foreign greetings from San Francisco thecube is live this is our second day of wall-to-wall coverage of VMware Explorer 2022. Lisa Martin and Dave Nicholson here we're going to be talking with some ladies from Rackspace next please welcome Adriana Bustamante VP of strategic alliances and Hannah Deuce director of strategic alliances from Rackspace it's great to have you on the program thank you so much for having us good afternoon good morning is it lunchtime already almost almost yes and it's great to be back in person we were just talking about the keynote yesterday that we were in and it was standing room only people are ready to be back they're ready to be hearing from VMware it's ecosystem its Partners it's Community yes talk to us Adriana about what Rackspace is doing with Dell and VMware particularly in the healthcare space sure no so for us Partnerships are a big foundation to how we operate as a company and um and I have the privilege of doing it for over over 16 years so we've been looking after the dell and VMware part partnership ourselves personally for the last three years but they've been long-standing partners for for us and and how do we go and drive more meaningful joint Solutions together so Rackspace you know been around since since 98 we've seen such an evolution of coming becoming more of this multi-cloud transformation agile Global partner and we have a lot of customers that fall in lots of different verticals from retail to public sector into Healthcare but we started noticing and what we're trying trying to drive as a company is how do we drive more specialized Solutions and because of the pandemic and because of post-pandemic and everyone really trying to to figure out what the new normal is addressing different clients we saw that need increasing and we wanted to Rally together with our most strategic alliances to do more Hannah talk about obviously the the pandemic created such problems for every industry but but Healthcare being front and center it still is talk about some of the challenges that Healthcare organizations are coming to Rackspace going help yeah common theme that we've heard from some of our large providers Healthcare Providers has been helped me do more with less which we're all trying to do as we navigate The New Normal but in that space we found the opportunity to really leverage some of our expertise long-term expertise and that the talent and the resource pool that we had to really help in a some of the challenges that are being faced at a resource shortage Talent shortage and so Rackspace is able to Leverage What what we've done for many many years and really tailor it to the outcomes that Health Care Providers are needing nowadays that more with less Mantra runs across the gamut but a lot of it's been helped me modernize helped me get to that next phase I can't I can't I don't have the resources to DIY it myself anymore I need to figure out a more robust business continuity program and so helping with business continuity Dr you know third copies of just all all this data that's growing so it's not just covered pandemic driven but it's that's definitely driving the the need and the requirement to modernize so much quicker it's interesting that you mentioned rackspace's history and expertise in doing things and moving that forward and leveraging that pivoting focusing on specific environments to create something net new we've seen a lot of that here if you go back 10 years I don't know if that's the perfect date to go back to but if you go back 10 years ago you think about VMware where would we have expected VMware to be in this era of cloud we may have thought of things very very differently differently Rackspace a Pioneer in creating off-premises hey we will do this for you didn't even really call it Cloud at the time right but it was Cloud yeah and so the ability for entities like Rackspace like VMware we had a NetApp talking to us about stuff they're doing in the cloud 10 years ago if you I would say no they'd be they'll be gone they'll be gone so it's really really cool to see Rackspace making this transition and uh you know being aware of everything that's going on and focusing on the best value proposition moving forward I mean am I am I you know do I sound like somebody who would who would fit into the Rackspace culture right now or do I not get it yes you sound like a rocker we'll make you an honorary record that's what we call a Rackspace employees yes you know what we've noticed too and is budgets are moving those decision makers are moving so again 10 years ago just like you said you would be talking to sometimes a completely different Persona than we do than we do today and we've seen a shift more towards that business value we have a really unique ability to bring business and Technical conversations together I did a lot of work in the past of working with a lot of CMO and and digital transformation companies and so helping bring it and business seeing the same and how healthcare because budgets are living in different places and even across the board with Rackspace people are trying to drive more business outcomes business driven Solutions so the technical becomes the back end and really the ingredients to make all of that all of that happen and that's what we're helping to solve and it's a lot it's very fast paced everyone wants to be agile now and so they're leaning on us more and more to drive more services so if you've seen Rackspace evolve we're driving more of that advisement and those transformation service type discussions where where our original history was DNA was very much always embedded in driving a great experience now they're just wanting more from us more services help us how help us figure out the how Adriana comment on the outcomes that you're helping Healthcare organizations achieve as as we as we it's such a relatable tangible topic Healthcare is Right everybody's everybody's got somebody who's sick or you've been sick or whatnot what are some of those outcomes that we can ex that customers can expect to achieve with Rackspace and VMware oh great great question so very much I can't mentioned earlier it's how do I modernize how do I optimize how do I take the biggest advantage of the budgets and the landscape that I have I want to get to the Cloud we need to help our patients and get access to that data is this ready to go into the cloud is this not ready to go into the cloud you know how do we how do we help make sure we're taking care of our patients we're keeping things secure and accessible you know what else do you think is coming up yeah and one specific one uh sequencing genetic sequencing and so we've had this come up from a few different types of providers whether it's medical devices that they may provide to their end clients and an outcome that they're looking for is how do we get how do we leverage um here's rip here's what we do but now we have so many more people we need to give this access to we need them to be able to have access to the sequencing that all of this is doing all of these different entities are doing and the outcome that they're trying to get to to is more collaboration so so that way we can speed up in the face of a pandemic we can speed up those resolutions we could speed up to you know whether it's a vaccine needed or something that's going to address the next thing that might be coming you know um so that's a specific one I've heard that from a handful of different different um clients that that we work with and so trying to give them a Consolidated not trying to we are able to deliver them a Consolidated place that their application and tooling can run in and then all of these other entities can safely and securely access this data to do what they're going to do in their own spaces and then hopefully it helps the betterment of of of us globally like as humans in the healthcare space we all benefit from this so leveraging the technology to really drive a valuable outcome helps us all so so and by the way I like trying to because it conveys the proper level of humility that we all need to bring to this because it's complicated and anybody who looks you in the eye it pretends like they know exactly how to do it you need to run from those people no it is and and look that's where our partners become so significant we we know we're Best in Class for specific things but we rely on our Partnerships with Dell and VMware to bring their expertise to bring their tried and true technology to help us all together collectively deliver something good technology for good technology for good it is inherently good and it's nice when it's used for goodness it's nice when it's yeah yeah talk about security for a second you know we've seen the threat landscape change dramatically obviously nobody wants to be the next breach ransomware becoming a household term it's now a matter of when we get a head not F where has security gone in terms of conversations with customers going help us ensure that what we're doing is delivering data access to the right folks that need it at the right time in real time in a secure fashion no uh that's another good question in hot and burning so you know I think if we think about past conversations it was that nice Insurance offering that seemed like it came at a high cost if you really need it I've never been breached before um I'll get it when I when I need it but exactly to your point it's the win and not the if so what we're finding and also working with a nice ecosystem of Partners as well from anywhere from Akamai to cloudflare to BT it's how do we help ensure that there is the security as Hannah mentioned that we're delivering the right data access to the right people and permissions you know we're able to help meet multitude of compliance and regulations obviously health care and other regulated space as well we look to make sure that from our side of the house from the infrastructure that we have the right building blocks to help them Reach those compliance needs obviously it's a mutual partnership in maintaining that compliance and that we're able to provide guidance and best practices on to make sure that the data is living in a secure place that the people that need access to it get it when they when they need it and monitor those permissions and back to your complexity comment so more and more complex as we are a global global provider so when you start to talk to our teams in the UK and our our you know clients there specializing um kind of that Sovereign Cloud mentality of hey we need to have um we need to have a cloud that is built for the specific needs that reside within Healthcare by region so it's not just even I mean you know we're we're homegrown out of San Antonio Texas so like we know the U.S and have spent time here but we've been Global for many years so we just get down into the into the nitty-gritty to customize what's needed within each region well Hannah is that part of the Rackspace value proposition at large moving forward because frankly look if I if I want if I want something generic I can I can swipe credit card and and fire up some Services sure um moving forward this is something that is going to more characterize the Rackspace experience and I and I understand that the hesitancy to say hey it's complicated it's like I don't want to hear that I want to hear that it's easy it's like well okay we'll make it easy for you yes but it's still complicated is that okay that's the honest that's that's the honest yeah that's why you need help right that's why we need to talk about that because people people have a legitimate question why Rackspace yep and we don't I don't want to put you on the spot but no yeah but why why Rackspace you've talked a little bit about it already but kind of encapsulate it oh gosh so good good question why Rackspace it's because you can stand up [Laughter] well you can you do it there's many different options out there um and if I had a PowerPoint slide I'd show you this like lovely web of options of directions that you could go and what is Rackspace value it's that we come in and simplify it because we've had experience with this this same use case whatever somebody is bringing forward to us is typically something we've dealt with at numerous times and so we're repeating and speeding up the ability to simplify the complex and to deliver something more simplified well it may be complex within us and we're like working to get it done the outcome that we're delivering is is faster it's less expensive than dedicating all the resources yourself to do it and go invest in all of that that we've already built up and then we're able to deliver it in a more simplified manner it's like the duck analogy the feet below the water yes exactly and a lot of expertise as well yes a lot talk a little bit about the solution that that Dell VMware Rackspace are delivering to customers sure so when we think about um Healthcare clouds or Cloud specific to the healthcare industry you know there's some major players within that space that you think epic we'll just use them as an example this can play out with others but we are building out a custom or we have a custom clouds able to host epic and then provide services up through the Epic help application through partnership so that is broadening the the market for us in the sense that we can tailor what the what that end and with that healthcare provider needs uh do they do they have the expertise to manage the application okay you do that and then we will build out a custom fit Cloud for that application oh and you need all the adjacent things that come with it too so then we have reference architecture you know built out already to to tailor to whatever all those other 40 80 90 hundreds of applications that need to come with that and then and then you start to think about Imaging platforms so we have Imaging platforms available for those specific needs whether it's MRIs and things like that and then the long-term retention that's needed with that so all of these pieces that build out a healthcare ecosystem and those needs we've built those we've built those out and provide those two to our clients yesterday VMware was talking about Cloud chaos yes and and it's true you talk about the complexity and Dave talks about it too like acknowledging yes this is a very complex thing to do yeah there's just so many moving parts so many Dynamics so many people involved or lack thereof people they they then talked about kind of this this the goal of getting customers from cloud chaos to Cloud smart how does that message resonate with Rackspace and how are you helping customers get from simplifying the chaos to eventually get to that cloud smart goal so a lot of it I I believe is with the power of our alliances and I was talking about this earlier we really believe in creating those powerful ecosystems and Jay McBain former for Forester analyst talks about you know the people are going to come ahead really are serve as that orchestration layer of bringing everybody together so if you look at all of that cloud chaos and all of the different logos and the webs and which decisions to make you know the ones that can help simplify that bring it all together like we're going to need a little bit of this like baking a cake in some ways we're going to need a little bit of sugar we'll need this technology this technology and whoever is able to put it together in a clean and seamless way and as Hannah said you know we have specific use cases in different verticals Healthcare specifically and talking from the Imaging and the Epic helping them get hospitals and different you know smaller