>> Announcer: Live, from Stanford University, it's the Cube, covering The Women in Data Science Conference, 2017. >> Welcome back to The Cube, we are live at Stanford University, at the 2nd annual Women in Data Science Conference, this great, fantastic one day technical conference. And we are so excited to be joined by Yael Garten, who was one of the career panelists. Yael, you are the Director of Data Science at LinkedIn, welcome to the cube. >> Yeah, thank you, thanks for having me. So excited to have you here, everybody knows LinkedIn. My parents even have probably multiple LinkedIn accounts, but they do. You've served, what 400 and plus million accounts, I'd love to understand, what is the role, what's the data scientist's role in the business overall? >> Yeah, so I guess when people ask me about data science, what I love to kind of start with is there are a couple different types of data science. And so I would basically say that there are two main categories by which we use data science at LinkedIn. If you think about it, there is really data science where a product of your work is for a human to consume. So using data to help inform business or product strategy, to make better products, make more informed decisions about how you're investing your resources. So that's one side, which is often called decision sciences, or advanced analytics. Another type of data science is where the consumer of the output is a machine. Alright so rather than a human, a machine. So basically they these are things like machine learning models and recommendation systems. So we have really both of those. The second category is what we call data products. And so we use those in virtually everything we do. So on the data products, much of LinkedIn is a data product, it's really based on date. Right, our profiles, our connection graph, the way that people are engaging with LinkedIn helps us improve the product for our members and clients. And then we use that data internally, to really make better decisions, to understand, you know how can we better serve the world's professionals, and make them more productive and successful? >> Right, fantastic, so tell us a little bit about your team. It sounds like it's sort of broken into those two domains. You must have quite a, a large team, or a lean team? >> So yeah, we have, the way we have our team is that we work really closely within all of our product verticals, and we embed closely with the business, to really understand kind of what are the needs. And then we work very cross-functionally. So we will typically have in any group, sort of a product manager, and engineer, a designer, a data scientist, often it's from both kinds of data scientists. So sort of one on the analytic side, one on the machine learning side. Right, marketing, business operation, so really very cross-functional teams working together, using this data. >> Very smart, it sounds very integrated from the beginning, where they kind of by design-- >> Yes. >> So that collaboration is really sort of natural within LinkedIn? >> Yes. >> That's fantastic, very progressive. And certainly it's something that everybody benefits from. >> Yes. >> Right because as whether you're on the advanced analytic side, or on the machine learning side, you're getting exposure to the business side, vice versa, which, that's really a great environment for success. >> Yes, yeah and part of, I think, what I love about LinkedIn is actually our data culture, and how kind of data is infused in the culture of how we do things. >> Right, which is really-- >> Right, not always the case. >> It's not, and it's, cultural shifts have, we were talking about that with a number of guests today, and especially the size of the organization, that's tough. >> Yael: Yes. >> So to have that built in and that integration as part of, this is how we do business is, really you can imagine all the potential and possibilities there. So would love to understand, how is LinkedIn using data to recommend ways to evolve products and services to best serve all of it's members? >> Yeah, so maybe two different examples of how we do this, one is, what we do is every launch that we have, so every feature that we generate, we really do it at an online experimentation setting. So we have a certain feature that we're about to roll out to our members. And we want to make sure that it's a better experience for our members. And better, as measured by kind of the metrics that we've defined in terms of measures of success. And so, which is really aligned to what value we believe we're delivering our members and customers. And so when we roll out features, we'll roll it out to a certain percentage of our users, test the downstream impacts of that, and then decide, based on that, whether we actually roll that feature out to 100% of members. And so that's one of the things that my team is heavily involved in, is really helping to use that data to make sure that we are structuring things in a way that's statistically sound, so that we can measure the impacts correctly, of rolling out certain features. So that's kind of one category of work. And the other category is really to, to do sort of opportunity identification, and kind of deep-dive insights into understanding into a certain product area. Where are there opportunities to improve the product? So one, let me give you a high-level example. One of the ways we might use data is to say okay, Are certain members in certain countries accessing via iOS or Android? And if so, should we be developing more in differentiating between iOS and Android apps? It's one simple example right, where we'll actually decide our R&D investments, based on the data that we're seeing in terms of how people are using our products and do we think that that's important enough of an investment to improve the products and invest in that area? >> Wow very, very smart. What are some of the basic ways that data scientists can deliver more value for their stakeholders, whether they're internal stakeholders, across different functions within the organization, or the members, the external stakeholders? >> Yeah, I think one of the most important things is to really embed closely into these kind of functional or domain areas, and understand qualitatively and quantitatively, what's important. Right, so understanding what the business context is and what problem you're trying to solve. And I think one of the most important that data scientists play a role is actually helping to ensure are we even answering the right question? So as an example, a product manager might ask a data scientist to pull certain data, or to do a certain analysis, and a part of the conversation and the culture has to be what are you trying to get at? What are you trying to understand? And really thinking through is that even the right question to be asking? Or could we ask it in a different way? Because that's going to inform what analysis you do, right what, really what, how you're delivering the results of this analysis to make better decisions. So I think that's a big part of it is, having this iterative process of doing data science. >> Really, it sounds like such and innovative culture, and you're right, looking at the data to determine is this the right next step? Is it not? How do we maybe adapt and change based on really what this data is telling us. If we kind of look at collaboration for a second. You talked about the integrated teams, but I'm wondering how do you scale collaboration within LinkedIn across so many businesses and engineering stakeholders? >> Yeah, so the way I kind of like to think about it is, there's really, you have to invest in culture, process, and tools. So let me start from the bottom up. So on the tools or technology, one of the ways to do it, is actually to create self-served tools, to really democratize the data. So first of all investing in foundations of really good data quality, right, whether you're creating that data yourself, or you're collecting that from externally, from different organizations. Once you have really good data quality, making sure that you have foundations that enable self-serve data basically. So for example, some of the things that data scientists are used today in various companies, really doesn't need a data scientist if you've invested in ways where business partners, let's say, can quarry that data themselves. So they don't need a data scientist to be doing this role. So that's an important investment on the technology side. In addition, making data scientists really productive, by using and investing in tools that will enable them to access the data is really important. So once you have that sort of technology, it enables your data scientist to be productive. The process is really important. So just as an example we have a sort of playbook in terms of how do we launch features? And part of that is kind of bring in data insights, in terms of which features we should be building. And then once you've determined how using the data on those insights, it's okay how are we going to launch this in terms of experimental design and setting? And then what are the success metrics? How are we going to know that this actually a good-- (speaker drowned out by crashing sound) And then once we've launched the experiment, analyzing that, where all of the stakeholders are part of this right? The project manager, the executive, the engineer, the data scientist, and then kind of iterating on the results and deciding what the decision is. So having actually a process that the whole team or the company abides by, really helps at having this collaboration where it's clear what everyone is doing and kind of what's the process by which we use data to develop and to innovate? And then finally culture, I think that's such an important part, and that really needs to be sort of bottoms up, top down, everywhere. It really needs to be a community and a culture where data is discussed and where data is expected, and where decision making really is grounded on, on data. I fundamentally believe that any product being developed, or any decision being made really should be data informed if not data driven. >> Right absolutely. One of the things that I'm hearing in what you're doing is enabling some of business users to be self-sufficient. So you're taking that feedback and that input from the business side to be able to determine what tools they need to have and how you need to enable them so that you've got your resources aligned on certain products. >> Yeah, just as an example, one of the things that we do for example, is we realized over time that, this isn't actually productive, and how do we make ourselves scale, so we started doing data boot camps, for example. >> Interviewer: Okay. >> Where we'll actually train new people coming into the company, on data, and on self-serve tools, and on how to run experiments. And so a variety of different kind of aspects, and even how to work with data scientists productively. So we have actually train that >> fantastic. >> So this data boot camp really helps us to instill a data culture, and it rally empowers the team. >> So this is, anybody coming in, whether they're coming in for a marketing role, or a sales ops role, they get this data boot camp? >> Yeah. >> Wow. >> And it's open to anyone and you know, it yeah, typically is going to be a certain subset of those people, but it really is open to anyone, and we're talking about more ways of how do we scale that and maybe how we put that on LinkedIn learning and make that more broadly accessible. >> Yeah. >> Yeah. >> So you have quite a big team, how do you keep all of the data scientists that you've got happy, what are the challenges that they face, how do you evaluate those challenges and move forward so that they have an opportunity to make an impact at LinkedIn? >> Yeah, so part of the things are actually the things that I mentioned right? So a culture of data so a, it's really important when we see that this is not happening, actually addressing that. So data scientists are going to thrive in a community where data is valued, and where data scientists are valued, so that's actually a really important aspect. And you know luckily people come to use because they know that we do value data. But I think that that's very important for any company and so, I advise startups as well, and this is one of the things that I tell people that are founding companies, is you have to have a culture which values data to attract data scientists, because otherwise they have other options. The other thing is having these, these foundations that enable them to be productive. Right, so these tools and these systems that enable them to really do high-value work, and invest in the right areas. So start graduating from doing things that are more, maybe repetitive or low-level and figure out how do you scale that so that you can have data scientists really, efficiently using their time for things that only they can do? >> Right, I love that this culture is sort of grooming them. One of the things that, a couple things I read recently. One, was that, I think it was Forbes that said, 2017, the best job to apply for is data scientist. But, from an trends perspective, it's looking that by 2018, there's going to be a demand so high, there's not going to be enough talent. How are, what's your perspective on LinkedIn? Are you, have you, it sounds like from a foundational perspective, it is a data driven company that really values data, is that something that you see as a potential issue or you really have built a culture of such, not just collaboration and innovation, but education that LinkedIn is in a very good position? >> Yeah, well so one thing is that, I didn't mention in terms of the happiness factor right? Is that it is actually a place where data scientists look for a place where they can also grow and learn and be with other like-minded data scientists. So I think that's something that we strongly support, again for companies that, people that may be viewing this and are not in such environments, there are a lot of ways to do this. So keeping data scientists happy also can be facilitating meetups, right with data scientists from your local region, and so those are ways that people share information and share techniques and share challenges even right? >> Interviewer: Yeah. >> Because this a growing and evolving field. And so that's, having that community and one of the things that's amazing about this conference is that it's creating this community of data scientists that are all sharing successes and failures as data science is evolving. The other thing is that data science draws from so many different backgrounds right? >> Yeah. >> It's a broad field, right, and there's so many different kinds of data science, and even that is getting both more specialized and more broad. So I think that part of it is also looking at different backgrounds, different educational backgrounds and figuring out how can you expand the pool of people that you're looking at, you know that are data scientists? >> Interviewer: Right. >> And how do you augment what skills they may not have yet, you know, on the job or through training or through online education, and so we're looking at all of these ways so. >> That's fantastic, we've heard a lot of that today. The fact that, the core data science skills are still absolutely vital, but there's some other sort of softer skills, you talked about sharing. Communication has come up a number of times today. It's really a key, not only to be able to understand and interpret the data from a creative perspective and communicate what the data say. But to your point, to grow and learn and keep the data scientists happy, that social skill element is quite important. >> Yael: Yes. >> So that was, that was an interesting learning that I heard today, and I'm sure you've heard many interesting things today that have inspired you as well. >> Yeah, and that's something that you know, creating this culture is something that even data science leaders around the world, where we're discussing this and talking about this, you know what are the challenges? And how do we evolve this field? And how do we help define and help kind of groom the next generation of data scientists? >> Interviewer: Right. >> And to be in a more stable and be in a better place than where we were and to help to continue to evolve it, and so it is yeah. >> Evolution, it's a great word. I think that that's another theme that we've heard today and as much as I'm sure you've inspired and educated these women that are here. Not just in person today, but all the what 70, 70 cities and 25 countries it's being live streamed. >> Yael: Yeah, it was 80 cities and six continets. >> It's growing it's amazing. >> And yeah. >> And I'm sure that they'd vote a 10 from you, but it's probably just in the little bit that we've had a time to chat, I'm sure that you're probably gleaning a lot from them as well. >> Yeah, definitely, absolutely. >> And it's the, we're scratching the surface. >> Yes, absolutely and so there are many more years to come. >> Interviewer: Exactly, Yeal thank you so much for joining us on The Cube. >> Thank you, it's pleasure. >> It's a pleasure talking to you, we wish you continued success at LinkedIn. >> Thank you, it's a pleasure. >> And we want to thank you for watching The Cube. We've had a great day at the 2nd annual Women in Data Science conference at Stanford University. Join the conversation #wids2017. Thanks so much for watching, we'll see ya next time. 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>> Narrator: Live from Stanford University, it's theCUBE. Covering the Women in Data Science Conference 2017. >> Welcome back to theCUBE, we are live at Stanford at the second annual Women in Data Science Conference. I am Lisa Martin, joined by one of today's speakers from the event, Stephanie Gottlib. Stephanie, welcome to theCUBE. >> Thank you. >> You had a very interesting talk, which we'll get to in a minute, but you are currently the president of Agyleo Sport. We want to talk about that as well. You've been in the software and technology industry with oil and gas for a very long time, you've got a Bachelors, Masters, just a few years. >> Okay, thank you. >> Just you're, you've got expertise. That many people would desire. So we'd love to understand what your talk was about today, with respect to oil and gas. Data, digital transformation in oil and gas. You said "Data is the new oil." Which I just love that. Talk to us about that, what does that mean with respect to digital business transformation, and that industry? >> Yeah, so first of all, I say Data Science is definitely an area in which a woman, which I think is one of the main topic of today, will have a huge opportunity to move the needle. It's, I mean when you look at the, some numbers, I start in my talk with this example. In France, what is the proportion of women entrepreneurs involved in technology startups? And the answer is in the range of 8 to 12 percent. >> Lisa: Wow. >> I mean, in France right, I mean, economic-wise it's not perfect. But we have a long history, I think, human rights are there and so on, we are open. And to still be at this level, it's not dramatic, but to honest a lot remains to be done. And Data Science, it's a fantastic opportunity for women to change that drastically in the future. So that was cool to be invited to this presentation and see the huge potential that all those womans present for the future. So, having said that, now regarding my talk. What I wanted to bring on the table was about to put all the main foundational story to move into this new digital world. I mean, for industries which have been very conservative for a long time with old legacy aspect in it, moving to this digital world is not trivial. And you have three main components to handle with, which they have to address a bit differently. Which are about the goals, they have to adapt the way to think about, what are the new goals now? Which is mainly about asset utilization and maximizing the efficiency, the cost efficiency, the effectiveness, the safety and reliability and so on. How to integrate all of those technical new stuff, I mean, we are talking about Internet of Things, with plenty of new sensors everywhere in the field. HPC, High Performance Computing, for heavy computation, et cetera, et cetera. So that's some big topic, right? To digest for those industrial guys, and the last pillar which is, for me, the most crucial one is about the control change. Because beyond everything, you know, technical stuff. It's a matter of time, it's easy. But the control aspect is really essential. If you don't get the control right to instill some change management, you will likely fail. And a successful and valuable transformation comes with organization that have learned how to involve all of the entities, not just technical but legal, HR, accounting, sales marketing, all together to be aligned and to go to it. >> That's such a great point. Cultural evolution is critical, it's so hard. >> Stephanie: Absolutely. >> Right? You talk about whether it's a big oil company, or a big tech company, or another company that's large in another industry. Are you saying, though, I completely agree with you that cultural transit is the essential component. In oil and gas industry, how have you seen Data Science drive or influence cultural transformation? >> For sure, I mean the data now is in the center of everything. When I said, and you repeated, "Data is the new oil." Until recent past, we were driven by product centric approach. Today it's all about services and it's all about data. And that is a different paradigm that we need to integrate in the industry and in the oil and gas that I know better. To get the best benefit from it. It's a challenge but it's a fantastic and very passionate challenge to handle in the future. So that's why we have opened a center actually here, for example, in the Bay Area, to be close to the heart of what is happening in Data Science. >> Oh, fantastic, one of the things that you also said in your talk was that transformation through data analytics is equally as relevant on the operational side of a business as it is on the financial side. Expand upon that a little bit. >> Yeah, actually on the financial side, so the operational exploration prediction aspect I think it's more or less understandable. On the financial side it's a bit more hidden. But for too long our industry, I mean the oil and gas industry, have been substantially blind by not understanding how to best choose their commercial data in a holistic way. And now new startups, actually, have instilled some new way to think about that. Instill and develop new products based on machine learning combining machine learning, financial analysis. Et cetera, et cetera. Together to gain in accuracy, to gain in predictability, and a key factor is to... Get access to this information in a much faster time. And you know in our, in any industry, but in oil and gas industry time and precision cost a lot of money. >> Absolutely. What are some of the things that you would recommend to some of the young girls that are here, young women that are here, in terms of being able to influence an industry and elicit cultural change from an education perspective, is it just Data Science or what are some of the other skills and backgrounds do you think they need to be able to drive such change? >> Yeah, I think the conference was touching this point since this morning, and there is no clear answer obviously. There is no recipe, but for sure, I think many industrial today are still mirrored in the old ways. And they really need some fresh input, some fresh... Insight to really drive the culture right, the strategy right, that is necessary to move on the valuable and the successful transformation. And this fresh input, this fresh insight, I think can be completely an opportunity for woman to jump into this... This jobs or this, this aspect of the story. And with either the technical angle or the managerial angle I think it can be both right? And it's not exactly the same sort of skills that are behind. So skill wise, you know, let's be passionate. If you love the data, if you enjoy playing with the data, I think you will be perfect, doesn't matter if you are a man, a woman, I mean you are just a data scientist at the end. With skills and it's all about what you can bring and value to the company that you will work for. >> Lisa: Right. >> So go for it, I mean the Data Science world is an oyster, right? >> Absolutely. >> So go for it! >> Yes. >> I mean, really. It's a fantastic opportunity. >> It is, and some of the things that we heard today from the skills perspective is kind of opening it up or maybe broadening it a bit, absolutely the core Data Science skills are essential. The blend of hacker, statistician, mathematician, scientist, but also looking at some of the softer skills, creativity. Communication. >> Stephanie: Correct. >> And being able to understand enough of the business. >> Stephanie: Correct. >> To bring and really marry those two together. Have you seen that trend in kind of this ideal background coming up in the oil and gas industry? >> Yeah, of course, at the end of the day you've perfectly summarized all the skill set that a good data scientist needs to have. And this curiosity for the domain of application because Data Science either you can work for university then you can approach Data Science from an academic and fundamental thinking, but to be honest most of the time and most of the jobs are using Data Science for a purpose and for an application, so then you need to adapt yourself and be sure that you will have this curiosity, you need to adapt yourself to the knowledge world. And not the opposite, so this ability of adaptation, of curiosity, of passion for the type of problems or challenges, issues, that you will have to address through the Data Science world will be key, and it's really up to everybody to analyze if they want to go for it or not. >> I think that's a great point that you brought up, that adaptation. We have actually heard that a number of times today, that person needs to have the skills but also the adaptation, the flexibility. >> Stephanie: Correct. >> Along those lines, adaptation maybe, talk to us about what your current role is at Agyleo Sport. >> Yeah, with not real transition. (laughter) I moved, I quit Schlumberger a few months ago. My job, I loved my job, but I still live in France. It was difficult to be abroad so often. Anyway, I decided to change life but still I tried to stop working and I almost died. (laughter) So I decided to move forward to another challenge, really. And the new challenge is to combine and reconciliate my two passions, which are digital and sports. >> I love that, tell me more about that. >> So the idea is to raise a fund which would be the first independent fund in France, venture capital fund I mean. Addressing the sport and technology vertical. So domain, market, industry. You know sport, to make the link with what I express today, in fact sport is almost an industry like any other one. And the transformation of sport with integration of all this new tech have to be addressed and everything has to be done. So when you think how to revolutionize the way sport is handling either on the professional side or amateur side. You know, and the more I am digging into this new market for me, it's amazing. The opportunities are tremendous. And so we are pretty close to close our fund and to be, to get ready to invest in some passionating startups. Dynamic statups on this topic. I've just closed some partnership as well with, in LA, where sport tech is already booming. So it's going on and it's quite an exciting new, different, but, challenge that I am taking right now. >> It sounds so interesting. And wrapping things up, you bring up a great point that you've adapted but you've also been able to recognize the linkage between your favorite passion, sports, and technology and digital. And these days especially, we're a bit biased living in Silicon Valley where every company is a tech company, car companies et cetera. It's a really great message for the younger generation to understand, follow your passion. And there's technology there, and were going to need those diverse perspectives to help bring it to life and evolve it. >> Absolutely, so I think I realize that it's a luxury. At the point to have a choice to decide what you like to do in life, but it's also true that you have to address one in your early stage, early years, and giving you the maximum opportunities for the future is important. And then you can have this luxury, effectively to decide for your passion and to be driven by your passion. >> There's the Nirvana exactly. Well Stephanie thank you for those wise words of wisdom. Thanks so much for, >> Thank you very much. >> Stopping by theCUBE today, it's been a pleasure having you on. >> Me too, thank you. >> And we are going to be right back. We are live at the Women in Data Science Conference. Stick around, coming right back. (gentle electronic music)

