would say the first thing is to be aware of this. I think Uber and Lyft and Airbnb potentially were caught off guard with the amount of discrimination that was taking place. So the research that we preformed, and the research on Airbnb gives new startups a head start on designing their platforms. >> Just knowing that this is an issue. >> Knowing it's an issue, and potentially designing their platforms to think of ways to limit the amount of discrimination. >> Another question, did you look at gender bias? Do you have any indication that drivers discriminate based on gender? >> We did look at gender bias. The experiments weren't set up to necessarily nail that, but one thing that we found, for example in Boston, is that there is some evidence that women drivers were taken on longer trips. Again, both the male and the female RAs are going from the same point A to the same point B. >> Rebecca: That was a controlled part of the setting. >> That was the controlled part of the experiment. And we found evidence that women passengers were taken on longer trips and in fact, one of our RAs commented that she remembers going through the same intersection three times before she finally said something to the driver. >> And you think... So you didn't necessarily study this as part of it, but do you have any speculation, conjecture about why this was happening? >> Well, there's two potential motives. One is a financial motive that, by taking the passenger on a longer drive. They potentially get a higher fare. But I've heard anecdotal evidence that a more social motive might also be at play. For example, I have a colleague here at Sloan, who's told me that she's been asked out on dates three times while taking Uber and Lyft rides. >> So drivers taking the opportunity to flirt a little bit. >> Chris: Sure. >> Another question, can you comment on the hashtag DeleteUber campaign? This of course, is about the backlash against Uber responding that it was intending to profit from President Trump's executive order, the banning immigrants and refugees from certain countries from entering the United States. Uber maintains that its intentions were misunderstood, but it didn't stop the hashtag DeleteUber campaign. >> Yeah, I haven't followed that super closely, but to me it seems like Uber's getting a bit of a bad rap. One potential reason why they allowed Uber drivers to continue working is that, maybe they wanted to bring protesters to the airports to protest. So from that perspective, actually having Uber and Lyft still in business would be beneficial. >> Another question, did your study take into account the race of the drivers themselves? >> We actually we not allowed to. So any time you do a randomized control trial in the field like this, you have to go through a campus committee that approves or disapproves the research, and they were worried that if we collected information on the driver, that potentially, Uber and Lyft could go back into their records and find the drivers that discriminate, and then have penalties assigned to those drivers. >> So it just wouldn't be allowed to... >> At least in this first phase, yeah. They didn't want us to collect those data. >> Last question, we have time for one more. Why aren't there more experiments in the field of applies economics like this one? That's a good question. >> That's a great question, and in fact, I think many of us are trying to push experiments as much as possible. My other line of research is actually in energy and climate change research, and we've been- >> Rebecca: You like the hot topic. (lauhging) >> We've been designing a bunch of experiments to look at how information impacts consumers' choices in terms of what cars to buy, how it impacts their use of electricity at home. And experiments, randomized control trials actually started in developmental economics, where MIT has actually pioneered their use. And again, it's the best way to actually test, the most rigorous way to test whether intervention actually has an effect because you have both the controlled group and the treatment group. >> So why aren't they done more often? >> Well, it's tough, often you need to find a third party, for example, we didn't need a third party in the sense that we could just send RAs out with Uber and Lyft. But if we wanted to do anything with the drivers, for example, an information campaign, or if we wanted to change the platform at all, we would've needed Uber and Lyft to partner with us, and that can sometimes be difficult to do. And also experiments, let's be honest, are pretty expensive. >> Expensive because, you obviously weren't partnered with Uber and Lyft for this one, but... >> Right, but we had research assistants take 1500 Uber and Lyft rides, so we had to pay for each of those rides, and we also had to give them an hourly rate for their time. >> Well, Chris Knittle, thank you so much. This has been great talking to you, and you've given us a lot to think about. >> It's been fun, thanks for having me. >> And thank you for joining us on this edition of the MIT Sloan Expert Series. We hope to see you again soon.

>> Narrator: Live from New York, it's The Cube covering the IBM Machine Learning Launch Event brought to you by IBM. Here are your hosts, Dave Vellante and Stu Miniman. >> Good morning everybody, welcome to the Waldorf Astoria. Stu Miniman and I are here in New York City, the Big Apple, for IBM's Machine Learning Event #IBMML. We're fresh off Spark Summit, Stu, where we had The Cube, this by the way is The Cube, the worldwide leader in live tech coverage. We were at Spark Summit last week, George Gilbert and I, watching the evolution of so-called big data. Let me frame, Stu, where we're at and bring you into the conversation. The early days of big data were all about offloading the data warehouse and reducing the cost of the data warehouse. I often joke that the ROI of big data is reduction on investment, right? There's these big, expensive data warehouses. It was quite successful in that regard. What then happened is we started to throw all this data into the data warehouse. People would joke it became a data swamp, and you had a lot of tooling to try to clean the data warehouse and a lot of transforming and loading and the ETL vendors started to participate there in a bigger way. Then you saw the extension of these data pipelines to try to more with that data. The Cloud guys have now entered in a big way. We're now entering the Cognitive Era, as IBM likes to refer to it. Others talk about AI and machine learning and deep learning, and that's really the big topic here today. What we can tell you, that the news goes out at 9:00am this morning, and it was well known that IBM's bringing machine learning to its mainframe, z mainframe. Two years ago, Stu, IBM announced the z13, which was really designed to bring analytic and transaction processing together on a single platform. Clearly IBM is extending the useful life of the mainframe by bringing things like Spark, certainly what it did with Linux and now machine learning into z. I want to talk about Cloud, the importance of Cloud, and how that has really taken over the world of big data. Virtually every customer you talk to now is doing work on the Cloud. It's interesting to see now IBM unlocking its transaction base, its mission-critical data, to this machine learning world. What are you seeing around Cloud and big data? >> We've been digging into this big data space since before it was called big data. One of the early things that really got me interested and exciting about it is, from the infrastructure standpoint, storage has always been one of its costs that we had to have, and the massive amounts of data, the digital explosion we talked about, is keeping all that information or managing all that information was a huge challenge. Big data was really that