clinics get to the edge so we have all of the building blocks to get them what they need and we can't do that without Partners but we help simplify those outcomes for those customers yep so there's where they're Cloud smart so then they're like I want I want to be agile I want to work on my cost I want to be able to leverage a multi-cloud fashion because some things may may inherently need to be on Azure some things we inherently need to be on VMware how do we make them feel like they still have that modernized platform and Technology but still give the secure and access that they need right yeah we like to think of it as are you multi-cloud by accident or multi-cloud by Design and help you get to that multi-cloud by Design and leveraging the right yeah the right tools the right places and Dell was talking about that just that at Dell Technologies world just a couple months ago that most most organizations are multi-cloud by default not designed are you seeing any customers that are are able or how are you able to help customers go from that we're here by default for whatever reason acquisition growth.oit line of business and go from that default to a more strategic multi-cloud approach yes it takes planning and commitment you know you really need the business leaders and the technical leaders bought in and saying this is what I'm gonna do because it is a journey because exactly right M A is like inherited four different tools you have databases that kind of look similar but they're a little bit different but they serve four different things so at Rackspace we're able to help assess and we sit down with their teams we have very amazing rock star expertise that will come in and sit with the customers and say what are we trying to drive for it let's get a good assessment of the landscape and let's figure out what are you trying to get towards in your journey and looking at what's the best fit for that application from where it is now to where it is where it wants to be because we saw a lot of customers move to the cloud very quickly you know they went Cloud native very fast some of it made sense retailers who had the spikiness that completely made sense we had some customers though that we've seen move certain workloads they've been in the public Cloud now for a couple years but it was a static website it doesn't make as much sense anymore for certain things so we're able to help navigate all of those choices for them so it's interesting you just you just said something sort of offhand about having experts having them come in so if I am a customer and I have some outcome I want to achieve yes the people that I'm going to be talking to from Rackspace or from Rackspace and the people from Rackspace who are going to be working with the actual people who are deploying infrastructure are also Rackspace people so the interesting contrast there between other circumstances oftentimes is you may have a Global Systems integrator with smart people representing what a cloud provider is doing the perception if they try to make people perceive that okay everybody is working in lockstep but often there are disconnects between what the real capabilities are and what's being advertised so is that I mean I I know it's like a leading question it's like softball get your bats out but I mean isn't that an advantage you've got a single you know the saying used to be uh one throat to show now it's one back to pack because it's kind of Contour friendly yeah yeah but talk about that is that a real Advantage it does it really helps us because again this is our our this is our expertise this is where we where we live we're really close to the infrastructure we're great at the advisement on it we can help with those ongoing and day two management and Opera in operations and what it feels like to grow and scale so we lay this out cleanly and and clearly as possible if this is where we're really good we can we can help you in these areas but we do work with system integrators as well and part of our partner Community because they're working on sometimes the bigger overall Transformations and then we're staying look we understand this multi-cloud but it helps us because in the end we're doing that end to end for for them customer knows this is Rackspace and on hand and we we really strive to be very transparent in what it is that we want to drive and outcomes so sometimes at the time where it's like we're gonna talk about a certain new technology Dell might bring some of their Architects to the table we will say here is Dell with us we're doing that actively in the healthcare space today and it's all coming together but you know at the end of the day this is what Rackspace is going to drive and deliver from an end to end and we tap those people when needed so you don't have to worry about picking up the phone to call Dell or VMware so if I had worded the hard-hitting journalist question the right way it would have elicited the same responses that yeah yeah it drives accountability at the end of the day because what we advised on what we said now we got to go deliver yeah and it's it's all the same the same organization driving accountability so from a customer perspective they're engaging Rackspace who will then bring in dell and VMware as needed as we find the solution exactly we have all of the certification I mean the team the team is great on getting all of the certs because we're getting to handling all of the level one level two level three business they know who to call they have their dedicated account teams they have engagement managers that help them Drive what those bigger conversations are and they don't have to worry about the experts because we either have it on hand or we'll pull them in as needed if it's the bat phone we need to call awesome ladies thank you so much for joining Dave and me today talking about what Rackspace is up to in the partner ecosystem space and specifically what you're doing to help Healthcare organizations transform and modernize we appreciate your insights and your thoughts yeah thank you for having us thank you pleasure for our guests and Dave Nicholson I'm Lisa Martin you're watching thecube live from VMware Explorer 2022 we'll be back after a short break foreign [Music]

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

>> From Mountain View California, it's theCUBE covering the 15th Annual Grow! Awards. Brought to you by ACG SV. >> Hi, Lisa Martin on the ground with theCUBE at the Computer History Museum in Mountain View, California for the 15th Annual ACG SV Grow! Awards. This is a event with nearly 300 attendees, about 100 plus C-levels, and I'm excited to welcome to theCUBE for the first time, Hannah Kain, the CEO of Alom. Hannah, welcome to theCUBE. >> Thank you. I'm so glad to be here. >> And here you are, and I are, in the lobby where there is a lot of innovation and collaboration going on right here, so thank you for joining me in this energetic time at the event. >> Oh, I absolutely love it. You can feel the energy of Silicon Valley here. >> Yeah, you're right, you can. So tell me about, you are the CEO and founder of Alom. Tell me about your company, what you guys do, what makes you different. >> So, we do supply chain excellence. We execute and plant supply chains for very large corporations out of 19 locations globally. We are headquartered right here in Silicon Valley. Using technology to help our customers be agile and get the products to the right place exactly when their customers need it, and protect their brand, do their risk management that makes sure they do everything right in the supply chain. It's super exciting, and no other place is technology used better than in the supply chain. >> So, you founded Alom in the 90s. You have seen a tremendous amount of technology innovation. I mean, things change faster than we can even keep track sometimes. Tell me a little bit about what has been a facilitator of you as the CEO being headquartered in Silicon Valley, and being able to take advantage of technology to grow and scale your business. >> I think back in the 90s, nobody really realized the potential of technology in supply chain. I mean, supply chain wasn't even a word. And so, I always thought that supply chain could be done much differently than it was done in the old days, and that technology would be the big facilitator of it. So right now, we have much more visibility in supply chain. We can see where products are, et cetera, but we've also increased the complexity of the supply chains, driven down the cost of products, but also at the same point of time, driven up the complexity with components being shipped all over the world and assembled in one place and distributed to another place. So, there's a lot of complexity that only technology can resolve. So, being in Silicon Valley, which is the first place of technology, is just fantastic when you're in supply chain. It really leverages innovation that's taking place. >> And you can, like we said when we started, you can feel the energy of the innovation going on here. I read on your LinkedIn profile that you are passionate about excellence, technology, collaboration, and community. The last two, collaboration and community, really underscore the association for corporate growth in Silicon Valley. Tell me about your involvement in ACG SV and what makes this event worthy of your time. >> So, I do believe in collaboration. I think collaboration is a core value in Silicon Valley. I believe that collaborative companies and collaborative people are going to win in the marketplace and also have more fun while doing it, creating much more value. And so, in ACG Silicon Valley, there's just a lot of collaboration, lots of different points of view, but also a lot of very focused, dedicated business people. And so, we get together and get ideas from each other, but also send business to and from each other, and use each other as resources. And I also believe, apart from collaboration, being resourceful is a real winner. You need to be resourceful and be able to make things happen and figure out a way to navigate new landscapes. And that's what having these great contacts in ACG and other associations in Silicon Valley really do for me. >> So last question, Hannah, for you as a female CEO, a leader in technology, what advice would you give to the subsequent generations of women in technology who aspire to be leaders like yourself? >> I think they should be leaders like themselves, not like me, but like themselves. I think you need to be authentic. Bring your own strength to every situation, and I think that's what I really wish for the new generation. That many of the women have paved the way such that the new generation can really be themselves and contribute. And I'd say, focus on what you can contribute and what you can do for the greater community and for business as such. >> I love that advice, Hannah. Authenticity is such a value. Well, thank you so much for spending some time here with us in this energetic 15th Annual Grow! Awards. We appreciate your time. >> Absolutely my pleasure. Thank you. >> You're watching theCUBE. I'm Lisa Martin, thanks for your time. >> [Upbeat Tech Music]

(soft music) >> Narrator: Live from Las Vegas Navada, it's theCUBE, covering Accelerate 2017, brought to you by Fortinet. Now here are your hosts, Lisa Martin, and Peter Burris. (soft music) >> Hey welcome back to theCUBE, I'm Lisa Martin with my co-host Peter Burris. We're coming to you live from Las Vegas, we're with Fortinet today, at their Accelerate 2017 event, which brings together end-users, over 700 partners from 93 countries, great buzz today, very excited to be joined by Richard Hannah, who is the VP of information services at Gibson Energy. Richard, welcome to theCUBE. >> Thank you for having me. >> Great to have you here, first and foremost, Richard, help us understand, what is VP of information services? >> So maybe first off, I'll just explain Gibson Energy. >> Yes, that was probably my first question. (laughs) >> So Gibson Energy is a Calgary Canada based midstream oil and gas company. But we do have locations throughout North America. In all the major oil base in throughout North America. We're considered a mid-stream oil and gas company, which if you, the categories of the, of the Energy industry is really, upstream would be the companies, that are taking the product under the ground. Downstream would be closer to retail, and we're in the middle, so midstream side, so basically that entails, logistics, so, think trucking, train, some moving of the oil and gas, um, infrastructure, around storage, um >> You're getting into refinery >> Pipelines that kind of stuff, yeah, and then the marketing side, would be, the actual going to the end customers, so our marketing group would be looking for the end customer, like refineries et cetera. So that's kind of what makes up, makes up our company. About two, over 200 locations, pretty complex business. So to your question, Gibson is a 60 year old company never had a kind of a senior IT leader in its history, but through a number of acquisitions, we had doubled in size, kind of coming into, 2013, and so I was hired as their first VP of IT, and basically look after all of the strategy around technology, the operations around technology, security of technology for the company. >> So a lot of companies are now looking at IT as not just handling the operations of known processes and by known processes, I mean accounting, HR et cetera, >> Right. >> But they're actually looking at IT to be a partner in going after opportunities, that may not be so well formed. >> Right. >> That may require analytics or be dependent upon analytics, is Gibson starting to think in those terms? Is that a part of your remit as an executive within Gibson, is to help think that process through? >> Definitely yeah, I think you know, there's obviously the normal day to day keep the lights on, of IT, and there were some, major investments, and transformations if you will, that needed to happen on the technology side, and that's kind of what went on in the, say the 2015 to 2016 range, but now we are actually, you know as you discussed, we're actually now looking at ways of using technology to add value to the company, I think, you know IoT, is a great example of that, we're doing some interesting things with IoT, doing some interesting things with HoloLens, so we're actually starting to, you know, be that true, kind of, strategic enabler for the company. >> Well talk about some of those IoT opportunities, I mean, certainly, in the midstream oil and gas universe, there's a lot of very, very expensive equipment >> Right. >> But it has to be maintained and taken care of. So how is IoT starting to impact the way, the business operates >> [Right. So yeah, as you mentioned, we have, you know, thousands and thousands of devices in the field. >> Peter: Not little tiny things. >> Not little tiny things >> No. >> These are big things. >> Yeah. >> Bigger than a bread box kind of stuff. >> Exactly. So, um, you know, before the concept of IoT, um, any monitoring, or data that you had to get off any of those devices, was largely manual, or didn't exist at all. So a great example of our first, interest in IT was with one of our disposal wells, well sites, in the middle of Alberta, and, basically, you know, it disposes of things that can't be used within the, you know, within the downstream side of the business, so it environmentally safely disposes of dirt and mud and those types of things, water, a lot of water that obviusly comes out of the production side. So that disposal well, think of it as a large