>>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.

>> Live, from Orlando, Florida. It's theCUBE! Covering Enterprise Connect 2019. Brought to you by Five9. >> Welcome back to Orlando at Enterprise Connect 2019, I'm Lisa Martin with Stu Miniman. It may sound like we're at a party, this is the buzz of the event, this is day one, and we have had a great day so far of talking with lots of guests. We're welcoming back to theCUBE an alumni, Jim Lundy, see applause for you, Jim, CEO of Aragon Research, welcome back to theCUBE. >> Thank you, great to be here. [Lisa] - That was cute, by the way, so I hope we get some credit for that. >> Yeah, yeah, very cute. >> So Jim, you have been coming to Enterprise Connect since before it was even branded Enterprise Connect, back when it was VoiceCon. Tell us a little bit about your observations about the evolution, not only of the events, but also of all the collaboration and communication tools that consumers now are expecting and demanding of businesses. >> So, I think my first event was called VoiceCon in '07, and then it was all about phones. There was no software here. There was no video. There was no messaging. There was certainly no AI. And there were a lot of the players were not here, they were not in business then. So, if you actually look at some of the bigger players here today, they did not exist in 2007. So you look at the advent of Cloud, that's powered a whole new generation of services and opportunities, and it's great for buyers because there's so much more choice. So, VoiceCon almost died and they rebranded it but they've had to expand their focus. There's still a lot of voice focused stuff, but as you can see it's really shifted, we think it's shifting to communications and collaboration, we think contact center, particularly Cloud, is hot. We've got through overall Tam for communication, collaboration, contact center, by 2024, about 120 billion dollars, which makes it bigger than Enterprise secured. >> Yeah, we just had a great type-in with Blair Pleasant, and said, I'm a new channel, absolutely is where it is, but voice is still the number one preferred channel, when you talk about context center, there's lots of ways you can get in touch, but when something's wrong, I want to pick up my device and talk to a human eventually, so yeah, Cloud, and AI, and everything else, but there's still people in this center of everything going on here. >> Well, I think one of the things for contact center in particular you mentioned is the power of Cloud. So you look at some of the players here like we're in the Five9 booth, they've grown because of their Cloud focus, and Cloud is a lot of what's powering everybody here. And buyers want flexibility, so I think that's one of the big things that's changed, is there's still a lot of On Premise, and hybrid Cloud, but the power and the demand for 'I want to deploy something fast, and maybe I'm not even that big of a shop,' Cloud gives me that flexibility. >> When I look at the market as a whole, there's all those arguments about it's private Cloud, public Cloud, hybrid Cloud, multi Cloud, but if we think of Cloud as an operational model, and not a place, I want speed, I want to be able to update to my latest thing, whether that's for security or the cool new feature, and if I'm not Cloud, or Cloud-like, then I probably install something and what I do now and what I do a few years from now looks pretty close to what I did when I installed it. No? Does that resonate in this phase? >> Yeah, yeah. I think there's a couple things, also there's the operational nature of do I want to be in the server update business? Some people do, because of the nature of their business, but a lot of people don't. So then I can focus on the client experience, providing better journeys, and I think that's up the game. I think there's an awful lot of competition in this market because, really because of Cloud, but On Premise or private Cloud is not a bad word, and like I said, I think the bigger play is to be able to do a combination of things and meet the needs of the customer. The only thing I would say about the show is there's a lot of feature wars at this show and needs to be maybe a little more focused on what the customer needs versus hey, my box is better than your box. >> On that front, in terms of focusing on the customer experience, we talk a lot about that, there's a lot of the messaging and branding around the shows you were just pointing out, but something that is always interesting is where does a company balance the customer experience with the agent experience, because the customer experience is directly related to the agents being in power. >> Oh, totally! Well, you got to really do both and do both well. If the agent can't do their job, then the customer is not going to have a good experience. I do think that overall, there's been a pretty good focus on the agent, because that's where it kind of all started, and if you really look at contact center, it's really a heavy-duty application. You've got to be able to do all those things to service the inbound calls or inbound messages, and you're right, there is a lot of focus on the customer, because in some cases there is so much focus on the agent, well, we took the calls even though a lot of the calls, 10% might've gone to voicemail? Sometimes? Well, we serviced it, so. Little unknown fact is that in a lot of enterprises, marketing and the contact center group never talk. Interesting opportunity. >> Yeah, Jim, it's interesting, you talked about in tech we often get to that feature battle. Battle by power point or by product stack and oh, I've got 147 features and they only have 125 features, when you look at most customers they only know how to use three of the features they've got on there. So what differentiates from a customer standpoint, how do they choose, how do they make sure that they get something that is going to help their overall customer experience, and help their products and their marketing? >> Well, a couple things. First of all, you're right, they don't care as much about 'I've got this feature, you don't', they want to know can the provider take care of me if I buy from them? Are they reputable? Do