>>Hey everyone. Welcome to the Cube's Special Program series. Women of the Cloud Drops You by aws. I'm your host for the series, Lisa Martin. Very pleased to welcome Elle Mans to the program VP of North America Cloud platforms at Converge Technology Solutions. Rochelle, great to have you on the program. Thank you for your time today. >>Thank you, Lisa. Excited to be here. >>Tell me a little bit about you, a little bit about your role so the audience gets that understanding. >>Sure. So my role here is to help our customers migrate to public cloud or, or adopt public cloud as part as their overall digital transformation strategy. I've been in this role a little over two years supporting our, our customers and, and our organization as, as a whole. My background in technology, I've actually been a, a woman in technology since 1989. I'm one of those rare breeds that from a very, very young age, I knew I loved computers and, and always wanted something, something to do with it. The last 10 years of, of my career really has been working with clients and, and companies in the, in the industry with disruptive technologies, adopting new and and emerging technologies and, and cloud has been my focus for, for the last two years. >>But you're an og it sounds like when it comes >>To >>Tech, that's outstanding. >>It's surprises a lot of folks, >>Doesn't it? Yeah. Yes. Sometimes it surprises me as well, like how long I've been doing something, and I'm sure the same for you, but you have such wisdom and such experience that I would love to be able to share with the audience. Talk a little bit about some of your recommendations. Are they tactical, they strategic for those in the audience watching who really want to grow their careers and tech and climb that ladder? >>Yeah, I, I think, you know, our younger generations right now have, have, I think, a little bit of an easier path to, to take than, than some of us have with the amount of information that's out there, the access to, to information and the opportunity. I think one of the biggest recommendations that, that I can put out there is to always continue learning and find a mentor. Find a sponsor. You know, I, with being a female in tech, there weren't that many when I started out in the industry. And it's just, I get amazed every time I meet another, another female. And whether she's been in the industry for, you know, 20 years or two or five years, it's just exciting to see and listen to the stories of other people's paths and their journey. So mentor and, and your tribe, you definitely need your tribe. >>Absolutely. You know, something I didn't understand until a few years ago was the difference between a mentor and a sponsor. And it's so incredibly important to understand differences between the two, how they can help you lev get leverage in the career path that you're on, but, but people need to know you. You have a network, it's there. You might not think you do, but it is there. And think about those who are mentors, those that can be sponsors to help elevate you along your journey. >>Yeah, it's, it's amazing. I, I think about, I have three, three really good friends that we've grown up in the industry together, but the, sometimes even having those three really good friends, we went through many things by ourselves that we didn't have to, as you mentioned it, it, it, it took me longer than, than than I should to understand that I have someone that we can lean on sometimes just having those conversations and saying, Is this what I should do? Is this something or did did that just happen to me? You know, and having those that, that mentor and, and that partnership with someone that they may be in your organization, they may be outside of your organization, but definitely that you can have those candid conversations about what your growth or goal, what you'd like to strive for. You know, especially if it's something that may on the surface appear to be out of, out of reach. You know, if you have, have someone that is maybe not as invested in what you're trying to achieve, but can look at you and have that objective conversation, I think makes, makes all the difference and makes all the difference in the world. >>It does. And it's, it's a little bit about vulnerability, about raising your hand, saying, Hey, I'm very interested in this. I may not meet every single written criteria in the job description, but I have an interest and a passion. Can you help me navigate the, the path to, in order to get there? It's part of, it's just really raising your hand. >>It's, that's such a, such a great point, Lisa, because in some ways we can't be vulnerable because we are underrepresented as, as women in technology, but at the same time, we have to have that ability to have those same conversations that, you know, I don't know everything. Can I do this? What do I need to learn? So it, it really is finding that that balance and when you have a mentor that can help you in that area, that's the way you can show that vulnerability without, without looking like you don't have strength. >>Right. There's a balance there for sure. Speaking of that, vulnerability, diversity, we talk a lot about diversity when it comes to technology. There's a lot of strides being made. There's also some challenges, there's some gaps. What are some of the