>> 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.

>> 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)