bit flip. How do we take all that information and make it an opportunity? How do we get new revenue streams? Dave, IBM has been at the center of this and looking at the higher-level pieces of not just storing data, but leveraging it. Obviously huge in analytics, lots of focus on everything from Hadoop and Spark and newer technologies, but digging in to how they can leverage up the stack, which is where IBM has done a lot of acquisitions in that space and leveraging that and wants to make sure that they have a strong position both in Cloud, which was renamed. The soft layer is now IBM Bluemix with a lot of services including a machine learning service that leverages the Watson technology and of course OnPrem they've got the z and the power solutions that you and I have covered for many years at the IBM Med show. >> Machine learning obviously heavily leverages models. We've seen in the early days of the data, the data scientists would build models and machine learning allows those models to be perfected over time. So there's this continuous process. We're familiar with the world of Batch and then some mini computer brought in the world of interactive, so we're familiar with those types of workloads. Now we're talking about a new emergent workload which is continuous. Continuous apps where you're streaming data in, what Spark is all about. The models that data scientists are building can constantly be improved. The key is automation, right? Being able to automate that whole process, and being able to collaborate between the data scientist, the data quality engineers, even the application developers that's something that IBM really tried to address in its last big announcement in this area of which was in October of last year the Watson data platform, what they called at the time the DataWorks. So really trying to bring together those different personas in a way that they can collaborate together and improve models on a continuous basis. The use cases that you often hear in big data and certainly initially in machine learning are things like fraud detection. Obviously ad serving has been a big data application for quite some time. In financial services, identifying good targets, identifying risk. What I'm seeing, Stu, is that the phase that we're in now of this so-called big data and analytics world, and now bringing in machine learning and deep learning, is to really improve on some of those use cases. For example, fraud's gotten much, much better. Ten years ago, let's say, it took many, many months, if you ever detected fraud. Now you get it in seconds, or sometimes minutes, but you also get a lot of false positives. Oops, sorry, the transaction didn't go through. Did you do this transaction? Yes, I did. Oh, sorry, you're going to have to redo it because it didn't go through. It's very frustrating for a lot of users. That will get better and better and better. We've all experienced retargeting from ads, and we know how crappy they are. That will continue to get better. The big question that people have and it goes back to Jeff Hammerbacher, the best minds of my generation are trying to get people to click on ads. When will we see big data really start to affect our lives in different ways like patient outcomes? We're going to hear some of that today from folks in health care and pharma. Again, these are the things that people are waiting for. The other piece is, of course, IT. What you're seeing, in terms of IT, in the whole data flow? >> Yes, a big question we have, Dave, is where's the data? And therefore, where does it make sense to be able to do that processing? In big data we talked about you've got masses amounts of data, can we move the processing to that data? With IT, the day before, your RCTO talked that there's going to be massive amounts of data at the edge and I don't have the time or the bandwidth or the need necessarily to pull that back to some kind of central repository. I want to be able to work on it there. Therefore there's going to be a lot of data worked at the edge. Peter Levine did a whole video talking about how, "Oh, Public Cloud is dead, it's all going to the edge." A little bit hyperbolic to the statement we understand that there's plenty use cases for both Public Cloud and for the edge. In fact we see Google big pushing machine learning TensorFlow, it's got one of those machine learning frameworks out there that we expect a lot of people to be working on. Amazon is putting effort into the MXNet framework, which is once again an open-source effort. One of the things I'm looking at the space, and I think IBM can provide some leadership here is to what frameworks are going to become popular across multiple scenarios? How many winners can there be for these frameworks? We already have multiple programming languages, multiple Clouds. How much of it is just API compatibility? How much of work there, and where are the repositories of data going to be, and where does it make sense to do that predictive analytics, that advanced processing? >> You bring up a good point. Last year, last October, at Big Data CIV, we had a special segment of data scientists with a data scientist panel. It was great. We had some rockstar data scientists on there like Dee Blanchfield and Joe Caserta, and a number of others. They echoed what you always hear when you talk to data scientists. "We spend 80% of our time messing with the data, "trying to clean the data, figuring out the data quality, "and precious little time on the models "and proving the models "and actually getting outcomes from those models." So things like Spark have simplified that whole process and unified a lot of the tooling around so-called big data. We're seeing Spark adoption increase. George Gilbert in our part one and part two last week in the big data forecast from Wikibon showed that we're still not on the steep part of the Se-curve, in terms of Spark adoption. Generically, we're talking about streaming as well included in that forecast, but it's forecasting that increasingly those applications are going to become more and more important. It brings you back to what IBM's trying to do is bring machine learning into this critical transaction data. Again, to me, it's an extension of the vision that they put forth two years ago, bringing analytic and transaction data together, actually processing within that Private Cloud complex, which is what essentially this mainframe is, it's the original Private Cloud, right? You were saying off-camera, it's the original converged infrastructure. It's the original Private Cloud. >> The mainframe's still here, lots of Linux on it. We've covered for many years, you want your cool Linux docker, containerized, machine learning stuff, I can do that on the Zn-series. >> You want Python and Spark and Re and Papa Java, and all the popular programming languages. It makes sense. It's not like a huge growth platform, it's kind of flat, down, up in the product cycle but it's alive and well and a lot of companies run their businesses obviously on the Zn. We're going to be unpacking that all day. Some of the questions we have is, what about Cloud? Where does it fit? What about Hybrid Cloud? What are the specifics of this announcement? Where does it fit? Will it be extended? Where does it come from? How does it relate to other products within the IBM portfolio? And very importantly, how are customers going to be applying these capabilities to create business value? That's something that we'll be looking at with a number of the folks on today. >> Dave, another thing, it reminds me of two years ago you and I did an event with the MIT Sloan school on The Second Machine Age with Andy McAfee and Erik Brynjolfsson talking about as machines can help with some of these analytics, some of this advanced technology, what happens to the people? Talk about health care, it's doctors plus machines most of the time. As these two professors say, it's racing with the machines. What is the impact on people? What's the impact on jobs? And productivity going forward, really interesting hot space. They talk about everything from autonomous vehicles, advanced health care and the like. This is right at the core of where the next generation of the economy and jobs are going to go. >> It's a great point, and no doubt that's going to come up today and some of our segments will explore that. Keep it right there, everybody. We'll be here all day covering this announcement, talking to practitioners, talking to IBM executives and thought leaders and sharing some of the major trends that are going on in machine learning, the specifics of this announcement. Keep it right there, everybody. This is The Cube. We're live from the Waldorf Astoria. We'll be right back.