>> Announcer: Live from Stanford University. It's theCUBE. Covering the Women in Data Science conference, 2017. >> Hi, welcome back to theCUBE, live from Stanford University at the second annual Women in Data Science tech conference. We are here with the COO of Products & Innovation at SAP, Sinead Kaiya. Sinead, welcome to theCUBE! >> Thanks very much! It's great to be here. >> It's great to have you. You were one of the keynote speakers today. >> Sinead: I was. >> Talk to us about your role at SAP and some of the topics that you discussed to the large audience here today. >> Yeah, absolutely. So one of the things I was happy to open my keynote with was letting them know that I'm actually not a data scientist. Because while I think it's important that that community gets together and shares their knowledge, I'm actually coming from the industry business angle. And for the young women who are here starting out in data science, I thought it's also very interesting and important for them to also hear the business perspective on data science. So that was my main contribution to the talk today. And I got a lot of great feedback, that they really appreciated getting that perspective. >> I can't imagine that you wouldn't, because data science is a boardroom conversation now. You report to the CEO. Talk to us about the connection that you help the CEO understand about the value that data science can bring to organizations like SAP. >> Right. It's actually funny. We have recently re-equipped some of our major boardrooms in SAP with huge digital touchscreens. They're absolutely phenomenal, and the reason is because the CEO truly understands, as do the board members, that the power of many of their decisions are lying today in the data. And what they don't want is a static printout on some slides or some chart that somebody hands to them. They want to be able to touch the data and explore the data, and really try to dig into it themselves. So when it comes to the question of the data, I think for CEO's this is a no-brainer. Right, they're drowning in data. They have a lot of data. They understand that. But the point of my talk today was more about the science. So I think where CEO's need to go next, is understanding that just having reams of data and being able to slice and dice it is not going to cut it anymore. You need the young women in these professions that bring the scientific discipline to that data, which is incredibly technical, around machine learning algorithms, to actually start to make sense of that data. So this is a switch for CEO's. The data is a no-brainer, but the science is a new thing that's starting to creep into the boardroom. And they're starting to learn that machine learning and these technologies are going to be very important in how they drive their businesses. >> What's the perception of that at SAP, and what are some of the things that are going on on the technology side to bring that data science in, to make sense of this data and extract value for SAP? >> So obviously SAP has a very strong portfolio of analytics products as well as our SAP HANA in-memory data platform, but where the power of it, is when we start co-innovating with our customers, because it all comes to life once it reaches the customer. So I gave a couple of examples in my keynote today, on how we're co-innovating with, for example, our customer Trenitalia. So Trenitalia is the largest provider of train service in Italy. They move about two million passengers a day. >> Wow. >> And about 80 million tons of freight a year. And they're collaborating with SAP to not only, how do you say, equip all their trains with sensors and be able to be getting that real-time data, how do they connect that with the IT data in their maintenance systems, so that when a train, let's say we know before it's going to break, before it does, and the machine already has triggered the maintenance technician, has already scheduled it, and everything happens in a very smooth and automated way. So it's once we go to the real problems that our customers are having, and we can apply our in-memory technology to their problems, that we get the real value. >> Right. That's such an interesting example. Like, intelligent train, digital train, how do those come together to enable them to meet their customers' objectives. >> Absolutely. Another interesting topic that I talked about was business without bias. So this is a new feature set that we're building into our HR systems. So SAP SuccessFactors has systems that people use for recruiting, and then taking you through the whole HR life cycle from promotions to talent management to compensation. But obviously, anybody who's been through these processes know that there's a certain element of human bias along the way. So, one of the things I talked about is how we're using machine learning to enhance our HR product, so we can try to at least identify some of the bias, if not start to remove it from the system. So... >> This is, sorry. We actually were speaking with someone on the show earlier today, who was looking at how to remove bias from the recruiting process, and creating technology for college campuses and students to be able to use. It's game-based technology, and I thought it was really interesting, because oftentimes recruiting, looking at GPA's, test scores, maybe some of those other hard factors, but now with data science and the ability to understand and add some of the behavioral insights in, really interesting applicability and how that can influence the next generation of people working for lots of different industries and companies, including SAP. >> And it's not just because it's technically interesting, or because it's the right thing to do. To take it from the CEO angle, CEO's today recognize that if they want to solve the big challenges that are on their plate, they not only need the best talent, they need the most diverse talent. But I can see from my experience, just because the CEO decides that diversity should be a corporate priority, and just because people say "yeah, we think that's a good idea," how do you actually codify that in the systems that your employees are using in the business? So the question of, do we need diversity in business, is no longer on the table. But it's rather, how do we actually start to implement that in a more systematic way, so that it's not just wishful thinking. It's actually something that's built in. >> Right. Talk to us about who your collaborators are within SAP, on things like that. Who do you work with, departmentally, function-group-wise, to help make that "yes, we understand, we need to do this" into actually real-world applicability? >> Well, one of the things I talk to, and some advice I gave the young women today, which is true for software in general, is they have to collaborate with the end user. So if you want to build in these bias checks into the HR system, do not sit alone in your laboratory. Do not sit in front of your computer and try to guess what you think is needed. Go out and shadow a recruiter for a week. Go and sit with the end user. Go and understand and truly see what their problems are, and then really involve them in the solution. So, I think that will also help when we talk about how do the young women here take all the academics and all of the, how do you say, theory that they're creating, and start to apply that in a real business context. If you haven't involved the end user, that's going to be quite hard to do. So one of the things I told them is, go to the user. >> That's great advice. I'm curious though, your perspective, coming from the business side, you know we look at data science, Forbes said it's going to be the best job to apply for in 2017. We're also seeing statistics that show, by 2018 there's going to be a shortage. The demand will be so high for data scientists that there will be a shortage. If we kind of look at the evolution of data science and where we are now, you look at the traditional skills. Stats, math, sciences, computing, maybe former hackers. Some of the things that we've heard today that I'd love to get your opinion on, being a businesswoman, is people are now saying, you know, it's the ability to be creative, to analyze and interpret, but also to communicate the information. Another thing that came up that I thought was really interesting was the factor of empathy when you're evaluating different types of data. I thought that was really interesting. I'd love to get your advice for a young woman who might be thinking about majoring in computer science, but maybe her interests really lie in sports or something that you think, is there a technology there? Well yeah. What advice would you give, and what are some of the additional core skills that you see a successful data scientist of the future needs to have? >> Right. So I love that you brought up the topic of communication, because I see in the business world, this is so important. So when you talk about competitive advantage, all of the companies can go out and hire people with, let's say, equivalent technical skills. So we can all get to the same level of technical prowess, let's say, in an industry. But do you have the people who, like you said, can apply the creativity and then find a way to communicate the results back in a superior way? So I think they are going to find that just having the technical skills in business is never enough to really break that ceiling. You have to have absolutely phenomenal communication skills. >> Definitely. >> I also gave them the advice to take a couple of business courses. It really helps to understand how the decision-makers, who you're trying to influence, what are the strategies that they use? What are the challenges that they face? And how do you actually look at some of the problems of data science more from a business perspective? I told them, what I thought is, absolutely the most hireable data scientist would be someone with some domain expertise, someone with the technical background, but somebody who also knows about business. So we need the full package. >> Absolutely! Well and that's an important point, because technology evolves. It's also the catalyst for our evolution, and naturally, any role will change and evolve. I think communication is a core, a very horizontal skill. But I definitely also would agree with your recommendations that having some business acumen in some form or fashion is really going to be key. Tell us a little bit about, what are some of the things, when somebody's coming on to SAP as a data scientist, if they maybe don't have that business background, are they able to get that within, because the culture at SAP kind of supports sort of, cross-collaboration, cross-pollination, so that they might be able to just start to learn different perspectives, to become that package that we talked about. >> Right. So in SAP, of course we have multiple opportunities for employees to either move between departments and see different areas of the company, but as a data scientist at SAP, the best experience you're going to have is working with our customers. It's one of our greatest assets and our greatest pride, is the wonderful relationship we have with hundreds of thousands of leading businesses around the world. So by joining SAP, you get to collaborate with some of the really top companies and industries. And that is when it doesn't become business theory in books. You actually get to go to the customer and see how it touches their business, and where it becomes real. And I think this is what attracts so many people to SAP, and gets them to really engage and stay at SAP, is that phenomenal customer base that we have. >> That's fantastic. Well, that real-world applicability, there isn't anything better than that. You can learn a lot of theory in textbooks, and maybe obviously be able to apply some of it, but having that expertise when something doesn't go the way that it's printed, is really really key to helping shape someone. Speaking of shaping, I'm interested in how you've been at SAP for quite some time, you've had posts in Germany and France, which is amazing. Now you're based in New York. Tell us how you've seen, because you really clearly understand the business side and you understand the importance of the business side and the data science side, the needs there and how they need to work together to drive more value, innovation, drive products, drive revenue. How have you seen SAP's culture evolve to become open to, for example, business and data science merging and being core collaborators? >> Yeah, so I mean, SAP's industry has changed a lot over the recent years. And we've done that along with our customers. So our customers are obviously in a much more tight competitive situation in the whole digitization side of things. So we've been evolving along together with them. But to go back to my other point, one of the major changes or cultural shifts that I've seen in SAP is this tight collaboration with the end user. It used to be that we were only given access to the IT departments of our customers. So we literally had to work through the filter of the IT department to find out what it is we should build. Suddenly, the IT departments are realizing that the end user in companies have quite a bit of power these days, you know. >> Lisa: Yes they do. >> And they're now opening the doors and asking us to collaborate with them, and that shift has allowed our engineers to get even closer to the end users in our customers. >> Fantastic, and I'm sure that's really a key for driving innovation. Last question for you. We're at the second annual WiDS conference. I mean, what an amazing event. Live streamed, reaching so many people. You yourself were a keynote this afternoon. Diane Greene was a keynote this morning. As you look around this very energetic atmosphere that we're in, what has inspired you? What are you going to take away from WiDS 2017 that you're like, wow, that was really fantastic? >> Well, one of the things is the diversity of the speakers. I mean, the breadth of this topic is amazing. Being a woman in tech, of course it's wonderful to see so many highly intelligent and engaged women in one room, which is something we don't usually get to see. So that's one of the other key takeaways for me. >> Fantastic. Well Sinead, we so appreciate you stopping by theCUBE. We wish you continued success as COO of Products & Innovation, and we look forward to seeing you next time on the program. >> Thanks so much! >> And we want to thank you for watching theCUBE. We are live at the second annual Women in Data Science conference, #WiDS2017, but stick around. We'll be right back.

>> Commentator: Live from Stanford University it's theCUBE covering the Women in Data Science Conference 2017. (jazzy music) >> Hi, welcome back to theCUBE. I'm Lisa Martin live at Stanford University at the second annual Women in Data Science WiDS tech conference. We are here with Ann Rosenberg from SAP. She's the VP head of Global SAP Alliances and SAP Next-Gen. Ann, welcome to the program. >> Thank you so much. >> So SAP is a sponsor of WiDS. Talk to us a little bit about that, and why is it so important for SAP to be involved in this great womens organization. >> So first of all, in my role as working with SAP's relationship to academia and also building up innovation network we see that data science is a very, very key skill set, and we also would like to see many more women get involved into this. Actually (mumbling) right now as we speak we are at the same time in 20 different countries around the world, 24 events we have. So we are both in Berlin, we are in New York, we are all over the world. So it's very important. I call it kind of a movement what we are doing here. It's important that all over the world that we inspire women to go into data science and into tech in general. So it is important thing for SAP. First of all, we need a lot of data science interested people. You also need our entire SAP ecosystem to go out to universities and be able to recruit a data science student both from a diversity perspective, whatever you are a female or a man of course. >> Absolutely, you're right. This is a very inspiring event. It's something that you can really actually feel. You're hearing a lot of applause from the speakers. When you're looking enabling even SAP people to go out and educate and recruit data scientists, what are some of the key skills that you're looking for as the next generation of data scientists? >> This is an interesting thing because you can say that you need like a very strong technical skill set, but we see more and more, and I saw that after I moved to Silicon Valley for two years that also the whole thing about design thinking, the combination of design thinking and data science is becoming something which is extremely important, but also the whole topic about empathy and also, so when you build solution you need to have this whole purpose driven in mindset. So I think what we're seeing more and more is that it's great to be a great data science, but it takes more than that. And that's what I see Stanford and Berkeley are doing a lot, that they're kind of mixing up kind of like the classes. And so you can be a strong data science, but at the same time you also have the whole design thinking background. That's some of the things that we look for at SAP. >> And that's great. We're hearing more and more of that, other skills, critical thinking, being able to not only analyze and interpret the information, but apply it and explain it in a way that really reflects the value. So I know that you have a career, you've been in industry, but you've also been a lecturer. Is this career that you're doing now, this job in alliances and next-gen for SAP sort of a match made in heaven in terms of your background? >> I actually love that question, probably the best question I ever got because it is definitely my dream job. When I was teaching in Copenhagen for some years ago I saw the mind of young people. I saw the thesis, the best of master thesis. I saw what they were able to do, and I'm an old management consultant, and I kept on thinking that the quality of work, the quality of ideas and ideations that the students come with were something that the industry could benefit so much from. So I always wanted to do this matchmaking between the industries and the mind of young people. And it's actually right now I see that it's started kind of, what I at least saw for the last two years that the industries that go to academia, go to universities to educate or to students to work on new ideas. And of course in Silicon Valley this has been going on for some time now, but we see all over the world. And the network that I'm responsible for at SAP, we work in more than 106 countries around the world, with 3,100 universities. And what I really want to do now, I call it the Silicon Valleys of the world where you are mapping the industries with academia with the accelerators and start ups. It's just an incredible innovation network, and this is what I see is just so much growing right now. So it's a great opportunity for academia, but equally also for the industry. >> I love that. Something that caught my eye, I was doing some research, and April 2016 SAP announced a collaboration with the White House's Computer Science for All Initiative. Tell us about that. >> I mean the whole DNA of SAP is in education. And therefore we do support a number of entity around the world. Whatever we talk about building up a skill set within data science, building skill set in design thinking, or in any kind of development skills is really, really important for us. So we do a lot of work together with the governments around the world. Whatever you talk about the host communication, for example, we have programs called Young Thinkers, Beatick, where you go out to high schools or you go into academia, to universities. So when this institute came up, we of course went in and said we want to support this. So if I look at United States, so we have a huge amount of universities part of the network that I'm driving with my team. So we have data curriculums, education material, we have train to train our faculties, boot camps. We do hackathons, coach games. We do around 1,200 to 1,600 hackathon coach games per year around the world. We engage with the industries out to the universities. So therefore it was a perfect match for us to kind of support this institute. >> Fantastic. Are there any things that SAP does as we look at the conference where we are, this Women in Data Science, are there things that you're doing specifically to help SAP, maybe even universities bring in more females into the programs, whether it's a university program or into SAP? >> Yeah, so for SAP in our whole recruiting process we definitely are looking into that. There is a great mix between female and male people who get hired into the company, but we also, it all start with that you actually inspire young women to go into a data science education or into a development education. So my team, we actually go in before SAP recruiting get involved where we, that's why we build up the strong relationships with universities where we inspire young women, like we do at this event here to why should they go in and have a career like this. So therefore you can see there's a lot of pre=work we need to be done for us to be able to go in and go into the recruiting process afterwards. So SAP do a lot of course in the United States, but all over the world to inspire young women to go into tech. And SAP does what we see today all over the world we have huge amount of female from SAP, female speakers at all our events who stand as role models to show that they are women, they are working for SAP, and are very, very strong female speakers and are female role models for all young women to get involved. So we do a lot of stuff to show that to the next generation of data science of whatever it is in tech. >> Yeah, and I can imagine that that's quite symbiotic. It's probably a really nice thing for that female speaker to be able to have the opportunity to share what she's doing, what she's working on, but also probably nice for her to have the opportunity to be a mentor and to help influence someone else's career. So you mentioned accelerators a minute ago, and I wanted to understand a little bit more about SAP Next-Gen Consulting, this collaboration of SAP with accelerators or start ups. How are you partnering to help accelerate innovation, and who is geared towards? Is it geared more towards student? Or is SAP also helping current business leaders to evolve and really