heater that, heats up to you known large, you know, temperatures and as part of the disposal process. So prior to IoT, there was no way to really have any data on how that well was functioning, and when was the proper time to actually do preventative maintenance on the well. So we connected the well to you know, using IoT technology, through to the Cloud, and then, and then provide an analytics on the back end, to actually provide information on how that well was actually performing, from a heating standpoint, et cetera. So the operation team can actually, now real time, look at how that well is performing, and then perform maintenance when it's actually time to do it versus just doing it, you know based on gut feel. So save you know, thousands of hours of maintenance, thousands of man time, et cetera, so that's just one example of how we're connecting, you know, some of our devices. We are actually now starting to connect our our weight scale, which is part of our our logistic side of things. So again, prior to connecting those, the weight scale, somebody actually had to go out and take the measurements, write them down, take them back and put them into the operational system. Now, we can do that real time as well. So considerable efficiencies gained at the same time, you mentioned the word transformation before, I think you both did, you also talked about this growth there, so from a Cloud journey perspective, as we think of transformation in that sense, what is what's been the strategy that you've been employing as your generating, bringing more IoT devices online, to support the business, make it more efficient. What has your journey to the Cloud been, especially related to the growth that's happened in such a quick pace? >> Right. So, when I arrived back in 2013, as I mentioned, there was a fair bit of transformation that had to happen, on the IT side, and we're talking, you know, new ERP, new, so a lot on the application side including, new ERP et cetera, but on the infrastructure side, we required, again, a lot of transformations, sorry to keep using that word, but I think it's overused a lot, but it's the best way to describe what was happening. >> Evolution, transformation >> But, everything from our network, to our data centers, to security et cetera. So on the data center side, because of, the number of acquisitions the company went through, we actually, were sitting with seven data centers, and for a company our size, I mean way too many data centers a lot of cost, a lot of, you know, man power, to maintain those data centers, four of them in the US, three of them in Canada. So part of our strategy as a pertain to data center, was to consolidate, and you know I remember the kind of as we spoke about the strategy, was we need to move from somewhere from seven to less than seven, and zero was the right answer. (laughs) So meaning, wanted to get out of the data center business, and wanted to to go to the Cloud as much as possible. So we're now on that journey, we have, by the end of 2017, we'll have one physical data center, and the rest will be in the Cloud with Azure. >> And you're on that journey with Microsoft Azure, which is a big technology, alliance partner with Fortinet. Talk to us about the consolidation of data centers, and where does the security angle enter the picture, is it there from the beginning or is it something that has evolved as you transformed? >> I would say, largely evolved, so as we started architecting our, our cloud strategy with Azure, I mean Azure comes with, you know, a lot of security components, but at the same time we wanted to be in control of our own destiny as it were, as it pertains a security, so we wanted to have access to the firewall side of things, so that's how we got into working with Fortinet. And it was, we had never been a Fortinet customer prior to that, but as we looked at how to we secure Azure and how do we provide access to our network team, as it pertains to our connectivity to the cloud. Fortinet kind of, came out as the clear winner, through our due diligence, and we've been quite impressed with their capabilities, their partnership with Microsoft and Azure and their, you know, their ability that helped us architect a real secure solution as pertains to our cloud connectivity. So over the next couple of years, you're going to see more IoT? >> Definitely, that's 2017, I's say you know, two main strategies for 2017, security and IoT. >> So are you going to be seeing more edge oriented IoT >> Yes. >> So you're going to be, doing a fair amount of processing close to the end because of physics, so one of the things that we say, is we think that there's going to be less data move back to the Cloud, and more Cloud move to the edge. >> Right. >> How are, how do you see the relationship between, midstream oil and gas, being, processing at the edge, doing, running models at the edge, and making sure that the data that's in flight, which can be very strategic and very valuable, a lot of different dimensions remains secure. >> So you know as I mentioned at the outset, very complex company, and moving a lot you know, a lot of might, you know, what we call, oil and gas, and the other products that go with that. And I think, so if, as we look at IT, similar, right, very complex, network, very complex system that we have in place. And so, analytics is becoming, you know, quite important, to our whole running of the business, and obviously IT being the enabler of analytics, so, that is, you know, that's really what's moving us towards, and to do that, sorry, and to do that with, devices in the field, thinking your network is becoming very complex. So, not just wired devices any longer, wireless is a huge part of our network now, and keeping those things secure, and the fact that we're actually connecting to things that run, you know, the crown jewel, so to speak, makes it even more imperative that we have, you know, very, focus on security, and obviously great partners like Fortinet to help us keep those assets secure. >> From a security perspective, just curious from your standpoint, are you kind of the, the leader of that digital army, within Gibson or with your other peers on that c-suite to facilitate not only this journey to cloud, and I really liked how you about it Peter with the cloud moving out to the end points, what's your role in sort of, and how is it measured, facilitating security from, from that, eventually one data center out to those mobile IoT devices in the field. >> Right. So, I mean you know, as I mentioned, security is kind of one of our top strategies, unfortunately, I guess it has to be. But it's not hard to sell the importance of security, with, you know, the other senior leaders of the team. I think, the you know, the incidence that are happening in the world and the media, attention on security, makes it, makes >> Even in Canada. >> Even in Canada, yeah. (laughs) Makes it, you know, apparent that, that is kind of one of the questions that everybody's asking, >> Right. >> And in our business energy business as well, I mean, health, you know HSS and eHealth, security is paramount to what we do, you know, physically in the field, so security, from a digital standpoint is, I guess an easy sell. To your question, it's very top-of-mind everybody and IT kind of holds that banner as it, as it pertains to um, you know, the security of our digital assets. >> In some, in some senses, you might be able to say that some of the recent breaches, and we know that now they happen daily, but some of the ones that have been, in the media that you mentioned, could in some cases, in your role, maybe even be an advocate or an advantage for, you were saying it's kind of an easy sell, we understand the importance here. We want to get out ahead of it. Understanding, at some point, we're probably go into, get to the point of really being able to limit damage, that it's not a challenge in terms of the buy-in from your executive management. >> Right, and you know, the risk I think for us is disruption, um, and you see, you know, there's incidences around the globe, where, whether it's, you know, other utilities have been disrupted, you know, through breaches, so you know, that is our focus is, how do we ensure that our day to day operations are not disrupted by you know something that could have happened to from a, you know, from a digital security standpoint. >> Got it. Well it sounds like you have a quite a big 2017 ahead, continued success in the big data center, from seven to eventually zero with Microsoft Azure, that you're going to do. We thank you Richard Hannah, VP of information services, at Gibson Energy, thank you so much for joining us on theCUBE today. >> Alright, thank you for having me. >> And on behalf of Peter Burris my co-host, and myself Lisa Martin, thank you so much for watching theCUBE, stick around and we'll be right back. (upbeat music)

(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(energetic music) >> Everyone, welcome back to theCUBE's coverage of WiDS 2023. This is the eighth annual Women in Data Science Conference. As you know, WiDS is not just a conference or an event, it's a movement. This is going to include over 100,000 people in the next year WiDS 2023 in 200-plus countries. It is such a powerful movement. If you've had a chance to be part of the Livestream or even be here in person with us at Stanford University, you know what I'm talking about. This is Lisa Martin. I have had the pleasure all day of working with two fantastic graduate students in Stanford's Data Journalism Master's Program. Hannah Freitag has been here. Tracy Zhang, ladies, it's been such a pleasure working with you today. >> Same wise. >> I want to ask you both what are, as we wrap the day, I'm so inspired, I feel like I could go build an airplane. >> Exactly. >> Probably can't. But WiDS is just the inspiration that comes from this event. When you walk in the front door, you can feel it. >> Mm-hmm. >> Tracy, talk a little bit about what some of the things are that you heard today that really inspired you. >> I think one of the keyword that's like in my mind right now is like finding a mentor. >> Yeah. >> And I think, like if I leave this conference if I leave the talks, the conversations with one thing is that I'm very positive that if I want to switch, say someday, from Journalism to being a Data Analyst, to being like in Data Science, I'm sure that there are great role models for me to look up to, and I'm sure there are like mentors who can guide me through the way. So, like that, I feel reassured for some reason. >> It's a good feeling, isn't it? What do you, Hannah, what about you? What's your takeaway so far of the day? >> Yeah, one of my key takeaways is that anything's possible. >> Mm-hmm. >> So, if you have your vision, you have the role model, someone you look up to, and even if you have like a different background, not in Data Science, Data Engineering, or Computer Science but you're like, "Wow, this is really inspiring. I would love to do that." As long as you love it, you're passionate about it, and you are willing to, you know, take this path even though it won't be easy. >> Yeah. >> Then you can achieve it, and as you said, Tracy, it's important to have mentors on the way there. >> Exactly. >> But as long as you speak up, you know, you raise your voice, you ask questions, and you're curious, you can make it. >> Yeah. >> And I think that's one of my key takeaways, and I was just so inspiring to hear like all these women speaking on stage, and also here in our conversations and learning about their, you know, career path and what they learned on their way. >> Yeah, you bring up curiosity, and I think that is such an important skill. >> Mm-hmm. >> You know, you could think of Data Science and think about all the hard skills that you need. >> Mm, like coding. >> But as some of our guests said today, you don't have to be a statistician or an engineer, or a developer to get into this. Data Science applies to every facet of every part of the world. >> Mm-hmm. >> Finances, marketing, retail, manufacturing, healthcare, you name it, Data Science has the power and the potential to unlock massive achievements. >> Exactly. >> It's like we're scratching the surface. >> Yeah. >> But that curiosity, I think, is a great skill to bring to anything that you do. >> Mm-hmm. >> And I think we... For the female leaders that we're on stage, and that we had a chance to talk to on theCUBE today, I think they all probably had that I think as a common denominator. >> Exactly. >> That curious mindset, and also something that I think as hard is the courage to raise your hand. I like this, I'm interested in this. I don't see anybody that looks like me. >> But that doesn't mean I shouldn't do it. >> Exactly. >> Exactly, in addition to the curiosity that all the women, you know, bring to the table is that, in addition to that, being optimistic, and even though we don't see gender equality or like general equality in companies yet, we make progress and we're optimistic about it, and we're not like negative and complaining the whole time. But you know, this positive attitude towards a trend that is going in the right direction, and even though there's still a lot to be done- >> Exactly. >> We're moving it that way. >> Right. >> Being optimistic about this. >> Yeah, exactly, like even if it means that it's hard. Even if it means you need to be your own role model it's still like worth a try. And I think they, like all of the great women speakers, all the female leaders, they all have that in them, like they have the courage to like raise their hand and be like, "I want to do this, and I'm going to make it." And they're role models right now, so- >> Absolutely, they have drive. >> They do. >> Right. They have that ambition to take something that's challenging and complicated, and help abstract end users from that. Like we were talking to Intuit. I use Intuit in my small business for financial management, and she was talking about how they can from a machine learning standpoint, pull all this data off of documents that you upload and make that, abstract that, all that complexity from the end user, make something that's painful taxes. >> Mm-hmm. >> Maybe slightly less painful. It's still painful when you have to go, "Do I have to write you a check again?" >> Yeah. (laughs) >> Okay. >> But talking about just all the different applications of Data Science in the world, I found that to be very inspiring and really eye-opening. >> Definitely. >> I hadn't thought about, you know, we talk about climate change all the time, especially here in California, but I never thought about Data Science as a facilitator of the experts being able to make sense of what's going on historically and in real-time, or the application of Data Science in police violence. We see far too many cases of police violence on the news. It's an epidemic that's a horrible problem. Data Science can be applied to that to help us learn from that, and hopefully, start moving the needle in the right direction. >> Absolutely. >> Exactly. >> And especially like one sentence from Guitry from the very beginnings I still have in my mind is then when she said that arguments, no, that data beats arguments. >> Yes. >> In a conversation that if you be like, okay, I have this data set and it can actually show you this or that, it's much more powerful than just like being, okay, this is my position or opinion on this. And I think in a world where increasing like misinformation, and sometimes, censorship as we heard in one of the talks, it's so important to have like data, reliable data, but