other people, are they happy with the service? We do a lot of vender evaluations, we call them Aragon research globes and we usually spend six months working on understanding where the vender is this year, and we talk to references and things like that. So I think that sometimes when you, they read a report and they get some insight, they still want to talk to somebody versus just reading a peer review on somebody's consumer website, and really get that insight, so I think that's one lens and I think the other lens is that the smarter players are doing those things where they can provide really high touch support, I'd probably say Five9's pretty good at that, because contact center is really, really complicated, you just don't turn them on sometimes, there's things you have to do to make them work, and I think overall in this space, there are some products you can buy, maybe not contact center where you can spin them up and turn them, configure phones and go, I've actually deployed some of them, and there's some that would be such a nightmare, like who in the world would ever buy this product? So, I think it really varies a gambit and again, sometimes that doesn't always come out with an online review and again, sometimes the buyer, still buyer beware, in a lot of cases, some of the things you read online are not true. >> One of the things we were chatting with a number of the Five9 executs about today is that they have a five billion recorded customer conversations, tremendous potential there to really glean actionable insights about retaining that customer, increasing their CLV, but there's also the concern of data privacy and security in sharing, when you're talking with customers that might have this massive pull of data from which they can really expand their business and become competitive, where is the security and the privacy concerns there? >> It's a good question. There's a lot of focus on GDPR in Europe, there's a lot of focus in California on that, even though there's not been talked about in California. The rest of the US is kind of behind a little bit what Europe has done, but here's the thing. They've got ways to mass sensitive data in a recording like credit card data, that's pretty standard stuff, the big thing is data residency. I want my data in a certain country, Canadians do not want their data resident in the United States, Europeans don't either. Germans don't want their data resident in Belgium, so there's a big sensitivity in Europe about that, and even in fact, Microsoft's even gotten in trouble in Germany over that last year, because they eliminated a relationship with Doy to Telecom, sometimes you can kind of go overboard on that, but however, what I would say though is, some of the big Cloud companies have done this, brought this problem onto themselves, where they have not respected data privacy, there's even a bill now on facial recognition, because of some of the things that have gone on like IBM disclosed, they're doing something, so it is still an issue, it's always going to be an issue, I do think that there needs to be more protect, but here's the question. Who owns your data? Who owns your face, or my face? I don't think that because I upload a photo that I should give my rights away. I think we're going to catch up on that, I do think for the B-to-B though, a lot of these companies, first of all, they are certified, they have Cloud certifications, they definitely do certain things relative to privacy, and so they have to pass a lot of tests that are certified by an auditor, so I think there's a lot of things that most of the B-to-B buyers are not going to have to worry about with a lot of the people here, it's more of the personal side of things, the personal Cloud, Facebook, but usually not the kind of stuff you're dealing with here. >> So, Jim, when I look at the overall contact center market, the Cloud portion of that is still relatively small, if I saw right somewhere, 10, 15%, but it's been growing at a steady clip, where are we in their adoption, is there a plateau that it will hit that, is it take a third of a market, half the market, what do you see happening? >> I would say, we're on a journey and you're right, there is still a small part, which means the large address will market, not that much different than unified communications where it's mainly On Premise, going Cloud. We've got contact center going about 24 billion, and we think a lot of that will be eventually converted to a Cloud, except for maybe the ultra, ultra large call centers, and I think just like email migration 10 years, I've covered that, 10 years ago it was all On Premise. Today it's the opposite. It's like 90-10. So I think that eventually is going to start to happen. >> It's interesting, a lot of that was Microsoft really turned the lever, Microsoft on email, and Microsoft is like, we're going sass, you are going sass if you use Office, you are going Office 365. So I'm curious, is there a lever like that from a licensing standpoint or from a vender standpoint, that would push contact center? >> If you look at the contact center market, we've got it, growth rates around 9% overall, but then you've got people like Five9 that are growing 31%, alright? So if you starting looking at that, why is a Cloud company growing that much when the overall market, well because there's demand. They want the flexibility of Cloud, they don't want to run the servers and upgrade the servers, and I think that they've learned lessons from that, and you're right, Microsoft did do that, but Google forced them to do that. So I think that, are fast growing companies like Five9 forcing some of the bigger players to go more Cloud? And I can say absolutely yes, that a lot of the bigger players are looking over their shoulders saying, and they bought Cloud contact center players so they can keep up with some of the young startups, and Five9's not young, but they would still be considered young in the relative terms of this event. >> I'm curious, Jim, when you're talking with venders and the Aragon research that you do, companies of different sizes, whether they're born in the Cloud or they're legacy companies, where does cultural transformation come into