drive digital transformation within their companies? >> So the big (mumbling) I'm working on right now too is as mentioned you said SAP Next-Gen is called SAP Next-Gen Innovation With Purpose. So it's linked to the 17 U.N. global goals. We've seen from now in Silicon Valley when you innovate you actually make innovation web purposes included. And that's why we kind of agreed on in SAP why don't we make an innovation network where the main focus is that all the innovation we get out of this is purpose driven linked to the 17 global goals. Like the event here is the goal number five, gender equality. In that network we actually do the matchmaking between academia. We look at all the disrupted new technologies, experience the technologies like machine learning like what's being discussed a lot here, block chain IOT. And then we look at the industry out there because the industries, they need all the new ideas and how to work with all the new opportunities that technology can provide, but then we also look into accelerator start ups. The huge amount, and often when you're in Silicon Valley you kind of think this is the world of the start ups of the world. So when you travel around the world, that's we we looked into a lot the last two years. We call the Silicon Valleys of the world, any big city around the world, or even smaller cities, they have tech hub. So you have Ferline Valley, you have Silicon Roundabout in London, you have Silicon Alley in New York, and that is where there is a huge amount of gravity of start ups and accelerators. And when you begin to link them together with the university network of the world and together with the industry network of the world, you suddenly realize that there is an incredible activity of creativity and ideations and start ups, and you can begin to group that into industries. And that give industries the opportunity not only to develop solution inside the company, but kind of like go in and tap into that incredible innovation network. So we work a lot with seeding in start up, early start ups into corporates, and also crowd source out to academia and the mind of young people all Next-Gen Consulting project where you similar work with students at universities on projects. It could be big data science project. It could be new applications. So I see like as the next generation type of consultancy and research what is happening in that whole network. But that is really what SAP Next-Gen is, but it is linked to the 17 U.N. global goals. It is innovation with purpose, which I'm really happy to see because I think when you build innovation, you really think about in the bigger, the whole (mumbling) thing that we know from singularity. You should think about a bigger purpose of what you're doing. >> Right, right. It sounds like though that this Next-Gen Consulting is built on a foundation of collaboration and sharing. >> It is, it is, and we have three Next-Gen lab types we set up. In this year we built, last year, we are a new year now, we built 20 Next-Gen labs at university campuses and at SAP locations. And here in the new year more labs is being set up. We are opening up a big lab in New York. We just recently opened up one in Valdov at SAP's headquarter. We have one here in Silicon Valley, and then we have a number of universities around the world where SAP's customers go in and work with academia, with educators and students because what do you do today if you're in industry? You need to find students who are strong in machine learning and all the new technologies, right? So there's a huge need for in industry now to engage with academia, an incredible opportunity for both sides. >> Right, and one last question. Who are you, in the spirit of collaboration, who do you collaborate back with at SAP corporate? Who are all the beneficiaries or the influencers of Next-Gen Consulting? >> So I collaborate, inside SAP I collaborate, SAP have a number of, we have ICN, Innovation Center Network. We have our start up focus program. We have a number of innovation, the labs, a number of basically do all our software developments, so they're heavily involved. We have our whole go to market organization with all our SAP customers and industry, I call them clubs. And then externally is of course academia, universities, and then it is the start up communities, accelerators and of course, the industry. So it is really like a matchmaking. That's like, when people ask me what do you do, and I'm a matchmaker. That's really what I am. (Lisa laughs) >> I like that, a matchmaker of technology and people all over. So you're on the planning committee for WiDS. Wrapping things up here, what does this event mean to you in terms of what you've heard today? And what are you excited about for next year's event? >> So for me, one year ago when I heard about this year I kind of said this is important, this is very important. And it's not just an event, it's a movement. And so that was where I went in and said you know, we want to be part of this, but it must be more than just an event here. It's staying for the need to be much more than that. And this is where we all teamed up, all the sponsors together with ISMIE, and we said okay, let us crowd source it out, let us live stream it out much more than ever. And this is also what the assignment is now, that we to so many locations. This is just the beginning. Next year is going to be even bigger, and it's not like that we will wait to next year. We this week announced the SAP Next-Gen global challenges linked to the 17 U.N. global goals. So we are inspiring everybody to go in and work on those global challenges, and one of them is goal number five, which is linked to this event here. So for us and for me this is just the beginning, and next year is going to be even bigger. But we are going to do so many event and activity up to next year. My team in APJ, because of the Chinese New Year, have already been planned coming up here. >> Lisa: Fantastic. >> And we have been doing pre-event, (mumbling) events. So again, it is a movement, and it's going to be big. That's for sure. >> I completely can feel that within you. And you're going to be driving this momentum to make the movement even louder, ever more visible next year. >> Ann: Yeah. >> Well Ann, thank you so much for joining us on The Cube. We're happy to have you. >> Thank you so much for the opportunity. >> And we thank you for watching The Cube. I am Lisa Martin. We are live at Stanford University at the second annual Women in Data Science Conference. Stick around, we'll be right back. (jazzy music)

>> Announcer: Live from Stanford University, it's the Cube, covering the Women in Data Science Conference 2017. (electronic music) >> Hi, and welcome back to the Cube. I'm Lisa Martin live at the Women in Data Science Conference, second annual, here at Stanford University, #WiDS2017. Fortunate to be joined next by Miriah Meyer, who is an Assistant Professor at the University of Utah in the School of Computing. Miriah, welcome to the Cube. >> Thank you. >> It's great to have you here. You're a speaker at this event this year. >> Yes. >> Tell us a little bit about how you got involved in WiDS and what excites you about being able to speak to this very passionate, invigorating audience? >> Yeah, so I got an invitation from one of the organizers, seems like quite some time ago, and when I looked into the conference, it just looked fantastic. I was so impressed with the speakers they had last year and the speakers for this year. It's a really amazing powerhouse of a community here. The fact that it's a great technical conference that, oh, just happens to be all women, it was pretty awesome, I was pretty flattered to get invited. Then the sort of, the energy in there is really awesome. It is different, it feels different than other technical conferences I go to. >> I completely agree. I love that you talked about just the community, because that's really what it is, and I think some of the, just the vibe that you can feel sitting here is one of excitement, it's one of passion of women who have been in this industry for a very long time in computer science, and then those young girls who are looking for inspiration. I think it's very symbiotic, right? They're learning from you, but I think you're probably also learning from them. >> Definitely. I find that every time I present my work to another group of people, a different community, I always have to come up against what my own assumptions are about how easy or not it is to understand the kind of work I do. I personally find it just so important to communicate clearly, it's probably partly why I do the work that I do. But I learn a lot every time I give a talk at a place like this. >> Wow, outstanding. Well, speaking of your talk, your research is in visualization systems. Share with us what you shared with the audience today, goals, outcomes, current outcomes of your visualization research. >> Mm-hmm. My research passion is around helping people make sense of complex data. I've particularly done a lot of work with scientists, particularly that in biology, where there's just been this amazing explosion of data and people are just trying to wrap their heads around what they have and what kinds of amazing discoveries they're sitting on. But it's really interesting, we've gotten so good at creating data, but then, that's wonderful, but if you can't make sense of it, who cares? >> Lisa: Right. >> I have this incredibly privileged position where I get to go and work with people who are at the cutting edge of their field and learn about this amazing work that they've been spending their lifetime on. Then I help them, I design tools with them that sometimes changes even the way that they're thinking about the problem. It's incredibly satisfying and it's very much in the spirit of team science and it's a lot of fun. I was talking about just some of the basics behind how do you create effective visualizations, which, for me, it also draws heavily on the notion of how do we collaborate effectively, how do you get at people's deep needs when it comes to making sense of data, when they often times can't articulate it themselves. I refer to it as data counseling, because it feels very much like, I talk with people who have problems but they can't articulate it, so I ask them lots of questions to help them uncover the root of their problems. >> Lisa: Right. >> That's basically what I do. >> That data counseling. That's fantastic. >> Yeah, and then you use what you discover in order to design tools. >> Share with us a little bit about the courses that you teach in Computer Science at the University of Utah. >> Yeah, so I teach a graduate level visualization course. It is just about the basic foundational principles we have behind perception and cognition and what that means for how we encode information, and then also, the process of how do you evaluate visualizations effectively. It's a really wonderful course where we have people from, actually, all across campus, so a lot of people are bringing problems that they have in other fields and trying to learn how to be more effective in their own exploration with visualizations. Then at the undergrad level, I actually teach our second semester programming course, so these things are worlds apart. This is one of our large 200 person introduction to data structures and algorithms. >> OK. What are some of the things that are inspiring? We'll talk about your graduate students for a moment. What are some of the things that you find are inspiring them to want to understand data in this way? Is it because they were kids that grew up in STEM programs, or they just had a computer since the time they were little, or are there other factors that you're finding that are really drivers of them wanting this type of education? >> So the students that I work with directly, I think, kind of fall into two camps. One camp is, they're a sort of non-traditional computer scientist, where they enjoy the engineering, they enjoy the programming, but they also really enjoy people and are passionate about making a difference. They also really enjoy the interaction that we have to go through in trying to understand what someone needs. There's also a design component, it's really fun to get to create things that feel good and look good. That's definitely one class, so it's the sort of non-traditional computer scientist. The other class, I have a couple of students who come from a science background, who love science, but find that they like building things more than they like doing the science itself, and visualization is kind of a wonderful place in the middle where you can be part of science but doing the making and building that we do in computer science, as opposed to doing the sort of experimentation and studying that you do as a scientist. That was definitely, for myself, I have a background in science and that's what really drew me, when I discovered computer science and visualization itself. >> What are some of the traditional skills that a good educated computer scientist needed maybe five years ago, and how are you seeing that change? Are there new behavioral traits or skills that really are going to be essential for these people going forward? >> Yeah, I think especially in the space of data science and remembering that at the end of the pipeline there's a person sitting there either bringing their knowledge to bear or that you're trying to tell a story to you from data. I think one trait is the idea of having empathy and being able to connect with people, and to just understand that as technologists, we're, not all of us, but largely creating technology for people. That's something that I think has traditionally been undervalued and perhaps a little bit filtered out by perceptions of what a computer scientist is. But as technology is becoming more ubiquitous and people are understanding the impact that they could have, I think it is bringing in a different group of people that have different motivations for coming to the field. >> What are some of the, as your graduate students finish their education and go on to different industries, what are some of the industries that you're seeing that they're using their skills in? >> Yeah, so a lot of it is getting hired in companies that, their core product that they develop isn't necessarily a piece of technology. But they're using data now to really understand their business needs and things like that. I have a student right now who's actually at a government organization in DC, working with some amazing global health specialists. But these are midwives and social workers and they don't have the deep skills in data analysis. So there's opportunities for people in visualization and data science to go and really make an impact in a whole variety of interesting fields. That's actually one of the things that I always love to tell undergrads who come to talk to me about, "Oh, should I do computer science?" The thing I love most about it is that, whatever your passion is in life, whether it's medicine or whether it's music, or whether it's skiing, there is a technology problem there. If you have those skill sets, you can go and apply it to anything that you care deeply about. >> I couldn't agree more. That's such an important message to get out. I mean, every company, we're sitting here in Silicon Valley, where car companies are technology companies, every company these days, Walmart is a technology company. I think that's an important message for those kids to understand, following their passion. I don't think that that can be repeated enough, because you're right, whatever it is, there's a technology component to that. With that tip, let me ask you, what were some of your passions when you were younger in school? You mentioned your science degrees. But what were some of the things that really helped or maybe people shape your career and where you are today? >> Yeah, growing up, I was, my dad's a scientist, my mother's an artist, so there's definitely, both of those. >> Lisa: Art and science, so yes. >> Yeah, influences of both, and I really wanted to be an astronaut, but it turns out I get really motion sick. >> Oh, that's a bummer. >> So I had to give up that dream. I studied science, but at the same time, my mom always had me creating and doing things with her in her studio. I think I found this love of just being able to make something and how satisfying that is. I think that was influential. Then also, when I was in college, I was an astronomy major, and I had the opportunity to take lots of electives, which, in hindsight, I think was really important, because it let me explore many things. I found myself taking a lot of women's studies classes. What was interesting about that is just the way that you think and problem solve in a discipline like that where it's all critical analysis. That, sort of coupled with the deep analytics that I was, skills I was learning in physics, made for this just really interesting, I think, multiple, gave me perspectives to look at problems in multiple different ways. I think that that's been really important for being able to bring that suite of perspectives to how we solve problems. It's not all just quantitative, and it's just all qualitative. But it's really a nice mixture of both, if it gets us to good places. >> Absolutely. I think that zigzag career path that you're sounding like you're talking about, I know I had one as well, gives you perspectives that you wouldn't have even thought to seek, had you not been on these trails. >> Mm-hmm. >> I think that's great advice that people that are, whether they're in your classes or they're being able to listen to you here, should be able to know that it's OK to try things. >> Yes. Yes, exactly. I think back to the person I was when I was, say, 18. I didn't know. I think the one sort of constant in my career trajectory has been just, wow, this thing looks really interesting, I don't know where it's going to go, but I'm going to follow that path. Inevitably, if it's something that catches your attention, there's going to be something interesting that can come out of it. I think sort of letting go of this need to have everything defined from day one and instead following your passions is, that's the theme I've heard over and over again from the speakers in here, too. >> Absolutely. Don't be afraid to fail is one of the themes that has come out from this morning. Diane Greene, SVP of Google Cloud, who was in morning keynote, had even said, "Don't be afraid to get fired." I mean, could you imagine your parents saying that to you? >> Yeah. >> I couldn't, but it's also something that just shows you that there is tremendous opportunity in many different disciplines and domains for this type. >> By the way, if you have a technical computer science background, you can always find another job. (laughter) >> That is true. What is next on your plate in terms of research, what are you looking forward to the rest of 2017? >> Wow. >> Lisa: Sorry, was that too big of a question? >> Yeah. We have a couple of really interesting problems around color, around some new tools for helping designers and journalists work with data. I think also, I'm starting to think about trying to focus more on K through 12 education and trying to understand what some of the roadblocks are to getting computer science to a younger community of people. In Utah, we have a lot of rural populations. We also have Native American reservations. I think there's some really interesting challenges with getting computer science into those communities. I'm sort of thinking about working with some folks to try to understand more about that. >> That's fantastic. I mean, you bring up a good point, that kind of depending, then, where you are, here we are sitting at Stanford University, one of the pre-eminent universities in the world, and there's a tremendous amount of technology and resources available. But then you look at, really, the needs of communities in Utah, and they need people like you to help, go, "You know what, we have challenges here, and we need to solve that." Because that's part of the next generation of the people that are here speaking at these types of events. >> Miriah: Right. >> Absolutely critical problem. Well, Miriah, thank you so much for being on the Cube. >> Thank you for the opportunity. >> It's been a pleasure, we wish you the best of luck with your big plans for 2017. >> Thanks. >> Lisa: Hopefully, we'll see you next time. >> Great. >> We thank you for watching the Cube again, Lisa Martin, live at Stanford University at the Women in Data Science Second Annual Conference. Stick around, we've got more, we'll be right back. (electronic music)

(upbeat music) >> Voiceover: Live from Stanford University. It's the Cube covering the Women in Data Science Conference, 2017. >> Hi, welcome back to the Cube. I'm Lisa Martin and we are at the second annual Women in Data Science Conference at Stanford University. Such an inspiring day that we've had so far and right now we're joined by Megan Price, the executive director of the human rights data analysis group. Megan, welcome to the Cube. >> Thank you. >> It's so exciting to have you here. Megan, you're background is statistics. You have a PhD as a statistician. The Human Rights Data and Analysis Group, HRDAG, is focused on statistical analysis of mass violence. Talk to us about sort of the merger of your bio statistician or your statistician background with human rights. Was that something that you were always interested in? >> Sure. It was and I have to say I was really lucky. I got my Bachelor's and my Master's in statistics from a very technical engineering school in Ohio, where honestly, a lot of people would sort of, pat me on the head and say, "That's nice, that you're interested in human rights. You'll outgrow that." And fortunately I had one very thoughtful mentor, who said to me, "You know, I really think Public Health school is the direction you should go in", and so I got my PhD in biostatistics from Public Health school and it was really there that I was exposed to people who kind of said, "Yeah, social justice, human rights, do that as a day job. Get on it.", and so that was really great that I was exposed to that as something I can move into as a career. >> Exposed to them, but also you had the confidence. You obviously had a mentor that was very influential, but that takes some courage and some guts to go, you know what, yeah, this is needed. >> It's true, yeah. (laughs) >> So talk to us about some of the ... The HRDAG, we talked about it a little bit before we went live. The evolution. Show to our viewers, how it's evolved to what it is today. >> Sure. So the organization, the name and work started with work that my colleague, Dr. Patrick Ball started doing in El Salvador and in Guatemala in the 90s. And at the time, he was working ... He's formed a team to do the work at the American Association for the Advancement of Science. And so that was about 25 years ago. And then the work evolved and the team just kept kind of moving to where the right home was to get that work done and so in nearly 2000s, they moved out here to Paul Walter just up the street to Benetech, another technical non-profit. And they provided us a really nice home for our work for nine years. And then in 2013, the time had really come to be the right time for Patrick and I to spin out HRDAG as it's own non-profit organization. We're fiscally sponsored right now, but we're our own institution, which we're really excited about. >> So you mentioned some of the projects that Patrick was working on. What are some of the things that were really compelling to you, specifically within human rights, that really are catalysts for the work that you're doing today? >> Sure. I think that there are a lot of quantitative questions that get raised in looking at these questions about widespread patterns of violence, and asking questions about accountability and responsibility for violence. And to answer those questions, you have to look at statistical patterns, and so you need to bring a deep understanding of the data that are available and the appropriate way to analyze and answer those questions. >> How do you from an accuracy perspective, I understand that that's incredibly vital, especially where these important issues are concerned, how does HRDAG eliminate, mitigate inaccuracy issues with respect to data? >> Yeah, well we're always thinking about each of our projects as taking place in an adversarial environment, because we ultimately assume that at the end of the day our results are going to be either subjected to the kind of deep scrutiny that comes along with any kind of socially and politically sensitive topic, or with the kind of scrutiny that happens in a court room. And so that's really what motivates the level of rigor that we require in our work. And we maintain that by maintaining our relationship with mostly academicians, who are really pushing these methods forward and staying on top of what is the most cutting edge approach to this problem and how can we really know that we're being as transparent as possible in the way this data were collected, the way they were analyzed, the way they were processed and the limitations of those analysis. You know, the uncertainty present in any estimates that we put out. >> Give us an example of some of the type data sources that you're evaluating, say for the conflict in Syria. >> So in the case of Syria, we have relationships with four organizations that are all collecting information about victims who've been killed in the ongoing conflict in Syria. Those groups are the Syrian Center for Statistics and Research, Syrian Network for Human Rights, the Damascus Center for Human Rights Studies, and the Violations Documentation Center. And those are all citizen led, by groups that are maintaining networks