also acknowledge, and we talked about it with one of our interviewees that there's spices in data and we also need to be aware of this, and how to, you know, move this forward and use Data Science for social good. >> Mm-hmm. >> Yeah, for social good. >> Yeah, definitely, I think they like data, and the question about, or like the problem-solving part about like the social issues, or like some just questions, they definitely go hand-in-hand. Like either of them standing alone won't be anything that's going to be having an impact, but combining them together, you have a data set that illustrate a point or like solves the problem. I think, yeah, that's definitely like where Data Set Science is headed to, and I'm glad to see all these great women like making their impact and combining those two aspects together. >> It was interesting in the keynote this morning. We were all there when Margot Gerritsen who's one of the founders of WiDS, and Margot's been on the program before and she's a huge supporter of what we do and vice versa. She asked the non-women in the room, "Those who don't identify as women, stand up," and there was a handful of men, and she said, "That's what it's like to be a female in technology." >> Oh, my God. >> And I thought that vision give me goosebumps. >> Powerful. (laughs) >> Very powerful. But she's right, and one of the things I think that thematically another common denominator that I think we heard, I want to get your opinions as well from our conversations today, is the importance of community. >> Mm-hmm. >> You know, I was mentioning this stuff from AnitaB.org that showed that in 2022, the percentage of females and technical roles is 27.6%. It's a little bit of an increase. It's been hovering around 25% for a while. But one of the things that's still a problem is attrition. It doubled last year. >> Right. >> And I was asking some of the guests, and we've all done that today, "How would you advise companies to start moving the needle down on attrition?" >> Mm-hmm. >> And I think the common theme was network, community. >> Exactly. >> It takes a village like this. >> Mm-hmm. >> So you can see what you can be to help start moving that needle and that's, I think, what underscores the value of what WiDS delivers, and what we're able to showcase on theCUBE. >> Yeah, absolutely. >> I think it's very important to like if you're like a woman in tech to be able to know that there's someone for you, that there's a whole community you can rely on, and that like you are, you have the same mindset, you're working towards the same goal. And it's just reassuring and like it feels very nice and warm to have all these women for you. >> Lisa: It's definitely a warm fuzzy, isn't it? >> Yeah, and both the community within the workplace but also outside, like a network of family and friends who support you to- >> Yes. >> To pursue your career goals. I think that was also a common theme we heard that it's, yeah, necessary to both have, you know your community within your company or organization you're working but also outside. >> Definitely, I think that's also like how, why, the reason why we feel like this in like at WiDS, like I think we all feel very positive right now. So, yeah, I think that's like the power of the connection and the community, yeah. >> And the nice thing is this is like I said, WiDS is a movement. >> Yes. >> This is global. >> Mm-hmm. >> We've had some WiDS ambassadors on the program who started WiDS and Tel Aviv, for example, in their small communities. Or in Singapore and Mumbai that are bringing it here and becoming more of a visible part of the community. >> Tracy: Right. >> I loved seeing all the young faces when we walked in the keynote this morning. You know, we come here from a journalistic perspective. You guys are Journalism students. But seeing all the potential in the faces in that room just seeing, and hearing stories, and starting to make tangible connections between Facebook and data, and the end user and the perspectives, and the privacy and the responsibility of AI is all... They're all positive messages that need to be reinforced, and we need to have more platforms like this to be able to not just raise awareness, but sustain it. >> Exactly. >> Right. It's about the long-term, it's about how do we dial down that attrition, what can we do? What can we do? How can we help? >> Mm-hmm. >> Both awareness, but also giving women like a place where they can connect, you know, also outside of conferences. Okay, how do we make this like a long-term thing? So, I think WiDS is a great way to, you know, encourage this connectivity and these women teaming up. >> Yeah, (chuckles) girls help girls. >> Yeah. (laughs) >> It's true. There's a lot of organizations out there, girls who Code, Girls Inc., et cetera, that are all aimed at helping women kind of find their, I think, find their voice. >> Exactly. >> And find that curiosity. >> Yeah. Unlock that somewhere back there. Get some courage- >> Mm-hmm. >> To raise your hand and say, "I think I want to do this," or "I have a question. You explained something and I didn't understand it." Like, that's the advice I would always give to my younger self is never be afraid to raise your hand in a meeting. >> Mm-hmm. >> I guarantee you half the people weren't listening or, and the other half may not have understood what was being talked about. >> Exactly. >> So, raise your hand, there goes Margot Gerritsen, the founder of WiDS, hey, Margot. >> Hi. >> Keep alumni as you know, raise your hand, ask the question, there's no question that's stupid. >> Mm-hmm. >> And I promise you, if you just take that chance once it will open up so many doors, you won't even know which door to go in because there's so many that are opening. >> And if you have a question, there's at least one more person in the room who has the exact same question. >> Exact same question. >> Yeah, we'll definitely keep that in mind as students- >> Well, I'm curious how Data Journalism, what you heard today, Tracy, we'll start with you, and then, Hannah, to you. >> Mm-hmm. How has it influenced how you approach data-driven, and storytelling? Has it inspired you? I imagine it has, or has it given you any new ideas for, as you round out your Master's Program in the next few months? >> I think like one keyword that I found really helpful from like all the conversations today, was problem-solving. >> Yeah. >> Because I think, like we talked a lot about in our program about how to put a face on data sets. How to put a face, put a name on a story that's like coming from like big data, a lot of numbers but you need to like narrow it down to like one person or one anecdote that represents a bigger problem. And I think essentially that's problem-solving. That's like there is a community, there is like say maybe even just one person who has, well, some problem about something, and then we're using data. We're, by giving them a voice, by portraying them in news and like representing them in the media, we're solving this problem somehow. We're at least trying to solve this problem, trying to make some impact. And I think that's like what Data Science is about, is problem-solving, and, yeah, I think I heard a lot from today's conversation, also today's speakers. So, yeah, I think that's like something we should also think about as Journalists when we do pitches or like what kind of problem are we solving? >> I love that. >> Or like kind of what community are we trying to make an impact in? >> Yes. >> Absolutely. Yeah, I think one of the main learnings for me that I want to apply like to my career in Data Journalism is that I don't shy away from complexity because like Data Science is oftentimes very complex. >> Complex. >> And also data, you're using for your stories is complex. >> Mm-hmm. >> So, how can we, on the one hand, reduce complexity in a way that we make it accessible for broader audience? 'Cause, we don't want to be this like tech bubble talking in data jargon, we want to, you know, make it accessible for a broader audience. >> Yeah. >> I think that's like my purpose as a Data Journalist. But at the same time, don't reduce complexity when it's needed, you know, and be open to dive into new topics, and data sets and circling back to this of like raising your hand and asking questions if you don't understand like a certain part. >> Yeah. >> So, that's definitely a main learning from this conference. >> Definitely. >> That like, people are willing to talk to you and explain complex topics, and this will definitely facilitate your work as a Data Journalist. >> Mm-hmm. >> So, that inspired me. >> Well, I can't wait to see where you guys go from here. I've loved co-hosting with you today, thank you. >> Thank you. >> For joining me at our conference. >> Wasn't it fun? >> Thank you. >> It's a great event. It's, we, I think we've all been very inspired and I'm going to leave here probably floating above the ground a few inches, high on the inspiration of what this community can deliver, isn't that great? >> It feels great, I don't know, I just feel great. >> Me too. (laughs) >> So much good energy, positive energy, we love it. >> Yeah, so we want to thank all the organizers of WiDS, Judy Logan, Margot Gerritsen in particular. We also want to thank John Furrier who is here. And if you know Johnny, know he gets FOMO when he is not hosting. But John and Dave Vellante are such great supporters of women in technology, women in technical roles. We wouldn't be here without them. So, shout out to my bosses. Thank you for giving me the keys to theCube at this event. I know it's painful sometimes, but we hope that we brought you great stories all day. We hope we inspired you with the females and the one male that we had on the program today in terms of raise your hand, ask a question, be curious, don't be afraid to pursue what you're interested in. That's my soapbox moment for now. So, for my co-host, I'm Lisa Martin, we want to thank you so much for watching our program today. You can watch all of this on-demand on thecube.net. You'll find write-ups on siliconeangle.com, and, of course, YouTube. Thanks, everyone, stay safe and we'll see you next time. (energetic music)

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

(gentle music) >> Hey! Welcome back to theCUBE's coverage of WiDS 2023, the eighth Annual Women In Data Science Conference. I'm your host, Lisa Martin. We are at Stanford University, as you know we are every year, having some wonderful conversations with some very inspiring women and men in data science and technical roles. I'm very pleased to introduce Tracy Zhang, my co-host, who is in the Data Journalism program at Stanford. And Tracy and I are pleased to welcome our next guest, Rhonda Crate, Principal Data Scientist at Boeing. Great to have you on the program, Rhonda. >> Tracy: Welcome. >> Hey, thanks for having me. >> Were you always interested in data science or STEM from the time you were young? >> No, actually. I was always interested in archeology and anthropology. >> That's right, we were talking about that, anthropology. Interesting. >> We saw the anthropology background, not even a bachelor's degree, but also a master's degree in anthropology. >> So you were committed for a while. >> I was, I was. I actually started college as a fine arts major, but I always wanted to be an archeologist. So at the last minute, 11 credits in, left to switch to anthropology. And then when I did my master's, I focused a little bit more on quantitative research methods and then I got my Stat Degree. >> Interesting. Talk about some of the data science projects that you're working on. When I think of Boeing, I always think of aircraft. But you are doing a lot of really cool things in IT, data analytics. Talk about some of those intriguing data science projects that you're working on. >> Yeah. So when I first started at Boeing, I worked in information technology and data analytics. And Boeing, at the time, had cored up data science in there. And so we worked as a function across the enterprise working on anything from shared services to user experience in IT products, to airplane programs. So, it has a wide range. I worked on environment health and safety projects for a long time as well. So looking at ergonomics and how people actually put parts onto airplanes, along with things like scheduling and production line, part failures, software testing. Yeah, there's a wide spectrum of things. >> But I think that's so fantastic. We've been talking, Tracy, today about just what we often see at WiDS, which is this breadth of diversity in people's background. You talked about anthropology, archeology, you're doing data science. But also all of the different opportunities that you've had at Boeing. To see so many facets of that organization. I always think that breadth of thought diversity can be hugely impactful. >> Yeah. So I will say my anthropology degree has actually worked to my benefit. I'm a huge proponent of integrating liberal arts and sciences together. And it actually helps me. I'm in the Technical Fellowship program at Boeing, so we have different career paths. So you can go into management, you can be a regular employee, or you can go into the Fellowship program. So right now I'm an Associate Technical Fellow. And part of how I got into the Fellowship program was that diversity in my background, what made me different, what made me stand out on projects. Even applying a human aspect to things like ergonomics, as silly as that sounds, but how does a person actually interact in the space along with, here are the actual measurements coming off of whatever system it is that you're working on. So, I think there's a lot of opportunities, especially in safety as well, which is a big initiative for Boeing right now, as you can imagine. >> Tracy: Yeah, definitely. >> I can't go into too specifics. >> No, 'cause we were like, I think a theme for today that kind of we brought up in in all of our talk is how data is about people, how data is about how people understand the world and how these data can make impact on people's lives. So yeah, I think it's great that you brought this up, and I'm very happy that your anthropology background can tap into that and help in your day-to-day data work too. >> Yeah. And currently, right now, I actually switched over to Strategic Workforce Planning. So it's more how we understand our workforce, how we work towards retaining the talent, how do we get the right talent in our space, and making sure overall that we offer a culture and work environment that is great for our employees to come to. >> That culture is so important. You know, I was looking at some anitab.org stats from 2022 and you know, we always talk about the number of women in technical roles. For a long time it's been hovering around that 25% range. The data from anitab.org showed from '22, it's now 27.6%. So, a little increase. But one of the biggest challenges still, and Tracy and I and our other co-host, Hannah, have been talking about this, is attrition. Attrition more than doubled last year. What are some of the things that Boeing is doing on the retention side, because that is so important especially as, you know, there's this pipeline leakage of women leaving technical roles. Tell us about what Boeing's, how they're invested. >> Yeah, sure. We actually have a publicly available Global Diversity Report that anybody can go and look at and see our statistics for our organization. Right now, off the top of my head, I think we're hovering at about 24% in the US for women in our company. It has been a male majority company for many years. We've invested