things that you see from your lens, from your seat with respect to diversity and some of the challenges that are still out there? >>Yeah, I, I look at companies like AWS with much respect on where, you know, their diversity and inclusion goals. It's not just a checkbox. You can actually see that when it is part of the culture, the room looks diverse. There are so many companies that have have the diversity and and inclusion goals, but when you go into the room or you, you're sitting in a meeting or you have a board, it is, it's, it's still, it's, it's still not seeing yourself in that, in that room. I go to a lot of conferences, attend, attend a lot of meetings, and it, and it's still surprising to see, you know, the lack of minority representation, leadership and the lack of women in, in, in leadership. So while there's been amazing strides that we've seen happen, you know, particularly, like I said, with companies like aws, we've got a long way to go. >>And I think you mentioned the difference between a mentorship and, and sponsorship. That's one thing within these organizations, particularly in leadership, there, there needs to be that sponsorship of, of the individuals in your organization that can help change what the landscape looks like at the top through your leadership. You'd be surprised how how problems are solved differently. Problems can be solved more quickly and talk about innovation when you've got more a diverse lens. There's more ways to innovate if you've got different people bringing different perspectives to the, to the conversation. So looking forward to seeing that continuing changing of, of the landscape. When I look inside the room and, and I count, >>I do the same thing and there's so much value in thought diversity for organizations and that data clearly speaks for us stuff. We, we can't have a tech conversation without talking about data, but data demonstrate that for organizations that have diversity emails, for example, in the C-suite, those organizations are more profitable. So bringing in different tracks of thought, different perspectives, the thought diversity, diversity and gender diversity and other things is so valuable. It's invaluable to organizations in every industry. >>Yeah, it's, it's invaluable. And it, and it's funny because our industry tech right now, I mean data is, you know, it's the, it's the new water, it's the, the gold mine. It's the asset. And it's, it's funny that in this area that the data is, is almost ignored. It's, it's, the data proves itself. So it doesn't have to be a checkbox for these, you know, diversity inclusion goals because the data's there to, to prove that we're all here to be profitable, follow that data. >>Exactly. And sometimes it seems so simple. Follow the data and we, we think the same recommendation holds true to, to any industry that any company and any industry that needs to be a data company to be able to deliver what the demanding consumers want, follow the data, it won't leave you astray. So I wanna get though back to talking about you and some of the impact that you've been able to have in your career. Talk a little bit about some of the specific success stories of problems that you've helped solve related to cloud computing. >>Yeah, I, this last, I'd say 16 to 18 months for us as an organization has been amazing for me as well as my team. Some of our, you know, the majority of our success, we couldn't be, I couldn't be here having this conversation without my team. And for, for us as a, as an organization where our heritage is legacy data center, and we've got customers that we've had a 10, 15, 20 year selling relationship with that now via our acquisition strategy and growth strategy, we're going to them in saying, let us help you with your cloud journey. And it's something that they haven't known our, our organization for in the past. And so when we go in there and, and meet with CIOs and CEOs and ask for them to trust us to take them on this cloud journey, and many of our clients are, are what you term greenfield, that they've got very little activity in public cloud. >>And so it's a, it's a disruption, it's an internal disruption that can be a very emotional journey that has to start with trust because you transform so much of the business. And so each and every of our wins, particularly when we have, when we have wins with brands that are recognizable, particularly when we have wins against competitors that have been in the cloud space, and that's all they do. For me, I take that as, as a personal stamp of endorsement because we've, we've shown and demonstrated to those clients that we're the right ones to, to take 'em on that journey. And we've created that, we've created that, that trust. So for me, we've had some incredible wins with our clients and those conversations can get tough sometimes we're in, we're in the middle of a migration and the operational change that'll happen. And sometimes there's tough conversations to say, you know, you think your organization is here, it's not, it's here. And we're not calling that out to say, you haven't done something, we're calling that out so that your journey ends where you'd like it to be, where we've all agreed for it to to be. And so when we, you know, have that final party or have, you know, that final sign off at the end