collecting that information to the best of their ability. And they share with us, largely Excel spreadsheets that contain names of victims and any other information they were able to collect about those victims. >> You mentioned University collaboration a minute ago. From a methodology standpoint. Give me an insight into ... You're getting data from these various sources, largely Excel, where we know with Excel comes humans, comes sometimes, "Oops". How are you working with universities to help evaluate the data or what are some of the methodologies that they're recommending, given the data sources and the tools that you have? >> So there's really two stages that the data go through and the first one is within the groups themselves, who do that first layer of verification, and that is the human verification prior to, kind of all the risks of data entry problems. And so they're doing the on the ground, making sure that they've collected and confirmed that information, but then you're absolutely right, we get this data that's been hand entered and with all of the risks and potential down sides of hand entered data and so primarily what we do is fairly conventional data processing and data cleaning to just check for things like outliers, contradictory information. We'll do that using Python and using R. And then our friends and colleagues in academia, where they're really helping us out is, because there are these multiple sources collecting names of individual victims, what we have is a record linkage problem. And so we have multiple records that refer to the same individual. >> Okay. >> And so we work a lot with our academic partners to stay on top of the latest ways to de-duplicate databases, that might have multiple entries that refer to the same person. And so that's been really great lately. >> Okay. What are some of the methods that you've used in Syria to quantify mass violence and what have some of the outcomes been to date? >> So we rely primarily on methods from record linkage and that gets us to what we know and can observe. And then from there we need to build an estimate, what we don't know and what we can't observe, because inevitably in conflict violence, some of that violence is hidden. Some of those victims have not been identified or their stories have not been told yet. And it's our job as data scientists to use the tools at our disposal to estimate how much we don't know. And so for that step we use a class of statistical tools, called multiple systems estimation. And essentially what that does, is it builds on the patterns of data as they're collected by these multiple sources to model what the underlying population must have been. To generate what we were able to see. >> Okay. >> And so that's been the primary analysis we've done in Syria. And what we found from that analysis, is that as valuable and important as the documented data are, they often are overwhelmed, for example when violence peaks. It may be too dangerous and it may be impossible to accurately record how many people have been killed. >> Okay. >> And so we need a statistical model that can help us identify when data we observe seem to plateau, but perhaps our estimates tell us no, in fact that was a very violent period. And then we can dig in with field experts and interpret, was that a time when we know that territorial control was in contention. Or was that a time when we know, that there were clashes between certain groups. And so then we can infer further from that about responsibility for violence. >> So applying some additional attributers. Things that are attributing to this. What are some of the differences that you think that this has made so far? >> What I hope this has done so far, is simply to raise awareness about the scale of the violence that's happening in Syria. And what I hope ultimately, is that it helps to attribute accountability to those who are responsible for this violence. >> You've also got some projects going on in Guatemala. Can you share a little bit about that? >> We do. Yeah, we have a couple of projects in Guatemala. The one that I've worked on most closely, is looking at the historic archive of the national police in Guatemala. And that's actually the project that I started working on when I joined HRDAG. And Guatemala suffered an armed internal conflict from 1960 to 1996. And during that time period, many witnesses came forward and said that the national police force participated in the violence, but at the time that the UN, the United Nations broke our peace treaties, they weren't able to find any documentary evidence of the role the police played. And then in 2005, quite by accident, this archive, that's this cache of the police forces bureaucratic documents was discovered. And so we've been studying it since then. And it's been this really fascinating problem, if you have this building full of millions and millions and millions of pieces of paper, that are not really organized in any way. And how do you go about studying that? And so we partnered with other experts from the American Statistical Association, to design a random sample of the archive, so that we could learn about it as quickly as possible. >> What are some of the learnings that you've discovered so far? >> What we've discovered so far is just the sheer magnitude of the archive and in particular the amount of documents that were generated during the conflict. And then the other thing that we have discovered is the communication flow. The pattern of documents being sent to and from leadership the National Police Force. And specifically, Patrick Ball testified about that communication flow, to help establish command responsibility for the former chief of police, for a kidnapping that occurred in 1984. >> Wow, incredibly impactful work. But you've got some things on the domestic frontier. With us a little bit about what you're working on stateside. >> We do, yeah. In the past year, we've started our first US based project, which we're really excited about. And it's looking at the algorithms that are being used both in predictive policing and in criminal justice risk assessment. So decisions like whether or not someone should get bail or pre trial hearings, things like that. And we've been working with partners, primarily lawyers, to help assess, sort of, how are those algorithms working and what's the underlying data that's being fed into those algorithms. And what's the ways in which that data are biased. And so the algorithms are replicating the bias that exists in the data. >> Tell me, how does that conversation go, as a statistician with a lawyer, who is, you know, a business person. What sort of educating do you need to do to them about the impact that this data can make and how imperative it is that it'd be accurate. >> Yeah, well those conversations are really interesting, because there's so much education going in both directions. Where both we are helping them to turn their substantive question into an analytical question and sort of develop it in a way that we can do an analysis to get at that question, but then they're also helping us to understand, what's the way in which this information needs to be conveyed, so that it holds up in court, and so that it establish some sort of precedence, so that they can make policy change. >> It makes me think of, sort of the topic or the skill of communication. A number of our guests this morning on the program and those that we've heard speaking today, talk about the traditional data scientist skills. You know hybrid, hacker, someone that has statistics, mathematical skills, but now really looking at somebody who also has to have other behavioral skills. Be able to be creative, interpretive, but also to communicate it. I'd love to get your perspective as you've seen data science evolve in your own career. How have you maybe trained your team on the importance of communicating this information, so that it has a value and it has impact? >> Absolutely. I think creativity and communication are probably the two most important skills for a data scientist to have these days and that's definitely something that on our team, you know, it's always a painful process, but every time we give a talk, if we're fortunate enough that it's been videoed, we always have to go back and watch that. And I recommend to my teammates to do it quietly at home alone, maybe with their preferred beverage of choice, but that's the way that you learn and you discover, oh I could have said that differently or I could have said that another way, or I could have thought about a different way to present that, because I do think that that's absolutely vital. >> I'm just curious what you're perspective is from a curriculum standpoint, we've got a lot of students here, we've got some professors here. Is there something that you would recommend as part of ... Look back to your education. Would you think, you know what, being able to understand statistics is one thing, I need to be able to communicate it. Was that something that was part of your curriculum or something that you think, you know what, that's a vital component of this? >> It's absolutely a vital component. It was not part of my formal curriculum, but it was something that I got out of graduate school, because I was very lucky that I got to teach, essentially statistics 101 to introductory Public Health students. So they were graduate students, but there were a lot of students who maybe hadn't had a math class in a decade and were fairly math phobic. >> Lisa: Sounds like me. (both laughing) >> We could, you know, hold hands and get through it together. >> Okay, oh good. Beverage of my choice, awesome. (laughs) >> Exactly. And I really feel like that was what improved my communication skills, was experience with those students and thinking about how to convey the information to that class and going in day after day and designing that curriculum and really thinking about how to teach that class, is really the way that I have learned my communication skills. >> Oh that's fab. That real world experience, there's nothing that beats that. What are some of the things that have excited you about participating in (mumbles) this year? >> Oh my gosh, it is so much fun to be in an audience and to speak to an audience, that is so predominantly female. I mean of course, that's not something that we get to do very often. And so young, I mean this audience is really full of very energetic, ready to go tackle the world's problems women and it's very invigorating for me. It helps me to kind of go back and think, alright how can we do more and do bigger and create more opportunities for these folks to fill? >> It's a very symbiotic relationship, I think. They learn so much from you and you're learning so much from them. It's really nice. You can feel it. Right, you can feel it here in this environment. >> Absolutely. >> Well, Megan, thank you so much for joining us on the program today. We wish you the best of luck with HRDAG and your impending new little girl. >> Thank you. (laughs) I appreciate that. >> Absolutely. Well we thank you for watching the Cube. Again, we're live at the Women and Data Science Conference at Stanford University, second annual event. Stick around, we'll be right back. (upbeat music)

>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. (upbeat music) >> Hi and welcome back to theCUBE, I'm Lisa Martin and we are at Stanford University for the second annual Women in Data Science Conference. Fantastic event with leaders from all different industries. Next we're joined by Finale Doshi-Velez. You are the Associate Professor of Computer Science at Harvard University. Welcome to the program. >> Excited to be here. >> You're a technical speaker so give us a little bit of insight as to what some of the attendees, those that are attending live and those that are watching the livestream across 75 locations. What are some of the key highlights from your talk that they're going to learn? >> So my main area is working on machine learning for healthcare applications and what I really want people to take away from my talk is all the needs and opportunities there are for data science to benefit patients in very very tangible ways. There's so much power that you can use with data science these days and I think we should be applying it to problems that really matter, like healthcare. >> Absolutely, absolutely. So talking about healthcare you kind of see the intersection, that's your big focus, is the intersection of machine learning and healthcare. What does that intersection look like from a real world applicability perspective? What are some of the big challenges? And can you talk about maybe specific diseases that you're maybe working on-- >> Sure, absolutely. So I'll tell you about two examples. One example that we're working on is with autism spectrum disorder. And as the name suggests, it's a really broad spectrum. And so things that might work well for one sort of child might not work for a different sort of child. And we're using big data and machine learning to figure out what are the natural categories here and once we can divide this disease into subgroups, we can maybe do better treatment, better prognosis for these children, rather than lumping them into this big bucket-- >> Lisa: And treating everybody the same? >> Exactly. >> Lisa: Right. >> And another area we're working on is personalizing treatment selection for patients with HIV and with depression. And again, in these cases, there's a lot of heterogeneity in how people respond to the diseases. >> Lisa: Right. >> And with the large data sets that we now have available, we actually have huge opportunities in getting the right treatments to the right people. >> That's fantastic, so exciting. And it's really leveraging data as a change agent to really improve the lives of patients. >> Finale: Absolutely. >> From a human interaction perspective, we hear that machine learning is going to replace jobs. It's really kind of a known fact. But human insight is still quite important. Can you share with us-- >> Finale: Absolutely. >> where the machines and the humans come into play to help some of these dis-- >> Yes, so a big area that we work on is actually in formalizing notions of interpretability because in the healthcare setting, the data that I use is really really poor quality. There's lots of it. It's collected in a standard of care everyday but it's biased, it's messy. And you really need the clinician to be able to vet the suggestions that the agent is making. Because there might be some bias, some confounder, some reason why the suggestions actually don't make sense at all. And so a big area that we're looking at is how do you make these algorithms interpretable to domain experts such as clinicians, but not data experts. And so this is a really important area. And I don't see that clinician being replaced anytime soon in this process. But what we're allowing them to do is look at things that they couldn't look at before. They're not able to look at the entire patient's record. They certainly can't look at all the patient records for the entire hospital system when making recommendations. But they're still going to be necessary because you also need to talk to the patient and figure out what are their needs, do they care about a drug, that might cause weight gain for example, when treating depression. And all of these sorts of things. Those are not factors again that the machine are going to be able to take over. >> Lisa: Right. >> But it's really an ecosystem where you need both of these agents to get the best care possible. >> Got it, that's interesting. From an experimentation perspective, are you running these different experiments simultaneously, how do you focus your priorities, on the autism side, on the depression side? >> I see, well I have a lab, so that helps makes things easy. >> Lisa: Yup, you got it. >> I have some students working on some projects-- >> Lisa: Excellent >> And some students working on other projects, And we really, we follow the data. My collaborations are largely chosen based on areas where there are data available and we believe we can make an impact. >> Fantastic, speaking of your students, I'd love to understand a little bit more. You teach computer science to undergrads. >> Yes. >> As we look at how we're at this really inflection point with data science; there's so much that can be done in that, to your point, in tangible ways the differences that we can make. Kids that are undergrads at Harvard these days grew up with technology and the ability to get something like that; we didn't. So what are some of the things that have influenced them to want to become the next generation of computer or data scientists? >> I mean, I think most of them just realize that computers and data are essential in whatever field they are. They don't necessarily come to Harvard thinking that they're going to become data scientists. But in whatever field that they end up in, whether it's economics or government, they quickly realize, or business, they quickly realize that data is very important. So they end up in my undergraduate machine learning course. And for these students, my main focus is just to teach them, what the science, what the field can do, and also what the field can't do. And teach them that with great power comes great responsibility. So we're really focused on evaluation and just understanding on how to use these methods properly. >> So looking at kind of traditional computer, data science skills: data analytics, being able to interpret, mathematics, statistics, what are some of the new emerging skills that the future generation of data and computer scientists needs to have, especially related to the social skills and communication? >> So I think that communication is absolutely essential. At Harvard, I think we're fortunate because most of these people are already in a different field. They're also taking data science so they're already very good at communicating. >> Lisa: Okay. >> Because they're already thinking about some other area they want to apply in. >> So they've got, they're getting really a good breadth. >> They're getting a really great breadth, but in general, I think it is on us, the data scientists, to figure out how do we explain the assumptions in our algorithms to people who are not experts again in data science, because that could have really huge downstream effects. >> Absolutely. I like what you said that these kids understand that the computers and technology are important whatever they do. We've got a great cross section of speakers at this event that are people of, that are influencing this in retail, in healthcare, in education, and as well as in sports technology, on the venture capital side. And it really shows you that this day and age, everything is technology, every company we're in, we're sitting in Silicon Valley of course, where a car company is a technology company. But that's a great point that the next generation understands that it's prolific. I can't do anything without understanding this and knowing how to communicate it. So from your background perspective, were you a STEM kid from way back and you really just loved math and science? Is that what shaped your career? >> So I grew up in a family with like 15 generations back, accounting, finance, small business, and I was like, I'm never going to do any of this. (Lisa laughs) I am going to do something completely different. >> Lisa: You were determined, right. >> And so now I'm a data scientist. (laughing) >> At Harvard, that's pretty good, they must be proud. >> Working on healthcare applications. So I think numbers were definitely very much part of my upbringing, from the beginning. But one thing that I think did take a while for me to put together is that I came from a family where my great uncle was part of India's independence movement. My role models were people like Martin Luther King and Mother Teresa and I liked numbers. >> Lisa: Yeah. >> And, like how to put those together? And I think it definitely took me a while to figure out okay, how do you deliver those warm fuzzies with like cold hard facts. >> Lisa: Right. >> And I'm really glad that we're in a place today where the sort of skills that I have can be used to do enormous social good. >> What are some of the things that you're most excited about about this particular conference and being involved here? >> So I think conferences like these, like the Women in Data Science, I'm also involved in the Women in Machine Learning Conference, are a tremendous opportunity for people to find mentors and cohorts. So I went to my first Women in Machine Learning Conference over 10 years ago, and those are the people I still talk to whenever I need career advice, when I'm trying to figure out what I want to do with my research and what directions, or just general support. And when you're in a field where you maybe don't see that many women around you, it's great to have this connection so that you can draw on that wherever you end up. Your workplace may or may not have that many women but you know that they're out there and you can get support. >> Now that there's so much data available, a lot of the spirit of corporations that use data as a change agent have adopted cultures or tried, of try it, it might fail, but we're going to learn something from this. Do you see that mentality in your students about being free or being confident enough to try experiments and if they fail, take learnings from it and move forward as a positive? >> I mean, certainly that's what I try to teach my students. >> Lisa: Yeah, yeah. >> My graduate students I tell them, I expect you to make consistent progress. Progress includes failure if you can explain why it failed. And that's huge, that's how we learn and that's how we develop new algorithms, absolutely. >> Yeah, and I think that confidence is a key factor. You mention that Women in Machine Learning Conference, you've been involved in that for 10 years, how have you seen women's perspectives, maybe confidence evolve and change and grow as a result of this continued networking? Are you seeing people become more confident-- >> Finale: I think so. >> To be able to try things and experiments. >> I mean certainly, as people stay involved in the field, I've noticed that you develop that network, you develop that confidence, it's amazing. The first event had less than a hundred people. The last event that we had had over 500 people. The number of people at just the Women in Machine Learning event, was the same as the number of people at the entire conference 10 years ago. >> Right. >> Right, and so the field has grown but the number of women involved that you see through this events like WIDS and WIML I think is enormous. >> And the great thing that's happening here at WIDS 2017 is it's being live streamed. >> Finale: Right. >> Over 75 locations. >> So it's accessible to so many people. >> Exactly. >> Yes. >> We're expecting up to 6,000 people on the live stream. So the reach and the extension is truly global. >> Which is fantastic. >> It is fantastic and just the breadth of speakers that are here to influence. You mentioned a couple of your key influencers: Martin Luther King and Mother Teresa. From an education perspective, when you were trying to figure out your love of math and numbers and that, who were some of the people in your early career that were really inspiring and helped you gain that confidence that you would need to do what you're doing? >> So I think if I had to pick one person, it was probably a professor at MIT that I interacted quite a bit in my undergrad and continued to mentor me, Leslie Kaelbling, who is just absolutely fearless in just telling people to follow their passions. Because we really are super privileged as was mentioned earlier: we lose our jobs, we can just get another one. >> Lisa: Right. >> Right? And our skills are so in need that we can and we should try to do amazing things that we care about. And I think that message really stayed with me. >> Absolutely. >> So you got research going on in autism. You mentioned depression. What's next for you? What are some of your next interests? Cancer research, other things like that? >> So I'm actually really interested in mental health because I think that that's, you know, talk about messy spaces, in terms of data. (laughing) It's very hard to quantify but it has a huge, huge burden both to the people who suffer from mental health disorders, which is like close to 15 percent, 20 percent, depending on how you count. But also it has a huge burden on everyone else too, on like lost work, on the people around them. And so we're working with depression and autism, as I mentioned. And we're hoping to branch out into other neurodevelopmental disorders, as well as adult psychiatric disorders. And I feel like in this phase, it's even harder to find the right treatments. And the treatments take so long to test, six to eight weeks. And it can be so hard to keep up the morale, to keep trying out a treatment when your disorder is one that makes it hard to keep up trying whatever you need to try. >> Lisa: Right. >> So that's an area that I'm really focusing on these days. >> Well then your passion is clearly there. That intersection of machine learning and healthcare. You're right, you're talking about something that maybe isn't talked about nearly as much as some of other big diseases but it's one that is prolific. It affects so many. And it's exciting to know that there are people out there like you who really have a passion for that and are using data as a change agent to help current generations and future to come. So Finale, such a pleasure to have you on theCUBE. We wish you the best of luck in your technical talk and know that you're going to be mentoring a lot of people from far and wide. >> Thank you, my pleasure to be here. >> Absolutely, so I'm Lisa Martin. You've been watching theCUBE. We are live at the Women in Data Science Conference at Stanford University, but stick around, we'll be right back. (upbeat music)