this conversation about evolving a contact center such that an agent is empowered with the right content to deliver it through the right channel, to make a decision that really positively impacts the customer? I can imagine multiple generations, multiple countries, cultural transformation is hard. >> It is a big issue, I think there's more awareness on both the culture of the agent and the culture of the buyer, and I think there's more stuff going on relative to sentiment, sentiment analysis. I do think that's a bigger issue, I think there's more time being spent on training, the better digital companies are investing tons of money in training, so I think there's more awareness relative to cultural differences, cultural nuances, and being more sensitive to maybe things that they would say sorry, can't help you with that, since they've been trained to be maybe more sensitive, they're going to be more understanding when they're actually on a call. >> So, Jim, in your research, where's the white space? Where's the real opportunity for growth and transformation, we've had some discussions here, it's early days in AI's, at AI, or is it not the technology, is it the cultural changes, that Lisa brings up, where are some of impediments and room for growth in the industry? >> So we do think that the enterprise will become more intelligent, and that the providers are going to lead that charge, where instead of you say to AI, we call it intelligent contact center, and we think that there's going to be more of a demand for automation, and that there will be more assistance that might take care of a customer's problem before it ever gets to a human. I do think that we're not going to, that's going to be something that's never going to go away, it's just that they're going to get smarter and more supportive. We have helped clients deploy chat bots for help desk internally for customer facing help desk, I think it's still early here, that people have them, but they're more rules based than AI based. AI's coming in the next two years but there's no doubt that is going to be one of the drivers, and by the way, sometimes people be like, is this the problem we were having, is this the question you have? Yes. Here's this answer, and it's the right answer, the correct answer, that's what people really want, they want the instant gratification, we all kind of grew up, we were used to that with our phones, I need the answer, and I do think that I would probably say the demand for Cloud is going to out-strip everything, so if somebody that's an On Premise provider doesn't have a Cloud option, then I would be worried about them. But I do think AI is not going to go away, we don't think it's going to be an AI or nothing, it's going to be basically intelligent digital assistance, it can answer questions intelligently and have a conversation with you, there's some tools that do that today, but most of them are very basic question and answer, they're not high-end, it can't be like Jarvis on Iron Man, where yes, yes, Mr. Spark, I will do that for you, they're not quite there yet, but the movies glamify that whole thing. Some people expect, well, why doesn't it talk back to me? >> Any last questions, Jim, are there any industries that you see is going to be early adopters to start creating and actually deploying the intelligent contact center? >> Well, let's put it this way. Every client we've talked to in survey work said we wish we had more intelligence in our contact center. I think they're a little scared that they want to make sure they do it right, but if you do it and deploy it and test it, you'd be amazed it's for some of the basic Q&A, how rockstar stuff that is, but sometimes people rush too quickly and deploy it when it's not quite ready. I think a lot of the providers here, including Five9, are going to try to do AI the right way, and not try to rush it, but I would also say this. There's an awful lot of fud about AI, and most of it's not true. >> Lisa, final, final question for Jim here, since John Ferger's not here to ask it, Five9's gone through a lot of changes here, brought in some pretty high-profile executives, any commentary on our host here? >> Look, I knew Rowan and Jonathan Rosenberg at Cisco, they had a rockstar team there, they've even, since they've joined here brought more talent in, and so, the Five9 people I knew have been blown away by the level of talent that has come in, and I think that's just going to help them continue to grow. The question is, when did they declare how big they're going to be? And that's what we're looking for them to do. >> To be continued, Jim, thanks so much for joining Stu and me on theCUBE this afternoon. >> Thank you very much. >> For Stu Miniman, I'm Lisa Martin, you're watching theCUBE. (light beat music)

>>Hey everyone. Welcome back to the cubes. Live coverage of women in data science, worldwide conference widths 2022. I'm Lisa Martin coming to you from Stanford university at the Arriaga alumni center. And I'm pleased to welcome my next guest. Hannah Sperling joins me business process intelligence or BPI, academic and research alliances at SAP HANA. Welcome to the program. >>Hi, thank you so much for having me. >>So you just flew in from Germany. >>I did last week. Yeah. Long way away. I'm very excited to be here. Uh, but before we get started, I would like to say that I feel very fortunate to be able to be here and that my heart and vicious still goes out to people that might be in more difficult situations right now. I agree >>Such a it's one of my favorite things about Wiz is the community that it's grown into. There's going to be about a 100,000 people that will be involved annually in woods, but you walk into the Arriaga alumni center and you feel this energy from all the women here, from what Margo and teams started seven years ago to what it has become. I was happened to be able to meet listening to one of the panels this morning, and they were talking about something that's just so important for everyone to hear, not just women, the importance of mentors and sponsors, and being able to kind of build your own personal board of directors. Talk to me about some of the mentors that you've had in the past and