 Live from Boston, Massachusetts, it's theCUBE, covering Red Hat Summit 2017, brought to you by Red Hat. Welcome back, I'm your host, Rebecca Knight, along with Stu Miniman, my co-host. We are joined by Sandra Rivera, Vice President and General Manager Network Platform Groups at Intel. Thanks so much, Sandra. Thank you for having me. I want to talk about a point you made during your keynote address, and you talked about the transformative power of data, and just about how data will change the face of so many industries, from healthcare to airlines, to the financial services industry. And yet, there are so many challenges that companies and developers themselves face in dealing with this avalanche of data, sifting through it, understanding it, sorting it, chunking it the right way, and really understanding what it's saying. Can you talk about the challenges, and then also what companies are doing to overcome the challenge? So it is really at the crux of the both challenge and opportunity is what do you do with all the massive amounts of data that are being generated, and I spoke about how an average user really generates or consumes about one and a half gigabytes of data per day. But if you fast forward of what's happening in the rest of the industry with connected cars at four terabytes of data per day, or connected planes at five terabytes, or a smart factory at one petabyte of data a day, what do you do with all of that? Because today, much of that goes wasted and unutilized, right? We create these large data lakes, and yet the value creation portion that you need to turn it into something useful and profitable is really challenging. And the things that we're doing to address those challenges collectively with the ecosystem are really building standardized sets of software interfaces and APIs through our contributions in open source and open standards, because we do believe that these are problems that are best addressed when you're doing it in community and in parallel. And much of the investments that we're making in the underlying ingredient technologies, be it hardware or software, have to be exposed at a much higher level that for the application developer, they know that there are some tools underneath giving them performance or capabilities that they desire for their customers, but not having to know a lot of those intricacies. So a lot of that abstraction work that we do collectively with the ecosystem, and I mean, Red Hat being a great partner of ours in that vein, in that effort, is really to abstract all those complexities and make it easier to onboard the developers and let them innovate and really focus on the value creation portion of the problem statement. And so do developers now need a new layer of education to get the data? Yeah, well, in fact- You need any data scientists. Exactly. Well, and a lot, you see a lot of the larger corporations hiring in data scientists, but everyone is not going to be a data scientist and everyone's not going to be able to afford one on their payroll. So our job is really to have, again, this abstraction capability, but one that takes advantage of the underlying innovations that we invest in, both from a hardware and a software perspective, and then to really try to provide some of that education capability. And some of the things that I spoke about are as part of our community, a community that we call the Builders Community. Builders, in fact, I was trying to get folks to go. Look at builders.intel.com because you see we have hundreds and hundreds of publications there, solution briefs and technical documents and reference architectures and tips and tricks and techniques for how you can optimize your software to take advantage of all of these innovations underneath, instead of doing that trial and error that you would do if you're just kind of starting from ground up and doing it and repeating that same process over and over again. It's really embracing much more of that DevOps model, which is new to the networking industry, but very familiar to the IT type developer. Senator, I wonder if you can help us connect the dots. I think back to when we started talking about the term big data, one of the terms I loved, it was the bit flip from that, all this data is going to be a challenge to, hey, this is an opportunity for us to do good things. But when you start talking about the evolution now to machine learning, artificial intelligence, big data, there's so many companies that are like, we tried these initiatives and over 50% of them were failing. We just weren't delivering on the value, we were investing, but we weren't there. Why will it be different? How has the ecosystem matured and this kind of maturation of the market? Yeah, well, a lot of it is really about how do you make the access to all of that data look like another compute problem? And we have a lot of compute application developers that are very familiar with the types of software tools and optimization capabilities that we have, not just in the Intel portfolio, but in the ecosystem through our efforts in open source and open standards. So I think that we learn that trying to dig down and get every ounce of optimization from the hardware by hard coding to a lot of those interfaces is not the fastest way to bring a broad community of developers on board. And the investments that we have been making is in trying to both build up from a software stack perspective, but also build out our capabilities in our existing software tool chains that we have, that we have hundreds of thousands of developers that are familiar with developing to those interfaces. And when you do that or when we've been doing that, we don't think that the application developer will particularly care or should particularly care if that workload is running partly on a general purpose processing CPU, partly in an FPGA, which is another asset and capability that we have and is highly programmable or running in an ASIC environment, which is another capability that we brought into the company specifically around machine learning and artificial intelligence through an acquisition of a company by the name of Nirvana. So again, all of those are your building blocks, but our job is to create the software environment that just lets you put it together like Lego blocks, as opposed to really having to know all the intricacies and complexities of the underlying ingredient technologies. And how does the open source initiatives help us get to that customization that I might need for specific verticals and help accelerate the growth for everyone? Well, a lot of the investments that we've been making is both in the virtualization layer, but also in container types of technologies. I talked about the OpenShift initiative that we have with Red Hat and with other partners where we're looking at Docker and Kubernetes and container types of deployment models in addition to VM types of deployment models. If you look at everything that is happening in the industry and the investment that's going there, it really is very much around up-leveling the tools so that you can take advantage of the underlying capabilities, but you do have opportunities for customization that don't require necessarily programming micro engines down at the bare metal layer or lower layers of the hardware stack. So it very much is the playbook around if you want to enable a broad ecosystem, you have to lower the barriers to entry, you have to give them a tool chain that they can more easily adapt to or program to, and you have to show them opportunities by working directly with the end customers to, again, we talked about financial industry or healthcare industry that allows you to optimize for the problems that they're facing or the