 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, hashtag WIDDS 2018. Participate in the conversation and you're going to see people at WIDDS events in over 177 regions in over 53 countries. This event is aiming to reach about 100,000 people in the next couple of days, which in its third year is remarkable. It's aimed at inspiring and educating data scientists worldwide and of course supporting females in the field. It's also got keynotes, technical vision tracks, and a career panel, and we're excited to welcome back to theCUBE a CUBE alumni, Zia Ma, the Vice President of Software and Services Group and the Director of Big Data Technologies at Intel Zia. Welcome back to theCUBE. Thanks for having me, Lisa. You have been, this is your first time coming to a WIDDS event in person and your first year here, you are on the career panel. Yes. That's pretty cool. Tell us about, you just came from that career panel. Tell us about that. What were some of the things that excited you? What are some of the things that surprised you and what you've heard in that panel? So I think one thing that was really exciting is to see the passion from the audience. So many women excited with data science and with the future of what data science can bring. That's the most exciting part and also it's very exciting to get connected with so many women professionals. And in terms of surprise, I think it's a good surprise to see so much advancement in women development in data science comparing where we are and where we were two years ago. It's great to see so many women speakers and leaders talking about their work in the data science space, applying data science to solve real business problem, to solve transportation problem, to solve education, healthcare problems. I think that's the happy surprise, the fast advancement with women development in this field. What were some of the things that you shared, maybe recommendations or advice? You've been in industry for a long time. You've been at Intel for quite a long time. What were some of the things that you felt important to share with the audience? Those in person here at Stanford, which is about 400 or plus and those watching the live stream. Yeah, Lisa, I provide a career coaching actually for many women professionals at Intel and also from the industry. And a lot of them expressed interest of getting into data science field and they asked me what is the skill set that I need to develop in order to get this into this field. I think first you need to ask yourself what kind of job you want to get into in this field. There are marketing jobs, there are sales jobs and even for technical jobs, there are data engineering type of jobs, data visualization, statistician, data science or AI engineer, machine learning, deep learning engineer. So you have to ask yourself what kind of job you want to move to and then assess your skill set gap and work to close that gap. Another advice I give to many women professionals is that data science appeared to have a high bar today and it may be too significant a job to move from where you are to data science field. You may want to move to adjacent field first and to have a sense of what it is it like to work in the data science field and also have more insights with what's going on and then to better prepare you for eventually moving into this field. Great advice. I think one of the things that jumped out at me is talk about skill sets and we often hear a lot of the technical skills that are essential for a data scientist but there's also softer skills. Maybe it's more left brain, right brain, creativity, empathy, communication. Tell me in your ascension to now the VP level at Intel, what are some of the other skills besides the technical skills that you find as data science as a field grows and infiltrates everything? What are some of those softer skills that you think are really advantageous? Great question. I think openness and a collaboration are very important soft skills because as a data scientist you need to work with data engineering team because a data scientist, you extract business insights from the data but then you cannot work alone. You have to work with the data engineering team who prepares the data infrastructure, stores and manage the data very efficiently for you to consume. You also have to work with a domain expert. Let's say if you're applying data science solution to solve a real business problem, let's say in a medical field, you need to work with a domain expert from the medical field so that you can tailor your solution towards addressing some medical problems. So you need to work with that domain expert who knows the business operations and processes in medical field really, really well. So I think that collaboration is key and of course you also want to collaborate maybe with academia and open source community where a lot of real innovations are happening and you want to leverage the latest technology, building blocks so that you can accelerate your data science application or solution advancement. So collaboration and openness are the key. Openness is a great one. I'm glad that you brought that up. We had another guest on talking about that earlier in terms of being open one to not expecting, you know, the scientific method. You go into it with a hypothesis and you think you know what you're going to find or you want to know I want to find this and you might not. And being open to going, okay, that's okay. I'm going to course correct because failure in this sense is not a bad F word but also being open to other opinions, other perspectives that seems to be kind of a theme that we're hearing more about today. It's be willing to be open-minded. You know, that's an excellent point, Lisa. You know, I can share one example, one coming from engineering background. When I first moved into this field, we always had the assumption that when we talk with our customers they must be looking for something that's high performance. So our initial discussion with our customers centered around Intel product roadmap that will give you the highest performance for deep learning training or for analytics solution. But as we went deeper with the discussion, we realized that's not what customer are looking for in many cases. The fact is that many of them have collected massive amount of data over the years. They have built analytics applications and to end on top of that. And so as the data representation get more complex, they want to extract more complex insights. That's the time they want to apply deep learning but to the existing application infrastructure. So they're looking for something, let's say deep learning capability that can be easily integrated into the existing analytics solution stack, into its existing infrastructure and reuse its existing infrastructure for lower cost of ownership. That's what they're looking for. And high performance is just nice to have. So once we are open-minded to that learning, that totally changed the conversation. Actually in the last couple of years, we have applied that learning and we have collaborated with top cloud service providers like Amazon, Microsoft, Google and Alibaba and Baidu and a few others to deploy Intel-based deep learning capabilities libraries, frameworks into cloud so that more businesses and individuals can have access. But again, it's that openness. You really need to understand what is the problem you're solving before simply just selling a technology. Absolutely. And that was one of the best examples of openness. It's obviously in this case, listening to customers. We think we know the problem that we need to solve and they're telling you actually it's not that. It's a nice to