>>Hey, everyone. Welcome to the cubes coverage of women in data science, 2022. I'm Lisa Martin. And I'm here with one of the key featured keynotes for this year is with events. So the Aragon, the professor and department of human centered design and engineering at the university of Washington Cecilia, it's a pleasure to have you on the cube. >>Thank you so much, Lisa Lisa, it's a pleasure to be here as well. >>You got an amazing background that I want to share with the audience. You are a professor, you are a data scientist, an aerobatic pilot, and an author with expertise in human centered, data science, visual analytics, aviation safety, and analysis of extremely large and complex data sets. That's quite the background. >>Well, thank you so much. It's it's all very interesting and fun. So, >>And as a professor, you study how people make sense of vast data sets, including a combination of computer science and art, which I love. And as an author, you write about interesting things. You write about how to overcome fear, which is something that everybody can benefit from and how to expand your life until it becomes amazing. I need to take a page out of your book. You were also honored by president Obama a few years back. My goodness. >>Thank you so much. Yes. I I've had quite a journey to come here, but I feel really fortunate to be here today. >>Talk about that journey. I'd love to understand if you were always interested in stem, if it was something that you got into later, I know that you are the co-founder of Latinas in computing, a passionate advocate for girls and women in stem. Were you always interested in stem or was it something that you got into in a kind of a non-linear path? >>I was always interested in it when I was a young girl. I grew up in a small Midwestern town and my parents are both immigrants and I was one of the few Latinas in a mostly white community. And I was, um, I loved math, but I also wanted to be an astronaut. And I remember I, when we were asked, I think it was in second grade. What would you like to be when you grow up? I said, oh, I want to be an astronaut. And my teacher said, oh, you can't do that. You're a girl pick something else. And um, so I picked math and she was like, okay. >>Um, so I always wanted to, well, maybe it would be better to say I never really quite lost my love of being up in the air and potentially space. But, um, but I ended up working in math and science and, um, I, I loved it because one of the great advantages of math is that it's kind of like a magic trick for young people, especially if you're a girl or if you are from an underrepresented group, because if you get the answers right on a math test, no one can mark you wrong. It doesn't matter what the color of your skin is or what your gender is. Math is powerful that way. And I will say there's nothing like standing in a room in front of a room of people who think little of you and you silence them with your love with numbers. >>I love that. I never thought about math as power before, but it clearly is. But also, you know, and, and I wish we had more time because I would love to get into how you overcame that fear. And you write books about that, but being told you can't be an astronaut. You're a girl and maybe laughing at you because you liked Matt. How did you overcome that? And so nevermind I'm doing it anyway. >>Well, that's a, it's a, okay. The short answer is I had incredible imposter syndrome. I didn't believe that I was smart enough to get a PhD in math and computer science. But what enabled me to do that was becoming a pilot and I B I learned how to fly small airplanes. I learned how to fly them upside down and pointing straight at the ground. And I know this might sound kind of extreme. So this is not what I recommend to everybody. But if you are brought up in a way where everybody thinks little of you, one of the best things you can possibly do is take on a challenge. That's scary. I was afraid of everything, but by learning to fly and especially learning to fly loops and rolls, it gave me confidence to do everything else because I thought I appointed the airplane at the ground at 250 miles an hour and waited, why am I afraid to get a PhD in computer science? >>Wow. How empowering is that? >>Yeah, it really was. So that's really how I overcame the fear. And I will say that, you know, I encountered situations getting my PhD in computer science where I didn't believe that I was good enough to finish the degree. I didn't believe that I was smart enough. And what I've learned later on is that was just my own emotional, you know, residue from my childhood and from people telling me that they, you know, that they, that I couldn't achieve >>As I look what, look what you've achieved so far. It's amazing. And we're going to be talking about some of the books that you've written, but I want to get into data science and AI and get your thoughts on this. Why is it necessary to think about human issues and data science >>And what are your thoughts there? So there's been a lot of work in data science recently looking at societal impacts. And if you just address data science as a purely technical field, and you don't think about unintended consequences, you can end up with tremendous injustices and societal