some of the ones that you have at SAP now. >>Yeah. Thank you. Um, that's actually a great starting point. So maybe talk a bit about how I got involved in tech. Yeah. So SAP is a global software company, but I actually studied business and I was hired directly from university, uh, around four years ago. And that was to join SAP's analytics department. And I've always had a weird thing for databases, even when I was in my undergrad. Um, I did enjoy working with data and so working in analytics with those teams and some people mentoring me, I got into database modeling and eventually ventured even further into development was working in analytics development for a couple of years. And yeah, still am with a global software provider now, which brought me to women and data science, because now I'm also involved in research again, because yeah, some reason couldn't couldn't get enough of that. Um, maybe learn about the stuff that I didn't do in my undergrad. >>And post-grad now, um, researching at university and, um, yeah, one big part in at least European data science efforts, um, is the topic of sensitive data and data privacy considerations. And this is, um, also topic very close to my heart because you can only manage what you measure, right. But if everybody is afraid to touch certain pieces of sensitive data, I think we might not get to where we want to be as fast as we possibly could be. And so I've been really getting into a data and anonymization procedures because I think if we could random a workforce data usable, especially when it comes to increasing diversity in stem or in technology jobs, we should really be, um, letting the data speak >>And letting the data speak. I like that. One of the things they were talking about this morning was the bias in data, the challenges that presents. And I've had some interesting conversations on the cube today, about data in health care data in transportation equity. Where do you, what do you think if we think of international women's day, which is tomorrow the breaking the bias is the theme. Where do you think we are from your perspective on breaking the bias that's across all these different data sets, >>Right. So I guess as somebody working with data on a daily basis, I'm sometimes amazed at how many people still seem to think that data can be unbiased. And this has actually touched upon also in the first keynote that I very much enjoyed, uh, talking about human centered data science people that believe that you can take the human factor out of any effort related to analysis, um, are definitely on the wrong path. So I feel like the sooner that we realize that we need to take into account certain bias sees that will definitely be there because data is humanly generated. Um, the closer we're going to get to something that represents reality better and might help us to change reality for the better as well, because we don't want to stick with the status quo. And any time you look at data, it's definitely gonna be a backward looking effort. So I think the first step is to be aware of that and not to strive for complete objectivity, but understanding and coming to terms with the fact just as it was mentioned in the equity panel, that that is logically impossible, right? >>That's an important, you bring up a really important point. It's important to understand that that is not possible, but what can we work with? What is possible? What can we get to, where do you think we are on the journey of being able to get there? >>I think that initiatives like widths of playing an important role in making that better and increasing that awareness there a big trend around explainability interpretability, um, an AI that you see, not just in Europe, but worldwide, because I think the awareness around those topics is increasing. And that will then, um, also show you the blind spots that you may still have, no matter how much you think about, um, uh, the context. Um, one thing that we still need to get a lot better at though, is including everybody in these types of projects, because otherwise you're always going to have a certain selection in terms of prospectus that you're getting it >>Right. That thought diversity there's so much value in thought diversity. That's something that I think I first started talking about thought diversity at a Wood's conference a few years ago, and really understanding the impact there that that can make to every industry. >>Totally. And I love this example of, I think it was a soap dispenser. I'm one of these really early examples of how technology, if you don't watch out for these, um, human centered considerations, how technology can, can go wrong and just, um, perpetuate bias. So a soap dispenser that would only recognize the hand, whether it was a certain, uh, light skin type that w you know, be placed underneath it. So it's simple examples like that, um, that I think beautifully illustrate what we need to watch out for when we design automatic decision aids, for example, because anywhere where you don't have a human checking, what's ultimately decided upon you end up, you might end up with much more grave examples, >>Right? No, it's, it's I agree. I, Cecilia Aragon gave the talk this morning on the human centered guy. I was able to interview her a couple of weeks ago for four winds and a very inspiring woman and another herself, but she brought up a great point about it's the humans and the AI working together. You can't ditch the humans completely to your point. There are things that will go wrong. I think that's a sends a good message that it's not going to be AI taking jobs, but we have to have those two components working better. >>Yeah. And maybe to also refer to the panel discussion we heard, um, on, on equity, um, I very much liked professor Bowles point. Um, I, and how she emphasized that we're never gonna get to this perfectly objective state. And then also during that panel, um, uh, data scientists said that 80% of her work is still cleaning the data most likely because I feel sometimes there is this, um, uh, almost mysticism around the role of a data scientist that sounds really catchy and cool, but, um, there's so many different aspects of work in data science that I feel