of the project, that that's, that's a personal personal win for, for me, I, I, I enjoy solving problems and, and, and taking customers on those journeys, >>Solving problems and, and helping customers navigate the journey, whether it's the journey to cloud, the journey to digital, the journey to being more competitive than their competitors is, is just that, it's a journey. It's a multi-phased multi-step process. And to your point, underpinning that has to be trust between the organization and the people that are working to get them successfully on that journey. >>It does, and it's, it's funny, some of the, some of the conversations we're, we're starting out. Our, our approach, our team is very prescriptive and we'll get a lot of customers that just wanna go, go, go. And it's, it's, I'm, I'm road racing as, as my hobby. And so the old adage, sometimes you've gotta go slow to go fast and we, we talk to our customers and there's a lot of interviewing and they just wanna deliver. They just wanna jump in. And we're, we're like, it, it is, we know this may feel like we're going slow, but if we can really truly understand what that business outcome looks like, if we can uncover how you can leverage your investment and your, your movement to cloud, many, many customers are looking at it from a total cost of owner ownership. Can I, can I get outta the data center? >>If just that moving out of the data center, if we do those interviews with your different teams, and then we can understand an area where we can improve a customer experience, you know, make an offering that's been a, a cost center for you, a profit center for you. Those are things that we're looking for. So we really get to know our client's business. So it's not just about the technology or the destination, it's, it's what do you do when you get there? And so having those deep conversations with, with our, with our clients is, is the approach that we like to take. >>It's really about, to your point, it's about technology, but also processes and people, we can't forget the people part of this. Talk to me a little bit from the people perspective about how you see cloud evolving in the industry. Where are people involved and what are some of the things that you're excited about in terms of the evolution of your role? >>Yeah, In, in some ways for both our, our team internally and when we're working with clients, people in operations tend to be the things that are minimized. It, it tends to focus a lot on the technology, and we like to tell folks, you have to operate in the cloud and operating requires people in process. And so the, the people we know individuals with cloud skills are very much high in demand. And so how do you attract those skills? How do you retain those skills or how do you upskill the individuals in your organization? There's so much opportunity to bring people along. We go back to one of your earlier questions and, and you know, what's the evolution or roles that people, people can look at in, in the cloud, individuals that are in organizations right now where there hasn't been much public cloud adoption, taking those initiatives. >>Going back to another comment of learning, AWS provides so much free training and so much opportunity for individuals to upskill themselves to have growth in, in technology. And cloud is an area, you know, we're going through a recession. Cloud is an area that is still going to be one of the, the, the places that organizations look for answers to say, how do we drive innovation, right? How do we, how do we advance what we're doing from a, a profitability standpoint? And can we leverage, leverage cloud to cloud to do that? So upscaling and investing in yourself in those areas is, is, there's a great opportunity for that. >>There's a huge opportunity in upscaling and investing in ways to improve your own skills. My last question for you is, if we think back the last few years, talk about some of the changes in tech innovation in the workforce that you've seen and what are some of the things that you think are on the horizon? >>Yeah, so there, there's still a great opportunity to, to exploit cloud and in general, I mean, we see so many companies, software companies looking at sas, business models, subscription models. That's still changing. If we think about cloud economics and, and how we, how, how we purchase today. There's still an evolution there. But I think for me, being a, a self, self-proclaimed tech nerd, everything that's happening with AI and ML from an advanced analytics standpoint, the good and the bad. I mean, I think we've gotta look at the, the, the social responsibility behind this. When you talk about models and, and models themselves being diverse. If, if there isn't diverse background building those miles, the, the intentional bias gets built into, into some of those. But then I look at the, the advancements, I mean, it's exciting. We're working with, with, with one of our clients where autonomous taxis is, is something that they're trying to bring to market. >>You know, these are things that we saw in cartoons growing up that are reality and becoming reality in this day and age. So, you know, that's through AI and machine learning and just, you know, all of the new services that, you know, companies like AWS continue to bring out so that