>> Male Voiceover: Live from Stanford University, it's The Cube covering the Women in Data Science Conference 2017. >> Hi, welcome back to The Cube, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science Technical Conference. It's a one day event here, incredibly inspiring morning we've had. We're joined by Janet George, who is the chief data scientist at Western Digital. Janet, welcome to the show. >> Thank you very much. >> You're a speaker at-- >> Very happy to be here. >> We're very happy to have you. You're a speaker at this event and we want to talk about what you're going to be talking about. Industrialized data science. What is that? >> Industrialized data science is mostly about how data science is applied in the industry. It's less about more research work, but it's more about practical application of industry use cases in which we actually apply machine learning and artificial intelligence. >> What are some of the use cases at Western Digital for that application? >> One of the use case that we use is, we are in the business of creating new technology nodes and for creating new technology nodes we actually create a lot of data. And with that data, we actually look at, can we understand pattern recognition at very large scale? We're talking millions of wafers. Can we understand memory holes? The shape, the type, the curvature, circularity, radius, can we detect these patterns at scale? And then how can we detect if the memory hole is warped or deformed and how can we have machine learning do that for us? We also look at things like correlations during the manufacturing process. Strong correlations, weak correlations, and we try to figure out interactions between different correlations. >> Fantastic. So if we look at big data, it's probably applicable across every industry. How has it helped to transform Western Digital, that's been an institution here in Silicon Valley for a while? >> We in Western Digital we move mountains of data. That's just part of our job, right? And so we are the leaders in storage technology, people store data in Western Digital products, and so data's inherently very familiar to us. We actually deal with data on a regular basis. And now we've started confronting our data with data science. And we started confronting our data with machine learning because we are very aware that artificial intelligence, machine learning can bring a different value to that data. We can look at the insides, we can develop intelligence about how we build our storage products. What we do with our storage. Failure analysis is a huge area for us. So we're really tapping into our data to figure out how can we make artificial intelligence and machine learning ingrained in the way we do work. >> So from a cultural perspective, you've really done a lot to evolve the culture of Western Digital to apply the learnings, to improve the values that you deliver to all of your customers. >> Yes, believe it or not, we've become a data-driven company. That's amazing, because we've invested in our own data, and we've said "Hey, if we are going to store the world's data, we need to lead, from a data perspective" and so we've sort of embraced machine learning and artificial intelligence. We've embraced new algorithms, technologies that's out there we can tap into to look at our data. >> So from a machine learning, human perspective, in storage manufacturing, is there still a dependence on human insight where storage manufacturing devices are concerned, or are you seeing the machine learning really, in this case, take more of a lead? >> No, I think humans play a huge role, right? Because these are domain experts. We're talking about Ph.D.'s in material science and device physics areas so what I see is the augmentation between machine learning and humans, and the domain experts. Domain experts will not be able to scale. When the scale of wafer production becomes very large. So let's talk about 3 million wafers. How is a machine going to physically look at all the failure patterns on those wafers? We're not going to be able to scale just having domain expertise. But taking our core domain expertise and using that as training data to build intelligence models that can inform the domain expert and be smart and come up with all the ideas, that's where we want to be. >> Excellent. So you talked a little bit about the manufacturing process. Who are some of the other constituents that you collaborate with as chief data scientist at Western Digital that are demanding access to data, marketing, etcetera, what are some of those key collaborators for your group? >> Many of our marketing department, as well as our customer service department, we also have collaborations going on with universities, but one of the things we found out was when a drive fails, and it goes to our customer, it's much better for us to figure out the failure. So we've started modeling out all the customer returns that we've received, and look at that and see "How can we predict the life cycle of our storage?" And get to those return possibilities or potential issues before it lands in the hands of customers. >> That's excellent. >> So that's one area we've been focusing quite a bit on, to look at the whole life cycle of failures. >> You also talked about collaborating with universities. Share a little bit about that in terms of, is there a program for internships for example? How are you helping to shape the next generation of computer scientists? >> We are very strongly embedded in universities. We usually have a very good internship program. Six to eight weeks, to 12 weeks in the summer, the interns come in. Ours is a little different where we treat our interns as real value add. They come in, and they're given a hypothesis, or problem domain that they need to go after. And within six to eight weeks, and they have access to tremendous amounts of data, so they get to play with all this industry data that they would never get to play with. They can quickly bring their academic background, or their academic learning to that data. We also take really hard research-ended problems or further out problems and we collaborate with universities on that, especially Stanford University, we've been doing great collaborations with them. I'm super encouraged with Feliz's work on computer vision, and we've been looking into things around deep neural networks. This is an area of great passion for me. I think the cognitive computing space is just started to open up and we have a lot to learn from neural networks and how they work and where the value can be added. >> Looking at, just want to explore the internship topic for a second. And we're at the second annual Women in Data Science Conference. There's a lot of young minds here, not just here in person, but in many cities across the globe. What are you seeing with some of the interns that come in? Are they confident enough to say "I'm getting access to real world data I wouldn't have access to in school", are they confident to play around with that, test out a hypothesis and fail? Or do they fear, "I need to get this right right away, this is my career at stake?" >> It's an interesting dichotomy because they have a really short time frame. That's an issue because of the time frame, and they have to quickly discover. Failing fast and learning fast is part of data science and I really think that we have to get to that point where we're really comfortable with failure, and the learning we get from the failure. Remember the light bulb was invented with 99% negative knowledge, so we have to get to that negative knowledge and treat that as learning. So we encourage a culture, we encourage a style of different learning cycles so we say, "What did we learn in the first learning cycle?" "What discoveries, what hypothesis did we figure out in the first learning cycle, which will then prepare our second learning cycle?" And we don't see it as a one-stop, rather more iterative form of work. Also with the internships, I think sometimes it's really essential to have critical thinking. And so the interns get that environment to learn critical thinking in the industry space. >> Tell us about, from a skills perspective, these are, you can share with us, presumably young people studying computer science, maybe engineering topics, what are some of the traditional data science skills that you think are still absolutely there? Maybe it's a hybrid of a hacker and someone who's got, great statistician background. What about the creative side and the ability to communicate? What's your ideal data scientist today? What are the embodiments of those? >> So this is a fantastic question, because I've been thinking about this a lot. I think the ideal data scientist is at the intersection of three circles. The first circle is really somebody who's very comfortable with data, mathematics, statistics, machine learning, that sort of thing. The second circle is in the intersection of implementation, engineering, computer science, electrical engineering, those backgrounds where they've had discipline. They understand that they can take complex math or complex algorithms and then actually implement them to get business value out of them. And the third circle is around business acumen, program management, critical thinking, really going deeper, asking the questions, explaining the results, very complex charts. The ability to visualize that data and understand the trends in that data. So it's the intersection of these very diverse disciplines, and somebody who has deep critical thinking and never gives up. (laughs) >> That's a great one, that never gives up. But looking at it, in that way, have you seen this, we're really here at a revolution, right? Have you seen that data science traditionalist role evolve into these three, the intersection of these three elements? >> Yeah, traditionally, if you did a lot of computer science, or you did a lot of math, you'd be considered a great data scientist. But if you don't have that business acumen, how do you look at the critical problems? How do you communicate what you found? How do you communicate that what you found actually matters in the scheme of things? Sometimes people talk about anomalies, and I always say "is the anomaly structured enough that I need to care about?" Is it systematic? Why should I care about this anomaly? Why is it different from an alert? If you have modeled all the behaviors, and you understand that this is a different anomaly than I've normally seen, and you must care about it. So you need to have business acumen to ask the right business questions and understand why that matters. >> So your background in computer science, your bachelor's Ph.D.? >> Bachelor's and master's in computer science, mathematics, and statistics, so I've got a combination of all of those and then my business experience comes from being in the field. >> Lisa: I was going to ask you that, how did you get that business acumen? Sounds like it was by in-field training, basically on-the-job? >> It was in the industry, it was on-the-job, I put myself in positions where I've had great opportunities and tackled great business problems that I had to go out and solve, very unique set of business problems that I had to dig deep into figuring out what the solutions were, and so then gained the experience from that. >> So going back to Western Digital, how you're leveraging data science to really evolve the company. You talked about the cultural evolution there, which we both were mentioning off-camera, is quite a feat because it's very challenging. Data from many angles, security, usage, is a board level, boardroom conversation. I'd love to understand, and you also talked about collaboration, so talk to us a little bit about how, and some of the ways, tangible ways, that data science and your team have helped evolve Western Digital. Improving products, improving services, improving revenue. >> I think of it as when an algorithm or a machine learning model is smart, it cannot be a threat. There's a difference between being smart and being a threat. It's smart when it actually provides value. It's a threat when it takes away or does something you would be wanting to do, and here I see that initially there's a lot of fear in the industry, and I think the fear is related to "oh, here's a new technology," and we've seen technologies come in and disrupt in a major way. And machine learning will make a lot of disruptions in the industry for sure. But I think that will cause a shift, or a change. Look at our phone industry, and how much the phone industry has gone through. We never complain that the smart phone is smarter than us. (laughs) We love the fact that the smartphone can show us maps and it can send us in the right, of course, it sends us in the wrong direction sometimes, most of the time it's pretty good. We've grown to rely on our cell phones. We've grown to rely on the smartness. I look at when technology becomes your partner, when technology becomes your ally, and when it actually becomes useful to you, there is a shift in culture. We start by saying "how do we earn the value of the humans?" How can machine learning, how can the algorithms we built, actually show you the difference? How can it come up with things you didn't see? How can it discover new things for you that will create a wow factor for you? And when it does create a wow factor for you, you will want more of it, so it's more, to me, it's most an intent-based progress, in terms of a culture change. You can't push any new technology on people. People will be reluctant to adapt. The only way you can, that people adopt to new technologies is when they the value of the technology instantly and then they become believers. It's a very grassroots-level change, if you will. >> For the foreseeable future, that from a fear perspective and maybe job security, that at least in the storage and manufacturing industry, people aren't going to be replaced by machines. You think it's going to maybe live together for a very long, long time? >> I totally agree. I think that it's going to augment the humans for a long, long time. I think that we will get over our fear, we worry that the humans, I think humans are incredibly powerful. We give way too little credit to ourselves. I think we have huge creative capacity. Machines do have processing capacity, they have very large scale processing capacity, and humans and machines can augment each other. I do believe that the time when we had computers and we relied on our computers for data processing. We're going to rely on computers for machine learning. We're going to get smarter, so we don't have to do all the automation and the daily grind of stuff. If you can predict, and that prediction can help you, and you can feed that prediction model some learning mechanism by reinforced learning or reading or ranking. Look at spam industry. We just taught the Spam-a-Guccis to become so good at catching spam, and we don't worry about the fact that they do the cleansing of that level of data for us and so we'll get to that stage first, and then we'll get better and better and better. I think humans have a natural tendency to step up, they always do. We've always, through many generations, we have always stepped up higher than where we were before, so this is going to make us step up further. We're going to demand more, we're going to invent more, we're going to create more. But it's not going to be, I don't see it as a real threat. The places where I see it as a threat is when the data has bias, or the data is manipulated, which exists even without machine learning. >> I love though, that the analogy that you're making is as technology is evolving, it's kind of a natural catalyst >> Janet: It is a natural catalyst. >> For us humans to evolve and learn and progress and that's a great cycle that you're-- >> Yeah, imagine how we did farming ten years ago, twenty years ago. Imagine how we drive our cars today than we did many years ago. Imagine the role of maps in our lives. Imagine the role of autonomous cars. This is a natural progression of the human race, that's how I see it, and you can see the younger, young people now are so natural for them, technology is so natural for them. They can tweet, and swipe, and that's the natural progression of the human race. I don't think we can stop that, I think we have to embrace that it's a gift. >> That's a great message, embracing it. It is a gift. Well, we wish you the best of luck this year at Western Digital, and thank you for inspiring us and probably many that are here and those that are watching the livestream. Janet George, thanks so much for being on The Cube. >> Thank you. >> Thank you for watching The Cube. We are again live from the second annual Women in Data Science conference at Stanford, I'm Lisa Martin, don't go away. We'll be right back. (upbeat electronic music)

>> Narrator: Live from Stanford University, it's theCUBE covering the Women in Data Science Conference 2017. >> Hi welcome back to theCUBE, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science one day tech conference. We are joined by one of the speakers for the event today, Claudia Perlich, the Chief Scientist at Dstillery, Claudia, welcome to theCUBE. >> Claudia: Thank you so much for having me. It's exciting. >> It is exciting! It's great to have you here. You are quite the prolific author, you've won data mining competitions and awards, you speak at conferences all around the world. Talk to us what you're currently doing as the Chief Scientist for Dstillery. Who's Dstillery? What's the Chief Scientist's role and how are you really leveraging data and science to be a change agent for your clients. I joined Dstillery when it was still called Media6Degrees as a very small startup in the New York ad tech space. It was very exciting. I came out of the IBM Watson Research Lab and really found this a new challenging application area for my skills. What does a Chief Scientist do? It's a good question, I think it actually took the CEO about two years to finally give me a job description, (laughter) and the conclusion at that point was something like, okay there is technical contribution, so I sit down and actually code things and I build prototypes and I play around with data. I also am referred to as Intellectual Leadership, so I work a lot with the teams just kind of scoping problems, brainstorming was may work or dozen, and finally, that's what I'm here for today, is what they consider an Ambassador for the company, so being the face to talk about the more scientific aspects of what's happening now in ad tech, which brings me to what we actually do, right. One of the things that happened over the recent past in advertising is it became an incredible playground for data signs because the available data is incomparable to many other fields that I have seen. And so Dstillery was a pioneer in that space starting to look at initially social data things that people shared, but over the years it has really grown into getting a sense of the digital footprint of what people do. And our primary business model was to bring this to marketers to help them on a much more individualized basis identify who their customers current as well as futures are. Really get a very different understanding than these broad middle-aged soccer mom kind of categories to honor the individual tastes and preferences and actions that really truly reflect the variety of what people do. I'm many things as you mentioned, I publish mom, what's a mom, and I have a horse, so there are many different parts to me. I don't think any single one description fully captures that and we felt that advertising is a great space to explore how you can translate that and help both sides, the people that are being interacted with, as well as the brands that want to make sure that they reach the right individuals. >> Lisa: Very interesting. Well, as buyers journey as changed to mostly online, >> Exactly. >> You're right, it's an incredibly rich opportunity for companies to harness more of that behavioral information and probably see things that they wouldn't have predicted. We were talking to Walmart Labs earlier and one of the interesting insights that they shared was that, especially in Silicon Valley where people spend too much time in the car commuting-- (laughter) You have a long commute as well by train. >> Yes. >> And you'd think that people would want, I want my groceries to show up on my doorstep, I don't want to have to go into the store, and they actually found the opposite that people in such a cosmopolitan area as Silicon Valley actually want to go into the store and pick up-- >> Claudia: Yep. >> Their groceries, so it's very interesting how the data actually can sometimes really change. It's really the scientific method on a very different scale >> Claudia: Much smaller. >> But really using the behavior insights to change the shopping experience, but also to change the experience of companies that are looking to sell their products. >> I think that the last part of the puzzle is, the question is no longer what is the right video for the Super Bowl, I mean we have the Super Bowl coming up, right? >> Lisa: Right. Right. >> They did a study like when do people pay attention to the Super Bowl. You can actually tell, cuz you know what people don't do when they pay attention to the Super Bowl? >> Lisa: Mm,hmm. >> They're not playing around with their phones. They're actually not playing-- >> Lisa: Of course. >> Candy Crush and all these things, so what we see in the ad tech environment, we actually see that the demand for the digital ads go down when people really focus on what's going on on the big screen. But that was a diversion ... >> Lisa: It's very interesting (laughter) though cuz it's something that's very tangible and very ... It's a real world applications. Question for you about data science and your background. You mentioned that you worked with IBM Watson. Forbes has just said that Data Scientist is the best job to apply for in 2017. What is your vision? Talk to us about your team, how you've grown that up, how you're using big data and science to really optimize the products that you deliver to your customers. >> Data Science is really many, many different flavors and in some sense I became a Data Scientist long before the term really existed. Back then I was just a particular weird kind of geek. (laughter) You know all of a sudden it's-- >> Now it has a name. (laughter) >> Right and the reputation to be fun and so you see really many different application areas depending very different skillsets. What is originally the focus of our company has always been around, can we predict what people are going to do? That was always the primary focus and now you see that it's very nicely reflected at the event too. All of sudden communicating this becomes much bigger a part of the puzzle where people say, "Okay, I realize that you're really "good at predicting, but can you tell me why, "what is it these nuggets of inside-- >> Interpretation, right. >> "That you mentioned. Can you visualize what's going on?" And so we grew a team initially from a small group of really focused machine learning and predictive skills over to the broader can you communicate it. Can you explain to the customer archieve brands what happened here. Can you visualize data. That's kind of the broader shift and I think the most challenging part that I can tell in the broader picture of where there is a bit of a short coming in skillset, we have a lot of people who are really good today at analyzing data and coding, so that part has caught up. There are so many Data Science programs. What I still am looking for is how do you bring management and corporate culture to the place where they can truly take advantage of it. >> Lisa: Right. >> This kind of disconnect that we still have-- >> Lisa: Absolutely. >> How do we educate the management level to be comfortable evaluating what their data science group actually did. Whether they working on the right problems that really ultimately will have impact. I think that layer of education needs to receive a lot more emphasis compared to what we already see in terms of this increased skillset on just the sheer technical side of it. >> You mentioned that you teach-- >> Claudia: Mm,hmm. >> Before we went live here, that you teach at NYU, but you're also teaching Data Science to the business folks. I would love for you to expand a little bit more upon that and how are you helping to educate these people to understand the impact. Cuz that's really, really a change agent within the company. That's a cultural change, which is really challenging-- >> Claudia: Very much so. >> Lisa: What's their perception? What's their interest in understanding how this can really drive value? >> What you see, I've been teaching this course for almost six years now, and originally it was really kind of the hardcore coders who also happened to get a PhD on the side, who came to the course. Now you increasingly have a very broad collection of business minded people. I typically teach in the part-time, meaning they all have day jobs and they've realized in their day jobs, I need this. I need that. That skill. That knowledge. We're trying to get on the ground where without having to teach them python and ARM whatever the new toys are there. How can you identify opportunities? How do you know which of the many different flavors of Data Science, from prediction towards visualization to just analyzing historical data to maybe even causality. Which of these tools is appropriate for the task at hand and then being able to evaluate whether the level of support that a machine can only bring, is it even sufficient? Because often