>> Hello and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. Got a variety of interviews across the gamut from topics, women in tech, mentoring, pipelining, developers, open source, executives. Stanford's having International Women's Day celebration with the women in data science, which we're streaming that live as well. Variety of programs. In this segment, Meagen Eisenberg, friend of theCUBE, she's the CMO of Laceworks, is an amazing executive, got a great journey story as a CMO but she's also actively advising startups, companies and really pays it forward. I want to say Meagen, thank you for coming on the program and thanks for sharing. >> Yeah, thank you for having me. I'm happy to be here. >> Well, we're going to get into some of the journey celebrations that you've gone through and best practice what you've learned is pay that forward. But I got to say, one of the things that really impresses me about you as an executive is you get stuff done. You're a great CMO but also you're advised a lot of companies, you have a lot of irons in the fires and you're advising companies and sometimes they're really small startups to bigger companies, and you're paying it forward, which I love. That's kind of the spirit of this day. >> Yeah, I mean, I agree with you. When I think about my career, a lot of it was looking to mentors women out in the field. This morning I was at a breakfast by Eileen and we had the CEO of General Motors on, and she was talking about her journey nine years as a CEO. And you know, and she's paying it forward with us. But I think about, you know, when you're advising startups, you know, I've gathered knowledge and pattern recognition and to be able to share that is, you know, I enjoy it. >> Yeah. And the startups are also fun too, but it's not always easy and it can get kind of messy as you know. Some startups don't make it some succeed and it's always like the origination story is kind of rewritten and then that's that messy middle. And then it's like that arrows that don't look like a straight line but everyone thinks it's great and you know, it's not for the faint of heart. And Teresa Carlson, who I've interviewed many times, former Amazon, now she's the president of Flexport, she always says, sometimes startups on certain industries aren't for the faint of heart so you got to have a little bit of metal, right? You got to be tough. And some cases that you don't need that, but startups, it's not always easy. What have you learned? >> Yeah, I mean, certainly in the startup world, grit, creativity. You know, when I was at TripActions travel company, pandemic hits, nobody's traveling. You cut budget, you cut heads, but you focus on the core, right? You focus on what you need to survive. And creativity, I think, wins. And, you know, as a CMO when you're marketing, how do you get through that noise? Even the security space, Lacework, it's a fragmented market. You've got to be differentiated and position yourself and you know, be talking to the right target audience and customers. >> Talk about your journey over the years. What have you learned? What's some observations? Can you share any stories and best practices that someone watching could learn from? I know there's a lot of people coming into the tech space with the generative AI things going on in Cloud computing, scaling to the edge, there's a lot more aperture for technical jobs as well as just new roles and new roles that haven't, you really don't go to college for anymore. You got cybersecurity you're in. What are some of the things that you've done over your career if you can share and some best practices? >> Yeah, I think number one, continual learning. When I look through my career, I was constantly reading, networking. Part of the journey is who you're meeting along the way. As you become more senior, your ability to hire and bring in talent matters a lot. I'm always trying to meet with new people. Yeah, if I look at my Amazon feed of books I've bought, right, it kind of chronicle of my history of things I was learning about. Right now I'm reading a lot about cybersecurity, how the, you know, how how they tell me the world ends is the one I'm reading most recently. But you've got to come up to speed and then know the product, get in there and talk to customers. Certainly on the marketing front, anytime I can talk with the customer and find out how they're using us, why they love us, that, you know, helps me better position and differentiate our company. >> By the way, that book is amazing. I saw Nicole speak on Tuesday night with John Markoff and Palo Alto here. What a great story she told there. I recommend that book to everyone. It goes in and she did eight years of research into that book around zero day marketplaces to all the actors involved in security. And it was very interesting. >> Yeah, I mean, it definitely wakes you up, makes you think about what's going on in the world. Very relevant. >> It's like, yeah, it was happening all the time, wasn't it. All the hacking. But this brings me, this brings up an interesting point though, because you're in a cybersecurity area, which by the way, it's changing very fast. It's becoming a bigger industry. It's not just male dominated, although it is now, it's still male dominated, but it's becoming much more and then just tech. >> Yeah, I mean it's a constantly evolving threat landscape and we're learning, and I think more than ever you need to be able to use the data that companies have and, you know, learn from it. That's one of the ways we position ourselves. We're not just about writing rules that won't help you with those zero day attacks. You've got to be able to understand your particular environment and at any moment if it changes. And that's how we help you detect a threat. >> How is, how are things going with you? Is there any new things you guys got going on? Initiatives or programs for women in tech and increasing the range of diversity inclusion in the industry? Because again, this industry's getting much wider too. It's not just specialized, it's also growing. >> Yes, actually I'm excited. We're launching secured by women, securedbywomen.com and it's very much focused on women in the industry, which some studies are showing it's about 25% of security professionals are women. And we're going to be taking nominations and sponsoring women to go to upcoming security events. And so excited to launch that this month and really celebrate women in security and help them, you know, part of that continual learning that I talked about, making sure they're there learning, having the conversations at the conferences, being able to network. >> I have to ask you, what inspired you to pursue the career in tech? What was the motivation? >> You know, if I think way back, originally I wanted to be on the art side and my dad said, "You can do anything as long as it's in the sciences." And so in undergrad I did computer science and MIS. Graduated with MIS and computer science minor. And when I came out I was a IT engineer at Cisco and you know, that kind of started my journey and decided to go back and get my MBA. And during that process I fell in love with marketing and I thought, okay, I understand the buyer, I can come out and market technology to the IT world and developers. And then from there went to several tech companies. >> I mean my father was an engineer. He had the same kind of thing. You got to be an engineer, it's a steady, stable job. But that time, computer science, I mean we've seen the evolution of computer science now it's the most popular degree at Berkeley we've heard and around the world and the education formats are changing. You're seeing a lot of people's self-training on YouTube. The field has really changed. What are some of the challenges you see for folks trying to get into the industry and how would you advise today if you were talking to your young self, what would you, what would be the narrative? >> Yeah, I mean my drawback then was HTML pages were coming out and I thought it would be fun to design, you know, webpages. So you find something you're passionate about in the space today, whether it's gaming or it's cybersecurity. Go and be excited about it and apply and don't give up, right? Do whatever you can to read and learn. And you're right, there are a ton of online self-help. I always try to hire women and people who are continual learners and are teaching themselves something. And I try to find that in an interview to know that they, because when you come to a business, you're there to solve problems and challenges. And the folks that can do that and be innovative and learn, those are the ones I want on my team. >> It's interesting, you know, technology is now impacting society and we need everyone involved to participate and give requirements. And that kind of leads my next question for you is, like, in your opinion, or let me just step back, let me rephrase. What are some of the things that you see technology being used for, for society right now that will impact people's lives? Because this is not a gender thing. We need everybody involved 'cause society is now digital. Technology's pervasive. The AI trends now we're seeing is clearly unmasking to the mainstream that there's some cool stuff happening. >> Yeah, I mean, I think ChatGPT, think about that. All the different ways we're using it we're writing content and marketing with it. We're, you know, I just read an article yesterday, folks are using it to write children's stories and then selling those stories on Amazon, right? And the amount that they can produce with it. But if you think about it, there's unlimited uses with that technology and you've got all the major players getting involved on it. That one major launch and piece of technology is going to transform us in the next six months to a year. And it's the ability to process so much data and then turn that into just assets that we use and the creativity that's building on top of it. Even TripActions has incorporated ChatGPT into your ability to figure out where you want when you're traveling, what's happening in that city. So it's just, you're going to see that incorporated everywhere. >> I mean we've done an interview before TripAction, your other company you were at. Interesting point you don't have to type in a box to say, I'm traveling, I want a hotel. You can just say, I'm going to Barcelona for Mobile World Congress, I want to have a good time. I want some tapas and a nice dinner out. >> Yes. Yeah. That easy. We're making it easy. >> It's efficiency. >> And actually I was going to say for women specifically, I think the reason why we can do so much today is all the technology and apps that we have. I think about DoorDash, I think about Waze you know, when I was