it's hard to put that all in a nutshell narrowed down to one role. Um, I think in the end, if you enjoy working with data, and maybe you can even combine that with a certain domain that you're particularly interested in, be it sustainability, or, you know, urban planning, whatever that is the perfect match >>It is. And having that passion that goes along with that also can be very impactful. So you love data. You talked about that, you said you had a strange love for databases. Where do you, where do you want to go from where you are now? How much more deeply are you going to dive into the world of data? >>That's a good question because I would, at this point, definitely not consider myself a data scientist, but I feel like, you know, taking baby steps, I'm maybe on a path to becoming one in the future. Um, and so being at university, uh, again gives me, gives me the opportunity to dive back into certain courses and I've done, you know, smaller data science projects. Um, and I was actually amazed at, and this was touched on in a panel as well earlier. Um, how outdated, so many, um, really frequently used data sets are shown the realm of research, you know, AI machine learning, research, all these models that you feed with these super outdated data sets. And that's happened to me like something I can relate to. Um, and then when you go down that path, you come back to the sort of data engineering path that I really enjoy. So I could see myself, you know, keeping on working on that, the whole data, privacy and analytics, both topics that are very close to my heart, and I think can be combined. They're not opposites. That is something I would definitely stay true to >>Data. Privacy is a really interesting topic. We're seeing so many, you know, GDPR was how many years did a few years old that is now, and we've got other countries and states within the United States, for example, there's California has CCPA, which will become CPRA next year. And it's expanding the definition of what private sensitive data is. So we're companies have to be sensitive to that, but it's a huge challenge to do so because there's so much potential that can come from the data yet, we've got that personal aspect, that sensitive aspect that has to be aware of otherwise there's huge fines. Totally. Where do you think we are with that in terms of kind of compliance? >>So, um, I think in the past years we've seen quite a few, uh, rather shocking examples, um, in the United States, for instance, where, um, yeah, personal data was used or all proxies, um, that led to, uh, detrimental outcomes, um, in Europe, thanks to the strong data regulations. I think, um, we haven't had as many problems, but here the question remains, well, where do you draw the line? And, you know, how do you design this trade-off in between increasing efficiency, um, making business applications better, for example, in the case of SAP, um, while protecting the individual, uh, privacy rights of, of people. So, um, I guess in one way, SAP has a, as an easier position because we deal with business data. So anybody who doesn't want to care about the human element maybe would like to, you know, try building models and machine generated data first. >>I mean, at least I would feel much more comfortable because as soon as you look at personally identifiable data, you really need to watch out, um, there is however ways to make that happen. And I was touching upon these anonymization techniques that I think are going to be, um, more and more important in the, in the coming years, there is a proposed on the way by the European commission. And I was actually impressed by the sophisticated newness of legislation in, in that area. And the plan is for the future to tie the rules around the use of data science, to the specific objectives of the project. And I think that's the only way to go because of the data's out there it's going to be used. Right. We've sort of learned that and true anonymization might not even be possible because of the amount of data that's out there. So I think this approach of, um, trying to limit the, the projects in terms of, you know, um, looking at what do they want to achieve, not just for an individual company, but also for us as a society, think that needs to play a much bigger role in any data-related projects where >>You said getting true anonymization isn't really feasible. Where are we though on the anonymization pathway, >>If you will. I mean, it always, it's always the cost benefit trade off, right? Because if the question is not interesting enough, so if you're not going to allocate enough resources in trying to reverse engineer out an old, the tie to an individual, for example, sticking true to this, um, anonymization example, um, nobody's going to do it right. We live in a world where there's data everywhere. So I feel like that that's not going to be our problem. Um, and that is why this approach of trying to look at the objectives of a project come in, because, you know, um, sometimes maybe we're just lucky that it's not valuable enough to figure out certain details about our personal lives so that nobody will try, because I am sure that if people, data scientists tried hard enough, um, I wonder if there's challenges they wouldn't be able to solve. >>And there has been companies that have, you know, put out data sets that were supposedly anonymized. And then, um, it wasn't actually that hard to make interferences and in the, in the panel and equity one lab, one last thought about that. Um, we heard Jessica speak about, uh, construction and you know, how she would, um, she was trying to use, um, synthetic data because it's so hard to get the real data. Um, and the challenge of getting the synthetic data to, um, sort of, uh, um, mimic the true data. And the question came up of sensors in, in the household and so on. That is obviously a huge opportunity, but for me, it's somebody who's, um, very sensitive when it comes to privacy considerations straight away. I'm like, but what, you know, if we generate all this data, then somebody uses it for the wrong reasons, which might not be better urban planning for all different communities, but simple profit maximization. Right? So this is something that's also very dear to my heart, and I'm definitely going to go down that path further. >>Well, Hannah, it's been great having you on the program. Congratulations on being a Wood's ambassador. I'm sure there's going to be a lot of great lessons and experiences that you'll take back to Germany from here. Thank you so much. We appreciate your time for Hannah Sperling. I'm Lisa Martin. You're watching the QS live coverage of women in data science conference, 2020 to stick around. I'll be right back with my next guest.