heavily in increasing the number of women in roles. One interesting thing about this year that came out is that even though with the great resignation and those types of things, the attrition level between men and women were actually pretty close to being equal, which is like the first time in our history. Usually it tends on more women leaving. >> Lisa: That's a good sign. >> Right. >> Yes, that's a good sign. >> And we've actually focused on hiring and bringing in more women and diversity in our company. >> Yeah, some of the stats too from anitab.org talked about the increase, and I have to scroll back and find my notes, the increase in 51% more women being hired in 2022 than 2021 for technical roles. So the data, pun intended, is showing us. I mean, the data is there to show the impact that having females in executive leadership positions make from a revenue perspective. >> Tracy: Definitely. >> Companies are more profitable when there's women at the head, or at least in senior leadership roles. But we're seeing some positive trends, especially in terms of representation of women technologists. One of the things though that I found interesting, and I'm curious to get your thoughts on this, Rhonda, is that the representation of women technologists is growing in all areas, except interns. >> Rhonda: Hmm. >> So I think, we've got to go downstream. You teach, I have to go back to my notes on you, did my due diligence, R programming classes through Boeings Ed Wells program, this is for WSU College of Arts and Sciences, talk about what you teach and how do you think that intern kind of glut could be solved? >> Yeah. So, they're actually two separate programs. So I teach a data analytics course at Washington State University as an Adjunct Professor. And then the Ed Wells program is a SPEEA, which is an Aerospace Union, focused on bringing up more technology and skills to the actual workforce itself. So it's kind of a couple different audiences. One is more seasoned employees, right? The other one is our undergraduates. I teach a Capstone class, so it's a great way to introduce students to what it's actually like to work on an industry project. We partner with Google and Microsoft and Boeing on those. The idea is also that maybe those companies have openings for the students when they're done. Since it's Senior Capstone, there's not a lot of opportunities for internships. But the opportunities to actually get hired increase a little bit. In regards to Boeing, we've actually invested a lot in hiring more women interns. I think the number was 40%, but you'd have to double check. >> Lisa: That's great, that's fantastic. >> Tracy: That's way above average, I think. >> That's a good point. Yeah, it is above average. >> Double check on that. That's all from my memory. >> Is this your first WiDS, or have you been before? >> I did virtually last year. >> Okay. One of the things that I love, I love covering this event every year. theCUBE's been covering it since it's inception in 2015. But it's just the inspiration, the vibe here at Stanford is so positive. WiDS is a movement. It's not an initiative, an organization. There are going to be, I think annually this year, there will be 200 different events. Obviously today we're live on International Women's Day. 60 plus countries, 100,000 plus people involved. So, this is such a positive environment for women and men, because we need everybody, underrepresented minorities, to be able to understand the implication that data has across our lives. If we think about stripping away titles in industries, everybody is a consumer, not everybody, most of mobile devices. And we have this expectation, I was in Barcelona last week at a Mobile World Congress, we have this expectation that we're going to be connected 24/7. I can get whatever I want wherever I am in the world, and that's all data driven. And the average person that isn't involved in data science wouldn't understand that. At the same time, they have expectations that depend on organizations like Boeing being data driven so that they can get that experience that they expect in their consumer lives in any aspect of their lives. And that's one of the things I find so interesting and inspiring about data science. What are some of the things that keep you motivated to continue pursuing this? >> Yeah I will say along those lines, I think it's great to invest in K-12 programs for Data Literacy. I know one of my mentors and directors of the Data Analytics program, Dr. Nairanjana Dasgupta, we're really familiar with each other. So, she runs a WSU program for K-12 Data Literacy. It's also something that we strive for at Boeing, and we have an internal Data Literacy program because, believe it or not, most people are in business. And there's a lot of disconnect between interpreting and understanding data. For me, what kind of drives me to continue data science is that connection between people and data and how we use it to improve our world, which is partly why I work at Boeing too 'cause I feel that they produce products that people need like satellites and airplanes, >> Absolutely. >> and everything. >> Well, it's tangible, it's relatable. We can understand it. Can you do me a quick favor and define data literacy for anyone that might not understand what that means? >> Yeah, so it's just being able to understand elements of data, whether that's a bar chart or even in a sentence, like how to read a statistic and interpret a statistic in a sentence, for example. >> Very cool. >> Yeah. And sounds like Boeing's doing a great job in these programs, and also trying to hire more women. So yeah, I wanted to ask, do you think there's something that Boeing needs to work on? Or where do you see yourself working on say the next five years? >> Yeah, I think as a company, we always think that there's always room for improvement. >> It never, never stops. >> Tracy: Definitely. (laughs) >> I know workforce strategy is an area that they're currently really heavily investing in, along with safety. How do we build safer products for people? How do we help inform the public about things like Covid transmission in airports? For example, we had the Confident Traveler Initiative which was a big push that we had, and we had to be able to inform people about data models around Covid, right? So yeah, I would say our future is more about an investment in our people and in our culture from my perspective >> That's so important. One of the hardest things to change especially for a legacy organization like Boeing, is culture. You know, when I talk with CEO's or CIO's or COO's about what's your company's vision, what's your strategy? Especially those companies that are on that digital journey that have no choice these days. Everybody expects to have a digital experience, whether you're transacting an an Uber ride, you're buying groceries, or you're traveling by air. That culture sounds like Boeing is really focused on that. And that's impressive because that's one of the hardest things to morph and mold, but it's so essential. You know, as we look around the room here at WiDS it's obviously mostly females, but we're talking about women, underrepresented minorities. We're talking about men as well who are mentors and sponsors to us. I'd love to get your advice to your younger self. What would you tell yourself in terms of where you are now to become a leader in the technology field? >> Yeah, I mean, it's kind of an interesting question because I always try to think, live with no regrets to an extent. >> Lisa: I like that. >> But, there's lots of failures along the way. (Tracy laughing) I don't know if I would tell myself anything different because honestly, if I did, I wouldn't be where I am. >> Lisa: Good for you. >> I started out in fine arts, and I didn't end up there. >> That's good. >> Such a good point, yeah. >> We've been talking about that and I find that a lot at events like WiDS, is women have these zigzaggy patterns. I studied biology, I have a master's in molecular biology, I'm in media and marketing. We talked about transportable skills. There's a case I made many years ago when I got into tech about, well in science you learn the art of interpreting esoteric data and creating a story from it. And that's a transportable skill. But I always say, you mentioned failure, I always say failure is not a bad F word. It allows us to kind of zig and zag and learn along the way. And I think that really fosters thought diversity. And in data science, that is one of the things we absolutely need to have is that diversity and thought. You know, we talk about AI models being biased, we need the data and we need the diverse brains to help ensure that the biases are identified, extracted, and removed. Speaking of AI, I've been geeking out with ChatGPT. So, I'm on it yesterday and I ask it, "What's hot in data science?" And I was like, is it going to get that? What's hot? And it did it, it came back with trends. I think if I ask anything, "What's hot?", I should be to Paris Hilton, but I didn't. And so I was geeking out. One of the things I learned recently that I thought was so super cool is the CTO of OpenAI is a woman, Mira Murati, which I didn't know until over the weekend. Because I always think if I had to name top females in tech, who would they be? And I always default to Sheryl Sandberg, Carly Fiorina, Susan Wojcicki running YouTube. Who are some of the people in your history, in your current, that are really inspiring to you? Men, women, indifferent. >> Sure. I think Boeing is one of the companies where you actually do see a lot of women in leadership roles. I think we're one of the top companies with a number of women executives, actually. Susan Doniz, who's our Chief Information Officer, I believe she's actually slotted to speak at a WiDS event come fall. >> Lisa: Cool. >> So that will be exciting. Susan's actually relatively newer to Boeing in some ways. A Boeing time skill is like three years is still kind of new. (laughs) But she's been around for a while and she's done a lot of inspiring things, I think, for women in the organization. She does a lot with Latino communities and things like that as well. For me personally, you know, when I started at Boeing Ahmad Yaghoobi was one of my mentors and my Technical Lead. He came from Iran during a lot of hard times in the 1980s. His brother actually wrote a memoir, (laughs) which is just a fun, interesting fact. >> Tracy: Oh my God! >> Lisa: Wow! >> And so, I kind of gravitate to people that I can learn from that's not in my sphere, that might make me uncomfortable. >> And you probably don't even think about how many people you're influencing along the way. >> No. >> We just keep going and learning from our mentors and probably lose sight of, "I wonder how many people actually admire me?" And I'm sure there are many that admire you, Rhonda, for what you've done, going from anthropology to archeology. You mentioned before we went live you were really interested in photography. Keep going and really gathering all that breadth 'cause it's only making you more inspiring to people like us. >> Exactly. >> We thank you so much for joining us on the program and sharing a little bit about you and what brought you to WiDS. Thank you so much, Rhonda. >> Yeah, thank you. >> Tracy: Thank you so much for being here. >> Lisa: Yeah. >> Alright. >> For our guests, and for Tracy Zhang, this is Lisa Martin live at Stanford University covering the eighth Annual Women In Data Science Conference. Stick around. Next guest will be here in just a second. (gentle music)

(upbeat music) >> Hey everyone. Welcome back to "The Cube"'s live coverage of "Women in Data Science 2023". As every year we are here live at Stanford University, profiling some amazing women and men in the fields of data science. I have my co-host for this segment is Hannah Freitag. Hannah is from Stanford's Data Journalism program, really interesting, check it out. We're very pleased to welcome our first guest of the day fresh from the keynote stage, Gayatree Ganu, the VP of Data Science at Meta. Gayatree, It's great to have you on the program. >> Likewise, Thank you for having me. >> So you have a PhD in Computer Science. You shared some really cool stuff. Everyone knows Facebook, everyone uses it. I think my mom might be one of the biggest users (Gayatree laughs) and she's probably watching right now. People don't realize there's so much data behind that and data that drives decisions that we engage with. But talk to me a little bit about you first, PhD in Computer Science, were you always, were you like a STEM kid? Little Gayatree, little STEM, >> Yeah, I was a STEM kid. I grew up in Mumbai, India. My parents are actually pharmacists, so they were not like math or stats or anything like that, but I was always a STEM kid. I don't know, I think it, I think I was in sixth grade when we got our first personal computer and I obviously used it as a Pacman playing machine. >> Oh, that's okay. (all laugh) >> But I was so good at, and I, I honestly believe I think being good at games kind of got me more familiar and comfortable with computers. Yeah. I think I always liked computers, I, yeah. >> And so now you lead, I'm looking at my notes here, the Engagement Ecosystem and Monetization Data Science teams at Facebook, Meta. Talk about those, what are the missions of those teams and how does it impact the everyday user? >> Yeah, so the engagement is basically users coming back to our platform more, there's, no better way for users to tell us that they are finding value on the things that we are doing on Facebook, Instagram, WhatsApp, all the other products than coming back to our platform more. So the Engagement Ecosystem team is looking at trends, looking at where there are needs, looking at how users are changing their behaviors, and you know, helping build strategy for the long term, using that data knowledge. Monetization is very different. You know, obviously the top, top apex goal is have a sustainable business so that we can continue building products for our users. And so, but you know, I said this in my keynote today, it's not about making money, our mission statement is not, you know, maximize as much money as you can make. It's about building a meaningful connection between businesses, customers, users, and, you know especially in these last two or three funky, post-pandemic years, it's been such a big, an important thing to do for small businesses all over all, all around the world for users to find like goods and services and products that they care about and that they can connect to. So, you know, there is truly an connection between my engagement world and the monetization world. And you know, it's not very clear always till you go in to, like, you peel the layers. Everything we do in the ads world is also always first with users as our, you know, guiding principle. >> Yeah, you mentioned how you supported especially small businesses also during the pandemic. You touched a bit upon it in the keynote speech. Can you tell our audience what were like special or certain specific programs you implemented to support especially small businesses during these times? >> Yeah, so there are 200 million businesses on our platform. A lot of them small businesses, 10 million of them run ads. So there is a large number of like businesses on our platform who, you know use the power of social media to connect to the customers that matter to them, to like you, you know use the free products that we built. In the post-pandemic years, we built a lot of stuff very quickly when Covid first hit for business to get the word out, right? Like, they had