younger you had to print out instructions. Now I get in the car real quick, I need to go to soccer practice, I enter it, I need to pick them up at someone's house. I enter it. It's everything's real time. And so it takes away all the things that I don't add value to and allows me to focus on what I want in business. And so there's a bunch of, you know, apps out there that have allowed me to be so much more efficient and productive that my mother didn't have for sure when I was growing up. >> That is an amazing, I think that actually illustrates, in my opinion, the best example of ChatGPT because the maps and GPS integration were two techs, technologies merged together that replace driving and looking at the map. You know, like how do you do that? Like now it's automatically. This is what's going to happen to creative, to writing, to ideation. I even heard Nicole from her book read said that they're using ChatGPT to write zero day exploits. So you seeing it... >> That's scary stuff. You're right. >> You're seeing it everywhere. Super exciting. Well, I got to ask you before you get into some of the Lacework things that you're involved with, cause I think you're doing great work over there is, what was the most exciting projects you've worked on in your career? You came in Cisco, very technical company, so got the technical chops, CSMIS which stands for Management of Information Science for all the young people out there, that was the state of the art back then. What are some of the exciting things you've done? >> Yeah, I mean, I think about, I think about MongoDB and learning to market to developers. Taking the company public in 2017. Launching Atlas database as a service. Now there's so much more of that, you know, the PLG motion, going to TripActions, you know, surviving a pandemic, still being able to come out of that and all the learnings that went with it. You know, they recently, I guess rebranded, so they're Navan now. And then now back in the security space, you know, 14 years ago I was at ArcSite and we were bought by HP. And so getting back into the security world is exciting and it's transformed a ton as you know, it's way more complicated than it was. And so just understanding the pain of our customers and how we protect them as is fun. And I like, you know, being there from a marketing standpoint. >> Well we really appreciate you coming on and sharing that. I got to ask you, for folks watching they might be interested in some advice that you might have for them and their career in tech. I know a lot of young people love the tech. It's becoming pervasive in our lives, as we mentioned. What advice would you give for folks watching that want to start a career in tech? >> Yeah, so work hard, right? Study, network, your first job, be the best at it because every job after that you get pulled into a network. And every time I move, I'm hiring people from the last job, two jobs before, three jobs before. And I'm looking for people that are working hard, care, you know, are continual learners and you know, add value. What can you do to solve problems at your work and add value? >> What's your secret networking hack or growth hack or tip that you can share? Because you're a great networker by the way. You're amazing and you do add a lot of value. I've seen you in action. >> Well, I try never to eat alone. I've got breakfast, I've got lunch, I've got coffee breaks and dinner. And so when I'm at work, I try and always sit and eat with a team member, new group. If I'm out on the road, I'm, you know, meeting people for lunch, going for dinner, just, you know, don't sit at your desk by yourself and don't sit in the hotel room. Get out and meet with people. >> What do you think about now that we're out of the pandemic or somewhat out of the pandemic so to speak, events are back. >> Yes. >> RSA is coming up. It's a big event. The bigger events are getting bigger and then the other events are kind of smaller being distributed. What's your vision of how events are evolving? >> Yeah, I mean, you've got to be in person. Those are the relationships. Right now more than ever people care about renewals and you are building that rapport. And if you're not meeting with your customers, your competitors are. So what I would say is get out there Lacework, we're going to be at RSA, we're going to be at re:Inforce, we're going to be at all of these events, building relationships, you know, coffee, lunch, and yeah, I think the future of events are here to stay and those that don't embrace in person are going to give up business. They're going to lose market share to us. >> And networking is obviously very key on events as well. >> Yes. >> A good opportunity as always get out to the events. What's the event networking trick or advice do you give folks that are going to get out to the networking world? >> Yeah, schedule ahead of time. Don't go to an event and expect people just to come by for great swag. You should be partnering with your sales team and scheduling ahead of time, getting on people's calendars. Don't go there without having 100 or 200 meetings already booked. >> Got it. All right. Let's talk about you, your career. You're currently at Lacework. It's a very hot company in a hot field, security, very male dominated, you're a leader there. What's it like? What's the strategies? How does a woman get in there and be successful? What are some tricks, observations, any data you can share? What's the best practice? What's the secret sauce from Meagen Eisenberg? >> Yes. Yeah, for Meagen Eisenberg. For Lacework, you know, we're focused on our customers. There's nothing better than getting, being close to them, solving their pain, showcasing them. So if you want to go into security, focus on their, the issues and their problems and make sure they're aware of what you're delivering. I mean, we're focused on cloud security and we go from build time to run time. And that's the draw for me here is we had a lot of, you know, happy, excited customers by what we were doing. And what we're doing is very different from legacy security providers. And it is tapping into the trend of really understanding how much data you have and what's happening in the data to detect the anomalies and the threats that are there. >> You know, one of the conversations that I was just having with a senior leader, she was amazing and I asked her what she thought of the current landscape, the job market, the how to get promoted through the careers, all those things. And the response was interesting. I want to get your reaction. She said interdisciplinary skills are critical. And now more than ever, the having that, having a set of skills, technical and social and emotional are super valuable. Do you agree? What's your reaction to that and what would, how would you reframe that? >> Yeah, I mean, I completely agree. You can't be a leader without balance. You've got to know your craft because you're developing and training your team, but you also need to know the, you know, how to build relationships. You're not going to be successful as a C-level exec if you're not partnering across the functions. As a CMO I need to partner with product, I need to partner with the head of sales, I need to partner with finance. So those relationships matter a ton. I also need to attract the right talent. I want to have solid people on the team. And what I will say in the security, cybersecurity space, there's a talent shortage and you cannot hire enough people to protect your company in that space. And that's kind of our part of it is we reduce the number of alerts that you're getting. So you don't need hundreds of people to detect an issue. You're using technology to show, you know, to highlight the issue and then your team can focus on those alerts that matter. >> Yeah, there's a lot of emerging markets where leveling up and you don't need pedigree. You can just level up skill-wise pretty quickly. Which brings me to the next question for you is how do you keep up with all the tech day-to-day and how should someone watching stay on top of it? Because I mean, you got to be on top of this stuff and you got to ride the wave. It's pretty turbulent, but it's still growing and changing. >> Yeah, it's true. I mean, there's a lot of reading. I'm watching the news. Anytime something comes out, you know, ChatGPT I'm playing with it. I've got a great network and sharing. I'm on, you know, LinkedIn reading articles all the time. I have a team, right? Every time I hire someone, they bring new information and knowledge in and I'm you know, Cal Poly had this learn by doing that was the philosophy at San Luis Obispo. So do it. Try it, don't be afraid of it. I think that's the advice. >> Well, I love some of the points you mentioned community and network. You mentioned networking. That brings up the community question, how could people get involved? What communities are out there? How should they approach communities? 'Cause communities are also networks, but also they're welcoming people in that form networks. So it's a network of networks. So what's your take on how to engage and work with communities? How do you find your tribe? If someone's getting into the business, they want support, they might want technology learnings, what's your approach? >> Yeah, so a few, a few different places. One, I'm part of the operator collective, which is a strong female investment group that's open and works a lot with operators and they're in on the newest technologies 'cause they're investing in it. Chief I think is a great organization as well. You've got a lot of, if you're in marketing, there's a ton of CMO networking events that you can go to. I would say any field, even for us at Lacework, we've got some strong CISO networks and we do dinners around you know, we have one coming up in the Bay area, in Boston, New York, and you can come and meet other CISOs and security leaders. So when I get an invite and you know we all do, I will go to it. I'll carve out the time and meet with others. So I think, you know, part of the community is get out there and, you know, join some of these different groups. >> Meagen, thank you so much for spending the time. Final question for you. How do you see the future of tech evolving and how do you see your role in it? >> Yeah, I mean, marketing's changing wildly. There's so many different channels. You think about all the social media channels that have changed over the last five years. So when I think about the future of tech, I'm looking at apps on my phone. I have three daughters, 13, 11, and 8. I'm telling you, they come to me with new apps and new technology all the time, and I'm paying attention what they're, you know, what they're participating in and what they want to be a part of. And certainly it's going to be a lot more around the data and AI. I think we're only at the beginning of that. So we will continue to, you know, learn from it and wield it and deal with the mass amount of data that's out there. >> Well, you saw TikTok just got banned by the European Commission today around their staff. Interesting times. >> It is. >> Meagen, thank you so much as always. You're a great tech athlete. Been following your career for a while, a long time. You're an amazing leader. Thank you for sharing your story here on theCUBE, celebration of International Women's Day. Every day is IWD and thanks for coming on. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE Studios in Palo Alto. Thank you for watching, more to come stay with us. (bright music)