>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem. Barker's >> Welcome back to San Diego. Everybody watching the Cube, the leader and live check coverage. My name is David Locke. I'm here with my co host student in recovering Day to hear Sisqo live. 2019 on. On On. On on. Oswald is here. Excuse me. Sees the senior vice president of enterprise networking Engineering at Cisco. And John A postal, a polis. Italians in the Greeks. We have a lot in common. He is the VP and CTO of Enterprise Network. And get Sisko. Gentlemen, welcome to the Cube. How'd I do? Do you know it? Also, that you're bad, right? Thank you. All right, Good. Deal it out. Let's start with you. You guys have had a bunch of news lately. Uh, you're really kind of rethinking access to the network. Can you explain what's behind that to our audience? >> Yeah, even think about it. The network is getting has running more and more critical. Infrastructure at the same time is increasing. Bottom scale and complexity. What? We expected that you'll only be obvious. Violence on workspace is on the move. Are you? You're working here in your office, in the cafe, The sock off everywhere you want. An uninterrupted unplugged experience for that is violence. First, it's cloud driven and is dead optimist. So we had to rethink our way to access. It's not just about your laptops and your fool on the wireless network. In the end of the digital management systems, Coyote devices, everything is going to provide us with means reaching the access on that. But >> so, John, this obviously ties into, you know, you hear all the buzz about five g and WiFi. Six. Can you explain the connection? And you know what? We need to know about that. >> Okay, it's so fine. Five. Jean WiFi 62 new wireless technologies coming about now, and they're really awesome. So y fi six is the new version. WiFi. It's available today, and it's going to be available for down predominately indoors as wi WiFi indoors and high density environments where you need a large number. Large data bait for square meter una WiFi. Once again, the new WiFi six fight in the coverage indoors uh, five is going to be used predominately outdoors in the cellular frequency. Replacing conventional for Geo lt will provide you The broad coverage is your roam around outdoors. And what happens, though, is we need both. You need great coverage indoors, which wife Isis can provide, and you need great coverage outdoors. Which five year cried >> for G explosion kind of coincided with mobile yet obviously, and that caused a huge social change. And, of course, social media took off. What should we expect with five G? Is it? You know, I know adoption is gonna take a while. I'll talk about that, but it feels like it's more sort of be to be driven, but but maybe not. Can you >> see why 5 65 gr actually billions Some similar fundamental technology building blocks? You know you will be in the ball game for the Warriors game like a few weeks ago when they were winning on DH. After a bit of time to send that message. Video your kid something on the WiFi slow laden Z with WiFi, 61 have a problem. The WiFi six has four times the late in C 14. The throughput and capacity has existing y find Lowell Agency and also the battery life. You know, people say that that is the most important thing today. Like in the mass Maharaj three times the battery life for WiFi, 16 points. So you're gonna see a lot of use cases where you have inter walking within 556 and five g WiFi six foot indoors and find you for outdoor and some small overlap. But the whole idea is how do you ensure that these two disparate access networks are talking to each other explaining security policy and it is invisibility. >> Okay, so first what? Your warriors fan, right? Yeah. Awesome way. Want to see the Siri's keep going, baby? That was really exciting. Because I'm a Bruins fan, sir, on the plane the other night and in the JetBlue TV. Shut down, you know, So I immediately went to the mobile, But it was terrible experience, and I was going crazy checks in my friends. What's happening? You say that won't happen? Yeah, with five Julia and WiFi sexy. Exactly. Awesome. >> So, John, help connect for us. Enterprise. Not working. We've been talking about the new re architectures. You know, there's a c I there now intent based networking. How does this play into the five G and WiFi six discussion that we're having today? >> So one of the things that really matters to our customers and for everybody, basically, they want these sort of entering capability. They had some device is they want to talk to applications. They want access to data. We want to talk with other people or try ot things. So you need this sort of end twin capability wherever the ends are. So one of the things I've been working on a number of years now it's first all intent Basin that working, which we announced two and 1/2 years ago. And then multi domain, we try to connect across the different domains. Okay, well across campus and when, and data center all the way to the cloud and across the Service Fighter network and trad security has foundational across all of these. This was something that David Buckler and Chuck Robbins talked about at their keynote yesterday, and this is a huge area for us because we're going to make this single orchestrated capability crop customers to connect and to and no matter where the end of ices are >> alright so sewn on I have to believe that it's not the port, you know, administrator saying, Oh my God, I have all these signs of them. Is this where machine learning in A I come in to help me with all these disparate system absolutely are going very simple. Any user on any device had access to any