harms and harms to individuals. And I think any of us who has dealt with an inflexible algorithm, even if you just call up, you know, customer service and you get told, press five for this press four for that. And you say, well, I don't fit into any of those categories, you know, or have the system hang up on you after an hour. I think you'll understand that any type of algorithmic approach, especially on very large data sets has the risk of impacting people, particularly from low income or marginalized groups, but really any of us can be impacted in a negative way. >>And so, as a developer of algorithms that work over very large data sets, I've always found it really important to consider the humans on the other end of the algorithm. And that's why I believe that all data science is truly human centered or should be human centered, should be human centered and also involves both technical issues as well as social issues. Absolutely correct. So one example is that, um, many of us who started working in data science, including I have to admit me when I started out assume that data is unbiased. It's scrubbed of human influence. It is pure in some ways, however, that's really not true as I've started working with datasets. And this is generally known in the field that data sets are touched by humans everywhere. As a matter of fact, in our, in the recent book that we're, that we're coming out with human centered data science, we talk about five important points where humans touch data, no matter how scrubbed of human influence it's support it's supposed to be. >>Um, so the first one is discovery. So when a human encounters, a data set and starts to use it, it's a human decision. And then there's capture, which is the process of searching for a data set. So any data that has to be selected and chosen by an individual, um, then once that data set is brought in there's curation, a human will have to select various data sets. They'll have to decide what is, what is the proper set to use. And they'll be making judgements on this the time. And perhaps one of the most important ways the data is changed and touched by humans is what we call the design of data. And what that means is whenever you bring in a data set, you have to categorize it. No, for example, let's suppose you are, um, a geologist and you are classifying soil data. >>Well, you don't just take whatever the description of the soil data is. You actually may put it into a previously established taxonomy and you're making human judgments on that. So even though you think, oh, geology data, that's just rocks. You know, that's soil. It has nothing to do with people, but it really does. Um, and finally, uh, people will label the data that they have. And this is especially critical when humans are making subjective judgments, such as what race is the person in this dataset. And they may judge it based on looking at the individual skin color. They may try to apply an algorithm to it, but you know what? We all have very different skin colors, categorizing us into race boxes, really diminishes us and makes us less than we truly are. So it's very important to realize that humans touch the data. We interpret the data. It is not scrubbed of bias. And when we make algorithmic decisions, even the very fact of having an algorithm that makes a judgment say on whether a prisoner's likely to offend again, the judge just by having an algorithm, even if the algorithm makes a recommended statement, they are impacted by that algorithms recommendation. And that has obviously an impact on that human's life. So we consider all of this. >>So you just get given five solid reasons why data science and AI are inevitably human centric should be, but in the past, what's led to the separation between data science and humans. >>Well, I think a lot of it simply has to do with incorrect mental models. So many of us grew up thinking that, oh, humans have biases, but computers don't. And so if we just take decision-making out of people's hands and put it into the hands of an algorithm, we will be having less biased results. However, recent work in the field of data science and artificial intelligence has shown that that's simply not true that algorithmic algorithms reinforce human biases. They amplify them. So algorithmic biases can be much worse than human biases and can greater impact. >>So how do we pull ethics into all of this data science and AI and that ethical component, which seems to be that it needs to be foundational. >>It absolutely has to be foundational. And this is why we believe. And what we teach at the university of Washington in our data science courses is that ethical and human centered approaches and ideas have to be brought in at the very beginning of the algorithm. It's not something you slap on at the end or say, well, I'll wait for the ethicists to weigh in on this. Now we are all human. We can all make human decisions. We can all think about the unintended consequences of our algorithms as we develop them. And we should do that at the very beginning. And all algorithm designers really need to spend some time thinking about the impact that their algorithm may have. >>Right. Do you, do you find that people are still in need of convincing of that or is it generally moving in that