>> live from the Congress Centre in London, England. It's the queue at M I t. And the digital economy. The second machine Age Brought to you by headlines sponsor M I T. >> Everybody, welcome to London. This is Dave along with student men. And this is the cube. The cube goes out, we go to the events. We extract the signal from the noise. We're very pleased to be in London, the scene of the first machine age. But we're here to talk about the second Machine age. Andrew McAfee and Erik Brynjolfsson. Gentlemen, first of all, congratulations on this fantastic book. It's been getting great acclaim. So it's a wonderful book if you haven't read it. Ah, Andrew, Maybe you could hold it up for our audience here, the second machine age >> and Dave to start off thanks to you for being able to pronounce both of our names correctly, that's just about unprecedented. In the history of this, >> I can probably even spell them. Whoa, Don't. So, anyway, welcome. We appreciate you guys coming on and appreciate the opportunity to talk about the book. So if you want to start with you, so why London? I mean, I talked about the first machine age. Why are we back here? One of the >> things we learned when we were writing the book is how big deal technological progress is on the way you learn that is by going back and looking at a lot of history and trying to understand what bet the curve of human history. If we look at how advanced our civilizations are, if we look at how many people there are in the world, if we look at GDP per capita around the world, amazingly enough, we have that data going back hundreds, sometimes thousands of years. And no matter what data you're looking at, you get the same story, which is that nothing happened until the Industrial Revolution. So for us, the start of the first machine machine age for us, it's a real thrill to come to London to come to the UK, which was the birthplace of the Industrial Revolution. The first machine age to talk about the second. >> So, Eric, I wonder if you could have with two sort of main vectors that you take away from the book won is that you know, machines have always replaced humans and maybe doing so at a different rate of these days. But the other is the potential of continued innovation, even though many people say Moore's law is dead. You guys have come up with sort of premises to how innovation will continue to double. So boil it down for the lay person. What should we think about? Well, sure. >> I mean, let me just elaborate on what you just said. Technology's always been destroying jobs, but it's also always been creating jobs, you know, A couple centuries ago, ninety percent of Americans worked in agriculture on farms in nineteen hundred is down to about forty one percent. Now is less than two percent. All those people didn't simply become unemployed. Instead, new industries were invented by Henry Ford, Steve Jobs, Bill Gates. Lots of other people and people got rather unemployed, became redeployed. One of the concerns is is, Are we doing that fast enough? This time around, we see a lot of bounty being created by technology. Global poverty rates are falling. Record wealth in the United States record GDP per person. But not everyone's participating in that. Not even when sharing the past ten fifteen years, we've actually to our surprise seem median income fall that's income of the person the fiftieth percentile, even though the overall pie is getting bigger. And one of the reasons that we created the initiative on the digital economy was to try to crack that, not understand what exactly is going on? How is technology behaving differently this time around in earlier eras and part that has to do with some of the unique characteristics of eventual goods? >> Well, your point in the book is that normally median income tracks productivity, and it's it's not this time around. Should we be concerned about that? >> I think we should be concerned about it. That's different than trying to stop for halt course of technology. That's absolutely not something you >> should >> be more concerned about. That way, Neto let >> technology move ahead. We need to let the innovation happen, and if we are concerned about some of the side effects or some of the consequences of that fine, let's deal with those. You bring up what I think is the one of most important side effects to have our eye on, which is exactly as you say when we look back for a long time, the average worker was taking home more pay, a higher standard of living decade after decade as their productivity improved. To the point that we started to think about that as an economic law, your compensation is your marginal productivity fantastic what we've noticed over the past couple of decades, and I don't think it's a coincidence that we've noticed this, as the computer age has accelerated, is that there's been a decoupling. The productivity continues to go up, but the wage that average income has stagnated. Dealing with that is one of our big challenges. >> So what you tell your students become a superstar? I mean, not everybody could become a superstar. Well, our students cats, you know, maybe the thing you know they're all aspired to write. >> A lot of people focus on the way that technology has helped superstars reach global audiences. You know, I had one student. He wrote an app, and about two or three weeks, he tells me, and within a few months he had reached a million people with that app. That's something that probably would have been impossible a couple of decades ago. But he was able to do that because he built it on top of the Facebook platform, which is on top of the Internet and a lot of other innovations that came before. So in some ways it's never been easier to become a superstar and to reach literally not just millions, but even billions of people. But that's not the only successful path in the second machine age. There's also other categories where machines just aren't very good. Yet one of the ones that comes to mind is interpersonal skills, whether that's coaching or underst picking up on other cues from people nurturing people carrying for people. And there are a whole set of professions around those categories as well. You don't have to have some superstar programmer to be successful in those categories, and there are millions of jobs that are needed in those categories for to take care of other P people. So I think there's gonna be a lot of ways to be successful in the second machine age, >> so I think >> that's really important because one take away that I don't like from people who've looked at our work is that only the amazing entrepreneurs or the people with one forty plus IQ's are going to be successful in the second machine age. That's it's just not correct. As Eric says, the ability to negotiate the ability Teo be empathetic to somebody, the ability to care for somebody machines they're lousy of thes. They remain really important things to do. They remain economically valuable