application. Sitting in a data center in a cloud of multiple clouds over any network, you want that securely and seamlessly. You also wanna have nature. Its whole network is orchestrator automated, and you're the right visibility's recipes for idea on with the business insights on the eye. An ML. What's happening is there for the next book is going in complexity and skill. The number of alerts are growing up, so you are not able to figure it out. That's where the power of a I and machine learning comes. Think about it in the industry revolution, the Industrial Revolution made sure that you are. You don't have limitations or what humans can do right, like machines. And now we want to make sure businesses can benefit in the digital revolution, you know, in limited by what I can pass through all the logs and scrolls on ornament. Everything and that's the power of air and machine learning >> are there use cases where you would want some human augmentation. We don't necessarily want the machine taking over for you or Or Do you see this as a fully automated type of scenario? >> Yeah, so what happens is first ball visibility is really, really important. The operator of an effort wants the visibility and they want entwined across all these domains. So the first thing we do is we apply a lot of machine learning to get to take that immense amount of data is an unmentioned and to translate it into piece of information to insights into what's happening so that we could share to the user. And they can have visibility in terms of what's happened, how well it's happening. Are they anomalies? Are is this security threat so forth? And then we can find them additional feedback. Hate. This is anomaly. This could be a problem. This is the root cause of the problem, and we believe these are the solutions for what do you want to do? You wantto Do you want actuate one of these solutions and then they get to choose. >> And if you think of any other way, our goal is really take the bits and bytes of data on a network. Convert that data into information that information into insights that inside that lead to outcomes. Now you want. Also make sure that you can augment the power of a machine. Learning on those insights, you can build on exactly what's happening. For example, you want first baseline, your network, what's normal for your environment and when you have deviations and that anomalies. Then, you know, I don't know exactly what the problem is. Anyone automated the mediation of the problem. That's the power of A and women you >> When you guys as engineers, when you think about, you know, applying machine intelligence, there's a lot of, you know, innovation going on there. Do you home grow that? Do you open source it? Do you borrow? Explain the philosophy there in terms of it. From a development standpoint, >> development point of it is a combination of off all the aspects, like we will not green when they leave it all the exists. But it's always a lot of secrets are that you need to apply because everything flows through the network, right? If everything first netbooks, this quarter of information is not just a data link, their data source as well. So taking this district's also information. Normalizing it, harmonizing it, getting a pretty language. Applying the Alberta and machine learning, for example. We do that model, model learning and training in the clouds. Way to infants in the cloud, and you pushed the rules down. There's a combination, all of all, of that >> right, and you use whatever cloud tooling is available. But it sounds like it's really from an interest from a Cisco engineering standpoint. It's how you apply the machine intelligence for the benefit of your customers and those outcomes versus us. Thinking of Sisko is this new way I company right. That's not the ladder. It's the former. Is that >> fair? One of the things that's really important is that, as you know, Cisco has been making, uh, we've been designing a six for many years with really, really rich telemetry and, as you know, Data's key to doing good machine learning and stuff. So I've been designing the A six to do really time at wire speed telemetry and also to do various sorts of algorithmic work on the A six. Figure out. Hey, what is the real data you want to send up? And then we have optimized the OS Iowa sexy to be able to perform various algorithms there and also post containers where you could do more more machine learning at the switch at the router, even in the future, maybe at the A P and then with DNA Center way, have been able to gather all the data together in a single data life where we could form a machine learning on top. >> That's important, Point John mentioned, because you want Leo want layers and analytics. And that's why the cattle's 91 191 20 access point we launch has Cisco are basic that provides things like cleaning for spectrum were also the analytic from layer one level are literally a seven. I really like the line, actually from Chuck Robbins, yesterday said. The network sees everything, and Cisco wants to give you that visibility. Can you walk us through some of the new pieces? What, what what people, Either things that they might not have been aware of our new announcements this week as part of the Sisko, a network analytics, announced three things. First thing is automated based lining. What it really means. Is that what's normal for your environment, right? Because what's normal for your own environment may not be the same for my environment. Once I understand what that normal baseline is, then, as I have deviations I canto anomaly detection, I can call it an aggregate issues I can really bring down. Apply here and machine learning and narrow down the issues that are most critical for you to look at right now. Once and Aragon exact issue. I wanted the next thing, and that is what we call machine. Reasoning on machine reasoning is all about ordering the workflow off what you need to do to debug and fix the problem. You want the network to become smarter and smarter, the more you use it on. All of this is done through model learning and putting in the clouds infants in the cloud and pushing it down the rules as way have devices on line on time. So, >> do you see the