>>We're back at Supercomputing 22 in Dallas, winding down day four of this conference. I'm Paul Gillan, my co-host Dave Nicholson. We are talking, we've been talking super computing all week and you hear a lot about what's going on in the United States, what's going on in China, Japan. What we haven't talked a lot about is what's going on in Europe and did you know that two of the top five supercomputers in the world are actually from European countries? Well, our guest has a lot to do with that. Florian, bearish, I hope I pronounce that correctly. My German is, German is not. My strength is the operations director for price, ais, S B L. And let's start with that. What is price? >>So, hello and thank you for the invitation. I'm Flon and Price is a partnership for Advanced Computing in Europe. It's a non-profit association with the seat in Brussels in Belgium. And we have 24 members. These are representatives from different European countries dealing with high performance computing in at their place. And we, so far, we provided the resources for our European research communities. But this changed in the last year, this oral HPC joint undertaking who put a lot of funding in high performance computing and co-funded five PET scale and three preis scale systems. And two of the preis scale systems. You mentioned already, this is Lumi and Finland and Leonardo in Bologna in Italy were in the place for and three and four at the top 500 at least. >>So why is it important that Europe be in the top list of supercomputer makers? >>I think Europe needs to keep pace with the rest of the world. And simulation science is a key technology for the society. And we saw this very recently with a pandemic, with a covid. We were able to help the research communities to find very quickly vaccines and to understand how the virus spread around the world. And all this knowledge is important to serve the society. Or another example is climate change. Yeah. With these new systems, we will be able to predict more precise the changes in the future. So the more compute power you have, the better the smaller the grid and there is resolution you can choose and the lower the error will be for the future. So these are, I think with these systems, the big or challenges we face can be addressed. This is the climate change, energy, food supply, security. >>Who are your members? Do they come from businesses? Do they come from research, from government? All of the >>Above. Yeah. Our, our members are public organization, universities, research centers, compute sites as a data centers, but But public institutions. Yeah. And we provide this services for free via peer review process with excellence as the most important criteria to the research community for free. >>So 40 years ago when, when the idea of an eu, and maybe I'm getting the dates a little bit wrong, when it was just an idea and the idea of a common currency. Yes. Reducing friction between, between borders to create a trading zone. Yes. There was a lot of focus there. Fast forward to today, would you say that these efforts in supercomputing, would they be possible if there were not an EU super structure? >>No, I would say this would not be possible in this extent. I think when though, but though European initiatives are, are needed and the European Commission is supporting these initiatives very well. And before praise, for instance 2008, there were research centers and data centers operating high performance computing systems, but they were not talking to each other. So it was isolated praise created community of operation sites and it facilitated the exchange between them and also enabled to align investments and to, to get the most out of the available funding. And also at this time, and still today for one single country in Europe, it's very hard to provide all the different architectures needed for all the different kind of research communities and applications. If you want to, to offer always the latest technologies, though this is really hardly possible. So with this joint action and opening the resources for other research groups from other countries, you, we, we were able to, yeah, get access to the latest technology for different communities at any given time though. And >>So, so the fact that the two systems that you mentioned are physically located in Finland and in Italy, if you were to walk into one of those facilities and meet the people that are there, they're not just fins in Finland and Italians in Italy. Yeah. This is, this is very much a European effort. So this, this is true. So, so in this, in that sense, the geography is sort of abstracted. Yeah. And the issues of sovereignty that make might take place in in the private sector don't exist or are there, are there issues with, can any, what are the requirements for a researcher to have access to a system in Finland versus a system in Italy? If you've got a EU passport, Hmm. Are you good to go? >>I think you are good to go though. But EU passport, it's now it becomes complicated and political. It's, it's very much, if we talk about the recent systems, well first, let me start a praise. Praise was inclusive and there was no any constraints as even we had users from US, Australia, we wanted just to support excellence in science. And we did not look at the nationality of the organization, of the PI and and so on. There were quotas, but these quotas were very generously interpreted. So, and if so, now with our HPC joint undertaking, it's a question from what European funds, these systems were procured and if a country or being country are associated to this funding, the researchers also have access to these systems. And this addresses basically UK and and Switzerland, which are not in the European Union, but they were as created to the Horizon 2020 research framework. And though they could can access the systems now available, Lumi and Leono and the Petascale system as well. How this will develop in the future, I don't know. It depends to which research framework they will be associated or not. >>What are the outputs of your work at price? Are they reference designs? Is it actual semiconductor hardware? Is it the research? What do you produce? >>So the, the application we run or the simulation we run cover all different scientific domains. So it's, it's science, it's, but also we have industrial let projects with more application oriented targets. Aerodynamics for instance, for cars or planes or something like this. But also fundamental science like the physical elementary physics particles for instance or climate change, biology, drug design, protein costa, all these >>Things. Can businesses be involved in what you do? Can they purchase your, your research? Do they contribute to their, I'm sure, I'm sure there are many technology firms in Europe that would like to be involved. >>So this involving industry though our calls are open and is, if they want to do open r and d, they are invited to submit also proposals. They will be evaluated and if this is qualifying, they will get the access and they can do their jobs and simulations. It's a little bit more tricky if it's in production, if they use these resources for their business and do not publish the results. They are some, well, probably more sites who, who are able to deal with these requests. Some are more dominant than others, but this is on a smaller scale, definitely. Yeah. >>What does the future hold? Are you planning to, are there other countries who will be joining the effort, other institutions? Do you plan to expand your, your scope >>Well, or I think or HPC joint undertaking with 36 member states is quite, covers already even more than Europe. And yeah, clearly if, if there are other states interest interested to join that there is no limitation. Although the focus lies on European area and on union. >>When, when you interact with colleagues from North America, do you, do you feel that there is a sort of European flavor to supercomputing that is different or are we so globally entwined? No. >>So research is not national, it's not European, it's international. This is also clearly very clear and I can, so we have a longstanding collaboration with our US colleagues and also with Chap and South Africa and Canada. And when Covid hit the world, we were able within two weeks to establish regular seminars inviting US and European colleagues to talk to to other, to each other and exchange the results and find new collaboration and to boost the research activities. So, and I have other examples as well. So when we, we already did the joint calls US exceed and in Europe praise and it was a very interesting experience. So we received applications from different communities and we decided that we will review this on our side, on European, with European experts and US did it in US with their experts. And you can guess what the result was at the meeting when we compared our results, it was matching one by one. It was exactly the same. Recite >>That it, it's, it's refreshing to hear a story of global collaboration. Yeah. Where people are getting along and making meaningful progress. >>I have to mention you, I have to to point out, you did not mention China as a country you were collaborating with. Is that by, is that intentional? >>Well, with China, definitely we have less links and collaborations also. It's also existing. There, there was initiative to look at the development of the technologies and the group meet on a regular basis. And there, there also Chinese colleagues involved. It's on a lower level, >>Yes, but is is the con conversations are occurring. We're out of time. Florian be operations director of price, European Super Computing collaborative. Thank you so much for being with us. I'm always impressed when people come on the cube and submit to an interview in a language that is not their first language. Yeah, >>Absolutely. >>Brave to do that. Yeah. Thank you. You're welcome. Thank you. We'll be right back after this break from Supercomputing 22 in Dallas.