opportunities that they see as well. So some of the work we do is not just on the technology side, but very much in terms of matchmaking to the end customers and doing the proof of concepts and doing the learnings and that iterative process of just uncovering the things that you thought were going to be big problems, sometimes aren't, and the things that you didn't anticipate would be challenges, sometimes are. And I mean, it's hard work, but it actually is really being successful in terms of there's a lot of interest in this area, there's many more tools, there's more investment going in, and there's a lot of opportunity for innovation and growth. And particularly with the emerging force of artificial intelligence and 5G, that really will have a transformative effect on the way we customers, just individual customers, interact with these industries, and you had some great examples. Yeah, so I talked a little bit about banking application and that sort of natural language processing that happens and the ability to have an AI assistant that can help you when you just speak in the regular sentences and syntax, but also gets smarter over time to learn your individual habits and preferences and then can sometimes really will provide advice, not just answer questions, but actually provide some investment advice, let's say. We talked about AI in sports, which is another great area for application of artificial intelligence and learning movements and motion and form of an athlete's swing or an athlete's form or position as they're exercising their sport. But one of the other areas that we're seeing a lot of application is in something as old as agriculture, right, which is a 23,000 year old industry, but smart and connected cows and smart and connected wine, which is a wonderful application. But for the farmers to understand the soil quality and to know what, you know, the forecast and the moisture and the sunshine and the rainfall, I mean, all of these things really allow them to be more effective and have a higher output, more successful crops, more profits, and even their farming equipment, right, all the sensors that are in farming equipment to be able to predict a failure of the equipment or a service requirement for that piece of equipment. So all these things that you realize that once you're, anything that can be smart and connected is going to be smart and connected. I mean, we fundamentally believe that at Intel, that whether you're talking about sports and skateboards and bikes or you're talking about industries, financial, medical, certainly is a huge one. The education or agriculture, there's so many opportunities for you to, to really have that value creation element of the data collection process. I want to ask you also about the technology industry and the community within the technology industry. It's getting a bad rap these days. There's very little diversity. There's very few women, particularly in leadership positions, very few minorities. I know that this is a cause that you champion, personally and professionally. What, first of all, is it as bad as the headlines when you're in it? And second, what are you doing to change it, both as an individual leader and at, and what Intel is doing? Well, this is something that Intel is deeply committed to from our CEO, through our leadership team and really driving throughout the organization. And it isn't just because it's the right thing to do, diversity is the right thing to do, but it just makes business sense. If you look at just women in general and women and men, women make over half of the purchasing decisions in a family and actually in the household, they make more than half the purchasing decisions for the big ticket items. And so it's kind of dumb to not include more women in leadership positions that could have a different perspective on product development and features and trade-offs and capabilities and just organically what you do in terms of your own product innovation. But beyond that, we also know that any organization that has diversity and it's men, it's women, it's ethnicity, it's experience, large company, small company, it's different cultures and backgrounds, you will drive a better business result. And the data proves it over and over and over again that you are quicker to innovate, you're quicker to find and identify problems, you're quicker as a team to just move to something that is more innovative, faster, and it's proven that all of those, the companies that have more diversity on their boards and in their senior leadership team do drive better business outcomes. So from that perspective, it's again, it's the right thing to do, but it also makes good business sense. But it is a complex problem. And at Intel, I mean, we certainly know it's a pipeline problem that starts at very young age in terms of just getting particular more girls interested in science, technology, engineering, and math. Then when they graduate, it's attracting them to come and really be engineers and to maintain that technical passion that they have. And sometimes in the face of a lot of adversity because we know that sometimes their inputs get marginalized or discounted. But then we find that even if we've made it all through that that it's a retention problem from the perspective that women want to see a career progression just like men do. And typically that is just a bigger challenge for women because the people that make those decisions or provide those opportunities, there's not enough women that are advocating, frankly, not just women, but they're not men advocating for those women. So we have a lot that we're investing in this very multifaceted problem. It is a journey, but to your point, I'm not discouraged. I really do think it's better than it's ever been. And the bro culture, I mean, is that, I mean, you talked about the women who may get discouraged because their inputs, they're not called on in a meeting. They're not chosen for that cool new project. I mean, that is deflating. Well, it is deflating, and those are the things that you have to address. But one of the ways that we have found to do that is that you have to assume that there is a bias, right? We all have biases. This is one thing that we learn, is that if you have a brain, you have a bias. It's not good or bad, it just is. And so there's so many ways to overcome those biases. There's all kinds of ways. I mean, we know this from studies that were done, women that were trying out for the Philharmonic Orchestra in New York. If you did the audition behind the curtain, they were chosen like 50% more of the time than if they weren't behind the curtain because you just tend to, your bias is that, well, I didn't hear them play that well, but it's unconscious. You don't realize that you're actually doing that. So there's so many ways that you can overcome the unconscious bias, but you have to acknowledge that it exists, and once it exists, then there's a lot of tools and techniques that we employ at Intel in terms of having more diverse hiring panels, having more diverse candidates that you're bringing in, and establishing your criteria for hiring before you meet the candidates, and then assessing each candidate against that criteria so that you don't get to kind of change your mind afterwards. I mean, there's lots of ways. But truly, I am very encouraged. I do, I mean, I've been at this a long time, and I think it is a much better environment now than it was. It's nowhere where we need to be, but yeah, the culture is tough, but it's not as bad as it was, and it is getting better every day. Great, well, thank you so much, Sandra Rivera. We appreciate your time. Thank you. I'm Rebecca Knight for Stu Miniman. We'll be back with the wrap just after this.