>> 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.