to announce when special shopping hours existed for at-risk populations, or when certain goods and services were available versus not. We had grants, there's $100 million grant that we gave out to small businesses. Users could show sort of, you know show their support with a bunch of campaigns that we ran, and of course we continue running ads. Our ads are very effective, I guess, and, you know getting a very reliable connection with from the customer to the business. And so, you know, we've run all these studies. We support, I talked about two examples today. One of them is the largest black-owned, woman black-owned wine company, and how they needed to move to an online program and, you know, we gave them a grant, and supported them through their ads campaign and, you know, they saw 60% lift in purchases, or something like that. So, a lot of good stories, small stories, you know, on a scale of 200 million, that really sort of made me feel proud about the work we do. And you know, now more than ever before, I think people can connect so directly with businesses. You can WhatsApp them, I come from India, every business is on WhatsApp. And you can, you know, WhatsApp them, you can send them Facebook messages, and you can build this like direct connection with things that matter to you. >> We have this expectation that we can be connected anywhere. I was just at Mobile World Congress for MWC last week, where, obviously talking about connectivity. We want to be able to do any transaction, whether it's post on Facebook or call an Uber, or watch on Netflix if you're on the road, we expect that we're going to be connected. >> Yeah. >> And what we, I think a lot of us don't realize I mean, those of us in tech do, but how much data science is a facilitator of all of those interactions. >> Yeah! >> As we, Gayatree, as we talk about, like, any business, whether it is the black women-owned wine business, >> Yeah. >> great business, or a a grocer or a car dealer, everybody has to become data-driven. >> Yes. >> Because the consumer has the expectation. >> Yes. >> Talk about data science as a facilitator of just pretty much everything we are doing and conducting in our daily lives. >> Yeah, I think that's a great question. I think data science as a field wasn't really defined like maybe 15 years ago, right? So this is all in our lifetimes that we are seeing this. Even in data science today, People come from so many different backgrounds and bring their own expertise here. And I think we, you know, this conference, all of us get to define what that means and how we can bring data to do good in the world. Everything you do, as you said, there is a lot of data. Facebook has a lot of data, Meta has a lot of data, and how do we responsibly use this data? How do we use this data to make sure that we're, you know representing all diversity? You know, minorities? Like machine learning algorithms don't do well with small data, they do well with big data, but the small data matters. And how do you like, you know, bring that into algorithms? Yeah, so everything we do at Meta is very, very data-driven. I feel proud about that, to be honest, because while data gets a bad rap sometimes, having no data and making decisions in the blind is just the absolute worst thing you can do. And so, you know, we, the job as a data scientist at Facebook is to make sure that we use this data, use this responsibly, make sure that we are representing every aspect of the, you know, 3 billion users who come to our platform. Yeah, data serves all the products that we build here. >> The responsibility factor is, is huge. You know, we can't talk about AI without talking about ethics. One of the things that I was talking with Hannah and our other co-host, Tracy, about during our opening is something I just learned over the weekend. And that is that the CTO of ChatGPT is a woman. (Gayatree laughs) I didn't know that. And I thought, why isn't she getting more awareness? There's a lot of conversations with their CEO. >> Yeah. >> Everyone's using it, playing around with it. I actually asked it yesterday, "What's hot in Data Science?" (all laugh) I was like, should I have asked that to let itself in, what's hot? (Gayatree laughs) But it, I thought that was phenomenal, and we need to be talking about this more. >> Yeah. >> This is something that they're likening to the launch of the iPhone, which has transformed our lives. >> I know, it is. >> ChatGPT, and its chief technologist is a female, how great is that? >> And I don't know whether you, I don't know the stats around this, but I think CTO is even less, it's even more rare to have a woman there, like you have women CEOs because I mean, we are building upon years and years of women not choosing technical fields and not choosing STEM, and it's going to take some time, but yeah, yeah, she's a woman. Isn't it amazing? It's wonderful. >> Yes, there was a great, there's a great "Fast Company" article on her that I was looking at yesterday and I just thought, we need to do what we can to help spread, Mira Murati is her name, because what she's doing is, one of the biggest technological breakthroughs we may ever see in our lifetime. It gives me goosebumps just thinking about it. (Gayatree laughs) I also wanted to share some stats, oh, sorry, go ahead, Hannah. >> Yeah, I was going to follow up on the thing that you mentioned that we had many years with like not enough women choosing a career path in STEM and that we have to overcome this trend. What are some, like what is some advice you have like as the Vice-President Data Science? Like what can we do to make this feel more, you know, approachable and >> Yeah. >> accessible for women? >> Yeah, I, there's so much that we have done already and you know, want to continue, keep doing. Of course conferences like these were, you know and I think there are high school students here there are students from my Alma Mater's undergrad year. It's amazing to like get all these women together to get them to see what success could look like. >> Yeah. >> What being a woman leader in this space could look like. So that's, you know, that's one, at Meta I lead recruiting at Meta and we've done a bunch to sort of open up the thinking around data science and technical jobs for women. Simple things like what you write in your job description. I don't know whether you know this, or this is a story you've heard before, when you see, when you have a job description and there are like 10 things that you need to, you know be good at to apply to this job, a woman sees those 10 and says, okay, I don't meet the qualifications of one of them and she doesn't apply. And a man sees one that he meets the qualifications to and he applies. And so, you know, there's small things you can do, and just how you write your job description, what goals you set for diversity and inclusion for your own organization. We have goals, Facebook's always been pretty up there in like, you know, speaking out for diversity and Sheryl Sandberg has been our Chief Business Officer for a very long time and she's been, like, amazing at like pushing from more women. So yeah, every step of the way, I think, we made a lot of progress, to be honest. I do think women choose STEM fields a lot more than they did. When I did my Computer Science I was often one of one or two women in the Computer Science class. It takes some time to, for it to percolate all the way to like having more CTOs and CEOs, >> Yeah. >> but it's going to happen in our lifetime, and you know, three of us know this, women are going to rule the world, and it (laughs) >> Drop the mic, girl! >> And it's going to happen in our lifetime, so I'm excited about it. >> And we have responsibility in helping make that happen. You know, I'm curious, you were in STEM, you talked about Computer Science, being one of the only females. One of the things that the nadb.org data from 2022 showed, some good numbers, the number of women in technical roles is now 27.6%, I believe, so up from 25, it's up in '22, which is good, more hiring of women. >> Yeah. >> One of the biggest challenges is attrition. What keeps you motivated? >> Yeah. >> To stay what, where you are doing what you're doing, managing a family and helping to drive these experiences at Facebook that we all expect are just going to happen? >> Yeah, two things come to mind. It does take a village. You do need people around you. You know, I'm grateful for my husband. You talked about managing a family, I did the very Indian thing and my parents live with us, and they help take care of the kids. >> Right! (laughs) >> (laughs) My kids are young, six and four, and I definitely needed help over the last few years. It takes mentors, it takes other people that you look up to, who've gone through all of those same challenges and can, you know, advise you to sort of continue working in the field. I remember when my kid was born when he was six months old, I was considering quitting. And my husband's like, to be a good role model for your children, you need to continue working. Like, just being a mother is not enough. And so, you know, so that's one. You know, the village that you build around you your supporters, your mentors who keep encouraging you. Sheryl Sandberg said this to me in my second month at Facebook. She said that women drop out of technical fields, they become managers, they become sort of administrative more, in their nature of their work, and her advice was, "Don't do that, Don't stop the technical". And I think that's the other thing I'd say to a lot of women. Technical stuff is hard, but you know, keeping up with that and keeping sort of on top of it actually does help you in the long run. And it's definitely helped me in my career at Facebook. >> I think one of the things, and Hannah and I and Tracy talked about this in the open, and I think you'll agree with us, is the whole saying of you can't be what you can't see, and I like to way, "Well, you can be what you can see". That visibility, the great thing that WiDS did, of having you on the stage as a speaker this morning so people can understand, everyone, like I said, everyone knows Meta, >> Yeah. >> everyone uses Facebook. And so it's important to bring that connection, >> Yeah. >> of how data is driving the experiences, the fact that it's User First, but we need to be able to see women in positions, >> Yes. >> like you, especially with Sheryl stepping down moving on to something else, or people that are like YouTube influencers, that have no idea that the head of YouTube for a very long time, Susan Wojcicki is a woman. >> (laughs) Yes. Who pioneered streaming, and I mean how often do you are you on YouTube every day? >> Yep, every day. >> But we have to be able to see and and raise the profile of these women and learn from them and be inspired, >> Absolutely. >> to keep going and going. I like what I do, I'm making a difference here. >> Yeah, yeah, absolutely. >> And I can be the, the sponsor or the mentor for somebody down the road. >> Absolutely. >> Yeah, and then referring back to what we talked in the beginning, show that data science is so diverse and it doesn't mean if you're like in IT, you're like sitting in your dark room, >> Right. (laughs) >> coding all day, but you know, >> (laughs) Right! >> to show the different facets of this job and >> Right! >> make this appealing to women, >> Yeah. for sure. >> And I said this in my keynote too, you know, one of the things that helped me most is complimenting the data and the techniques and the algorithms with how you work with people, and you know, empathy and alignment building and leadership, strategic thinking. And I think honestly, I think women do a lot of this stuff really well. We know how to work with people and so, you know, I've seen this at Meta for sure, like, you know, all of these skills soft skills, as we call them, go a long way, and like, you know, doing the right things and having a lasting impact. And like I said, women are going to rule the world, you know, in our lifetimes. (laughs) >> Oh, I can't, I can't wait to see that happen. There's some interesting female candidates that are already throwing their hats in the ring for the next presidential election. >> Yes. >> So we'll have to see where that goes. But some of the things that are so interesting to me, here we are in California and Palo Alto, technically Stanford is its own zip code, I believe. And we're in California, we're freaking out because we've gotten so much rain, it's absolutely unprecedented. We need it, we had a massive drought, an extreme drought, technically, for many years. I've got friends that live up in Tahoe, I've been getting pictures this morning of windows that are >> (laughs) that are covered? >> Yes, actually, yes. (Gayatree laughs) That, where windows like second-story windows are covered in snow. >> Yeah. >> Climate change. >> Climate change. >> There's so much that data science is doing to power and power our understanding of climate change whether it's that, or police violence. >> Yeah. (all talk together) >> We had talk today on that it was amazing. >> Yes. So I want more people to know what data science is really facilitating, that impacts all of us, whether you're in a technical role or not. >> And data wins arguments. >> Yes, I love that! >> I said this is my slide today, like, you know, there's always going to be doubters and naysayers and I mean, but there's hard evidence, there's hard data like, yeah. In all of these fields, I mean the data that climate change, the data science that we have done in the environmental and climate change areas and medical, and you know, medicine professions just so much, so much more opportunity, and like, how much we can learn more about the world. >> Yeah. >> Yeah, it's a pretty exciting time to be a data scientist. >> I feel like, we're just scratching the surface. >> Yeah. >> With the potential and the global impact that we can make with data science. Gayatree, it's been so great having you on theCUBE, thank you. >> Right, >> Thank you so much, Gayatree. >> So much, I love, >> Thank you. >> I'm going to take Data WiD's arguments into my personal life. (Gayatree laughs) I was actually just, just a quick anecdote, funny story. I was listening to the radio this morning and there was a commercial from an insurance company and I guess the joke is, it's an argument between two spouses, and the the voiceover comes in and says, "Let's watch a replay". I'm like, if only they, then they got the data that helped the woman win the argument. (laughs) >> (laughs) I will warn you it doesn't always help with arguments I have with my husband. (laughs) >> Okay, I'm going to keep it in the middle of my mind. >> Yes! >> Gayatree, thank you so much. >> Thank you so much, >> for sharing, >> Thank you both for the opportunity. >> And being a great female that we can look up to, we really appreciate your insights >> Oh, likewise. >> and your time. >> Thank you. >> All right, for our guest, for Hannah Freitag, I'm Lisa Martin, live at Stanford University covering "Women in Data Science '23". Stick around, our next guest joins us in just a minute. (upbeat music) I have been in the software and technology industry for over 12 years now, so I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCUBE. (upbeat music) Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, "Hey, I'm really interested in this. I love talking with customers, gimme a shot, let me come into the studio and do an interview and see if we can work together". I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech, we do a lot of women in technology events and one of the things I.