things >> love concern that they won't remain louse. If I'm a you know, student listening, you said in your book, Self driving cars, You know, decade ago, even five years ago so it can happen. So how do we predict with computers Will and won't be good at We >> basically don't. Our track record in doing that is actually fairly lousy. The mantra that I've learned is that objects in the future are closer than they appear on the stuff that seem like complete SciFi. You're never goingto happen keeps on happening now. That said, I am still going to be blown away the first time I see a computer written novel that that that works, that that I find compelling, that that seems like a very human skill. But we are starting to see technologies that are good at recognizing human emotions that can compose music that can do art paintings that I find pretty compelling. So never say never is another. >> I mean right, right. If if I look some of the examples lately, you know, basic news computers could do that really well. IBM, you know, the lots of machine can make recipes that we would have never thought of. Very things would be creative. And Ian, the technology space, you know, you know, a decade ago computer science is where you tell everybody to go into today is data scientists still like a hot opportunity for people to go in And the technology space? Where, where is there some good opportunity? >> Or whether or not that's what the job title on the business card is that going to be hot being a numerous person being ableto work with large amounts of data input, particular being able to work with huge amounts of data in a digital environment in a computer that skills not going anywhere >> you could think of jobs in three categories is ready to technology. They're ones that air substitutes racing against machine. They're ones that air compliments that are using technology under ones that just aren't really affected yet by technology. The first category you definitely want to stay away from. You know, a lot of routine information processing work. Those were things machines could do well, >> prepare yourself as a job. Is that for a job as a payroll clerk? There's a really bad wait. >> See that those jobs were disappearing, both in terms of the numbers of employment and the wages that they get. The second category jobs. That compliment data scientist is a great example of that or somebody who's AP Writer or YouTube. Those are things that technology makes your skills more and more valuable. And there's this huge middle category. We talked earlier about interpersonal skills, a lot of physical task. Still, where machines just really can't touch them too much. Those are also categories that so far hell >> no, I didnt know it like middle >> school football, Coach is a job. It's going to be around a human job. It's going to be around for a long time to come because I have not seen the piece of technology that can inspire a group of twelve or thirteen year olds to go out there and play together as a team. Now Erik has actually been a middle school football coach, and he actually used a lot of technology to help him get good at that job, to the point where you are pretty successful. Middle school football coach >> way want a lot of teams games, and part of it was way could learn from technology. We were able to break down films in ways that people never could've previously at the middle school level. His technology's made a lot of things much cheaper. Now then we're available. >> So it was learning to be competitive versus learning how to teach kids to play football. Is that right? Or was a bit? Well, actually, >> one of the most important things and being a coach is that interpersonal connection is one thing I liked the most about it, and that's something I think no robot could do. What I think it be a long, long time. If ever that inspiring halftime speech could be given by a robot >> on getting Eric Gipper bring the Olsen Well, the to me, the more, most interesting examples I didn't realise this until I read your book, is that the best chess player in the world is not a computer, it's a computer and a human. That's what those to me. It seemed to be the greatest opportunities for innovative way. Call a >> racing with machines, and we want to emphasize that that's what people should be focusing. I think there's been a lot of attention on how machines can replace humans. But the bigger opportunities how humans and machines could work together to do things they could never have been done before in games like chess. We see that possibility. But even more, interestingly, is when they're making new discoveries in neuroscience or new kinds of business models like Uber and others, where we are seeing value creation in ways that was just not possible >> previously, and that chess example is going to spill over into the rest of the economy very, very quickly. I think about medicine and medical diagnosis. I believe that work needs to be a huge amount, more digital automated than it is today. I want Dr Watson as my primary care physician, but I do think that the real opportunities we're going to be to combine digital diagnosis, digital pattern recognition with the union skills and abilities of the human doctor. Let's bring those two skill sets together >> well, the Staton your book is. It would take a physician one hundred sixty hours a week to stay on top of reading, to stay on top of all the new That's publication. That's the >> estimate. And but there's no amount of time that watching could learn how to do that empathy that requires to communicate that and learn from a patient so that humans and machines have complementary skills. The machines are strong in some categories of humans and others, and that's why a team of humans and computers could be so >> That's the killer. Since >> the book came out, we found another great example related to automation and medicine in science. There's a really clever experiment that the IBM Watson team did with team out of Baylor. They fed the technology a couple hundred thousand papers related to one area of gene expression and proteins. And they said, Why don't you predict what the next molecules all we should look at to get this tart to get this desired response out on the computer said Okay, we think these nine are the next ones that are going to be good candidates. What they did that was so clever they only gave the computer papers that had been published through two thousand three. So then we have twelve years to see if those hypotheses turned out to be correct. Computer was batting about seven hundred, so people say, didn't that technology could never be creative. I think coming up with a a good scientific hypothesis is an example of creative work. Let's make that work a lot more digital as well. >> So, you know, I got a question from the crowd here. Thie First Industrial Revolution really helped build up a lot of the cities. The question is, with the speed and reach of the Internet and everything, is this really going to help distribute the population? Maur. What? The digital economy? I don't I don't think so. I don't think we want to come to cities, not just because it's the only waited to communicate with somebody we actually want to be >> face to face with them. We want to hang out with urbanization is a really, really powerful trend. Even as our technologies have gotten more powerful. I don't think that's going to revert, but I do think that if you if you want to get away from the city, at least for a period of time and go contemplate and be out in the world. You can now do that and not >> lose touch. You know, the social undistributed workforce isn't gonna drive that away. It's It's a real phenomenon, but it's not going to >> mean that cities were going >> to be popular. Well, the cities have two unique abilities. One is the entertainment. If