>>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.

(upbeat music) >> Welcome to this Cube Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the head of High Performance Computing at Cineca a Dell Technologies customer. Sanzio, welcome to theCUBE. >> Thank you, it's a pleasure. >> Lisa Martin: Likewise. Nice to see you. So tell us a little bit about Cineca, this is a large computing center, but a very large Italian non-profit consortium. Tell us about it. >> Yes, Cineca has been founded 50 years ago, from the university systems in Italy to support the scientific discovery and the industry innovations using the high-performance computing, and the correlated mythologies like intelligence together with the big data processing, and the simulations. We are a corsortium, which means that is a private not-for-profit organization. Currently our member of the consortium, almost all the universities in Italy and also all the national agencies. >> Lisa Martin: And I also read that you are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our statutory visions in the last 10 to 15 years , we have been to say, frequent buyers in the top 10. The idea is that we're enabling the scientific discovery by mean of the providing the most advanced systems, and the co-designing the the most advanced HPC systems to promote to support the accents in science. And being part of the European high-performance computing ecosystems. >> Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the human brain project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> The human brain project is one of the flagship projects that has been co-funded by the European Commission and the participating member states that are two different, right now , flagships together with another that is just in progress, which is the the quantum of flagship we are participating indirectly together with the National Disaster Council. And we are core partners of the HPC constructors , that is the human brain project. One billion euro of investment, co-founded by the participating states and the European Commissions. it's a project that would combine both the technology issues and the designing of a high-performance computing systems that would meet the requirements of the community. And the big scientific challenges, correlated to the physiological functions of the human brains, including different related to the behavior of the, of the human brain, either from the pathological point of view either from the physiological point of view. In order to better understand the aging user, that it would impact the, the health the public health systems, some other that are correlating with what would be the support for the physiological knowledge of the human brains. And finally computational performance, the human brain is more than Exascale systems, but with a average consumption, which is very low. We are talking about some hundred of wards of energy would provide a, an extreme and computational performance. So if we put the organizing the technology high-performance computing in terms of interconnections now we're morphing the computing systems that would represent a tremendous step in order to facing the big challenges of our base and energies, personalized medicine, climate change, food for all those kinds of big social economic challenge that we are facing. >> Which reading them, besides the human brain project, there are other projects going on, such as that you mentioned. I'd like to understand how Cineca is working with Dell Technologies. You have to translate, as you mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell Technologies. >> In our computing architectures. We had the need to address the capability to facing the big data processing involved with respect of the Human Brain Project and generally speaking that evolved with the respect of the science-driven that would provide cloud access to the systems by means of containers technologies. And the capability also to address what will be the creation of a Federation for high performance computing facility in Europe. So at the end we manage a competitive dialogue procurement the processor, that in a certain sense would share together with the different potential technology providers, what would be the visions and also the constraints with respect to the divisions including budget constraints and at the end Dell had shown the characteristics of the solution, that it will be more, let's say compliant. And at the same time, flexible with respect of the combinations of very different constraints and requirements. >> Dell Technologies has been sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> The Italian commitment together with the European member states is that by mean of scientific methods and peer review process roughly speaking of the production capacity, would be shared at the European level, that it's a commitment that has been shared together with the France, Germany, Spain, and Switzerland. Where also of course, the Italian scientists, can apply and participate, but in a sort of emulations and the advanced competition for addressing what will be the excellence in science. The remaining 50% of our production capacity is for, for the national community and in somehow to support the Italian community to be competitive on the worldwide scenario that setting up would lead also to the agreement after the international level, with respect of some of the actions that are promoted in progress in the US and in Japan also that means the sharing options with the US researchers or Japanese researchers in an open space. >> It sounds like the human brain project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the human brain project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be lead by Euro SPC, which is called the phoenix that stands for Federation of a high-performance computing system in Europe. That provide open service based on two concepts One is the sharing of the ID at the European level. So it means that open the access to the Cineca system to the system in France , to UNIX system in Germany, to fifth system in Switzerland, and to the diocese the marine ocean system in Spain that is federated, ID management, others, et cetera, related to what will be the Federation of data access. The scientific community may share their data in a seamless mode, the actions is being supported by genetic, has to do with the two specific target. One is the elaborations of the data that are provided by the lens, laser, laboratory facility in Florence, that is one of the core parts of garnering the data that would come from the mouse brains, the time user for caviar. And the second part is for the meso scale studies of the cortex of the brain. In some situations they combinations of performance capability of the Federation systems for addressing what would be the simulations of the overall being of the human brain would take a lot of performance that are challenging simulation periodically that they would happen combining that they HPC facility as at European level. >> Right. So I was reading there's a case study by the way, on Cineca that Dell Technologies has published. And some of the results you talked about those at the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prosthesis for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the human brain project. One last question for you. What advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> There is a continuous sharing, of knowledge, experience, best practices, where the situation is different in the sense that there are, what would we be the integration of the high-performance computing technology into their production workflow. That is the sharing of the experience in order to provide a spreads and amplifications of the opportunity for supporting the innovation. That is part of our social mission in Italy, but it's also the objective. that is supported by the European Commission. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio, thank you so much for sharing with us, what Cineca is doing and the great research that's going on there. And across a lot of disciplines. We appreciate you joining the program today. Thank you. >> Thank you. Thank you very much. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this Cube Conversation. (upbeat music)