people can be innovative and, and and develop. But it's just, that's the, it's, it's exciting for me to, to see that across the board. So transportation from AI and ml, what we saw, what came out from, you know, covid and testing and the data and, and just the advancements of, of that. So there's, there's so many different ways to apply, apply that technology. >>There is the horizon I think is clearly bright. And thank you so much Rochelle, for sharing what you've done, your experiences, how you're helping to make that horizon even brighter. We appreciate your insights, we appreciate your time. Thank you for joining us in the program today. Thank you Lisa for Rochelle Mans. I'm Lisa Martin. You're watching The Cubes coverage of the special program series Women of the Cloud, brought to you by aws. Thanks for watching.

(energetic music) >> Announcer: Live, from Stanford University in Palo Alto, California, it's The Cube, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube. We are live at Stanford University at the third annual Women in Data Science Conference. I am Lisa Martin. We've had a really exciting day so far, talking with a lot of female leaders in different parts of STEM fields. And I'm excited to be joined by my next guest, who is a speaker at this year's WIDS 2018 event, Daniela Witten, the Associate Professor of Statistics and Biostatistics at the University of Washington. Daniela, thanks so much for stopping by The Cube. >> Oh, thanks so much for the invitation. >> So here we are at Stanford University. You spent quite a lot of time here. You've got three degrees from Stanford, so it's kind of like coming back home? >> Yeah, I've spent from 2001 to 2010 here. I started with a bachelor's degree in math and biology, and then I did a master's, and finally a PhD in statistics. >> And so now you're up at the University of Washington. Tell us about that. What is your focus there? >> Yeah, so my work is in statistical machine learning, with applications to large scale data coming out of biology. And so the idea is that in the last ten or 20 years, the field of biology has been totally transformed by new technologies that make it possible to measure a person's DNA sequence, or to see the activity in their brain. Really, all different types of measurements that would have been unthinkable just a few years ago. But unfortunately, we don't yet know really how to make sense of these data statistically. So there's a pretty big gap between the data that we're collecting, or rather, the data that biologists are collecting, and then the scientific conclusions that we can draw from these data. So my work focuses on trying to bridge this gap by developing statistical methods that we can use to make sense of this large scale data. >> That sounds exciting. So, WIDS, this is the third year, and they have grown this event remarkably quickly. So, we had Margot Garritsen on the program a little bit earlier, and she had shared 177 regional WIDS events going on today, this week, in 53 countries. And they're expecting to reach 100,000 people. So, for you, as a speaker, what is it that attracted you to participate in the WIDS movement, and share your topic, which we'll get to in a second, what was it that sort of attracted you to that? >> Well, first of all, it's an honor to be invited to participate in this event, which, as you mentioned, is getting live streamed and so many people are watching. But what's really special for me, of course, as a woman, is that there's so many conferences out there that I speak at, and the vast majority have a couple of female speakers, and it's not because there's a lack of talent. There are plenty of very qualified women who could be speaking at these conferences. But often, the conference organizers just don't think of women right away, or maybe add a couple women as an afterthought to their speaker lineups. And so it's really wonderful to be part of a conference where all of the speakers are women, and so we can really see the broad ways in which women are contributing to data science, both in and out of industry. >> And one of the things that Margot shared was, she had this idea with her co-founders only three years ago in 2015, and they got from concept to their first event in six months. >> Daniela: Women know how to get things done. >> We do, don't we? (laughs) But also what it showed, and even in 2015, and we still have this problem in 2018, is there's a massive demand for this. >> Yeah. >> The statistics, speaking of statistics, the numbers show very few women that are getting degrees in STEM subjects are actually working in their field. I just saw this morning, it's really cool, interactive infographic that someone shared with me on Twitter, thank you very much, that showed that 20 percent of females get degrees in engineering, but only 11 percent of them are working in engineering. And you think, "How have we gone backwards in the last 30 years?" But at least now we've got this movement, this phenomenon that is WIDS to start, even from an awareness perspective, of showing we don't have a lot of thought diversity. We have a great opportunity to increase that, and you've got a great platform in order to share your story. >> Yeah. Well, I think that you raise a good point though, as, even though the number of women majoring in STEM