you'd like to socialize with people in a face to face way most of the time, although people do it online as well, the other is that there's still a lot of types of communication that are best done in person. And, in fact, real estate value suggests that being able to be close toe other experts in your field. Whether it's in Silicon Valley, Hollywood, Wall Street is still a valuable asset. Eric and I >> travel a ton not always together. We could get a lot of our work done via email on via digital tools. When it comes time to actually get together and think about the next article or the next book, we need to be in the same room with the white bored doing it. Old school >> want to come back to the roots of innovation. Moore's law is Gordon Mohr put forth fiftieth anniversary next week, and it's it's It's coming to an end in terms of that actually has ended in terms of the way it's doubling every eighteen months, but looks like we still have some runway. But you know, experts can predict and you guys made it a point you book People always underestimate, you know, human's ability to do the things that people think they can't do. But the rial innovation is coming from this notion of combinatorial technologies. That's where we're going to see that continued exponential growth. What gives you confidence that that >> curve will continue? If you look at innovation as the work, not of coming up with some brand new Eureka, but as putting together existing building blocks in a new and powerful way, Then you should get really optimistic because the number of building blocks out there in the world is only going up with iPhones and sensors and banned weapon and all these different new tools and the ability to tap into more brains around the world to allow more people to try to do that recombination. That ability is only increasing as well. I'm massively optimistic about innovation, >> yet that's a fundamental break from the common attitude. We hear that we're using up all the low hanging fruit, that innovation. There's some fixed stock of it, and first we get the easy innovations, and then it gets harder and harder to innovate. We fundamentally disagree with that. You, in fact, every innovation we create creates more and more building blocks for additional innovations. And if you look historically, most of the breakthroughs have been achieved by combining previously existing innovations. So that makes me optimistic that we'LL have more and more of those building blocks going >> forward. People say that we've we've wrung all of the benefit out of the internal combustion engine, for example, and it's all just rounding error. For here. Know a completely autonomous car is not rounding error. That's the new thing that's going to change. Our lives is going to change our cities is going to change our supply chains, and it's making a new, entirely new use case out of that internal combustion. >> So you used the example of ways in the book, Really, you know, their software, obviously was involved, but it really was sensors and it was social media. And we're mobile phones and networks, just these combinations of technologies for innovation, >> none of which was an invention of the Ways team, none of which was original. Theyjust put those elements together in a really powerful way. >> So that's I mean, the value of ways isn't over. So we're just scratching the surface, and we could talk about sort of what you guys expect. Going forward. I know it's hard to predict well, another >> really important thing about wages in addition to the wake and combined and recombined existing components. It's available for free on my phone, and GPS would've cost hundreds of dollars a few years ago, and it wouldn't have been nearly as good at ways. And in a decade before that, it would have been infinitely expensive. You couldn't get it at any price, and this is a really important phenomenon. The digital economy that is underappreciated is that so much of what we get is now available at zero cost. Our GDP measures are all the goods and services they're bought and sold. If they have zero price, they show up is a zero in GDP. >> Wikipedia, right? Wikipedia, but that just wait here overvalue ways. Yeah, it doesn't. That >> doesn't mean zero value. It's still quite valuable to us. And more and more. I think our metrics are not capturing the real essence of the digital economy. One of the things we're doing at the Initiative initiative, the addition on the usual economy is to understand better what the right metrics will be for seeing this kind of growth. >> And I want to talk about that in the context of what you just said. The competitiveness. So if I get a piece of fruit disappears Smythe Digital economy, it's different. I wonder if you could explain that, >> and one of the ways it's different will use waze is an example here again, is network effects become really, really powerful? So ways gets more valuable to me? The more other ways er's there are out there in the world, they provide more traffic information that let me know where the potholes and the construction are. So network effects lead to really kind of different competitive dynamics. They tend to lead toward more winner, take all situations. They tend to lead toward things that look more not like monopolies, and that tends to freak some people out. I'm a little more home about that because one of the things we also know from observing the high tech industries is that today's near monopolist is yesterday's also ran. We just see that over and over because complacency and inertia are so deadly, there's always some some disruptor coming up, even in the high tech industries to make the incumbents nervous. >> Right? Open source. >> We'LL open source And that's a perfect example of how some of the characteristics of goods in the digital economy are fundamentally different from earlier eras and microeconomics. We talk about rival and excludable goods, and that's what you need for a competitive equilibrium. Digital goods, our non rival and non excludable. You go back to your micro economics textbook for more detail in that, but in essence, what it means is that these goods could be freely coffee at almost zero cost. Each copy is a perfect replica of the original that could be transmitted anywhere on the planet almost instantaneously, and that leads to a very different kind of economics that what we had for the previous few hundred years, >> or you don't work to quantify that. Does that sort of Yeah, wave wanted >> Find the effect on the economy more broadly. But there's also a very profound effects on business and the kind of business models that work. You know, you mentioned open source as an example. There are platform economics, Marshall Banal Stein. One of the experts in the field, is speaking here today about that. Maybe we get a chance to talk about it later. You can sometimes make a lot of money by giving stuff away for free and gaining from complimentary goods. These are things that >> way started. Yeah, Well, there you go. Well, that would be working for you could only do that for a little >> while. You'll like you're a drug dealer. You could do that for a little while. And then you get people addicted many. You start charging them a lot. There's a really different business model in the second machine age, which is just give stuff away for free. You can make enough off other ancillary streams like advertising to have a large, very, very successful business. >> Okay, I wonder if we could sort of, uh, two things I want first I want to talk about the constraints. What is the constraints to taking advantage of that? That innovation curve in the next day? >> Well, that's a great question, and less and less of the constraint is technological. More and more of the constraint is our ability as individuals to cope with change and said There's