just because you can analyze data doesn't mean that the reliability of the model is truly sufficient to support then a downstream business project. Being able to really understand those trade offs without necessarily being able to sit down and code it yourself. That knowledge has become a lot more valuable and I really enjoy the brainstorming when we're just trying to scope a project when they come with problems from their day job and say, "Hey, we're trying to do that." And saying, "Are you really trying to do that?" "What are you actually able to execute? "What kind of decisions can you make?" This is almost like the brainstorming in my own company now brought out to much broader people working in hospitals, people working in banking, so I get exposed to all of these kinds of problems said and that makes it really exciting for me. >> Lisa: Interesting. When Dstillery is talking to customer or prospective customers, is this now something that you're finding is a board level conversation within businesses? >> Claudia: No, I never get bored of that, so there is a part of the business that is pretty well understood and executed. You come to us, you give us money, and we will execute a digital campaign, either on mobile phones, on video, and you tell me what it is that you want me to optimize for. Do you want people to click on your ad? Please don't say yes, that's the worst possible things you may ask me to do-- (laughter) But let's talk about what you're going to measure, whether you want people to show up in your store, whether you really care about signing up for a test drive, and then the system automatically will build all the models that then do all the real-time bidding. Advertising, I'm not sure how many people are aware, as your New York Times page loads, every single ad slot on that side is sold in a real-time auction. About 50 billion times a day, we receive a request whether we want to bid on the opportunity to show somebody an ad. >> Lisa: Wow. >> So that piece, I can't make 50 billion decisions a day. >> Lisa: Right. >> It is entirely automated. There's this fully automated machine learning that just serves that purpose. What makes it interesting for me now that ... Now this is kind of standard fare if you want to move over and is more interesting parts. Well, can you for instance predict which of the 15 different creatives I have for Jobani, should I show you? >> Lisa: Mm,hmm. >> The one with the woman running, or the one with the kid opening, so there is no nuances to it and exploring these new challenges or going into totally new areas talking about, for instance churn prediction, I know an awful lot about people, I can predict very many things and a lot of them go far beyond just how you interact with ads, it's almost the most boring part. We can see people researching diabetes. We can provide snapshots to farmer telling them here's really where we see a rise of activity on a certain topic and maybe this is something of interest to understand which population is driving those changes. These kinds of conversations really making it exciting for me to bring the knowledge of what I see back to many different constituents and see what kind of problems we can possibly support with that. >> Lisa: It's interesting too. It sounds like more, not just providing ad technology to customers-- >> Claudia: Yeah. >> You're really helping them understand where they should be looking to drive value for their businesses. >> Claudia: That's really been the focus increasingly and I enjoy that a lot. >> Lisa: I can imagine that, that's quite interesting. Want to ask you a little bit before we wrap up here about your talk today. I was looking at your, the title of your abstract is, "Beware what you ask for: The secret life of predictive models". (laughter) Talk to us about some of the lessons you learn when things have gone a little bit, huh, I didn't expect that. >> I'm a huge fan of predictive modeling. I love the capabilities and what this technology can do. This being said, it's a collection of aha moments where you're looking at this and this, this doesn't really smell right. To give you an example from ad tech, and I alluded to this, when people say, "Okay we want a high click through rate." Yes, that means I have to predict who will click on an ad. And then you realize that no matter what the campaign, no matter what the product, the model always chooses to show the ad on the flashlight app. Yeah, because that's when people fumble in the dark. The model's really, really good at predicting when people are likely to click on an ad, except that's really not what you intended-- >> Right. >> When you asked me to do that. >> Right. >> So it's almost the best and powerful that they move off into a sidetracked direction you didn't even know existed. Something similar happened with one of these competitions that I won. For Siemens Medical where you had to identify an FMI images of breast, which of these regions are most likely benign or which one have cancer. In both models we did really, really well, all was good. Until we realized that the patient ID was by far the most predictive feature. Now this really shouldn't happen. Your social security number shouldn't be able to predict-- >> Lisa: Right. >> Anything really. It wasn't the social security number, but when we started looking a little bit deeper, we realized what had happened is the data set was a sample from different sources, and one was a treatment center, and one was a screening center and they had certain ranges of patient IDs, so the model had learned where the machine stood, not what the image actually contained about the probability of having cancer. Whoever assembled the data set possibly didn't think about the downstream effect this can have on modeling-- >> Right. >> Which brings us back to the data science skill as really comprehensive starting all the way from the beginning of where the data is collected, all the way down to be extremely skeptical about your own work and really make sure that it truly reflects what you want it to do. You asked earlier like what makes really good Data Scientists. The intuition to feel when something is wrong and to be able to pinpoint and trace it back with the curiosity of really needing to understand everything about the whole process. >> Lisa: And also being not only being able to communicate it, but probably being willing to fail. >> Claudia: That is the number one really requirement. If you want to have a data-driven culture, you have to embrace failure, because otherwise you will fail. >> Lisa: How do you find the reception (laughter) to that fact by your business students. Is that something that they're used to hearing or does it sound like a foreign language to them? >> I think the majority of them are in junior enough positions that they-- >> Lisa: Okay. >> Truly embrace that and if at all, they have come across the fact that they weren't allowed to fail as often as they had wanted to. I think once you go into the higher levels of conversation and we see that a lot in the ad tech industry where you have incentive problems. We see a lot of fraud being targeted. At the end of the day, the ad agency doesn't want to confess to the client that yeah they just wasted five million dollars-- >> Lisa: Right. >> Of ad spend on bots, and even the CMO might not be feeling very comfortable confessing that to the CO-- >> Right. >> Claudia: Being willing to truly face up the truth that sometimes data forces you into your face, that can be quite difficult for a company or even an industry. >> Lisa: Yes, it can. It's quite revolutionary. As is this event, so Claudia Perlich we thank you so much for joining us-- >> My pleasure. >> Lisa: On theCUBE today and we know that you're going to be mentoring a lot of people that are here. We thank you for watching theCUBE. We are live at Stanford University from the Women in Data Science Conference. I am Lisa Martin and we'll be right back (upbeat music)

>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. >> Hi, I'm Lisa Martin, welcome back to theCUBE. We are live at Stanford University at the second annual Women in Data Science Conference, the one-day tech conference and we are joined by Julie Yoo, who is the founder and chief data scientist of Pymetrics. Julie, you were on the customer panel today. So welcome to theCUBE. >> Thank you. >> It's great to have you, it's such an interesting background. >> Julie: Thank you. >> Neuroscience meets engineering, or engineering meets neuroscience. I'd love for us to understand a little bit more about those two, how they're combined, and also, about Pymetrics. But give us a little bit of a background, as a woman in the sciences, how you got to where you are now. >> As you mentioned, my background's in computer engineering and I went into PhD program in electrical and computer engineering 'cause I wanted to study artificial intelligence. I was fascinated by the notion of artificial intelligence. So my research topic started in automatic speech recognition systems, building computers to decode and decipher human speech. After a couple of years, I got frustrated with just the engineering approach or statistical methods-based approach to improving the existing speech recognition systems that are out there, 'cause I thought to myself, We're trying to make computers understand human speech and mimic human function when we don't really understand how our brain works and I don't really know exactly what happens when you listen to you speak, when I listen to you speak and when you listen to I speak, what is going on? We didn't really have a good sense, so I wanted to study neuroscience. So I quit engineering and I went into PhD program in neuroscience and there, I started doing a lot of neuroimaging study, just looking at human cognition and just figuring out what is going on when people perceive and process these signals that are out there. >> And was your idea to eventually marry the two? >> I didn't really think about it that way, but it just sort of happened, as in like, my background in engineering sort of homed me into doing some of the projects that I did when I was doing my PhD and my post-doc. And while I was doing all that, I just evolved to be a data scientist without, really, me realizing I was doing everything that a typical data scientist would do. And this was even before 2008. The job title of data scientist wasn't even around then, so it sort of happened because of where I came from and because what I was interested in and as I was doing that, it just ended up being a good marriage. >> And there it was. Talk to us, tell people what Pymetrics is and what the genesis of this company was. >> Pymetrics is a platform that uses neuroscience-based games and data science to promote predictive and bias-free hiring. How we became a product was because I was going through post-doc and my co-founder was also going through business school and we were both going through the phase of, Okay, we don't want to stay in academia. What do we want to do with our lives? And at the time, we realized a lot of the career-advising tools that are out there were not scientific and they were not data-driven and we felt that there is a clear need for a tool that can actually use all these data that are out there to help people figure out what they should be doing with their lives. So we thought we were uniquely positioned to use our background in engineering and neuroscience and build a product that could actually solve these challenging problem and that's how we started Pymetrics. >> That's fantastic. You started about three years ago in 2013. So, really getting rid of some of the biases, share with us what some of the biases are. Is it test scores, SATs, MCATs, GPAs? >> There are many, many different kinds of biases in hiring process right now, I think. There is a preconception of what an engineer should look like and I think that plays a lot. And when you do going to an interview, how you look and how you dress, it adds to the bias. There is ethnic bias, there's gender bias, and there is bias based on test scores and what school you went to. So we want to remove ourselves from that and really get down to what kind of person you are and are you really... I guess, have the right set of skills to succeed in certain job functions. We do that by measuring, instead of taking your subjective answers from questionnaires, we do that by objectively measuring your behavior and these games are based on neuroscience research so we know that they actually measure things that we want them to measure, for instance, your ability to pay attention, your risk appetite, and all those things that we think matters as to what makes you good at certain things and not so good at some other things. So we use these objective data and data science and predictive modeling to come up with predictions as to how good you will be in certain career versus some other career. >> Really, an incredible need for that. It's game-based, so it's an actual game that people will play that will help understand more of who they are as a person, their behaviors, those patterns. Tell us a little bit about the invention of the game, what was it like, who was it for? >> The games were actually sourced from neuroscience research community. We did not create these games. What we did was we actually just took them from research and medical settings and applied it through hiring. We know that these are relevant to measuring your attributes and your personality, so why not use it for hiring and career advising, because it makes sense. We're trying to measure your qualities, your soft skills and what-not, why not just use it for something that could really benefit from these sort of data. What we did do is we actually made these games, they're not really called games in research community, but we made it shorter and we made it more applicable to the things that we are trying to use if for. >> You took feedback from some of your earlier adopters who were saying maybe it's taking me too long, maybe some of the recruiters might say, they gave you some very viable feedback that have helped you optimize the products. >> Right, as a data scientist, I always think the more data, the better, but that also means that people would have to sit in front of their computers and play an hour-long battery of games and a lot of people were thinking that it might be just a tad too long and companies felt that spending 45 minutes to an hour could be a discouraging thing and people felt fatigue effect and we could see that in the results, so we ended up making it shorter. We went from 20 games to 12 games and we cut it down to 25 minutes long and I think, now, we're in the sweet spot where we do get enough data but, at the same time, we're not making it an hour long. >> Right, so this is really targeted for people coming out of university programs, whether it's bachelor's, master's, doctorate, et cetera, and also, what type of companies who are looking to hire, what's kind of your target market for that? >> I think mostly Fortune 500 companies 'cause a lot of these companies do hire in large volume, so it helps to have us go to these companies and build their models based off of their employees. And if a smaller company comes along and they only have 10 employees in the job function, then it's extremely difficult for us to build the model base off of their 10 employees, whereas if it's a larger corporation, then we can have 200 employees play and we can build the model based on their data. So generally, large corporations is our target clients. >> I'm curious, in terms of some of the data that you are seeing, that you're analyzing, are you seeing, we look at data science as a great example of the event that we're at, in report from Forbes recently that said it's the best job to apply for in 2017. We're looking at now what's going to be happening, predicted over the course of the next year, and that's a shortage in talent. Are you seeing, with some of the data that you're taking in, are you seeing things that are mapping to that, like people that are really geared towards that? Or are you seeing more companies that are looking for computer-industry, data-science type roles? Is that increasing, as well? >> I think companies are definitely looking for more data scientists and I think, also, people are figuring out that there are data science programs like graduate school programs and I think that supply of data scientists is definitely increasing, but at the same time, or more so, the demand for data scientists is increasing. And not to mention, the available data that's out there is increasing at a faster rate than anything else. Yeah, it is, I think, the best time to be a data scientist right now. >> Let me ask you one more question about looking at skills. We have such a great cross-section at this event of leaders in retail, in obviously, what you're doing in neuroscience-gaming-merging world. We've got professors here. Data science is such an interesting topic, it's obviously very horizontal. From a skill set perspective, kind of the traditional skills of being a statistician, mathematics, being a hacker, a lot of the things that we've been hearing around the show today, and really aligns with what you're doing is more on the behavioral insight side of, you have to be able to communicate what you're seeing and be able to apply it. I'd love to understand a profile of an ideal data scientist that you guys are seeing from your data. What are some of the other behavioral attributes that maybe are some of the non-teachable things that you're seeing that really come up that this would be a great career path for someone? >> Personally, I think intellectual curiosity is number one, and they would have to have strong self-motivation and discipline because you could love analyzing data and you could just be doing that for how many days, I don't know, and that's it. You could actually come up with a good story. You've got to be a good storyteller and if you have artistic flair to make the data beautiful, then even better. But it is important to go from the beginning of the project where you have a bunch of data set and actually come up with actionable results that people can use. And you're not only always going to be communicating with a data scientist, so you need to be able to present your data in a more succinct and easily-digestible way. >> That sounds like, as the chief data scientist for Pymetrics, that's what you're looking for to hire on your team. Give us a little bit, last question here, just a little bit of an overview of what your data science team looks like at Pymetrics, as you're helping to leverage this data to give people opportunities with careers. What does your team look like? >> Our team has a very diverse background. We have a few PhD's in Physics and you know, well, I have a PhD in Neuroscience and there's other data scientists with PhD's in Physics. We actually have one guy who majored in Data Science and we have another guy who majored in Bio Engineering. So it's definitely a diverse background. But the general theme is that you do need a good, quantitative foundation. So, whether it's engineering or physics, it is still helpful to have that statistical or analytical mind and if you can actually apply that, and actually love solving problems then I think data scientist is a right goal. >> So you're on the career panel at WiDS2017, is that the advice that you would give to kind of, the next generation of kids that are interested in this but aren't quite sure what industry they would want to go into? >> What industry? I think, I mean if they're even remotely interested in going into data science, I would encourage them to pursue it. I think it is one of the most fascinating fields right now and there's never going to be a shortage of needs for data scientists. So if you like it, if you think you are going to be pretty good at it, I say go for it. >> Fantastic. And you've got a great audience here. This is being live streamed in 20 cities, I think across the globe, or 75 cities, I have to get those stats right. But, there's a big opportunity here to be an influencer and we thank you for spending some time with us. Best of luck on the panel. >> Thank you. >> Thank you for watching. I'm Lisa Martin, we are live with theCUBE, at Women and Data Science 2017, #WiDS2017. Stick around, we'll be right back. (upbeat mellow music)

>> Announcer: Live from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. >> Hi, welcome to theCUBE. I'm Lisa Martin, and we are at the Women in Data Science second annual conference at Stanford University. Great event, very excited to be joined by one of the founders of the Women in Data Science, the Senior Director and Head of Data Science at Walmart Labs, Esteban Arcaute. Very nice to have you on the program. Thanks for joining us. >> Thank you for having me, Lisa. >> So talk to us about data science in retail. How is Walmart using data science too influence shoppers wherever they are, mobile, in store, dot com? >> So data science is a key component to how we create our experiences, especially now that our customers essentially don't really make a distinction between they're shopping in stores or they're actually using their mobile device, or they're at home with their desktop. So that means that for us it really is about creating a seamless experience that allows a customer to not feel that barrier of the medium that they're using to shop. So more practically, that means that the data that we're using to create the experience is essentially the same across all of these medias. >> So big data brings, and data science brings big opportunities, but also some challenges. Talk to us about some of the challenges that you've had with the tremendous amount of data because you've got what? Sixty million shoppers, 260 million, excuse me, globally. How are you dealing with some of those challenges and really turning them into opportunities to create that seamless experience? >> So for us it means that a lot of ready-made solutions that are available for other companies, they just don't work for us. The same way that other companies with large amounts of data, they actually have to create their own in-house solutions or technology. It is the same for us. Now in terms of how that is a very specific challenge, that means that when you actually go and train, let's say a model, that is trying to predict whether a customer is going to satisfied with a purchase or not, usually the amount of data that you have will make that model to not be that reliable unless you actually did it in-house. >> Okay, so from an accuracy perspective that really is what was driving being able to do that within Walmart Labs? >> Yes, and just sort of to give a plug to the department where I got my PhD, all of these numerical instabilities that in past you will only see when doing computational fluid dynamics, they actually start appearing in places like retail just because of the volume of data that is available. And so for us it's a great opportunity to be an ICME student. >> Excellent, and that's right, you got your Master's and your PhD right here at Stanford. Talk to us about from a scale and a speed perspective. How are you seeing the ability to influence the consumer experience? How quickly are you able to identify trends and act on them so that customer experience is better, and also the bottom line financials are improved as well for Walmart? >> That is a great question, Lisa, because our customers' expectations are changing really, really rapidly. If you remember back in the late 90s when you would go to a search engine and it worked, it was like a miracle. Everybody was really excited. Fast forward to today, you go to any search box, not a search engine, you put in a query. If it doesn't work, you're disappointed. When it works, it's just table stakes. That means that for us we need to be able to iterate as quickly as the customer expectations change, which is really, really fast. >> Absolutely. How do you collaborate with the business side? So first, let's talk about your team. What's the size of your team? As the head of data science, what are the different functions within your team, first and foremost? >> I'm also in charge of the search experience within Walmart Global eCommerce. It's a fairly large team because it is composed of basically the full stack from the back end, data science, dev ops, product management, so I cannot give you an exact size, but it's a fairly large team. >> And so how do you collaborate with the business to influence merchandising, for example? What is that collaboration like between Walmart Labs and the dot com side? >> So last year, Kelly Thompson was one of the speakers at the Women in Data Science Conference, and she talked about the importance of bringing the art of merchandising with the science of data science together. And it really is true that there're certain things that algorithms cannot catch as soon as a human expert actually knows about. And so the way we develop our products and enhance experiences for our customers is really bringing these two together in a partnership to ensure that there's never one side that is working on something that the other one cannot just leverage. >> From a priority perspective, how are some of the trends that you find driving priorities for investment? >> It goes both ways. Sometimes we find the trend. Sometimes the business finds the trend. And so sometimes the business asks us to try to automate or to predict something that we hadn't thought about, and that is actually very difficult, and hence we invest a lot in that. And sometimes we find some customer patterns that indicate a different behavior in a locality or with certain characteristics that then the business can go and better serve themselves. So it really is driven by whoever has a good idea, and they can