have and you go whoa, that changes everything. And it also changes, sounds like the downstream collaboration that Intel knew we need to have in order to drive our business forward and help our customers in every industry do the same thing. Exactly, exactly. So a couple of things that I'd love to get your perspective on is the culture at Intel. You've been there a long time. What is that culture like in terms of maybe fueling or being a nice opportunity for bringing in this diversity that we so need in every industry? Yeah, one thing I want to share actually just now during the panel discussion, I shared this. I said Intel will be the first high-tech company achieving full representation of a woman and underrepresented minority by the end of this year. Wow, by the end of 2018. Yes, we put in our timeline by two years. Yes, we're well on track for this year to achieve that. And I personally, I like this quote from Brian Kazanich, our CEO, that if we want tech to define the future, we must be representative of that future. So actually in the last actually few years now, Intel has put great effort into hiring and retention for diversity. We also have put great effort for inclusion. We want to make sure our employees, every one of them come to work, bring the full selves and for the value add. We also invest in diverse entrepreneur through Intel capital initiatives and most importantly, we also partner with academia, universities to build the pipeline for tech sectors. So we put a lot of effort and we committed about $300 million for closing the gap at the company but also for the high tech sector. So it definitely we're very committed to the diversity and inclusion. But that doesn't mean that we only focus on this and of course we make sure that our people are bringing the right skillsets and we bring the most qualified people to do the job. On the pipeline front, one of the things I was reading recently is some of the challenges that recruit that organizations that are going to say college campuses to recruit, some of the missteps they might be taking in terms of if they're trying to bring more females into their organization in STEM roles, don't staff a booth with men, right? Or have the only females that are at a recruitment event be doing, handing out swag or taking names. Obviously there's important roles to be had everywhere but that was one of the things that seems to be, well what is simple thing to change, just flip the model so that the pipeline to your point is fueling really what corporations like Intel want to achieve so that that future is really as inclusive and diverse as it should be. Second thing that you mentioned before we went live and from an Intel perspective is you guys were challenged on the talent acquisition front and so a few years ago you started the Women in Big Data Forum to solve that problem. Tell us about that and what have you achieved so far? So great question. So this is three or four years ago and Intel, because I managed the Big Data Engineering Organization within Intel and we are working to hire some diversity talents. So we opened some regs and we look at our candidate pool. There are very few women, actually barely any women in the candidate pool. Again, yes, we always want to hire the most qualified people but it also does not feel right that when you don't even have any diversity candidates in that pool, even though we exhausted all possible options even try to bring the relevant diversity candidates into the pool, but it's very challenging. So then we reached out to a few industry partners to see is Intel the only company that had this problem or you have the same problem, it turned out everyone had the same problem. So yes, people value diversity, they all see the value but it's very challenging to have a successful recruiting process for diversity. That's the time the few of us gather together. We said maybe there is something that we can do to support a stronger woman pipeline for future hiring. It may take a couple of years, it may take one year but unless we start doing something today, you know, we're going to talk about the same problem two years from now. Exactly. So then, you know, with sponsorship from our executive team, you know, Doug Fisher, the Intel software and so this is group GM and also Michael Green and a few others, we, you know, bring the team together. We started, you know, look at networking opportunities, training opportunities. We work with our industry partners to offer many free training classes. We also, you know, start reaching out to universities to build the pipeline and especially to motivate the female students to get passionate about big data, about analytics. So as of now, we have more than 2,000 members globally for the forum and also we have many chapters. We have chapters along West Coast in the Bay Area, also East Coast. We also have chapters in Europe and Asia. So we're definitely seeing more and more women getting excited with, you know, big data and analytics and also we have great collaboration with women in data science at Stanford, so. Yeah, and it sounds like the momentum, it doesn't sound like the momentum, you can feel it, right? You can feel it online with, I can see a Twitter stream in front of me here on this monitor. People are getting involved in droves all across the globe and I said to Margo, I asked her earlier, Margo Gerritzen, one of the founders of Woods, I said, first of all, you must be pleasantly, pretty shocked at how quickly this has ascended. And she said, yes. I said, what do you go from here? And she said, it's really now going to be about getting involved with Woods more frequently throughout the year. Also kind of going up funnel, if you will, to high school students and starting to encourage them, excite them and start that motivation track, if you will, even earlier. And I think that is, in terms to your point about we can't do anything if the pipeline isn't there to support it. One of the things that Woods is aiming to do, and it sounds like what you're doing as well, similar in women in big data form at Intel, is let's start creating a pipeline of women that are educated in the technical side and the software skill side that are interested and find their passion so that we can help motivate them that you can do this. The sky is the limit where data science is concerned. Absolutely, absolutely. And it's great to see actually everybody recognize the value of building the pipeline and it reaching out beyond the university students. Because we have to get more and more girls getting into the science and tech sector. And we have to start from young. And I totally agree. I think we really need to build our pipeline and a pipeline for our pipeline. Yes, exactly. And also that sort of sustaining momentum as women go in university and study STEM subjects, get into the field. Obviously retention is a big challenge that the tech industry and STEM fields alike have faced. But that retention, that motivation, and I think organizations like this, just with this, you can feel the passion when you walk into this alumni center at Stanford is really key. We thank you so much for carving out some time to share your insights and your career path and your recommendations on theCUBE and wish you continued success at Intel and with women in big data forum. I'm sure we'll see you back at WIDS next year. All right, thank you. Thanks, Lisa. Absolutely, my pleasure. We want to thank you. You have been watching theCUBE live from the Women in Data Science Conference 2018. Hashtag WIDS 2018, join the conversation, get involved. I'm Lisa Martin from Stanford. Stick around. I'll be right back with John Furrier to do a wrap of the day.