(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE Studios here in Palo Alto, California. We have a special Around theCUBE segment, Unpacking AI. This is a Get Smart Series. We have three great guests. Rajen Sheth, VP of AI and Product Management at Google. He knows well the AI development for Google Cloud. Dr. Kate Darling, research specialist at MIT media lab. And Professor Barry O'Sullivan, Director SFI Centre for Training AI, University of College Cork in Ireland. Thanks for coming on, everyone. Let's get right to it. Ethics in AI as AI becomes mainstream, moves out to the labs and computer science world to mainstream impact. The conversations are about ethics. And this is a huge conversation, but first thing people want to know is, what is AI? What is the definition of AI? How should people look at AI? What is the definition? We'll start there, Rajen. >> So I think the way I would define AI is any way that you can make a computer intelligent, to be able to do tasks that typically people used to do. And what's interesting is that AI is something, of course, that's been around for a very long time in many different forms. Everything from expert systems in the '90s, all the way through to neural networks now. And things like machine learning, for example. People often get confused between AI and machine learning. I would think of it almost the way you would think of physics and calculus. Machine learning is the current best way to use AI in the industry. >> Kate, your definition of AI, do you have one? >> Well, I find it interesting that there's no really good universal definition. And also, I would agree with Rajen that right now, we're using kind of a narrow definition when we talk about AI, but the proper definition is probably much more broad than that. So probably something like a computer system that can make decisions independent of human input. >> Professor Barry, your take on the definition of AI, is there one? What's a good definition? >> Well, you know, so I think AI has been around for 70 years, and we still haven't agreed the definition for it, as Kate said. I think that's one of those very interesting things. I suppose it's really about making machines act and behave rationally in the world, ideally autonomously, so without human intervention. But I suppose these days, AI is really focused on achieving human level performance in very narrowly defined tasks, you know, so game playing, recommender systems, planning. So we do those in isolation. We don't tend to put them together to create the fabled artificial general intelligence. I think that's something that we don't tend to focus on at all, actually if that made sense. >> Okay the question is that AI is kind of elusive, it's changing, it's evolving. It's been around for awhile, as you guys pointed out, but now that it's on everyone's mind, we see it in the news every day from Facebook being a technology program with billions of people. AI was supposed to solve the problem there. We see new workloads being developed with cloud computing where AI is a critical software component of all this. But that's a geeky world. But the real world, as an ethical conversation, is not a lot of computer scientists have taken ethics classes. So who decides what's ethical with AI? Professor Barry, let's start with you. Where do we start with ethics? >> Yeah, sure, so one of the things I do is I'm the Vice-Chair of the European Commission's High-Level Expert Group on Artificial Intelligence, and this year we published the Ethics Guidelines for Trustworthy AI in Europe, which is all about, you know, setting an ethical standard for what AI is. You're right, computer scientists don't take ethical standards, but I suppose what we are faced with here is a technology that's so pervasive in our lives that we really do need to think carefully about the impact of that technology on, you know, human agency, and human well-being, on societal well-being. So I think it's right and proper that we're talking about ethics at this moment in time. But, of course, we do need to realize that ethics is not a panacea, right? So it's certainly something we need to talk about, but it's not going to solve, it's not going to rid us of all of the detrimental applications or usages of AI that we might see today. >> Kate, your take on ethics. Start all over, throw out everything, build on it, what do we do? >> Well, what we do is we get more interdisciplinary, right? I mean, because you asked, "Who decides?". Until now it has been the people building the technology who have had to make some calls on ethics. And it's not, you know, it's not necessarily the way of thinking that they are trained in, and so it makes a lot of sense to have projects like the one that Barry is involved in, where you bring together people from different areas of expert... >> I think we lost Kate there. Rajen, why don't you jump in, talk about-- >> (muffled speaking) you decide issues of responsibility for harm. We have to look at algorithmic bias. We have to look at supplementing versus replacing human labor, we have to look at privacy and data security. We have look at the things that I'm interested in like the ways that people anthropomorphize the technology and use it in a way that's perhaps different than intended. So, depending on what issue we're looking at, we need to draw from a variety of disciplines. And fortunately we're seeing more support for this within companies and within universities as well. >> Rajen, your take on this. >> So, I think one thing that's interesting is to step back and understand why this moment is so compelling and why it's so important for us to understand this right now. And the reason for that is that this is the moment where AI is starting to have an impact on the everyday person. Anytime I present, I put up a slide of the Mosaic browser from 1994 and my point is that, that's where AI is today. It's at the very beginning stages of how we can impact people, even though it's been around for 70 years. And what's interesting about ethics, is we have an opportunity to do that right from the beginning right now. I think that there's a lot that you can bring in from the way that we think about ethics overall. For example, in our company, can you all hear me? >> Yep. >> Mm-hmm. >> Okay, we've hired an ethicist within our company, from a university, to actually bring in the general principles of ethics and bring that into AI. But I also do think that things are different so for example, bias is an ethical problem. However, bias can be encoded and actually given more legitimacy when it could be encoded in an algorithm. So, those are things that we really need to watch out for where I think it is a little bit different and a little bit more interesting. >> This is a great point-- >> Let me just-- >> Oh, go ahead. >> Yeah, just one interesting thing to bear in mind, and I think Kate said this, and I just want to echo it, is that AI is becoming extremely multidisciplinary. And I think it's no longer a technical issue. Obviously there are massive technical challenges, but it's now become as much an opportunity for people in the social sciences, the humanities, ethics people. Legal people, I think need to understand AI. And in fact, I gave a talk recently at a legal symposium, and the idea of this on a parallel track of people who have legal expertise and AI expertise, I think that's a really fantastic opportunity that we need to bear in mind. So, unfortunately us nerds, we don't own AI anymore. You know, it's something we need to interact with the real world on a significant basis. >> You know, I want to ask a question, because you know, the algorithms, everyone talks about the algorithms and the bias and all that stuff. It's totally relevant, great points on interdisciplinary, but there's a human component here. As AI starts to infiltrate the culture and hit everyday life, the reaction to AI sometimes can be, "Whoa, my job's going to get automated away." So, I got to ask you guys, as we deal with AI, is that a reflection on how we deal with it to our own humanity? Because how we deal with AI from an ethics standpoint ultimately is a reflection on our own humanity. Your thoughts on this. Rajen, we'll start with you. >> I mean it is, oh, sorry, Rajen? >> So, I think it is. And I think that there are three big issues that I see that I think are reflective of ethics in general, but then also really are particular to AI. So, there's bias. And bias is an overall ethical issue that I think this is particular here. There's what you mentioned, future of work, you know, what does the workforce look like 10 years from now. And that changes quite a bit over time. If you look at the workforce now versus 30 years ago, it's quite a bit different. And AI will change that radically over the next 10 years. The other thing is what is good use of AI, and what's bad use of AI? And I think one thing we've discovered is that there's probably 10% of things that are clearly bad, and 10% of things that are clearly good, and 80% of things that are in that gray area in between where it's up to kind of your personal view. And that's the really really tough part about all this. >> Kate, you were going to weigh in. >> Well, I think that, I'm actually going to push back a little, not on Rajen, but on the question. Because I think that one of the fallacies that we are constantly engaging in is we are comparing artificial intelligence to human intelligence, and robots to people, and we're failing to acknowledge sufficiently that AI has a very different skillset than a person. So, I think it makes more sense to look at different analogies. For example, how have we used and integrated animals in the past to help us with work? And that lets us see that the answer to questions like, "Will AI disrupt the labor market?" "Will it change infrastructures and efficiencies?" The answer to that is yes. But will it be a one-to-one replacement of people? No. That said, I do think that AI is a really interesting mirror that we're holding up to ourselves to answer certain questions like, "What is our definition of fairness?" for example. We want algorithms to be fair. We want to program ethics into machines. And what it's really showing us is that we don't have good definitions of what these things are even though we thought we did. >> All right, Professor Barry, your thoughts? >> Yeah, I think there's many points one could make here. I suppose the first thing is that we should be seeing AI, not as a replacement technology, but as an assistive technology. It's here to help us in all sorts of ways to make us more productive, and to make us more accurate in how we carry out certain tasks. I think, absolutely the labor force will be transformed in the future, but there isn't going to be massive job loss. You know, the technology has always changed how we work and play and interact with each other. You know, look at the smart phone. The smart phone is 12 years old. We never imagined in 2007 that our world would be the way it is today. So technology transforms very subtly over long periods of time, and that's how it should be. I think we shouldn't fear AI. I think the thing we should fear most, in fact, is not