come from anywhere. >> You mentioned the need still for human insight. Talk to us about that dynamic, machine learning and human insight. How does that work together, and again kind of thinking in the context of speed and skill to meet those changing customer demands? >> That is one of the best kept secrets for machine learning, is that most machine learning systems, the moment they have a human in the loop, the learning grade gets accelerated exponentially bcause essentially when a machine learning method is not working properly, it tends to be for certain types of cases that if they get resolved, just a few insights from a human being can actually go and make the machine learn a lot faster than if it's trying to figure it out on its own. So for us really even there is a partnership. We think of it as a systems with a human in the loop. That human, if it's an expert, it's even better, which is what we have. And so we create our systems to deeply integrate our merchandising capacity. >> So you actually see human intervention or interaction as a necessary component to speed to market leveraging data? >> That is the fastest way to get there. There might be other ways to do with that. We don't always have a human in the loop, but when we can have a human in the loop, we have seen that acceleration is actually measurable. >> Fantastic. So one of the things I wanted to chat about with you is looking at your team a little bit, as well as your involvement here in the Women in Data Science. You were one of the founders. Talk to us about Walmart's interest in helping to not only educate women, and further their education in data science, but also maybe to combat the predicted shortage of data scientists that's predicted to start even in 2018. How is that collaboration going to help in that sense? >> So let me address the question in two parts. First, the question related to women and minorities into data science. So Walmart is a very inclusive company. We win awards every year because of all of our work in there. And I think that starting with Women in Data Science, it's a natural place to start because there's always 50% of women everywhere. And so that means that really thinking that there should be an equal representation, or maybe not equal representation, there should be a way to funnel all of this talent into data science just makes sense. There's not a question as to whether there's sufficiently many of them or things like that. >> So culturally it was kind of a natural extension for Walmart Labs it sounds like. >> Absolutely, yes. And the second question is the shortage. So for us we're very lucky in that we have two things that any company needs to have to attract great data scientists. So first one is that we actually have data. Believe it or not, it is an asset that a lot of companies don't realize is actually (mumbling). And the second one is that we empower all of our associates with the ability to have impact from the get go. We don't put them in some small project that might have an impact in maybe three years. No, we actually put them in participating projects that might have, for instance in my team, impact within the first three to four months of being on the floor. >> That's fantastic, and I'm sure that really inspires them. They see that they can make an impact right away. And I would imagine just after chatting with you that they have the freedom probably to test and fail, and from that failure it becomes more opportunities to get and tweak and get things right. >> Absolutely. So especially in a field like retail, there's no laws of retail. There's not someone that just put in some nice equations and we just and study and do something. Actually you need to test over and interate constantly, especially when your customers expectations change so rapidly. >> So in terms of evolution of data science and skills, data presentation skills, analysis, stats, math, what are some of the other skills, maybe even social skills that you think are really key for the young next generation of data scientists to really get into this field regardless of industry and be successful? >> It's a question that I get very often, and especially because data science has not yet been formally properly defined in some sense. Data scientist is even less properly defined, so the term just started in 2010 or 11, so usually people think that they have to be hackers, have analytical skills and have some domain expertise. We actually flip that to say you have to have analytical skills, so that stays. You have to be a software engineer or have software engineering skills, and you have to project management skills. And the reason is that unless you are able to properly communicate what your insights are, to understand how they get incorporated into a real software system, and of course to have the expertise to know what you are doing, you're not going to be successful as a data scientist. So for us really those three components are the ones that drive what are we looking at data scientists. >> Excellent, so you mentioned hackers. Hackathons, you recently had a hackathon. How is Walmart Labs giving opportunities to maybe kids in grade school and high school, kids that are university to start developing that talent. >> So we have also an internship program every year. We have interns across all of Walmart Labs, and there is always a great opportunity to seed fresh new ideas that come from our interns, so that happens every year. We organize hackathons in very targeted way in places where we see that there is demand to have these kind of events organized. So I think one that we have in our website is one from 2015 with Tech Crunch Disrupt. It's a big one, but we do other things as well. >> But that actually has the ability, someone who's made a big difference or won at a hackathon that Walmart Lab sponsors has the ability to actually influence Walmart. >> Absolutely because as I said a couple of minutes ago, great ideas come from anywhere. And hackathons are great places where you see all of these ideas bubbling, and that you might not even realize that oh, that opportunity is right there. Someone can see it, and wants it seen, everybody can see it. So it's a great place. >> But that's a great, from a cultural perspective what you're saying sounds fantastic, that you're, there's a culture within Walmart Labs and Walmart that really is not only diverse from women in the sciences as well, but also one that really encourages test it, try it, you can make an impact here. And I think that's huge for attracting talent. What advice would you give to some of the young women that are here at the Women in Data Science Conference for the second annual to want to become successful data scientists? >> So I would give the advice that I have for myself, which is stay true to yourself, and anyone can be a great data scientist. >> What are some of the things that you're most looking forward to learning and hearing at this second annual event? >> The line up of speakers is amazing, and I think that the fact that they come from all places in industry, and all types of academic and professional journeys make it a very rich experience even for me to understand what are the possibilities. >> Absolutely, the cross section of speakers at the event is amazing. You've got obviously you know, data science into retail. We've got people that are using, that are going to be on the show later, data science to change the way college kids are recruited for jobs. Kind of getting away from that things that used to scare me, GPA, test scores, really leveraging science to open up those possibilities. And I think one of the things that that can enable from your comment earlier is the importance of being able to be a good communicator. It's not just about understanding the data. You've got to be able to explain it in a way that makes sense. Is this an impact? Also you mentioned we've got people that are here today on the academic side that are helping to educate the next generation of computer and data scientists. So I think it's a phenomenal opportunity for women of all ages to really understand it's not just technology. Every company this day and age is a technology company, and the opportunities are there to be influencers, and it sounds like at Walmart Labs, from the ground up. >> Yes, absolutely. >> Fantastic. Well, Esteban it's been such a pleasure having you on the program today. Thank you so much for joining. We look forward to having a great event and hopefully seeing you at the third annual next year. >> Definitely. Thank you very much for having me, Lisa. >> And you've been watching theCUBE. We are live at the Women in Data Science Conference at Stanford University. Stick around, be right back. (jazzy music)

(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat music)

(upbeat music) >> Hey, girls and guys. Welcome back to theCUBE. We are live at Stanford University, covering the 8th Annual Women in Data Science Conference. One of my favorite events. Lisa Martin here. Got a couple of guests from Total Energies. We're going to be talking all things data science, and I think you're going to find this pretty interesting and inspirational. Please welcome Alexandre Lapene, Tech Advisor Data Science at Total Energy. It's great to have you. >> Thank you. >> And Myriam Fayad is here as well, product and value manager at Total Energies. Great to have you guys on theCUBE today. Thank you for your time. >> Thank you for - >> Thank you for receiving us. >> Give the audience, Alexandre, we'll start with you, a little bit about Total Energies, so they understand the industry, and what it is that you guys are doing. >> Yeah, sure, sure. So Total Energies, is a former Total, so we changed name two years ago. So we are a multi-energy company now, working over 130 countries in the world, and more than 100,000 employees. >> Lisa: Oh, wow, big ... >> So we're a quite big company, and if you look at our new logo, you will see there are like seven colors. That's the seven energy that we basically that our business. So you will see the red for the oil, the blue for the gas, because we still have, I mean, a lot of oil and gas, but you will see other color, like blue for hydrogen. >> Lisa: Okay. >> Green for gas, for biogas. >> Lisa: Yeah. >> And a lot of other solar and wind. So we're definitely multi-energy company now. >> Excellent, and you're both from Paris? I'm jealous, I was supposed to go. I'm not going to be there next month. Myriam, talk a little bit about yourself. I'd love to know a little bit about your role. You're also a WiDS ambassador this year. >> Myriam: Yes. >> Lisa: Which is outstanding, but give us a little bit of your background. >> Yes, so today I'm a product manager at the Total Energies' Digital Factory. And at the Digital Factory, our role is to develop digital solutions for all of the businesses of Total Energies. And as a background, I did engineering school. So, and before that I, I would say, I wasn't really aware of, I had never asked myself if being a woman could stop me from being, from doing what I want to do in the professional career. But when I started my engineering school, I started seeing that women are becoming, I would say, increasingly rare in the environment >> Lisa: Yes. >> that, where I was evolving. >> Lisa: Yes. >> So that's why I was, I started to think about, about such initiatives. And then when I started working in the tech field, that conferred me that women are really rare in the tech field and data science field. So, and at Total Energies, I met ambassadors of, of the WiDS initiatives. And that's how I, I decided to be a WiDS Ambassador, too. So our role is to organize events locally in the countries where we work to raise awareness about the importance of having women in the tech and data fields. And also to talk about the WiDS initiative more globally. >> One of my favorite things about WiDS is it's this global movement, it started back in 2015. theCUBE has been covering it since then. I think I've been covering it for theCUBE since 2017. It's always a great day full of really positive messages. One of the things that we talk a lot about when we're focusing on the Q1 Women in Tech, or women in technical roles is you can't be what you can't see. We need to be able to see these role models, but also it, we're not just talking about women, we're talking about underrepresented minorities, we're talking about men like you, Alexander. Talk to us a little bit about what your thoughts are about being at a Women and Data Science Conference and your sponsorship, I'm sure, of many women in Total, and other industries that appreciate having you as a guide. >> Yeah, yeah, sure. First I'm very happy because I'm back to Stanford. So I did my PhD, postdoc, sorry, with Margot, I mean, back in 20, in 2010, so like last decade. >> Lisa: Yeah, yep. >> I'm a film mechanics person, so I didn't start as data scientist, but yeah, WiDS is always, I mean, this great event as you describe it, I mean, to see, I mean it's growing every year. I mean, it's fantastic. And it's very, I mean, I mean, it's always also good as a man, I mean, to, to be in the, in the situation of most of the women in data science conferences. And when Margo, she asked at the beginning of the conference, "Okay, how many men do we have? Okay, can you stand up?" >> Lisa: Yes. I saw that >> It was very interesting because - >> Lisa: I could count on one hand. >> What, like 10 or ... >> Lisa: Yeah. >> Maximum. >> Lisa: Yeah. >> And, and I mean, you feel that, I mean, I mean you could feel what what it is to to be a woman in the field and - >> Lisa: Absolutely. >> Alexandre: That's ... >> And you, sounds like you experienced it. I experienced the same thing. But one of the things that fascinates me about data science is all of the different real world problems it's helping to solve. Like, I keep saying this, we're, we're in California, I'm a native Californian, and we've been in an extreme drought for years. Well, we're getting a ton of rain and snow this year. Climate change. >> Guests: Yeah. We're not used to driving in the rain. We are not very good at it either. But the, just thinking about data science as a facilitator of its understanding climate change better; to be able to make better decisions, predictions, drive better outcomes, or things like, police violence or healthcare inequities. I think the power of data science to help unlock a lot of the unknown is so great. And, and we need that thought diversity. Miriam, you're talking about being in engineering. Talk to me a little bit about what projects interest you with respect to data science, and how you are involved in really creating more diversity and thought. >> Hmm. In fact, at Total Energies in addition to being an energy company we're also a data company in the sense that we produce a lot of data in our activities. For example with the sensors on the fuel on the platforms. >> Lisa: Yes. >> Or on the wind turbines, solar panels and even data related to our clients. So what, what is really exciting about being, working in the data science field at Total Energies is that we really feel the impact of of the project that we're working on. And we really work with the business to understand their problems. >> Lisa: Yeah. >> Or their issues and try to translate it to a technical problem and to solve it with the data that we have. So that's really exciting, to feel the impact of the projects we're working on. So, to take an example, maybe, we know that one of the challenges of the energy transition is the storage of of energy coming from renewable power. >> Yes. >> So I'm working currently on a project to improve the process of creating larger batteries that will help store this energy, by collecting the data, and helping the business to improve the process of creating these batteries. To make it more reliable, and with a better quality. So this is a really interesting project we're working on. >> Amazing, amazing project. And, you know, it's, it's fun I think to think of all of the different people, communities, countries, that are impacted by what you're doing. Everyone, everyone knows about data. Sometimes we think about it as we're paying we're always paying for a lot of data on our phone or "data rates may apply" but we may not be thinking about all of the real world impact that data science is making in our lives. We have this expectation in our personal lives that we're connected 24/7. >> Myriam: Yeah. >> I can get whatever I want from my phone wherever I am in the world. And that's all data driven. And we expect that if I'm dealing with Total Energies, or a retailer, or a car dealer that they're going to have the data, the data to have a personal conversation, conversation with me. We have this expectation. I don't think a lot of people that aren't in data science or technology really realize the impact of data all around their lives. Alexander, talk about some of the interesting data science projects that you're working on. >> There's one that I'm working right now, so I stake advisor. I mean, I'm not the one directly working on it. >> Lisa: Okay. >> But we have, you know, we, we are from the digital factory where we, we make digital products. >> Lisa: Okay. >> And we have different squads. I mean, it's a group of different people with different skills. And one of, one of the, this squad, they're, they're working on the on, on the project that is about safety. We have a lot of site, work site on over the world where we deploy solar panels on on parkings, on, on buildings everywhere. >> Lisa: Okay. Yeah. >> And there's, I mean, a huge, I mean, but I mean, we, we have a lot of, of worker and in term of safety we want to make sure that the, they work safely and, and we want to prevent accidents. So what we, what we do is we, we develop some computer vision approach to help them at improving, you know, the, the, the way they work. I mean the, the basic things is, is detecting, detecting some equipment like the, the the mean the, the vest and so on. But we, we, we, we are working, we're working to really extend that to more concrete recommendation. And that's one a very exciting project. >> Lisa: Yeah. >> Because it's very concrete. >> Yeah. >> And also, I, I'm coming from the R&D of the company and that's one, that's one of this project that started in R&D and is now into the Digital Factory. And it will become a real product deployed over the world on, on our assets. So that's very great. >> The influence and the impact that data can have on every business always is something that, we could talk about that for a very long time. >> Yeah. >> But one of the things I want to address is there, I'm not sure if you're familiar with AnitaB.org the Grace Hopper Institute? It's here in the States and they do this great event every year. It's very pro-women in technology and technical roles. They do a lot of, of survey of, of studies. So they have data demonstrating where are we with respect to women in technical roles. And we've been talking about it for years. It's been, for a while hovering around 25% of technical roles are held by women. I noticed in the AnitaB.org research findings from 2022, It's up to 27.6% I believe. So we're seeing those numbers slowly go up. But one of the things that's a challenge is attrition; of women getting in the roles and then leaving. Miryam, as a woman in, in technology. What inspires you to continue doing what you're doing and to elevate your career in data science? >> What motivates me, is that data science, we really have to look at it as a mean to solve a problem and not a, a fine, a goal in itself. So the fact that we can apply data science to so many fields and so many different projects. So here, for example we took examples of more industrial, maybe, applications. But for example, recently I worked on, on a study, on a data science study to understand what to, to analyze Google reviews of our clients on the service stations and to see what are the the topics that, that are really important to them. So we really have a, a large range of topics, and a diversity of topics that are really interesting, so. >> And that's so important, the diversity of topics alone. There's, I think we're just scratching the surface. We're just at the very beginning of what data science can empower for our daily lives. For businesses, small businesses, large businesses. I'd love to get your perspective as our only male on the show today, Alexandre, you have that elite title. The theme of International Women's Day this year which is today, March 8th, is "Embrace equity." >> Alexandre: Yes. >> Lisa: What is that, when you hear that theme as as a male in technology, as a male in the, in a role where you can actually elevate women and really bring in that thought diversity, what is embracing equity, what does it look like to you? >> To me, it, it's really, I mean, because we, we always talk about how we can, you know, I mean improve, but actually we are fixing a problem, an issue. I mean, it's such a reality. I mean, and the, the reality and and I mean, and force in, in the company. And that's, I think in Total Energy, we, we still have, I mean things, I mean, we, we haven't reached our objective but we're working hard and especially at the Digital Factory to, to, to improve on that. And for example, we have 40% of our women in tech. >> Lisa: 40? >> 40% of our tech people that are women. >> Lisa: Wow, that's fantastic! >> Yeah. That's, that's ... >> You're way ahead of, of the global average. >> Alexandre: Yeah. Yeah. >> That outstanding. >> We're quite proud of that. >> You should be. >> But we, we still, we still know that we, we have at least 10% >> Lisa: Yes. because it's not 50. The target is, the target is to 50 or more. And, and, but I want to insist on the fact that we have, we are correcting an issue. We are fixing an issue. We're not trying to improve something. I mean, that, that's important to have that in mind. >> Lisa: It is. Absolutely. >> Yeah. >> Miryam, I'd love to get your advice to your younger self, before you studied engineering. Obviously you had an interest when you were younger. What advice would you give to young Miriam now, looking back at what you've accomplished and being one of our female, visible females, in a technical role? What do you, what would you say to your younger self? >> Maybe I would say to continue as I started. So as I was saying at the beginning of the interview, when I was at high school, I have never felt like being a woman could stop me from doing anything. >> Lisa: Yeah. Yeah. >> So maybe to continue thinking this way, and yeah. And to, to stay here for, to, to continue this way. Yeah. >> Lisa: That's excellent. Sounds like you have the confidence. >> Mm. Yeah. >> And that's something that, that a lot of people ... I struggled with it when I was younger, have the confidence, "Can I do this?" >> Alexandre: Yeah. >> "Should I do this?" >> Myriam: Yeah. >> And you kind of went, "Why not?" >> Myriam: Yes. >> Which is, that is such a great message to get out to our audience and to everybody else's. Just, "I'm interested in this. I find it fascinating. Why not me?" >> Myriam: Yeah. >> Right? >> Alexandre: Yeah, true. >> And by bringing out, I think, role models as we do here at the conference, it's a, it's a way to to help young girls to be inspired and yeah. >> Alexandre: Yeah. >> We need to have women in leadership positions that we can see, because there's a saying here that we say a lot in the States, which is: "You can't be what you can't see." >> Alexandre: Yeah, that's true. >> And so we need more women and, and men supporting women and underrepresented minorities. And the great thing about WiDS is it does just that. So we thank you so much for your involvement in WiDS, Ambassador, our only male on the program today, Alexander, we thank you. >> I'm very proud of it. >> Awesome to hear that Total Energies has about 40% of females in technical roles and you're on that path to 50% or more. We, we look forward to watching that journey and we thank you so much for joining us on the show today. >> Alexandre: Thank you. >> Myriam: Thank you. >> Lisa: All right. For my guests, I'm Lisa Martin. You're watching theCUBE Live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. We'll be back after a short break, so stick around. (upbeat music)

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