direction of understanding? We need to bring ethics in from the beginning, >>It's moving in that direction, but there are still people who haven't modified their mental models yet. So we're working on it. And we hope that with the publication of our book, that it will be used as a supplemental textbook in many data science courses that are focused exclusively on the algorithms and that they can open up the idea that considering the human centered approaches at the beginning of learning about algorithms and data science and the mathematical and statistical techniques, that the next generation of data scientists and artificial intelligence developers will be able to mitigate some of the potentially harmful effects. And we're very excited about this. This is why I'm a professor, because I want to teach the next generation of data scientists and artificial intelligence experts, how to make sure that their work really achieves what they intended it to, which is to make the world a better place, not a worse place, but to enable humans to do better and to mitigate biases and really to lead us into this century in a positive way. >>So the book, human centered data science, you can see it there over Sicily, his right shoulder. When does this come out and how can folks get a copy of it? >>So it came out March 1st and it's available in bookstores everywhere. It was published by MIT press, and you can go online or you can go to your local independent bookstore, or you can order it from your university bookstore as well. >>Excellent. Got to, got to get a copy of, get my hands on that. Got cut and get a copy and dig into that. Cause it sounds so interesting, but also so thoughtful and, um, clear in the way that you described that. And also all the opportunities that, that AI data science and humans are gonna unlock for the world and humans and jobs and, and great things like that. So I'm sure there's lots of great information there. Last question I mentioned, you are keynoting at this year's conference. Talk to me about like the top three takeaways that the audience is going to get from your keynote. >>So I'm very excited to have been invited to wins this year, which of course is a wonderful conference to support women in data science. And I've been a big fan of the conference since it was first developed here, uh, here at Stanford. Um, the three, the three top takeaways I would say is to really consider the data. Science can be rigorous and mathematical and human centered and ethical. It's not a trade-off, it's both at the same time. And that's really the, the number one that, that I'm hoping to keynote will bring to, to the entire audience. And secondly, I hope that it will encourage women or people who've been told that maybe you're not a science person or this isn't for you, or you're not good at math. I hope it will encourage them to disbelieve those views. And to realize that if you, as a member of any type of unread, underrepresented group have ever felt, oh, I'm not good enough for this. >>I'm not smart enough. It's not for me that you will reconsider because I firmly believe that everyone can be good at math. And it's a matter of having the information presented to you in a way that honors your, the background you had. So when I started out my, my high school didn't have AP classes and I needed to learn in a somewhat different way than other people around me. And it's really, it's really something. That's what I tell young people today is if you are struggling in a class, don't think it's because you're not good enough. It might just be that the teacher is not presenting it in a way that is best for someone with your particular background. So it doesn't mean they're a bad teacher. It doesn't mean you're unintelligent. It just means the, maybe you need to find someone else that can explain it to you in a simple and clear way, or maybe you need to get some scaffolding that is Tate, learn extra, take extra classes that will help you. Not necessarily remedial classes. I believe very strongly as a teacher in giving students very challenging classes, but then giving them the scaffolding so that they can learn that difficult material. And I have longer stories on that, but I think I've already talked a bit too long. >>I love that. The scaffolding, I th I think the, the one, one of the high level takeaways that we're all going to get from your keynote is inspiration. Thank you so much for sharing your path to stem, how you got here, why humans, data science and AI are, have to be foundationally human centered, looking forward to the keynote. And again, Cecilia, Aragon. Thank you so much for spending time with me today. >>Thank you so much, Lisa. It's been a pleasure, >>Likewise versus silly Aragon. I'm Lisa Martin. You're watching the cubes coverage of women in data science, 2022.