Artificial Intelligence, but is actual stupidity. So I think we need to, I would encourage people not to think, it's very easy to talk negatively and think negatively about AI because it is such a impactful and promising technology, but I think we need to keep it real a little bit, right? So there's a lot of hype around AI that we need to sort of see through and understand what's real and what's not. And that's really some of the challenges we have to face. And also, one of the big challenges we have, is how do we educate the ordinary person on the street to understand what AI is, what it's capable of, when it can be trusted, and when it cannot be trusted. And ethics gets of some of the way there, but it doesn't have to get us all of the way there. We need good old-fashioned good engineering to make people trust in the system. >> That was a great point. Ethics is kind of a reflection of that mirror, I love that. Kate, I want to get to that animal comment about domesticating technology, but I want to stay in this culture question for a minute. If you look at some of the major tech companies like Microsoft and others, the employees are revolting around their use of AI in certain use cases. It's a knee-jerk reaction around, "Oh my God, "We're using AI, we're harming the world." So, we live in a culture now where it's becoming more mission driven. There's a cultural impact, and to your point about not fearing AI, are people having a certain knee-jerk reaction to AI because you're seeing cultures inside tech companies and society taking an opinion on AI. "Oh my God, it's definitely bad, our company's doing it. "We should not service those contracts. "Or, maybe I shouldn't buy that product "because it's listening to me." So, there's a general fear. Does this impact the ethical conversation? How do you guys see this? Because this is an interplay that we see that's a personal, it's a human reaction. >> Yeah, so if I may start, I suppose, absolutely there are, you know, the ethics debates is a critical one, and people are certainly fearful. There is this polarization in debate about good AI and bad AI, but you know, AI is good technology. It's one of these dual-use technologies. It can be applied to bad situation in ways that we would prefer it wasn't. And it can also, it's a force for tremendous good. So, we need to think about the regulation of AI, what we want it to do from a legal point of view, who is responsible, where does liability lie? We also think about what our ethical framework is, and of course, there is no international agreement on what is, there is no universal code of ethics, you know? So this is something that's very very heavily contextualized. But I think we certainly, I think we generally agree that we want to promote human well-being. We want to compute, we want to have a prosperous society. We want to protect the well-being of society. We don't want technology to impact society in any negative way. It's actually very funny. If you look back about 25-30 years ago, there was a technology where people were concerned that privacy was going to be a thing of the past. That computer systems were going to be tremendously biased because data was going to be incomplete and not representative. And there was a huge concern that good old-fashioned databases were going to be the technology that will destroy the fabric of society. That didn't happen. And I don't think we're going to have AI do that either. >> Kate? >> Yeah, we've seen a lot of technology panic, that may or may not be warranted, in the past. I think that AI and robotics suffers from a specific problem that people are influenced by science fiction and pop culture when they're thinking about the technology. And I feel like that can cause people to be worried about some things that maybe perhaps aren't the thing we should be worrying about currently. Like robots and jobs, or artificial super-intelligence taking over and killing us all, aren't maybe the main concerns we should have right now. But, algorithmic bias, for example, is a real thing, right? We see systems using data sets that disadvantage women, or people of color, and yet the use of AI is seen as neutral even though it's impinging existing biases. Or privacy and data security, right? You have technologies that are collecting massive amounts of data because the way learning works is you use lots of data. And so there's a lot of incentive to collect data. As a consumer, there's not a lot of incentive for me to want to curb that, because I want the device to listen to me and to be able to perform better. And so the question is, who is thinking about consumer protection in this space if all the incentives are toward collecting and using as much data as possible. And so I do think there is a certain amount of concern that is warranted, and where there are problems, I endorse people revolting, right? But I do think that we are sometimes a little bit skewed in our, you know, understanding where we currently are at with the technology, and what the actual problems are right now. >> Rajen, I want your thoughts on this. Education is key. As you guys were talking about, there's some things to pay attention to. How do you educate people about how to shape AI for good, and at the same time calm the fears of people at the same time, from revolting around misinformation or bad data around what could be? >> Well I think that the key thing here is to organize kind of how you evaluate this. And back to that one thing I was saying a little bit earlier, it's very tough to judge kind of what is good and what is bad. It's really up to personal perception. But then the more that you organize how to evaluate this, and then figure out ways to govern this, the easier it gets to evaluate what's in or out . So one thing that we did, was that we created a set of AI principles, and we kind of codified what we think AI should do, and then we codified areas that we would not go into as a company. The thing is, it's very high level. It's kind of like the constitution, and when you have something like the constitution, you have to get down to actual laws of what you would and wouldn't do. It's very hard to bucket and say, these are good use cases, these are bad use cases. But what we now have is a process around how do we actually take things that are coming in and figure out how do we evaluate them? A last thing that I'll mention, is that I think it's very important to have many many different viewpoints on it. Have viewpoints of people that are taking it from a business perspective, have people that are taking it from kind of a research and an ethics perspective, and all evaluate that together. And that's really what we've tried to create to be able to evaluate things as they come up. >> Well, I love that constitution angle. We'll have that as our last final question in a minute, that do we do a reset or not, but I want to get to that point that Kate mentioned. Kate, you're doing research around robotics. And I think robotics is, you've seen robotics surge in popularity from high schools have varsity teams now. You're seeing robotics with software advances and technology advances really become kind of a playful illustration of computer technology and software where AI is playing a role, and you're doing a lot of work there. But as intelligence comes into, say robotics, or software, or AI, there's a human reaction to all of this. So there's a psychology interaction to either AI and robotics. Can you guys share your thoughts on the humanization interaction between technology, as people stare at their phones today, that could be relationships in the future. And I think robotics might be a signal. You mentioned domesticating animals as an example back in the early days of when we were (laughing) as a society, that happened. Now we all have pets. Are we going to have robots as pets? Are we going to have AI pets? >> Yes, we are. (laughing) >> Is this kind of the human relationship? Okay, go ahead, share your thoughts. >> So, okay, the thing that I love about robots, and you know, in some applications to AI as well, is that people will treat these technologies like they're alive. Even though they know that they're just machine. And part of that is, again, the influence of science fiction and pop culture, that kind of primes us to do this. Part of it is the novelty of the technology moving into shared spaces, but then there's this actual biological element to it, where we have this inherent tendency to anthropomorphize, project human-like traits, behaviors, qualities, onto non-humans. And robots lend themselves really well to that because our brains are constantly scanning our environments and trying to separate things into objects and agents. And robots move like agents. We are evolutionarily hardwired to project intent onto the autonomous movement in our physical space. And this is why I love robots in particular as an AI use case, because people end up treating robots totally differently. Like people will name their Roomba vacuum cleaner and feel bad for it when it gets stuck, which they would never do with their normal vacuum cleaner, right? So, this anthropomorphization, I think, makes a huge difference when you're trying to integrate the technology, because it can have negative effects. It can lead to inefficiencies or even dangerous situations. For example, if you're using robots in the military as tools, and they're treating them like pets instead of devices. But then there are also some really fantastic use cases in health and education that rely specifically on this socialization of the robot. You can use a robot as a replacement for animal therapy where you can't use real animals. We're seeing great results in therapy with autistic children, engaging them in ways