fields, at least in some areas of STEM has increased, the number of women making it higher up in the STEM ladder hasn't, for the most part. And one reason for this is possibly the lack of female role models. So being able to attend a conference like this, for young women who are interested in developing their career in STEM, I'm sure is really inspirational and a great opportunity. So it's wonderful for Margot and the other organizers to have put this together. >> It is. Even on the recruiting side, some of the things that still surprise me are when some, whether it's universities or companies that are going to universities to recruit for STEM roles, they're still bringing mostly men. And if there are females at the events, they're, often times they're handing out swag, they're doing more event coordination, which is great. I'm a marketer. There's a lot of females in marketing. But it still shows the need to start from a visibility standpoint and a messaging standpoint alone. They've got to flip this. >> I completely agree with that, but it also works the other way. So, often a company or an academic department might have a few women in a particular role, and those women get asked to do everything. Because they'll say, "Oh, we're going to Stanford to recruit. We need a woman there. We're having some event, and we don't want it to look totally non-diverse, so we need a woman there too." And the small number of women in STEM get asked to do a lot of things that the men don't get asked to do, and this can also be really problematic. Even though the intent is good, to clearly showcase the fact that there's diversity in STEM and in academia, the end outcome can actually be hurtful to the women involved who are being asked to do more than their fair share. So we need to find a way to balance this. >> Right. That balance is key. So what I want to kind of pivot on next is, just looking at the field of data science, it's so interesting because it's very, I like 'cause it's horizontal. We just had a guest on from Uber, and we talk to on The Cube, people in many different industries, from big tech to baseball teams and things like that. And what it really shows, though, is, there's blurred lines, or maybe even lines that have evaporated between demarcated career A, B, C, D. And data science is so pervasive that it's impacting, people that are working in it, like yourself, have the ability to impact every sector, policy changes, things like that. Do you think that that message is out there enough? That the next generation understands how much impact they can make in data science? >> I think there is a lot of excitement from young people about data science. At U-dub, we have a statistics major, and it's really grown a lot in popularity in the last few years. We have a new master's degree in data science that just was started around the same time that WIDS was started, and we had 800 applicants this year. >> Wow. >> For a single masters program. Truly incredible. But I think that there's an element of it that also maybe people don't realize. So data science, there's a technical skill set that comes with it, and people are studying undergrad in statistics, and getting master's in data science in order to get that technical skill set. But there's also a non-technical skill set that's incredibly important, because data science isn't done in a vacuum. It's done within the context of interdisciplinary teams with team members from all different areas. So, for example, in my work, I work with biologists. Your previous guest from Uber, I'm sure is working with engineers and all different areas of the company. And in order to be successful in data science, you need to really not only have technical skills, but also the ability to work as a team player and to communicate your ideas. >> Yeah, you're right. Balancing those technical skills with, what some might call soft skills, empathy, collaboration, the ability to communicate, seems to be, we talked about balance earlier, a scale-wise. Would you say they're pretty equivalent, in terms of really, that would give somebody a great foundation as a data scientist? >> I would say that having both of those skill sets would give you a good foundation, yes. The extent to which either one is needed probably depends on the details of your job. >> True. So, I want to talk a little bit more about your background. Something that caught my eye was that your work has been featured in popular media. Forbes, three times, and Elle magazine, which of course, I thought, "What? I've got to talk to you about that!" Tell me a little bit about the opportunities that you've had in Forbes and in Elle magazine to share your story and to be a mentor. >> Yeah. Well, I've just been lucky to be getting involved in the field of statistics at a time when statistics is really growing in importance and interest. So the joke is, that ten years ago, if you went to a cocktail party, and you said that you were a statistician, then nobody would want to talk to you. (Lisa laughs) And now, if you go to a cocktail party and you say you're a statistician, everyone wants to know more and find out if you know of any job openings for them. >> Lisa: That's pretty cool! >> Yeah. So it's a really great time to be doing this kind of work. And there's really an increased appreciation