a race between technology and education, and an even more profound constraint is the ability of our organisations in our culture to adapt. We really see that it's a bottleneck. And at the MIT Sloan School, we're very much focused on trying to relieve those constraints. We've got some brilliant technologists that are inventing the future on the technology side, but we've got to keep up with our business. Models are economic systems, and that's not happening fast enough. >> So let's think about where the technology's aren't in. The constraints aren't and are. As Eric says, access to technology is vanishing as a constraint. Access to capital is vanishing as a constraint, at least a demonstrator to start showing that you've got a good idea because of the cloud. Because of Moore's law and a small team or alone innovator can demonstrate the power of their idea and then ramp it up. So those air really vanishing constraints are mindset, constraints, our institutional constraints. And unfortunately, increasingly, I believe regulatory constraints. Our colleague Larry Lessing has a great way to phrase the choice, he says, With our policies, with our regulations, we can protect the future from the past, or we could protect the past from the future. That choice is really, really write. The future is a better place. Let's protect that from the incumbents in the inertia. >> So that leads us to sort of some of the proposals that you guys made in terms of how we can approach this. Good news is, capitalism is not something that you're you're you're you're very much in favor of, you know, attacking no poulet bureau, I think, was your comments on DH some of the other things? Actually, I found pretty practical, although not not likely, but practical things, right? Yes, but but still, you know, feasible certainly, certainly, certainly intellectually. But what have you seen in terms of the reaction to your proposals? And do you have any once that the public policy will begin to shape in a way that wages >> conference that the conversation is shifting. So just from the publication date now we've noticed there's a lot more willingness to engage with these ideas with the ideas that tech progress is racing ahead but leaving some people behind in more people behind in an economic sense over time. So we've talked to politicians. We've talked to policy makers. We've talked to faint thanks. That conversation is progressing. And if we want to change our our government, you want to change our policies. I think it has to start with changing the conversation. It's a bottom out phenomenon >> and is exactly right. And that's really one of the key things that we learned, you know well, we talked to our political science friends. They remind us that in American other democracies, leaders are really followers on. They follow public opinion and the people are the leaders. So we're not going to be able to get changes in our policies until we change the old broad conversation. We get people recognizing the issues they're underway here, and I wouldn't be too quick to dismiss some of these bigger changes we describe as possible the book. I mean, historically, there've been some huge changes the cost of the mass public education was a pretty radical idea when it was introduced. The concept of Social Security were recently the concept of marriage. Equality with something I think people wouldn't have imagined maybe a decade or two ago so you could have some big changes in the political conversation. It starts with what the people want, and ultimately the leaders will follow. >> It's easy to get dismayed about the logjam in Washington, and I get dismayed once in a while. But I think back a decade ago, if somebody had told me that gay marriage and legal marijuana would be pretty widespread in America, I would have laughed in their face. And, you know, I'm straight and I don't smoke dope. I think these were both fantastic developments, and they came because the conversation shifted. Not not because we had a gay pot smoker in the white. >> Gentlemen, Listen, thank you very much. First of all, for running this great book, well, even I got one last question. So I understand you guys were working on your topic for you next, but can you give us a little bit of, uh, some thoughts as to what you're thinking. What do we do? We tip the hand. Well, sure, I think that >> it's no no mystery that we teach in a business school. And we spent a lot of time interacting with business leaders. And as we've mentioned in the discussion here, there have been some huge changes in the kind of business models that are successful in the second machine age. We want to elaborate on those describe nuts what were seeing when we talk to business leaders but also with the economic theory says about what will and what? What won't work. >> So second machine age was our attempt it like a big idea book. Let's write the Business guide to the Second Machine Age. >> Excellent. First of all, the book is a big idea. A lot of big ideas in the book, with excellent examples and some prescription, I think, for moving forward. So thank you for writing that book. And congratulations on its success. Really appreciate you guys coming in the Cube. Good luck today and we look forward to talking to in the future. Thanks for having been a real pleasure. Keep right. Everybody will be right back. We're live from London. This is M I t E. This is the cube right back

>>Live from the Mandalay convention center in Las Vegas, Nevada. It's not cue at IBM insight 2014 >>you're all your hosts. John furrier and Dave Volante.. >>Okay. Welcome back everyone. We're here live inside the cube at IBM insight. I'm Sean with Dave Volante. We go after the events, extract the signal and noise. We go wall to wall covers what we do here. I don't, of course we're excited to have awesome gas. We talked to the executives, entrepreneurs, but we get the media stars in here. Uh, Katie Lyndon doll. Welcome to the cube. You are with CNN, the today show. You're the tech correspondent and you get a lot of energy. I could just tell this is going to be fun. It's been fun to hear the last few days. So I mean, Watson is the geeky story of any what, what are you seeing? Let me get the wife in a second. But outside of Watson, what's the coolest thing you've seen? >>I'm constantly on the hunt for the latest innovations in technology and I think that's probably the best part about my job. And always chasing down high level stories. I recently just came back for a dive with NASA. I learned that NASA astronauts actually train underwater to simulate microgravity and I'm like, Oh my gosh, no way. And they're like, do you want to come down to the world's only Marine underwater habitat? I was like, yes, please. So went down to the Florida keys, it's an hour off the coast and was diving literally with NASA European space agency and the Canadian space agency underwater. And again, it's the world's only underwater Marine habitat and seeing how they train in everything from asteroid mining to um, underwater surgery to actually seeing how the body responds to exercise. I guess water simulates one sixth of gravity. So it was a pretty dynamic shoot. >>I was doing that for NBC news and it's just I, those are the types of stories. I, I am a diver. I actually was doing a story on big data last year and it required me to get my dive certs and the Island of Bermuda feel very bad. It was a presentation that I was speaking on here at insight a, there was all this crowdsourced information about how the lion fish, if you've ever heard of the lion fish has been, it's an invasion in the Atlantic ocean. I took all of this information and metrics and made a story for CNN and it required me to get my advanced dive certs. So now I'm getting all these dive stories cause there's not a lot of us dive reporters. So the lion