>> Narrator: Live from Santa Clara in the heart of Silicon Valley. It's the Cube. Covering Cloud Foundry Summit 2017. Brought to you by the Cloud Foundry Foundation and Pivotal. >> Welcome back, I'm Stu Miniman joined by my cohost, John Troyer. There's nothing we love more when we're at the User Conference is to actually be able to dig in and talk with the users. I want to welcome to the program Opal Perry who is a divisional CIO at Allstate. Did the keynote this morning. A really good community here. I know they were excited to hear your story and thank you so much for joining us. >> Thanks, it's great to be here with you. >> So Opal, we hear this term the digital transformation. Some people think it's just a buzz word but you talked in your keynote about the transformation that's going on in your world. Why don't you give us a quick overview of your role and what this transformation has been. >> Sure, so I've been with Allstate almost six years and I'm one of the vice presidents on the technology leadership team so we both work together as a whole team on initiatives that affect the entire enterprise. And then my particular day-to-day focus is Divisional CIO of Claims. We're a large insurer. The number publicly held insurer in the U.S. We support claims for auto, property, Allstate business insurance. It's a outstanding time to be in the business because there's just so much going on in technology. There's so many immersion areas and particularly when we are able to knit them together to serve our customers from insurance protection, restoration standpoint. It's really powerful. We do say and hear transformation so much that it feels sometimes like an overused term but I haven't found a better word for it yet because I think things really are transformative. We've been used to, for many years in the industry, change. Right, continuous improvement. We're always trying to change and get better. But what's happening now with this conversions of forces is truly transformative. We're not just replacing one way of doing things with a slightly improved way. We're changing the way people interact and serve the customer. >> And Opal, what was the driver for the change? Was there a pain point or competitive pressure? What drove this change? >> At Allstate, it's all about the customer opportunity. As I mentioned this morning, we've got 16 million customer households and that's just a tremendous responsibility and also a tremendous opportunity. To us, it was thinking about how do we bring the forces of this great 86-year-old company to bear and use the digital and technology changes emerging and really do that in support of giving our customer a better and better experience. How do we protect them? How do we restore them? >> As you are making this transformation to... We're here at the Cloud Foundry Summit, so interested in the Cloud Foundry story, how some of that decision process, obviously the tech is really cool, A. So was this coming out of the developers first, the technologists first or was it more of a needs analysis from the top-down that like a platform instead of technologies like Cloud Foundry? It could be what we need. >> It really came from a number of quarters but the tipping force was from our infrastructure area. As we looked like a lot of large companies do at what's the future of infrastructure, both in the data center, themes that have been emerging for many years in Cloud. There were a number of us that are leaders at Allstate that came from a banking background so we had seen previous era changes. Prior to Cloud Foundry been instantiated, I'd worked more in home-grown paths and seen that opportunity both from the developer but also from the infrastructure and so when Andy Zitney had joined us, he's with McKesson now, but he had joined and was our CTO for a period of time and had background from Chase and PayPal and various areas. He came in and build our platform team and really looked through their selection process, determined Cloud Foundry was a great option for us and something that we could grow with over time to start meeting the needs. But it was really an interest of saying hey, let's let infrastructure get out of the way, provide the foundation for the developers, and let the developers innovate great software for the business. But let's let the platform take care of things. He brought early awareness to a lot of those factors. >> Yeah, I think the joke is that nobody should be righting their own cryptographic software anymore (Stu chuckles). Nobody should be writing a distributed key-value ParaStore anymore. The Cloud Foundry people will tell you nobody should be writing their own platform anymore. That's hard enough, let somebody else take care of it. >> Yeah, maybe if you're a PhD student (interviewers chuckling) or researching the next great idea but in terms of being within an enterprise, whose primary role is to serve customers in a different way. Again, it just takes care of a lot of the lifting. That took a while when we introduced it for some people to understand. People would say to me why are you adding another layer? Getting them to understand the power of the abstraction and that's what we're really doing. We're lifting up above so we don't have to be worried so much about the exact infrastructure we're sitting on. >> That upscaling process that you're talking about, that training process. Both from the developer side and the operational side, there's a learning curve. Some people embrace it and some maybe not so much. Can you talk a little about how people have gotten trained up on the new skills, how you're helping people do that? >> Yes, in our platform team, it really started with Matt Curry who joined us a few years