for the fact that it's not enough to have access to a lot of data, but we really need the technical skills to make sense of that data. >> Right. So share with us a little bit about the session that you're doing here: More Data, More Statistical Problems. Tell us a little bit about that and maybe some of the three, what are the three key takeaways that the audience was hearing from you? >> Yeah. So I think the first real takeaway is, sometimes there's a feeling that, when we have a lot of data, we don't really need a deep understanding of statistics, we just need to know how to do machine learning, or how to develop a black box predictor. And so, the first point that I wanted to make is that that's not really right. Actually, the more data you have, often the more opportunity there is for your analysis to go awry, if you don't really have the solid foundations. Another point that I wanted to make is that there's been a lot of excitement about the promise of biology. So, a lot of my work has biomedical applications, and people have been hoping for many years that the new technologies that have come out in recent years in biology, would lead to improve understanding of human health and improve treatment of disease. And, it turns out, that it hasn't, at least not yet. We've got the data, but what we don't know how to do is how to analyze it yet. And so, the real gap between the data that we have and achieving its promise is actually a statistical gap. So there's a lot of opportunity for statisticians to help bridge that gap, in order to improve human health. And finally, the last point that I want to make is that a lot of these issues are really subtle. So we can try to just swing a hammer at our data and hope to get something out of it, but often there's subtle statistical issues that we need to think about, that could very much affect our results. And keeping in mind sort of the effects of our models, and some of these subtle statistical issues is very important. >> So, in terms of your team at University of Washington, or your classes that you teach, you work with undergrads. >> Yeah, I teach undergrads and PhD students, and I work mostly with PhD students. And I've just been lucky to work with incredibly talented students. I did my PhD here at Stanford, and I had a great advisor and really wonderful mentoring from my advisor and from the other faculty in the department. And so it's really great to have the opportunity now, in turn, to mentor grad students at University of Washington. >> What are some of the things that you help them with? Is it, we talk about inspiring women to get into the field, but, as you prepare these grad students to finish their master's or PhD's, and then go out either into academia or in industry, what are some of the other elements that you think is important for them to understand in terms of learning how to be assertive, or make their points in a respectful, professional way? Is that part of what you help them understand and achieve? >> That's definitely part of it. I would say another thing that I try to teach them, so everyone who I work with, all my students, they're incredibly strong technically, because you don't get into a top PhD program in statistics or biostatistics if you're not technically very strong, so what I try to help my students do is figure out not just how to solve problems, because they can solve any problem they set their mind to, but actually how to identify the problems that are likely to be high impact. Because there's so many problems out there that you can try to solve statistically, and, of course, we should all be focusing our efforts on the ones that are likely to have a really big impact on society, or on health, or whatever it is that we're trying to influence. >> Last question for you. If you look back to your education to now, what advice would you give your younger self? >> Gosh, that's a really great question. I think that I'm happy with many of the career decisions I've made. For example, getting a PhD in statistics, I think is a great career move. But, at the same time, maybe I would tell a younger version of me to take more risks, and not be so worried about meeting every requirement on time, and instead, expanding a little bit, taking more courses in other areas, and really broadening instead of just deepening my skill set. >> We've heard that sentiment echoed a number of times today, and one of the themes that I'm hearing a lot is don't be afraid to get out of your comfort zone. And it's so hard for us when we're in it, when we're younger, 'cause you don't know that, you don't have any experience there. But it's something that I always appreciate hearing from the women who've kind of led the way for those of us and then, the next generation, is, don't be afraid to get comfortably uncomfortable and as you said, take risks. It's not a bad thing, right? Well, Daniela, thanks so much for carving out some time to visit us on The Cube, and we're happy to have given you the opportunity to reach an even bigger audience with your message, and we wish you continued success at U-dub. >> Oh, thanks so much. >> We want to thank you for watching. I'm Lisa Martin live with The Cube at WIDS 2018 from Stanford University. Stick around, I'll be back with my next guest after a short break. (energetic music)