>> Narrator: Alumni. (upbeat music) >> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, WiDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WiDS movement and phenomenon, Dr. Margot Gerritsen. Welcome to theCUBE! >> It's great to be here, thanks so much for being at our conference. >> Oh, likewise. You were the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. >> Gerritsen: That's right, yep. >> Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, well, I would love to give our audience a sense of the history of WiDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30, over 30 years? >> Yeah, probably since I was 16, so that was 35 years ago. >> Yeah, and you were used to being one of few, or if not the only woman >> That's right. >> In a meeting, in a room. You were okay with that but you realized, you know what? There are probably women who are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WiDS that you and your co-founders had. >> So, May, 2015, Esteban from Walmart Labs, now at Facebook, and Karen Matthys, who's still very active, you know, one of the organizers of the conference, and I were having coffee at a cafe in Stanford and we were lamenting the fact that at another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them why? Did you notice? Because very often people are not even aware, it's just such the norm to only have male speakers, >> Right, right. >> That people don't even notice. And so we asked why is that? And they said, "Well, you know we really tried to find "speakers but we couldn't find any." And that really was, for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes, a little bit, we say it's sort of a revenge conference. (laughs) We said, let's show them we can get some really outstanding women, and in fact only women. And that's how it started. Now we were sitting at this coffee shop and I said, "Let's do a conference." And they said, "Well, that would be great, next year." And I said, "No, this year. "Let's just do it. "Let's do it in November." We had six months to put it together. It was just a local conference here. We got outstanding speakers, which were really great. Mostly from the area. And then we started live-streaming because we thought it would be fun to do. And to our big surprise, we had 6,000 people on the livestream just without really advertising. That made us realize, in November 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire a fantastic conference center in San Francisco and get 10,000 people in like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the livestream, that we will then provide to people, and asking for regional events, local events to be set up all around the world. And we created this ambassador program, that is now in its second year. the first year the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world >> In about 40 countries. >> This was last year, 2017? >> Yeah, almost exactly 13 months ago, and then this year now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. >> That's incredible. >> So we're on every continent apart from Antarctica but we're working on that one. >> Martin: I was going to say, that's probably next year. >> Yeah, that's right. >> The scale, though, that you've achieved in such a short time period, I think, not only speaks to the power, like you said, of using technology and using live-streaming, but also, there is a massive demand. >> Gerritsen: There is a great need, yeah. >> For not only supporting, like from the perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think, underscores, there is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. >> Gerritsen: That's right. >> So, what are some of the things, so, this year, not only are you reaching, hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WiDS Datathon. >> That's right. >> Tell us about the WiDS Datathon, what was the idea behind it? You announced some winners today? >> Yeah. Yeah, so with WiDS last year, we really felt that we hit a nerve. Now there is an incredible need for women to see other women perform so well in this field. And, you know, that's why we do it, to inspire. But it's a one-time event, it's once a year. And we started to think about, what are some of the ways that we can make this movement, because it's really become a movement, into something more than just an annual, once-a-year conference? And so, Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported again by the ambassadors, so the local WiDS organizations. So we thought we'd just try. But again, it's one of those things we say, "Oh, let's do it." We, I think, thought about this about six months ago. Finding a good data set is always a challenge but we found a wonderful data set, and we had a great response with 1100, almost 1200 people in the world participating. >> That's incredible. >> Several hundred teams. Yeah, and what we said at the time was, well, let's have the teams be 50% female at least, so that was the requirement, we have a lot of mixed teams. And ultimately, of course, that's what we want. We want 50-50, men-women, have them both at the table, to participate in data science activities, to do data science research, and answer a lot of these data questions that are now driving so many decisions. Now we want everybody around the table. So with this Datathon, it was just a very small event in the sense, and I'm sure next year it will be bigger, but it was a great success now. >> Well, congratulations on that. One of the things I saw you on a Youtube video talking about over the weekend when I was doing some prep was that you wanted this Datathon to be fun, creative, and I think those are two incredibly important ways to describe careers, not just in STEM but in data science, that yes, this can be fun. >> Yep. >> Should be if you're spending so much time every day, right, doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation? >> Oh. (laughs) You're hitting the biggest sore point in data science. And you could even turn it around, you say, because of creativity, we have a problem too. Because you can be very creative in how you interpret the data, and unfortunately, for most of us, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky, and it's tricky. 'cause you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer. Even though we always say we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data. So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem and the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government, the more important it is that we are at least aware. One of the really interesting things this conference, is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. We had Daniela Witten saying this, and various other speakers. And in the first year that we had this conference, you would not have heard this. >> Martin: Really? Only two years ago? >> Yeah. So even two years ago, some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmations bias, now at least people now are aware. And I'm always a great optimist, thinking if people are aware, and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness, and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist, you are somebody who needs to look at things taking into account ethics, and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Educational programs like ours too have embedded these sort of aspects into the education and I think there is a lot of hope for the future. But we're just starting. >> Right. But you hit the nail on the head. You've got to start with that awareness. And it sounds like, another thing that you just described is we often hear, the top skills that a data scientist needs to have is statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side, that seem to be, from what we hear on theCUBE, as important, >> Gerritsen: That's right. >> As really that technical training. To be more well-rounded and to also, as you mentioned earlier, to have to the chance to influence every single sector, every single industry, in our world today. >> And it's a pity that they're called softer skills. (laughs) >> It is. >> Because they're very very hard skills to really master. >> A lot of them are probably you're born with it, right? It's innate, certain things that you can't necessarily teach? >> Well, I don't believe that you cannot do this without innate ability. Of course if you have this innate ability it helps a little, but there's a growth mindset of course, in this, and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. >> Absolutely. >> This is one of the reasons why we want way more diversity in these companies, right. It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one, communication skills, presentation skills, visualization skills, negotiation skills, that they really are developed everywhere, in the companies, at the universities. >> Absolutely. We speak with some companies, and some today, even, on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners, you mentioned Walmart Labs, how challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey we have this idea for a conference, we want to do this in six months so strap on your seatbelts, what were those conversations like to get some of those partners onboard? >> We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting really big diversity initiatives. Intel is one of them, SAP is another one of them. We were connected with these companies. Walmart Labs, for example, one of the founders of the company was from Walmart Labs. And so when we said, look, we want to put this together, they said great. This is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WiDS events around the world. Like SAP built 30 WiDS events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that a changed culture is for the best of everybody. But they also believe that because they need the women. There is a great shortage of really excellent data scientists right now, so why not look at 50% of your population? >> Martin: Exactly. >> You know, there's fantastic talent in that pool and they want to track that also. So I think that within the companies, there is more awareness, there is an economic need to do so, a real need, if they want to grow, they need those people. There is an awareness that for their future, the long term benefit of the company, they need this diversity in opinions, they need the diversity in the questions that are being asked, and the way that the companies look at the data. And so, I think we're at a golden age for that now. Now am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student 30 some years ago, I was one of the very few women, and I thought, by the time I'm old, and now I'm old, you know, as far as my 18-year-old self, right, I mean in your 50s, you're old. I thought everything would be better. And we certainly would be at critical mass, which is 30% or higher, and it's actually gone down since the 80s, in computer science and in data science and statistics, so it is really very frustrating in that sense that we're really starting again from quite a low level. >> Right. Right. >> But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. >> Well I think you definitely, with WiDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine, WiDS 2018 reaching up to 100,000 people over these events, what do you do next year? Where do you go from here? (laughs) >> Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events, so we're going to be thinking about how to organize ourselves, maybe on every continent. >> Getting to Antarctica in 2019? >> Yeah, but have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful, and quite a few of them are looking at the live-stream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they are at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very very eager to self-educate, and so what we are going to be putting together, at least that's the plan now, we'll see, if we can make this, is educational tools, and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women, which will be very, very interesting. We'll start this next month, and so every week or every two weeks we'll have a new podcast out there. And then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year, >> Sustained inspiration. >> Sustained inspiration and resources. >> Wow, well, congratulations, Margot, to you and your co-founders. This is a movement, and we are very excited for the opportunity to have you on theCUBE as well as some of the speakers and the attendeees from the event today. And we look forward to seeing all the great things that I think are going to come for sure, the rest of this year and beyond. So thank you for giving us some of your time. >> Thank you so much, we're a big fan of theCUBE. >> Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin, we are live at the third annual Women in Data Science Conference coming to you from Stanford University, #WiDS2018, join the conversation. I'll be back with my next guest after a short break. (upbeat music)

>> Narrator: Live from Stanford University in Palo Alto, California. It's theCUBE covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE. Our continuing coverage live at the Women in Data Science Conference 2018, #WiDS2018. I'm Lisa Martin and I'm very excited to not only be at the event, but to now be joined by one of the speakers who spoke this morning. Mala Anand, the executive vice president at SAP and the president of SAP Leonardo Data Analytics, Mala Anand, Mala, welcome to theCUBE. >> Thank you Lisa, I'm delighted to be here. >> So this is your first WiDS and we were talking off camera about this is the third WiDS and 100,000 people they're expecting to reach today. As a speaker, how does that feel knowing that this is being live streamed and on their Facebook Live page and you have the chance to reach that many people? >> It's really exciting, Lisa and you know, it's inspiring to see that we've been able to attract so many participants. It's such an important topic for us. More and more I think two elements of the topic, one is the impact that data science is going to have in our industry as well as the impact that we want more women to participate with the right passion and being able to be successful in this field. >> I love that you said passion. I think that's so key and that's certainly one of the things, I think as my second year hosting theCUBE at WiDS, you feel it when you walk in the door. You feel it when you're reading the #WiDS2018 Twitter feed. It's the passion is here, the excitement is here. 150 plus regional WiDS events going on today in over 50 countries so the reach can be massive. What were maybe the top three takeaways from your talk this morning that the participants got to learn? >> Absolutely, and what's really exciting to see is that we see from a business perspective that customers are seeing the potential to drive higher productivity and faster growth in this whole new notion of digital technologies and the ability now for these new forms of systems of intelligence where we embed machine learning, big data, analytics, IoT, into the core of the business processes and it allows us to reap unprecedented value from data. It allows us to create new business models and it also allows us to reimagine experiences. But all of this is only possible now with the ability to apply data science across industries in a very deep and domain expertise way, and so that's really exciting and, moreover, to see diversity in the participants. Diversity in the people that can impact this is very exciting. >> I agree. You talked about digital business. Digital transformation opens up so many new business model opportunities for companies but the application of advanced analytics, for example, alone opens up so many more career opportunities because every sector is affected by big data. Whether we know it or not, right? And so the opportunity for those careers is exploding. But another thing that I think is also ripe for conversation is bringing in diverse perspectives to analyze and interpret that data. >> Absolutely. >> To remove some of the bias so that more of those business models and opportunities can really bubble up. >> Absolutely. >> Lisa: Tell me about your team at SAP Leonardo and from a diversity perspective, what's going on there? >> Yeah, absolutely. So I think your point is really valid which is, the importance of bringing in diversity and also the importance of diversity both from a gender perspective and a diversity in skills. And I think the key element of data and decision science is now it opens up different types of skills, right? It opens up the skills of course, the technology skills are fundamental. The ability to read data modeling is fundamental, but then we add in the deep domain expertise. The add in the business perspectives. The ability to story tell and that's where I see the ability to story tell with the right domain expertise opens up such a massive opportunity for different kinds of participants in this field and so within SAP itself, we are very driven by driving diversity. SAP had set a very aggressive goal for by 2017 to be at 25% of women in leadership positions and we achieved that. We've got an aggressive goal to be at 30% of women in leadership positions by 2020 and we're really excited to achieve that as well and very important as well both within Leonardo and data analytics as well, by diversity is fundamental to our growth and more importantly to the growth for the industry. I think that's going to be fundamental. >> I think that's a really important point, the growth of the industry. SAP does a lot with WiDS. We had Ann Rosenberg on last year. I saw her walking around. So from a cultural stand point, what you've described, there's really a dedicated focus there and I think it's a unique opportunity that SAP doesn't have. They're taking advantage of it to really show how a massive corporation, a huge enterprise, can really be very dedicated to bringing in this diversity. It helps the business, but it also, to your point, can make a big impact on industry. >> Absolutely, you know, culture is such a critical part of being succeeding in the business, and I think culture is an important lever that can help differentiate companies in the market. So of course it's technology, it's value creation for our customers, and I think culture is such an important part of it, and when you unpeel the lever of culture, within there comes diversity, and within there comes bringing a different diversity of skills base as well that is going to be really critical in the next generation of businesses that will get created. >> I like that. Especially sitting in Silicon Valley where there's new businesses being created every, probably 30 seconds. I'd love to understand, if we kind of take a walk back through your career and how you got to where you are now. What were some of the things that inspired you along the way, mentors? What were some of the things that you found really impactful and crucial to you being as successful as you are and a speaker at an event like WiDS? >> Oh, absolutely. It's really exciting to see that from my own personal journey, I think that one of the things that was really important is passion. And ensuring that you find those areas that you're passionate about. I was always very passionate about software and being able to look at data and analyze data. From doing my undergraduate in Computer Science, as well as my graduate work in Computer Science from Brown, and from there on out, always looking at any of the opportunities whether it was an individual contributor that I did. It's important to be passionate and I felt that that was really my guiding post to really being able to move up from a career perspective, and also looking to be in an environment, in an ecosystem, of people and environments that you're always learning from, right? And always never being afraid to reach a little bit further than your capabilities. I think ensuring that you always have confidence in the ability that you can reach, and even though the goals might feel a little bit far away at the moment. So I think also being around a really solid team of mentors and being able to constantly learn. So I would say a constant, continuous learning, and passion is really the key to success. >> I couldn't agree more. I think it's that we often, the word expert is thrown around so often and in so many things, and there certainly are people that have garnered a lot of expertise in certain areas, but I always think, "Are you really ever an expert?" There's so much to learn everyday, there's so many opportunities. But another thing that you mentioned that reminded me of, we had Maria Klawe on a little bit earlier today and one of the things that she said in her welcome address was, in terms of inspiration, "Don't worry if there's something "that you think you're not good at." >> Mala: Absolutely. >> It's sort of getting out of your comfort zone and one of my mentors likes to say, "getting comfortably uncomfortable." That's not an easy thing to achieve. So I think having people around, people like yourself, you're now a mentor to potentially 100,000 people today, alone. What are some of the steps that you recommend of, how does someone go, "I really like this, "but I don't know if I can do it." How would you help someone get comfortably uncomfortable? >> Yeah, I think first of all, building a small group I would say, of stakeholders that are behind you and your success is going to be really important. I think also being confident about your abilities. Confidence comes in failing a few times. It's okay to miss a few goals, it's okay to fail, but then you leap forward even faster. >> Failure is not a bad F word, right? >> Mala: Absolutely. >> It really can be, and I think, a lot of leaders, like yourself will say that it's actually part of the process. >> It's very much part of the process. And so I think, number one thing is passion. First you've got to be really clear that this is exactly what you're passionate about. Second is building a team around you that you can count on, you can rely on, that are invested in your success. And then thirdly is also just to ensure that you are confident. Being confident about asking for more. Being confident about being able to reach close to the impossible is okay. >> It is okay, and it should be encouraged, every day. No matter what gender, what ethnicity, that should just sort of be one of those level playing fields, I think. Unfortunately, it probably won't be but events like WiDS, and the reach that it's making today alone, certainly, I think, offer a great foundation to start helping break some of the molds that even as we sit in Silicon Valley, are still there. There's still massive discrepancies in pay grades. There's still a big percentage of females with engineering degrees that are not working in the field. And I think the more people like yourself, and some of your other colleagues that are here participating at WiDS alone today, have the opportunity to reach a broader audience, share their stories. Their failures, the successes, and all the things that have shaped that path, the bigger the opportunity we have and it's, I think, almost, sort of a responsibility for those of us who've been in STEM for a while, to help the next generation understand nobody got here with a silver spoon. Eh, some. >> Absolutely. >> But on a straight path. It's always that zig zaggy sort of path, and embrace it! >> Yeah, I think that's key, right? And the one point here is very relevant that you mentioned as well is, that it's very important for us to recognize that a love for an environment where you can embrace the change, right? In order to embrace change, it's not just people that are going through it, but people that are supporting it and sponsoring it because it's a big change. It's a change from what was an environment a few years ago to what is going to be an environment of the future, which is an environment full of diversity. So I think being able to be ambassadors of the change is really important. As well as to allow for confidence building in this environment, right? I think that's going to be really critical as well. And for us to support those environments and build awareness. Build awareness of what is possible. I think many times people will go through their careers without being aware of what is possible. Things that were certain thresholds, certain limits, certain guidelines, two years ago are dramatically different today. >> Oh yes. >> So having those ambassadors of change that can help us build awareness, with our growing community, I think is going to be really important. >> I think, some of the things too, that you're speaking to, there are boundaries that are evaporating. We're seeing them become perforated and sort of disappear, as well as maybe some of these structured careers. There's a career as this, as that. They used to be pretty demarcated. Doctor, lawyer, architect, accountant, whatnot. And now it's almost infinite. Especially having a foundation in technology with data science and the real world social implications alone, that a career in this field can deliver just kind of shows the sky's the limit. >> Yeah, absolutely. The sky's truly the limit, and I think that's where you're absolutely right. The lines are blurring between certain areas, and at the same time, I think, this opens up huge opportunity for diversity in skill set and diversity in domain. I think equally important is to ensure to be successful you want to start by driving focus, as well, right? So, how do you draw that balance? And for us to be able to mentor and guide the younger generation, to drive that focus. At the same time take leverage the opportunities open is going to be critical. >> So getting back to SAP Leondardo. What's next in this year, we're in March of 2018. What are some of the things that are exciting you that your team is going to be working on and delivering for SAP and your customers this year? >> SAP Leondardo is really exciting because it essentially allows for our customers to drive faster innovation with less risk. And it allows our customers to create these digital businesses where you have to change a business process and a business model that no single technology can deliver. So as a result we bring together machine learning, big data analytics, IoT, all running on a solid cloud platform with in-memory databases like Kana, at scale. So this year is going to be all about how we bring these capabilities together very specifically by industry and reimagine processes across different industries. >> I like that, reimagine. I think that's one of the things that you're helping to do for females in data science and computer sciences. Reimagine the possibilities. Not just the younger generation, but also those who've been in the field for a while that I think will probably be quite inspired and reinvigorated by some of the things that you're sharing. So, Mala, thank you so much for taking the time to stop by theCUBE and share your insights with us. We wish you continued success in your career and we look forward to seeing you WiDS next year. >> Thank you so much, Lisa. I'm delighted to be here. >> Excellent. >> Thank you. >> My pleasure. We want to thank you. You are watching theCUBE live from WiDS 2018, at Stanford University. I'm Lisa Martin. Stick around, my next guest will be joining me after this short break.