ago. He's a awesome engineer but also a great leader. He really set the tone culturally for the platform team to be learning environment and for people to share a lot. So a lot's really happened where he's led the hiring and training and seating of the platform team. From a developer perspective, when we looked across the enterprise and realized we've got a couple thousand developers that have worked for us for decades across different areas, we needed to do something more to reach scale more quickly. Initially, we were pairing with Pivotal and that was effective in getting some good results but we thought in order to make that scale and scale more quickly, we wanted to take a different approach. We partnered with Galvanize and brought in-house a 12-week bootcamp-style approach. >> Opal, one of the things that really resonated in your keynote, you talked about painting a picture as to how this technology really impacted your customers. There was a tree, there was a sun, there was your lab's environment and roots. Maybe if you could tease that out a little bit for us and explain how this technology really impacts your users. >> Yes, well, one I think in using that metaphor, it kind of acknowledges the environment is somewhat organic, right? The platform is still growing a lot, the ecosystem we're in, we have the chance to both contribute to the community and to take from it as it develops. To me, that's a really strong notion. The notion that particularly in leadership, we're kind of we're gardeners in a way, right? We're fostering the growth and so I thought that it's a really good example of thinking about as a tree or any plant really grows. It needs a variety of factors so I said our customers are like the sun to us, they're the reason for existing, and that's what we're all orbiting around. But the air represents all the business opportunity. The winds of change have been blowing mightily for years. The soil in which the tree is planted is like all the great Cloud Foundry instances. It's the training, it's the new role definition, it's the holistic program that really defines how we work as a digital product team. We put all that together and we need constant leadership support on a number of grounds to really make sure we take and cement the change. >> What about the developers? Where do they fit in this natural, organic analogy. >> They're the growing, thriving, strong plant itself. I think both. We aim for each individual product team and each individual, whether it's developer, product manager or designer to be continuously growing and using their creativity, discipline, strength, to bring us great business results. And then when you kind of back out and look at our network or product teams, that's a really important thing to me. An enterprise of our scale is very few breakthroughs will occur, I believe, because of a single digital product innovation. It's really in the ability to knit together different products to provide an end-to-end service or experience to the customer. >> How do you look at the public cloud? You know, Cloud Foundry allows? We were talking about BOSH, a multi-cloud environment. Where does your applications and deployments live today and how do you look at the public cloud? >> You know, we're still exploring some of the possibilities. Matt and his team have been very active looking. We started with on-premise installation for Cloud Foundry. And for myself, leading a development team, it's great as the platform is a look to kind of burst out into a multi-cloud environment. It'll be transparent to my team as long as we're operating to run on our Cloud Foundry instance, they can take us wherever we need to go. They've been doing a lot of work with our security team and other areas of the company to determine what's the right way to forge the path forward. I had a meeting with them Friday and they've got some great design things in the works. I think the next six moths to a year, are going to be looking at some real strong expansion of our cloud strategy. >> How does security fit into this whole picture? Obviously, a major concern for every CIO these days. >> Yeah, absolutely. I mean, to us, we've taken a real security-first approach. We're been our CISO team has been working really closely with Matt and the Cloud engineers and they're just defining how do we want to segregate parts of our environment? How do we follow the principle of trust no one and build security in from the get-go? Again, it's a little bit like the platform itself. I'm confident when they get a solution in place, they'll minimize the burden on my developers and we can just have a security-first mindset but have a lot of the hygiene taken care of by the platform implementation. >> Again, something you don't want to differentiate on. You want to be built into the foundation, or the roots, maybe of our metaphor here. >> Opal: Yes. I heard ya. >> Opal, can you talk a little bit about the apps? Obviously, we've already used words like scale here today. Allstate's a big company. You've got lots of apps. Legacy apps, many different kinds of stacks, generations of technology. How are you choosing what ends up being is this greenfield or things that are being moved? How are you all looking at different applications inside the