that we haven't seen previously. So there are a lot of really cool ways that we can make this work for us as well. >> Barry, your thoughts, have you ever thought that we'd be adopting AI as pets some day? >> Oh yeah, absolutely. Like Kate, I'm very excited about all of this too, and I think there's a few, I agree with everything Kate has said. Of course, you know, coming back to the remark you made at the beginning about people putting their faces in their smartphones all the time, you know, we can't crowdsource our sense of dignity, or that we can't have social media as the currency for how we value our lives or how we compare ourselves with others. So, you know, we do have to be careful here. Like, one of the really nice things about, one of the really nice examples of an AI system that was given some significant personality was, quite recently, Tuomas Sandholm and others at Carnegie Mellon produced this Liberatus poker playing bot, and this AI system was playing against these top-class Texas hold' em players. And all of these Texas hold 'em players were attributing a level of cunning and sophistication and mischief on this AI system that clearly it didn't have because it was essentially trying to just behave rationally. But we do like to project human characteristics onto AI systems. And I think what would be very very nice, and something we need to be very very careful of, is that when AI systems are around us, and particularly robots, you know, we do need to treat them with respect. And what I mean is, we do make sure that we treat those things that are serving society in as positive and nice a way as possible. You know, I do judge people on how they interact with, you know, sort of the least advantaged people in society. And you know, by golly, I will judge you on how you interact with a robot. >> Rajen-- >> We've actually done some research on that, where-- >> Oh, really-- >> We've shown that if you're low empathy, you're more willing to hit a robot, especially if it has a name. (panel laughing) >> I love all my equipment here, >> Oh, yeah? >> I got to tell ya, it's all beautiful. Rajen, computer science, and now AIs having this kind of humanization impact, this is an interesting shift. I mean, this is not what we studied in computer science. We were writin' code. We were going to automate things. Now there's notions of math, and not just math cognition, human relations, your thoughts on this? >> Yeah, you know what's interesting is that I think ultimately it boils down to the user experience. And I think there is this part of this which is around humanization, but then ultimately it boils down to what are you trying to do? And how well are you doing it with this technology? And I think that example around the Roomba is very interesting, where I think people kind of feel like this is more, almost like a person. But, also I think we should focus as well on what the technology is doing, and what impact it's having. My best example of this is Google Photos. And so, my whole family uses Google Photos, and they don't know that underlying it is some of the most powerful AI in the world. All they know is that they can find pictures of our kids and their grandkids on the beach anytime that they want. And so ultimately, I think it boils down to what is the AI doing for the people? And how is it? >> Yeah, expectations become the new user experience. I love that. Okay, guys, final question, and also humanization, we talked about the robotics, but also the ethics here. Ethics reminds me of the old security debate, and security in the old days. Do you increase the security, or do you throw it all away and start over? So with this idea of how do you figure out ethics in today's modern society with it being a mirror? Do we throw it all away and do a do-over, can we recast this? Can we start over? Do we augment? What's the approach that you guys see that we might need to go through right now to really, not hold back AI, but let it continue to grow and accelerate, educate people, bring value and user experience to the table? What is the path? We'll start with Barry, and then Kate, and then Rajen. >> Yeah, I can kick off. I think ethics gets us some of the way there, right? So, obviously we need to have a set of principles that we sign up to and agree upon. And there are literally hundreds of documents on AI ethics. I think in Europe, for example, there are 128 different documents around AI ethics, I mean policy documents. But, you know, we have to think about how are we actually going to make this happen in the real world? And I think, you know, if you take the aviation industry, that we trust in airplanes, because we understand that they're built to the highest standards, that they're tested rigorously, and that the organizations that are building these things are held account when things go wrong. And I think we need to do something similar in AI. We need good strong engineering, and you know, ethics is fantastic, and I'm a strong believer in ethical codes, but we do need to make it practical. And we do need to figure out a way of having the public trust in AI systems, and that comes back to education. So, I think we need the general public, and indeed ourselves, to be a little more cynical and questioning when we hear stories in the media about AI, because a lot of it is hyped. You know, and that's because researchers want to describe their research in an exciting way, but also, newspaper people and media people want to have a sticky subject. But I think we do need to have a society that can look at these technologies and really critique them and understand what's been said. And I think a healthy dose of cynicism is not going to do us any harm. >> So, modernization, do you change the ethical definition? Kate, what's your thoughts on all this? >> Well, I love that Barry brought up the aviation industry because I think that right now we're kind of an industry in its infancy, but if we look at how other industries have evolved to deal with some thorny ethical issues, like for example, medicine. You know, medicine had to develop a whole code of ethics, and develop a bunch of standards. If you look at aviation or other transportation industries, they've had to deal with a lot of things like public perception of what the technology can and can't do, and so you look at the growing pains that those industries have gone through, and then you add in some modern insight about interdisciplinary, about diversity, and tech development generally. Getting people together who have different experiences, different life experiences, when you're developing the technology, and I think we don't have to rebuild the wheel here. >> Yep. >> Rajen, your thoughts on the path forward, throw it all away, rebuild, what do we do? >> Yeah, so I think this is a really interesting one because of all the technologies I've worked in within my career, everything from the internet, to mobile, to virtualization, this is probably the most powerful potential for human good out there. And AI, the potential of what it can do is greater than almost anything else that's out there. However, I do think that people's perception of what it's going to do is a little bit skewed. So when people think of AI, they think of self-driving cars and robots and things like that. And that's not the reality of what AI is today. And so I think two things are important. One is to actually look at the reality of what AI is doing today and where it impacts people lives. Like, how does it personalize customer interactions? How does it make things more efficient? How do we spot things that we never were able to spot before? And start there, and then apply the ethics that we've already known for years and years and years, but adapt it to a way that actually makes sense for this. >> Okay, like that's it for the Around theCUBE. Looks like we've tallied up. Looks like Professor Barry 11, third place, Kate in second place with 13. Rajen with 16 points, you're the winner, so you get the last word on the segment here. Share your final thoughts on this panel. >> Well, I think it's really important that we're having this conversation right now. I think about back to 1994 when the internet first started. People did not have that conversation nearly as much at that point, and the internet has done some amazing things, and there have been some bad side effects. I think with this, if we have this conversation now, we have this opportunity to shape this technology in a very very positive way as we go forward. >> Thank you so much, and thanks everyone for taking the time to come in. All the way form Cork, Ireland, Professor Barry O'Sullivan. Dr. Kate Darling doing some amazing research at MIT on robotics and human psychology and like a new book coming out. Kate, thanks for coming out. And Rajen, thanks for winning and sharing your thoughts. Thanks everyone for coming. This is Around theCUBE here, Unpacking AI segment around ethics and human interaction and societal impact. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Hi. We're right outside of the Boston Haba. You're watching >> the cube on stew Minimum in. And this is active Geo data driven. 