(ambient music) >> Good morning, everyone. Lisa Martin with theCUBE, live at the eighth Annual Women in Data Science Conference. This is one of my absolute favorite events of the year. We engage with tons of great inspirational speakers, men and women, and what's happening with WiDS is a global movement. I've got two fabulous co-hosts with me today that you're going to be hearing and meeting. Please welcome Tracy Zhang and Hannah Freitag, who are both from the sata journalism program, master's program, at Stanford. So great to have you guys. >> So excited to be here. >> So data journalism's so interesting. Tracy, tell us a little bit about you, what you're interested in, and then Hannah we'll have you do the same thing. >> Yeah >> Yeah, definitely. I definitely think data journalism is very interesting, and in fact, I think, what is data journalism? Is definitely one of the big questions that we ask during the span of one year, which is the length of our program. And yeah, like you said, I'm in this data journalism master program, and I think coming in I just wanted to pivot from my undergrad studies, which is more like a traditional journalism, into data. We're finding stories through data, so that's why I'm also very excited about meeting these speakers for today because they're all, they have different backgrounds, but they all ended up in data science. So I think they'll be very inspirational and I can't wait to talk to them. >> Data in stories, I love that. Hannah, tell us a little bit about you. >> Yeah, so before coming to Stanford, I was a research assistant at Humboldt University in Berlin, so I was in political science research. And I love to work with data sets and data, but I figured that, for me, I don't want this story to end up in a research paper, which is only very limited in terms of the audience. And I figured, okay, data journalism is the perfect way to tell stories and use data to illustrate anecdotes, but to make it comprehensive and accessible for a broader audience. So then I found this program at Stanford and I was like, okay, that's the perfect transition from political science to journalism, and to use data to tell data-driven stories. So I'm excited to be in this program, I'm excited for the conference today and to hear from these amazing women who work in data science. >> You both brought up great points, and we were chatting earlier that there's a lot of diversity in background. >> Tracy: Definitely. >> Not everyone was in STEM as a young kid or studied computer science. Maybe some are engineering, maybe some are are philosophy or economic, it's so interesting. And what I find year after year at WiDS is it brings in so much thought diversity. And that's what being data-driven really demands. It demands that unbiased approach, that diverse, a spectrum of diverse perspectives, and we definitely get that at WiDS. There's about 350 people in person here, but as I mentioned in the opening, hundreds of thousands will engage throughout the year, tens of thousands probably today at local events going on across the globe. And it just underscores the importance of every organization, whether it's a bank or a grocer, has to be data-driven. We have that expectation as consumers in our consumer lives, and even in our business lives, that I'm going to engage with a business, whatever it is, and they're going to know about me, they're going to deliver me a personalized experience that's relevant to me and my history. And all that is powered by data science, which is I think it's fascinating. >> Yeah, and the great way is if you combine data with people. Because after all, large data sets, they oftentimes consist of stories or data that affects people. And to find these stories or advanced research in whatever fields, maybe in the financial business, or in health, as you mentioned, the variety of fields, it's very powerful, powerful tool to use. >> It's a very power, oh, go ahead Tracy. >> No, definitely. I just wanted to build off of that. It's important to put a face on data. So a dataset without a name is just some numbers, but if there's a story, then I think it means something too. And I think Margot was talking about how data science is about knowing or understanding the past, I think that's very interesting. That's a method for us to know who we are. >> Definitely. There's so many opportunities. I wanted to share some of the statistics from AnitaB.org that I was just looking at from 2022. We always talk at events like WiDS, and some of the other women in tech things, theCUBE is very much pro-women in tech, and has been for a very long, since the beginning of theCUBE. But we've seen the numbers of women technologists historically well below 25%, and we see attrition rates are high. And so we often talk about, well, what can we do? And part of that is raising the awareness. And that's one of the great things about WiDS, especially WiDS happening on International Women's Day, today, March 8th, and around event- >> Tracy: A big holiday. >> Exactly. But one of the nice things I was looking at, the AnitaB.org research, is that representation of tech women is on the rise, still below pre-pandemic levels, but it's actually nearly 27% of women in technical roles. And that's an increase, slow increase, but the needle is moving. We're seeing much more gender diversity across a lot of career levels, which is exciting. But some of the challenges remain. I mean, the representation of women technologists is growing, except at the intern level. And I thought that was really poignant. We need to be opening up that pipeline and going younger. And you'll hear a lot of those conversations today about, what are we doing to reach girls in grade school, 10 year olds, 12 year olds, those in high school? How do we help foster them through their undergrad studies- >> And excite them about science and all these fields, for sure. >> What do you think, Hannah, on that note, and I'll ask you the same question, what do you think can be done? The theme of this year's International Women's Day is Embrace Equity. What do you think can be done on that intern problem to help really dial up the volume on getting those younger kids interested, one, earlier, and two, helping them stay interested? >> Yeah. Yeah, that's a great question. I think it's important to start early, as you said, in school. Back in the day when I went to high school, we had this one day per year where we could explore as girls, explore a STEM job and go into the job for one day and see how it's like to work in a, I dunno, in IT or in data science, so that's a great first step. But as you mentioned, it's important to keep girls and women excited about this field and make them actually pursue this path. So I think conferences or networking is very powerful. Also these days with social media and technology, we have more ability and greater ways to connect. And I think we should even empower ourselves even more to pursue this path if we're interested in data science, and not be like, okay, maybe it's not for me, or maybe as a woman I have less chances. So I think it's very important to connect with other women, and this is what WiDS is great about. >> WiDS is so fantastic for that network effect, as you talked about. It's always such, as I was telling you about before we went live, I've covered five or six WiDS for theCUBE, and it's always such a day of positivity, it's a day of of inclusivity, which is exactly what Embrace Equity is really kind of about. Tracy, talk a little bit about some of the things that you see that will help with that hashtag Embrace Equity kind of pulling it, not just to tech. Because we're talking and we saw Meta was a keynote who's going to come to talk with Hannah and me in a little bit, we see Total Energies on the program today, we see Microsoft, Intuit, Boeing Air Company. What are some of the things you think that can be done to help inspire, say, little Tracy back in the day to become interested in STEM or in technology or in data? What do you think companies can and should be doing to dial up the volume for those youngsters? >> Yeah, 'cause I think somebody was talking about, one of the keynote speakers was talking about how there is a notion that girls just can't be data scientists. girls just can't do science. And I think representation definitely matters. If three year old me see on TV that all the scientists are women, I think I would definitely have the notion that, oh, this might be a career choice for me and I can definitely also be a scientist if I want. So yeah, I think representation definitely matters and that's why conference like this will just show us how these women are great in their fields. They're great data scientists that are bringing great insight to the company and even to the social good as well. So yeah, I think that's very important just to make women feel seen in this data science field and to listen to the great woman who's doing amazing work. >> Absolutely. There's a saying, you can't be what you can't see. >> Exactly. >> And I like to say, I like to flip it on its head, 'cause we can talk about some of the negatives, but there's a lot of positives and I want to share some of those in a minute, is that we need to be, that visibility that you talked about, the awareness that you talked about, it needs to be there but it needs to be sustained and maintained. And an organization like WiDS and some of the other women in tech events that happen around the valley here and globally, are all aimed at raising the profile of these women so that the younger, really, all generations can see what they can be. We all, the funny thing is, we all have this expectation whether we're transacting on Uber ride or we are on Netflix or we're buying something on Amazon, we can get it like that. They're going to know who I am, they're going to know what I want, they're going to want to know what I just bought or what I just watched. Don't serve me up something that I've already done that. >> Hannah: Yeah. >> Tracy: Yeah. >> So that expectation that everyone has is all about data, though we don't necessarily think about it like that. >> Hannah: Exactly. >> Tracy: Exactly. >> But it's all about the data that, the past data, the data science, as well as the realtime data because we want to have these experiences that are fresh, in the moment, and super relevant. So whether women recognize it or not, they're data driven too. Whether or not you're in data science, we're all driven by data and we have these expectations that every business is going to meet it. >> Exactly. >> Yeah. And circling back to young women, I think it's crucial and important to have role models. As you said, if you see someone and you're younger and you're like, oh I want to be like her. I want to follow this path, and have inspiration and a role model, someone you look up to and be like, okay, this is possible if I study the math part or do the physics, and you kind of have a goal and a vision in mind, I think that's really important to drive you. >> Having those mentors and sponsors, something that's interesting is, I always, everyone knows what a mentor is, somebody that you look up to, that can guide you, that you admire. I didn't learn what a sponsor was until a Women in Tech event a few years ago that we did on theCUBE. And I was kind of, my eyes were open but I didn't understand the difference between a mentor and a sponsor. And then it got me thinking, who are my sponsors? And I started going through LinkedIn, oh, he's a sponsor, she's a sponsor, people that help really propel you forward, your recommenders, your champions, and it's so important at every level to build that network. And we have, to your point, Hannah, there's so much potential here for data drivenness across the globe, and there's so much potential for women. One of the things I also learned recently , and I wanted to share this with you 'cause I'm not sure if you know this, ChatGPT, exploding, I was on it yesterday looking at- >> Everyone talking about it. >> What's hot in data science? And it was kind of like, and I actually asked it, what was hot in data science in 2023? And it told me that it didn't know anything prior to 2021. >> Tracy: Yes. >> Hannah: Yeah. >> So I said, Oh, I'm so sorry. But everyone's talking about ChatGPT, it is the most advanced AI chatbot ever released to the masses, it's on fire. They're likening it to the launch of the iPhone, 100 million-plus users. But did you know that the CTO of ChatGPT is a woman? >> Tracy: I did not know, but I learned that. >> I learned that a couple days ago, Mira Murati, and of course- >> I love it. >> She's been, I saw this great profile piece on her on Fast Company, but of course everything that we're hearing about with respect to ChatGPT, a lot on the CEO. But I thought we need to help dial up the profile of the CTO because she's only 35, yet she is at the helm of one of the most groundbreaking things in our lifetime we'll probably ever see. Isn't that cool? >> That is, yeah, I completely had no idea. >> I didn't either. I saw it on LinkedIn over the weekend and I thought, I have to talk about that because it's so important when we talk about some of the trends, other trends from AnitaB.org, I talked about some of those positive trends. Overall hiring has rebounded in '22 compared to pre-pandemic levels. And we see also 51% more women being hired in '22 than '21. So the data, it's all about data, is showing us things are progressing quite slowly. But one of the biggest challenges that's still persistent is attrition. So we were talking about, Hannah, what would your advice be? How would you help a woman stay in tech? We saw that attrition last year in '22 according to AnitaB.org, more than doubled. So we're seeing women getting into the field and dropping out for various reasons. And so that's still an extent concern that we have. What do you think would motivate you to stick around if you were in a technical role? Same question for you in a minute. >> Right, you were talking about how we see an increase especially in the intern level for women. And I think if, I don't know, this is a great for a start point for pushing the momentum to start growth, pushing the needle rightwards. But I think if we can see more increase in the upper level, the women representation in the upper level too, maybe that's definitely a big goal and something we should work towards to. >> Lisa: Absolutely. >> But if there's more representation up in the CTO position, like in the managing level, I think that will definitely be a great factor to keep women in data science. >> I was looking at some trends, sorry, Hannah, forgetting what this source was, so forgive me, that was showing that there was a trend in the last few years, I think it was Fast Company, of more women in executive positions, specifically chief operating officer positions. What that hasn't translated to, what they thought it might translate to, is more women going from COO to CEO and we're not seeing that. We think of, if you ask, name a female executive that you'd recognize, everyone would probably say Sheryl Sandberg. But I was shocked to learn the other day at a Women in Tech event I was doing, that there was a survey done by this organization that showed that 78% of people couldn't identify. So to your point, we need more of them in that visible role, in the executive suite. >> Tracy: Exactly. >> And there's data that show that companies that have women, companies across industries that have women in leadership positions, executive positions I should say, are actually more profitable. So it's kind of like, duh, the data is there, it's telling you this. >> Hannah: Exactly. >> Right? >> And I think also a very important point is work culture and the work environment. And as a woman, maybe if you enter and you work two or three years, and then you have to oftentimes choose, okay, do I want family or do I want my job? And I think that's one of the major tasks that companies face to make it possible for women to combine being a mother and being a great data scientist or an executive or CEO. And I think there's still a lot to be done in this regard to make it possible for women to not have to choose for one thing or the other. And I think that's also a reason why we might see more women at the entry level, but not long-term. Because they are punished if they take a couple years off if they want to have kids. >> I think that's a question we need to ask to men too. >> Absolutely. >> How to balance work and life. 