fish story for CNN too. Another good example of a piece that I go after. >>So you, you bring a lot of energy. What do you see here? I mean you see a lot of stories and you get pitched stories. I can imagine that your email box flux, I mean it's like, Oh >>I have 78,000 unread emails right now. I'm not proud of that. But yes, constantly being pitched. >>I had 40,000 I'm a little bit blind. I'm going to give that to you in the today show. Not too shabby. But what do you do? You get pitched all the time and so you got the vet stories. What's your formula for vetting stories? I mean, what gets your attention and how do you go outside your comfort zone to select good stories? What your attention. It's funny, >>you know, so I've been in television for the last 10 years and I feel like now I have this internal barometer and knowing when something's very good and the scope of the things that I cover from, you know, in the past month alone when I was talking about the NASA piece and then I'll flip the next day and do top Halloween gadgets on today's show. So it's, it's very vast, but I can instantly tell and it's, it's come through experience and being in a background in technology and knowing what's gonna work for the consumer and knowing a hot product. When I see it and I I T I gotten pretty good I think at it spotting a product that a consumer is going to love but also finding a story that is, maybe it's super nerdy, but my job is to take it and to bring it down to a level that's entertaining for any kind of audience, whether it be CNN or whether it be today. >>So it says your Guinness book of world record holder, share that in little nugget with the folks in. Yes, that is a true story. I have a Guinness world record holder in the most high fives and one minute. Okay, so this probably solicit some like how the heck did that happen? I've always been fascinated with Guinness world records and I always wanted one and I've always been obsessed with a high five like I am paranoid of huggers, there's nothing that scares me more or good high five just go for the five. I don't want to bring it in and okay, it's a little OCD. I will completely aware. So anyways, I found that this Guinness world record was held by a clown in England for the most high fives and one minute. So I convinced I was hosting a show on spike TV and I convinced them to allow me to break this record. >>So we had all these people line up in the MTV cafeteria and you have a Guinness world record adjudicator come onsite, you get two tries and if you win you get a plaque in a formal ceremony. The cube before we should do the most consecutive interviews to having a drink of water. We want to just come here and we could break something able to break something or like you said, it's his official. Yeah, we started to get like real nervous and like hot and yeah, so I had two tribes. Oh I was, I was giving him a big ass big fitness person. So I was like ready. And if clown beats me at this point, it's over your careers. division. You'll never work again because I beat it on the first try and then I advanced it on a single hand or you go, there's a whole process as you can imagine with the adjudicator's she's like real intense. >>She's like counting with her clicker on the high five so I go down this line of people and it has to be over there can't be like a mailed in like you know like a high five you go for the five names and then I got a couple that were disqualified, you know like a couple didn't count because it wasn't like a full on five four so like a film replay. Super slow motion. I like argued a few. I was like no, I was for sure up on that one. The flag, it was sponsored by PRL. It wasn't but it should have been but it was fun. So I have a plaque how many? 107 heard rumors that it's been broken but I didn't care as long as I've got a plan to that plan at one point. Okay. Let's cut to about IBM because Watson is the coolest thing I'll say is pretty mainstream. >>It hits your wheelhouse. I'll see for the day I've seen jeopardy. Absolutely. Now how does that translate into a story for sure. Stuff going on here. What do you, so what's very cool about Watson? I called my boyfriend because I've had a relationship with him now over the last few years, a few years ago on CVS. I actually got to challenge Watson on a full game of jeopardy and I think that was of course the most, the most memorable part of Watson when he took on the two, you know, jeopardy champions. But so this is like a lifetime moment for me. I got a full game of jeopardy, me Watson and another individual smoked me and actually I was doing okay and then it was like tennis vocab. I was like, Oh, I got this. You know, like I've been in sports my whole life. I've been worked at ESPN for seven years. >>I got this in the bag, I was doing good. And then they were like, Oh, we had them on the low setting. I was like, all right, really? Like really? Like I was just feeling good about myself. I finished with $2, two bucks. Um, and I thought it was so cool how gimmicky it was, you know, in a healthy beach in the tennis category. Oh, you smoking, you never in the low setting for sure. I got a few of those, a few. I actually got set in Tennessee vocab. You're going to have it right. Even watching tennis your whole life. Right. ESPN is embarrassing and disappointing. And then I weighed you too much and then the double jeopardy. Anyways, I digress. So how cool is it that I got to play Watson but then now years later seeing the power in it in many different developments and most notably I work over at as a volunteer at Sloan Memorial Kettering cancer center for a small group called Candlelighters that works with individuals that come in from around the world for cancer treatments. >>Now Sloan is one of those powerful cancer centers in the world is actually using it as predictive analysis. So here and I work with these kids and I, it's very complex. When they go in for a diagnosis, there's lots of different problems that they have and really it's, it's, it's, it's, it's guesswork for a doctor now. They can put all of these things that are happening with it, with a child into a machine, and they can pump out a hypotheses. Of course, you're going to have to have the human interaction tailored with that to have the emotional side, but I had been fascinated, especially on the medical side, watching your boyfriend at this point. That's interesting. We'll get that to the world of Facebook. It's complicated. I heard rumors that he's talks back and we'll listen to this a true statement. He's a lot smarter than I am. >>I'm intimidated by that, but what's the coolest demo with Watson that you've seen besides jeopardy? Yeah, that would have, well I actually learned something new from a few developers that I met yesterday about the new chef app. So being able to go into your pantry and to do some recipe from what you have, the ingredients you have insider, I think that's a little more consumer friendly. So I was kind of like, um, I'm excited to check that one out. Looking at the tech landscape, what are you most excited about? I mean, what's the coolest kind of consumer meats like gadget, short door, tech cloud. If you could pull a few favorites at what's, what's drawing your attention? Uh, one that we actually had here that's probably popped into mind. There's so many to choose from, but in the world of Oculus rift, and the reason I say that is not for the gaming aspect, but more for the potential in the landscape of physical therapy. >>The first time I got on Oculus raft, I was actually training on a Navy boat and I was doing a segment where all my camera men were all around me. I lost track of reality and I got so immersed into virtual reality and being there and even as a huge diver, I get very motion sick and I got motion sick on the boat. Being in this