day? If you think about the roadmap for for machine intelligence, do you see the day where the machine will actually do the remediation of that workflow. >> Absolutely. That's what we need to get you >> when you talk about the automated base lining is obviously a security, you know, use case there. Uh, maybe talk about that a little bit. And are there others? It really depends on your objective, right? If my objective is to drive more efficiency, lower costs, I presume. A baseline is where you start, right? So >> when I say baseline what I mean really, like, say, if I tell you that from this laptop to connect on a WiFi network, it took you three seconds and ask, Is that good or bad? You know, I don't know what the baseline for his environment. What's normal next time? If you take eight seconds on your baseline street, something is wrong. But what is wrong isn't a laptop issue isn't a version on the on your device is an application issue on network issue and our issue I don't know. That's why I'm machine learning will do exactly what the problem is. And then you use machine reasoning to fix a problem. >> Sorry. This is probably a stupid question, but how much data do you actually need. And how much time do you need to actually do a good job in that? That type of use case? >> What happens is you need the right data, Okay? And you're not sure where the right data is originally, which we do a lot of our expertise. It's this grass for 20 years is figuring out what the right data is and also with a lot of machine learning. We've done as well as a machine reason where we put together templates and so forth. We've basically gathered the right made for the right cause for the customer. And we refined that over time. So over time, like this venue here, the way this venue network, what it is, how it operates and so forth varies with time. We need to weigh need to refine that over time, keep it up to date and so forth. >> And when we talk about data, we're talking about tons of metadata here, right? I mean, do you see the day where there'll be more metadata than data? Yeah, it's a rhetorical question. All right, so So it's true you were hearing >> the definite zone. Lots of people learning about a building infrastructure is code. Tell us how the developer angle fits into what we've been discussing. >> Here we ask. So what happens is is part of intent based on African key parts of automation, right? And another key parts. The assurance. Well, it's what Devon it's trying to do right now by working with engineering with us and various partners are customers is putting together one of the key use cases that people have and what is code that can help them get that done. And what they're also doing is trying to the looking through the code. They're improving it, trying to instill best practice and stuff. So it's recently good po'd people can use and start building off. So we think this could be very valuable for our customers to help move into this more advanced automation and so forth. >> So architecture matters. We've touched upon it. But I want you to talk more about multi domain architectures wear Chuck Robbins. You know, talk about it. What is it? Why is it such a big deal on DH? How does it give Sisko competitive advantage? >> Think about it. I mean, my dad go being architectures. Nothing but all the components of a modern enterprise that look behind the scenes from giving access to a user or device to access for application and everything in between. Traditionally, each of these domains, like an access domain, the land domain can have 100 thousands off network know that device is. Each of these are configured General Manual to see a live my domain architectures almost teaching these various domains into one cohesive, data driven, automated programmable network. Your campus, your branch, your ran. But he doesn't and cloud with security as an integral part of it if it all. >> So it's really a customer view of an architecture isn't? Yeah, absolutely. Okay, that's good. I like that answer. I thought you're going to come out with a bunch of Cisco No mumbo jumbo in secret sauce. Now it really is you guys thinking about Okay, how would our customers need to architect there? >> But if you think about it, it's all about customer use case, for example, like we talked earlier today, we were walking everywhere on the bull's eye, in the cafe, in office and always on the goal. You're accessing your business school applications, whether it's webex salesforce dot com, 40 65. At the same time you're doing Facebook and what's happened. YouTube and other applications. Cisco has the van Domain will talk to Sisko. The domains action escalates and policies. So now you can cry tears the application that you want, which is business critical and fixing the night watchers but miss experience for you. But you want the best experience for that matter, where you are well >> on the security implications to I mean, you're basically busting down the security silos. Sort of the intent here, right? Right. Last thoughts on the show. San Diego last year. Orlando. We're in Barcelona earlier this year. >> I think it's been great so far. If you think about it in the last two years, we fill out the entire portfolio for the new access network when the cattle is 90. 100. Access points with WiFi six Switches Makes emission Campus core. Waterston, Controller Eyes for Unified Policy Data Center for Automation Analytics. Delia Spaces Business Insights Whole Access Network has been reinvented on It's a great time. >> Nice, strong summary, but John will give you the last word. >> What happens here is also everything about It says that we have 5,000 engineers have been doing this a couple years and we have a lot more in the pipe. So you're going to Seymour in six months from now Morn. Nine months and so forth. It's a very exciting time. >> Excellent. Guys. It is clear you like you say, completing the portfolio positioning for the next wave of of access. So congratulations on all the hard work I know a lot goes into it is Thank you very much for coming. All right, Keep it right there. David. Dante was stupid. And Lisa Martin is also in the house. We'll get back with the Cube. Sisqo live 2019 from San Diego.