company? Where they live on which cloud and how they get modernized? >> We're lighting the business needs and strategy, really drive how we prioritize. It really is a matter of a lot, at this point, triage and prioritization. We've got a rich set of opportunities. When we're building new apps in-house, we're certainly looking to take a cloud-first approach. Again, a lot of that's within our own walls today but we know that with the Foundry, it offers us the option to burst out at a later date and leaves us some optionality. The Allstate Corporation, the Allstate brand of insurance is what's best known but in Claims, I also support we have a brand called Encompass Insurance so we're looking to provide support for multiple companies and build technology that can serve everyone. There are a lot of cases too, in an ecosystem like ours, where we're working with third party vendors and they're increasingly offering cloud-based solutions. Again, we do a lot of work with them from the security and compliance perspective to make sure that their strategy is consistent with ours. To make sure we take appropriate care of our customer data. And then I personally get really excited by the refactoring opportunities. I'm really fortunate in Claims that our core claims system was implemented just about 10 years ago. I call it legacy now, but it's not, (John chuckles) as far back to the dark ages as some of the other systems that you'll find within the walls of enterprises. It was build as our last big monolithic implementation and we've been doing decoupling there. So whenever we know we're going to do a decoupling, we look for what opportunity to implement new cloud native microservices and again just stand that up in our environment with the platform team. >> I wanted to ask also about culture and technology adoption. We're sitting here in the middle of Silicon Valley. This cloud phenomenon driven a lot from Silicon Valley. Sometimes people think this cloud native stuff, it's for startups, it's for the kids, it's for whatever. You're based in the Midwest and I also, I'm an Illinois boy myself. You get sometimes, kind of a inferiority complex about the coast, both coasts. But this does not seem to be a coastal phenomenon. This does not seem to be something that only a startup can learn. This is Allstate, a mature company and with a Midwestern base, can you kind of talk a little about was there anything about that in terms of people saying we can't do that here or that sort of thing? >> No, no, I mean, in fact, I think it's a global phenomenon. I was living for almost two years in Belfast, Northern Ireland. We have a division there, Allstate Northern Ireland and we saw a lot of Foundry activity among different companies there. Of course, there's a European summit every year, as well, so I think it's just good common sense. A lot of us, again, before Cloud Foundry came through were working with the different predecessor technologies and Spring and Vmware, you know various aspects and kind of knitting together which felt like reinventing the wheel. So it's just good business sense, good common sense when there's a solution that you can leverage. I think it's just like you were commenting earlier, right? If it's there and you can use it and you can allow the focus to be on what really differentiates you as a business to your customers. That's the way to go. >> Opal, the last question I have for you is there either commentary on any of the announcements that were made this week or are there any things that you're hoping really, for either Pivotal, the fFundation in general, your ecosystem that would make your life easier that's kind of on your to-do list from the vendor side? >> There's so much to take in. I think it's probably still going to take me a week to absorb all the implications. It's great to watch the dynamics going on. I think Microsoft joining the Foundation, that's a very good move 'cause we have so many different technologies within our enterprise so to understand how different vendors are working and playing together in some way is really good. I think Abbey and the Foundation, they've been fantastic about always soliciting input from members like us and members of the community about what we want to see. For me, it's always a big eye-to-word scale. Again, we're a huge enterprise. There are even larger enterprises here that have started running and when this really becomes the we all achieve the aspirational goals and it becomes the day-to-day backbone. It's just making sure this is really hardened to run at true enterprise weight. I think that the enterprise scale of the future is going to be even bigger than what it has been historically because with all these new products, we're driving an appetite towards greater and greater customer interaction. I saw that in banking ten years ago and I think we're going to see it in insurance more and more so we just want to know that we're all working together to get that strength and that power that the customer needs. >> Opal Perry, really appreciate you sharing Allstate's digital transformation with us and our audience, for John and myself. We'll be back with more coverage here from the Cloud Foundry Summit. Thanks for watching the Cube. >> Opal: Thank you. (gentle lively music)