physical, this augmented reality world, we're actually shooting this at the birthplace of Oculus rift. So we really diving behind the scenes into the actual, uh, software and hardware and it was such a cool, immersive experience and realize that what this could do for physical therapy or even at the dentist at a lower end, I think the capabilities for augmented reality and taking yourself out of that moment are huge. So I think that's very exciting. How about drones? >>Oh my gosh. So yes, let's talk to, and my nephew the other day and he said, do you want to see the drone that I built? And I said, yeah, it's got this four or five quadcopter. It's a quadcopter. Yup. I said, where'd you get the software for? He goes, I'll download it. It's all open source. I hacked it a little bit. I actually have several drones. Okay. Nominal. Because this blew me away. I probably have what I consider is the best prosumer drone. It's a DGI Phantom, a DJI Phantom two and I have got some incredible aerial footage over the mountains of Montana and also over a Bermuda, the Island of Bermuda. I sent it up, put it over a shipwreck, gorgeous. And for me as a flake, being in photo and video and going out and getting my own video and not having to rely on a cop, a copter for, you know, that would be thousands of dollars worth of footage or relying on a cameraman. >>I just sent that baby up. I'm like, please don't hit anybody. It's a little hard to operate when you get the one, the higher end models. I have a couple of the parents too. There are a lot easier to operate and do it right from my iPhone, but I am just like, I'm so into it now. I think it's a little gimmicky when we talk about Amazon and pizza deliveries and taco deliveries and beer deliveries with a drone shooting surprises. Texas man, what am, I don't know about that. But uh, I think it's fascinating. I think it's a really cool technology. And again, I've personally saved tens and thousands of dollars using my drones. So you, when you flew over these sites yet proximate, so you had visual concepts. So the Phantom Jerome that I have, that's my favorite one. I actually attach a GoPro to it so I can send it up and I use the gyroscope or just kind of move my GoPro around in mid air. It goes hundreds of feet high. I mean, you've really got to get a grasp on it and know what you're doing. I had it out in a field well before I took it out to an Island on a beach. But I'm not, a drive is not something you really, it's not a remote control car. Now did you build it? Oh no. Goodness. Aww, that's totally on the market. Yeah, I got it at B and H photo >>sending them out. So in San Francisco off their balconies and then they're going out to, you know, angel Island, Alcatraz, and literally they're flying out then unregulated. It's like someday there'll be drone collisions, let's say this is unregulated. This is a huge, people are geeking out with the drones. It's super exciting. Dave camera's shooting down him sending him into football venues or you know, the world series delivering packages. But mom's a streaker. I mean Amazon. I like that. Okay. So what else is new for you? Tell us more about some, some cool behind the scenes at a today's show. Any sad night live, uh, opportunities for you next been >>to Saturday night live. Oh my gosh. By the way, that's like the hottest ticket in New York to get. I've had the opportunity to go to two shows cause my friend's a cameraman over there. The rehearsal for it is like amazing. I know that's a huge digression, but talking about something to see in person, that's one of my bucket lists. Phenomenal. Yeah. Phenomenal. What else is new in New York and the scene there? Uh, Oh, we constantly covering a lot of different pieces. Uh, one, I just came back from Africa a little bit ago. I was doing a number of pieces over there from an elephant orphanage to one of my favorite pieces that we'll be rolling out soon. I did it for cnn.com and also working on a video piece of it. I went in embedded myself in the second poorest part of the entire world in the slums of Kibera, Kenya, and it was amazing to see that in these very poor areas, 70 to 80% cell penetration. A lot of people don't think that a smartphone would be prevalent. It sure is. And these kids, yeah, absolutely. There's cell towers everywhere. These kids were, you know, they don't have much, but they have e-reader devices and they can have thousands of books when they're walking 10 miles to school. You walk into the school that doesn't have any electricity, it's a hundred degrees, but they all have e-readers, Kindles right on their desk. I was blown away. I went to several different schools around Eastern Kenya. Fascinating story to be able to cover. So >>yeah, that's a really good point. In mobile penetration. If I was talking to this startup that where their business plan is to build, sell a solar battery recharging stations because they have the exact points, like they have all these devices but it's not, they don't have the traditional electricity and the parks >>one outlet in the entire school. So fortunately for, you know, with wifi off it's about a week charge on a Kindle. So it is, >>yeah, I think, I think that's a great market opportunity. Certainly in emerging countries, the mobile penetration, I'm so suites about the IBM show here. Is this your first time here or, >>I have had the luxury and the opportunity to be a part of several IBM events and everyone is so uniquely different. And this one all about developers obviously. So something I get to nerd out in myself in that is an it girl and also a developer. It's fun to be able to learn. I picked up so much new information so I just kind of like, they're like, you can, you're done with, I'm like, I'm going to hang out for a little bit longer. >>You know, you know you're a, you know, you're a geek when you're geeking out, when you're off the clock, you know Steve and I the same way. We're like we should stop rookies now let's keep going. So CES, UFC, yes, >>yes, every year for sure. And for anyone that hasn't been to CF, it's kind of on the bucket list for anybody that's attending technology, 35 football fields full of gadgets. Amazing. Yeah, it's always one of my biggest times of the year. So we'll be back here. >>now do you enjoy CES or is it a hard slog for you because you must have to really get down and dirty for CS, I mean a lot of stuff to cover. >>I did and I tried to make it to like the most random boosts. I find someone of my best technology products and like the ma and PA type shops that don't have the million dollar booth and like you know that are really back in a corner and I'm like zero in, >>you go on to cover, by the way, do you go into cover? You kind of sneak in there and you go into the camera guys. No, I go for it. You go for it. Okay. Time. Okay. All right guys. Um, that's awesome. Well can. Thanks for coming on the cube. We really appreciate spending the time. We'd love the personality. I love the energy. I mean Dave and I think you know, we're, first of all we're huge fans of your work. Especially the ESPN part. No, we're, we're big sports fans. In fact we call this the ESPN of tech cause it's our kind of version of like trying to be like ESPN. But we think technology is going mainstream. People at this new generation are geeks and even too, you alluded to ESPN, even sports and technology, I can't tell you how many pieces I've covered in pro athletes and how tech is entering in that space. Everywhere. Disruption in the data, the social media, you know, limiting have agents that go direct to the audience. Just super exciting. I mean I'm real big fan of media, tech, sports and entertainment. Thanks for coming on the cube. We appreciate it. We'll be right back with this after the short break here inside the cube live in Las Vegas. I'm John and Dave. We write back.