2019 due date. Two days digging into, You >> know, the role of data inside Cos on, you know, in an ever changing world, happy to welcome to the program of first time guests are China Oven countrymen who's a research manager at I. D. C. Coming to us from across the pond in London. Thanks so much for joining us. Pleasure. So tell us a little bit. I d c. We know. Well, you know, the market landscapes, you know, watching what's happening. Thie said it 77 Zita bites that was put up in the keynote. Came came from I D. C. Tells you you're focused. >> Yeah, so I'm part of the data protection and storage research team, But I have, ah, European focus. I covered the Western European markets where data protection is almost off a neurotic interest to us. So a lot of our investment is actually made on the context of data protection. And how do I become data driven without compromising on security and sovereignty and data locality. So that's something that I look at. I'm also part of our broader multi cloud infrastructure team on also develops practice. I'm looking at all these modern new trends from data perspective as well. So it's kind of nice being >> keeping you busy, huh? Yeah. So about a year ago, every show that I went to there would be a big clock up on the Kino stage counting down until gpr went way actually said on the Q. Many times it's like we'll know when GPR starts with lawsuits. Sister and I feel like it was a couple of days, if not a couple of weeks before some of the big tech firms got sued for this. So here we are 2019. It's been, you know, been a while now since since since this launch. How important is GDP are you know what? How is that impacting customers and kind of ripple effect? Because, you know, here in the States, we're seeing some laws in California and beyond that are following that. But they pushed back from the Oh, hey, we're just gonna have all the data in the world and we'll store it somewhere sure will protect it and keep it secure. But but But >> yeah, yeah, so it's suggestive. Here is a game changer and it's interesting you said this big clock ticking and everybody has been talking about it. So when the European Commission >> announced repairs >> coming, organizations had about two years to actually prepare for it. But there were a lot of naysayers, and they thought, This is not gonna happen. The regulators don't have enough resources to actually go after all of these data breaches, and it's just too complicated. Not everyone's going complaints just not gonna happen. But then they realised that the regulators we're sticking to it on towards the end. Towards the last six months in the race to GDP, and there was this helter skelter running. Their organizations were trying to just do some Die Ryan patch of exercise to have that minimum viable compliance. So there they wanted to make sure that they don't go out of business. They don't have any major data breaches when Jean Pierre comes a difference that that was the story of 2018 although they have so much time to react they didn't on towards the end. They started doing a lot of these patch up work to make sure they had that minimum by the compliance. But over time, what we're seeing is that a lot off a stewed organizations are actually using GDP are as to create that competitive differentiations. If you look at companies like Barclays, they have been so much on top of that game on DH. They include that in their marketing strategies and the corporate social responsibility to say that, Hey, you know our business is important to us, but your privacy and your data is much more valuable to us, and that kind of instantly helps them build that trust. So they have big GDP, our compliance into their operations so much and so well that they can actually sell those kind of GPR consultancy services because they're so good at it. And that's what we are seeing is happening 2019 on DH. Probably the next 12 to 18 months will be about scaling on operational izing GDP are moving from that minimum viable compliance. >> Its interest weighed a conversation with Holly St Clair, whose state of Massachusetts and in our keynote this morning she talked about that data minimalist. I only want as much data as I know what I'm going to do. How I'm goingto leverage it, you know, kind of that pendulum swing back from the I'm goingto poured all the data and think about it later. It is that Did you see that is a trend with, you know, is that just governments is that, you know, you seeing that throughout industries and your >> interesting. So there was seven gpr came into existence. There were a lot of these workshops that were happening for on for organizations and how to become GDP. And there was this Danish public sector organization where one of the employees went to do that workshop was all charged up, and he came back to his employer and said, Hey, can you forget me on it Took that organization about 14 employees and three months to forget one person. So that's the amount of data they were holding in. And they were not dilating on all the processes were manual which took them so long to actually forget one person on. So if you don't cleanse a pure data act now meeting with all these right to be forgotten, Andi, all these specific clauses within GPR is going to be too difficult. And it's going to just eat up your business >> tryingto connecting the dots here. One of the one of the big stumbling blocks is if you look at data protection. If I've got backup, if I've got archive, I mean, if I've taken a snapshot of something and stuck that under a mountain in a giant tape and they say forget about me Oh, my gosh, Do I have to go retrieve that? I need to manage that? The cost could be quite onerous. Help! Help us connect the dots as to what that means to actually, you know, what are the ramifications of this regulation? >> Yeah, So I think so. Judy PR is a beast. It's a dragon off regulations. It's important to dice it to understand what the initial requirements are on one was the first step is to get visibility and classified the data as to what is personal data. You don't want to apply policies to all the data because I might be some garbage in there, so you need to get visibility on A says and classified data on what is personal data. Once you know what data is personal, what do you want to retain? That's when you start applying policies too. Ensure that they are safe and they're anonymous. Pseudonym ized. If you want to do analytics at a later stage on DH, then you think about how you meet. Individual close is so see there's a jeep airframe, but you start by classifying data. Then you apply specific policies to ensure you protect on back up the personal data on. Then you go about meeting the specific requirements. >> What else can you tell us about kind of European markets? You know, I I know when I look at the the cloud space, governance is something very specific to, and I need to make sure my data doesn't leave the borders and like what other trends in you know issues when you hear >> it from Jenny Peered forced a lot ofthe existential threat to a lot of companies. Like, say, hyper scale. Er's SAS men does so they were the first ones to actually become completely compliant to understand their regulations, have European data data hubs, and to have those data centres like I think At that time, Microsoft had this good good collaboration with T systems to have a local data center not controlled by Microsoft, but by somebody who is just a German organizations. You cannot have data locality more than that, right? So they were trying different innovative ways to build confidence among enterprises to make sure that cloud adoption continues on what was interesting. That came out from a research was that way thought, Gee, DPR means people's confidence and cloud is going to plunge. People's confidence in public cloud is going to pledge. That didn't happen. 42% of organizations were still going ahead with their cloud strategies as is, but it's just that they were going to be a lot more cautious. And they want to make sure that the applications and data that they were putting in the cloud was something that they had complete visibility in tow on that didn't have too much of personal data and even if it had, they had complete control over. So they had a different strategy off approaching public cloud, but it didn't slow them down. But over time they realised that to get that control ofthe idea and to get that control of data. They need to have that multiple multi cloud strategy because Cloud had to become a two way street. They need to have an exit strategy. A swell. So they tried to make sure that they adopted multiple cloud technologies and have the data interoperability. Ahs Well, because data management was one of their key key. Top of my prayer. >> Okay, last question I had for you. We're here at the active you event. What? What do you hear from your customers about Octavio? Any research that you have relevant, what >> they're doing, it's going interesting. So copy data management. That's how active you started, right? They created a market for themselves in this competition, a management and be classified copy data management within replication Market on replication is quite a slow market, but this copy data management is big issue, and it's one of the fastest growing market. So So So they started off from a good base, but they created a market for themselves and people started noticing them, and now they have kind of grown further and grown beyond and tried to cover the entire data management space. Andi, I think what's interesting and what's going to be interesting is how they keep up the momentum in building that infrastructure, ecosystem and platform ecosystem. Because companies are moving from protecting data centers to protecting centers of data on if they can help organizations protect multiple centers of data through a unified pane of glass, I have a platform approach to data management. Then they can help organizations become data drivers, which gives them the competitive advantage. So if they can keep up that momentum there going great guns, >> Thank you so much for joining us in Cheshire, sharing the data that you have in the customer viewpoints from Europe. So we'll be back with more coverage here from Active EO data driven 2019 in Boston. Mess fuses on stew Minimum. Thanks for watching the Q. Thank you.