'Cause we never ask that. We just ask the woman. >> No, they just get it done, probably because there's a woman on the other end whose making it happen. >> Exactly. So yeah, another thing to think about, another thing to work towards too. >> Yeah, it's a good point you're raising that we have this conversation together and not exclusively only women, but we all have to come together and talk about how we can design companies in a way that it works for everyone. >> Yeah, and no slight to men at all. A lot of my mentors and sponsors are men. They're just people that I greatly admire who saw raw potential in me 15, 18 years ago, and just added a little water to this little weed and it started to grow. In fact, theCUBE- >> Tracy: And look at you now. >> Look at me now. And theCUBE, the guys Dave Vellante and John Furrier are two of those people that are sponsors of mine. But it needs to be diverse. It needs to be diverse and gender, it needs to include non-binary people, anybody, shouldn't matter. We should be able to collectively work together to solve big problems. Like the propaganda problem that was being discussed in the keynote this morning with respect to China, or climate change. Climate change is a huge challenge. Here, we are in California, we're getting an atmospheric river tomorrow. And Californians and rain, we're not so friendly. But we know that there's massive changes going on in the climate. Data science can help really unlock a lot of the challenges and solve some of the problems and help us understand better. So there's so much real-world implication potential that being data-driven can really lead to. And I love the fact that you guys are studying data journalism. You'll have to help me understand that even more. But we're going to going to have great conversations today, I'm so excited to be co-hosting with both of you. You're going to be inspired, you're going to learn, they're going to learn from us as well. So let's just kind of think of this as a community of men, women, everything in between to really help inspire the current generations, the future generations. And to your point, let's help women feel confident to be able to stay and raise their hand for fast-tracking their careers. >> Exactly. >> What are you guys, last minute, what are you looking forward to most for today? >> Just meeting these great women, I can't wait. >> Yeah, learning from each other. Having this conversation about how we can make data science even more equitable and hear from the great ideas that all these women have. >> Excellent, girls, we're going to have a great day. We're so glad that you're here with us on theCUBE, live at Stanford University, Women in Data Science, the eighth annual conference. I'm Lisa Martin, my two co-hosts for the day, Tracy Zhang, Hannah Freitag, you're going to be seeing a lot of us, we appreciate. Stick around, our first guest joins Hannah and me in just a minute. (ambient music)

>> Now we're recording. >> All right. >> Appreciate that, Hannah. >> Yeah, so I mean, I think in general we continue to do very, very well as a company. I think like everybody, there's economic headwinds today that are unavoidable, but I think we have a couple things going for us. One, we're in the cyberspace, which I think is, for the most part, recession proof as an industry. I think the impact of a recession will impact some vendors and some categories, but in general, I think the industry is pretty resilient. It's like the power industry, no? Recession or not, you still need electricity to your house. Cybersecurity is almost becoming a utility like that as far as the needs of companies go. I think for us, we also have the ability to do the security, the security operations, for a lot of companies, and if you look at the value proposition, the ROI for the cost of less than one to maybe two or three, depending on how big you are as a customer, what you'd have to pay for half to three security operations people, we can give you a full security operations. And so the ROI is is almost kind of brain dead simple, and so that keeps us going pretty well. And I think the other areas, we remove all that complexity for people. So in a world where you got other problems to worry about, handling all the security complexity is something that adds to that ROI. So for us, I think what we're seeing is mostly is some of the larger deals are taking a little bit longer than they have, some of the large enterprise deals, 'cause I think they are being a little more cautious about how they spend it, but in general, business is still kind of cranking along. >> Anything you can share with me that you guys have talked about publicly in terms of any metrics, or what can you tell me other than cranking? >> Yeah, I mean, I would just say we're still very, very high growth, so I think our financial profile would kind of still put us clearly in the cyber unicorn position, but I think other than that, we don't really share business metrics as a private- >> Okay, so how about headcount? >> Still growing. So we're not growing as fast as we've been growing, but I don't think we were anyway. I think we kind of, we're getting to the point of critical mass. We'll start to grow in a more kind of normal course and speed. I don't think we overhired like a lot of companies did in the past, even though we added, almost doubled the size of the company in the last 18 months. So we're still hiring, but very kind of targeted to certain roles going forward 'cause I do think we're kind of at critical mass in some of the other functions. >> You disclose headcount or no? >> We do not. >> You don't, okay. And never have? >> Not that I'm aware of, no. >> Okay, on the macro, I don't know if security's recession proof, but it's less susceptible, let's say. I've had Nikesh Arora on recently, we're at Palo Alto's Ignite, and he was saying, "Look," it's just like you were saying, "Larger deal's a little harder." A lot of times customers, he was saying customers are breaking larger deals into smaller deals, more POCs, more approvals, more people to get through the approval, not whole, blah, blah, blah. Now they're a different animal, I understand, but are you seeing similar trends, and how are you dealing with that? >> Yeah, I think the exact same trends, and I think it's just in a world where spending a dollar matters, I think a lot more oversight comes into play, a lot more reviewers, and can you shave it down here? Can you reduce the scope of the project to save money there? And I think it just caused a lot of those things. I think, in the large enterprise, I think most of those deals for companies like us and Palo and CrowdStrike and kind of the upper tier companies, they'll still go through. I think they'll just going to take a lot longer, and, yeah, maybe they're 80% of what they would've been otherwise, but there's still a lot of business to be had out there. >> So how are you dealing with that? I mean, you're talking about you double the size of the company. Is it kind of more focused on go-to-market, more sort of, maybe not overlay, but sort of SE types that are going to be doing more handholding. How have you dealt with that? Or have you just sort of said, "Hey, it is what it is, and we're not going to, we're not going to tactically respond to. We got long-term direction"? >> Yeah, I think it's more the latter. I think for us, it's we've gone through all these things before. It just takes longer now. So a lot of the steps we're taking are the same steps. We're still involved in a lot of POCs, we're involved in a lot of demos, and I don't think that changed. It's just the time between your POC and when someone sends you the PO, there's five more people now got to review things and go through a budget committee and all sorts of stuff like that. I think where we're probably focused more now is adding more and more capabilities just so we continue to be on the front foot of innovation and being relevant to the market, and trying to create more differentiators for us and the competitors. That's something that's just built into our culture, and we don't want to slow that down. And so even though the business is still doing extremely, extremely well, we want to keep investing in kind of technology. >> So the deal size, is it fair to say the initial deal size for new accounts, while it may be smaller, you're adding more capabilities, and so over time, your average contract values will go up? Are you seeing that trend? Or am I- >> Well, I would say I don't even necessarily see our average deal size has gotten smaller. I think in total, it's probably gotten a little bigger. I think what happens is when something like this happens, the old cream rises to the top thing, I think, comes into play, and you'll see some organizations instead of doing a deal with three or four vendors, they may want to pick one or two and really kind of put a lot of energy behind that. For them, they're maybe spending a little less money, but for those vendors who are amongst those getting chosen, I think they're doing pretty good. So our average deal size is pretty stable. For us, it's just a temporal thing. It's just the larger deals take a little bit longer. I don't think we're seeing much of a deal velocity difference in our mid-market commercial spaces, but in the large enterprise it's a little bit slower. But for us, we have ambitious plans in our strategy or on how we want to execute and what we want to build, and so I think we want to just continue to make sure we go down that path technically. >> So I have some questions on sort of the target markets and the cohorts you're going after, and I have some product questions. I know we're somewhat limited on time, but the historical focus has been on SMB, and I know you guys have gone in into enterprise. I'm curious as to how that's going. Any guidance you can give me on mix? Or when I talk to the big guys, right, you know who they are, the big managed service providers, MSSPs, and they're like, "Poo poo on Arctic Wolf," like, "Oh, they're (groans)." I said, "Yeah, that's what they used to say about the PC. It's just a toy. Or Microsoft SQL Server." But so I kind of love that narrative for you guys, but I'm curious from your words as to, what is that enterprise? How's the historical business doing, and how's the entrance into the enterprise going? What kind of hurdles are you having, blockers are you having to remove? Any color you can give me there would be super helpful. >> Yeah, so I think our commercial S&B business continues to do really good. Our mid-market is a very strong market for us. And I think while a lot of companies like to focus purely on large enterprise, there's a lot more mid-market companies, and a much larger piece of the IT puzzle collectively is in mid-market than it is large enterprise. That being said, we started to get pulled into the large enterprise not because we're a toy but because we're quite a comprehensive service. And so I think what we're trying to do from a roadmap perspective is catch up with some of the kind of capabilities that a large enterprise would want from us that a potential mid-market customer wouldn't. In some case, it's not doing more. It's just doing it different. Like, so we have a very kind of hands-on engagement with some of our smaller customers, something we call our concierge. Some of the large enterprises want more of a hybrid where they do some stuff and you do some stuff. And so kind of building that capability into the platform is something that's really important for us. Just how we engage with them as far as giving 'em access to their data, the certain APIs they want, things of that nature, what we're building out for large enterprise, but the demand by large enterprise on our business is enormous. And so it's really just us kind of catching up with some of the kind of the features that they want that we lack today, but many of 'em are still signing up with us, obviously, and in lieu of that, knowing that it's coming soon. And so I think if you look at the growth of our large enterprise, it's one of our fastest growing segments, and I think it shows anything but we're a toy. I would be shocked, frankly, if there's an MSSP, and, of course, we don't see ourself as an MSSP, but I'd be shocked if any of them operate a platform at the scale that ours operates. >> Okay, so wow. A lot I want to unpack there. So just to follow up on that last question, you don't see yourself as an MSSP because why, you see yourselves as a technology platform? >> Yes, I mean, the vast, vast, vast majority of what we deliver is our own technology. So we integrate with third-party solutions mostly to bring in that telemetry. So we've built our own platform from the ground up. We have our own threat intelligence, our own detection logic. We do have our own agents and network sensors. MSSP is typically cobbling together other tools, third party off-the-shelf tools to run their SOC. Ours is all homegrown technology. So I have a whole group called Arctic Wolf Labs, is building, just cranking out ML-based detections, building out infrastructure to take feeds in from a variety of different sources. We have a full integration kind of effort where we integrate into other third parties. So when we go into a customer, we can leverage whatever they have, but at the same time, we produce some tech that if they're lacking in a certain area, we can provide that tech, particularly around things like endpoint agents and network sensors and the like. >> What about like identity, doing your own identity? >> So we don't do our own identity, but we take feeds in from things like Okta and Active Directory and the like, and we have detection logic built on top of that. So part of our value add is we were XDR before XDR was the cool thing to talk about, meaning we can look across multiple attack surfaces and come to a security conclusion where most EDR vendors started with looking just at the endpoint, right? And then they called themselves XDR because now they took in a network feed, but they still looked at it as a separate network detection. We actually look at the things across multiple attack surfaces and stitch 'em together to look at that from a security perspective. In some cases we have automatic detections that will fire. In other cases, we can surface some to a security professional who can go start pulling on that thread. >> So you don't need to purchase CrowdStrike software and integrate it. You have your own equivalent essentially. >> Well, we'll take a feed from the CrowdStrike endpoint into our platform. We don't have to rely on their detections and their alerts, and things of that nature. Now obviously anything they discover we pull in as well, it's just additional context, but we have all our own tech behind it. So we operate kind of at an MSSP scale. We have a similar value proposition in the sense that we'll use whatever the customer has, but once that data kind of comes into our pipeline, it's all our own homegrown tech from there. >> But I mean, what I like about the MSSP piece of your business is it's very high touch. It's very intimate. What I like about what you're saying is that it's software-like economics, so software, software-like part of it. >> That's what makes us the unicorn, right? Is we do have, our concierges is very hands-on. We continue to drive automation that makes our concierge security professionals more efficient, but we always want that customer to have that concierge person as, is almost an extension to their security team, or in some cases, for companies that don't even have a security team, as their security team. As we go down the path, as I mentioned, one of the things we want to be able to do is start to have a more flexible model where we can have that high touch if you want it. We can have the high touch on certain occasions, and you can do stuff. We can have low touch, like we can span the spectrum, but we never want to lose our kind of unique value proposition around the concierge, but we also want to make sure that we're providing an interface that any customer would want to use. >> So given that sort of software-like economics, I mean, services companies need this too, but especially in software, things like net revenue retention and churn are super important. How are those metrics looking? What can you share with me there? >> Yeah, I mean, again, we don't share those metrics publicly, but all's I can continue to repeat is, if you looked at all of our financial metrics, I think you would clearly put us in the unicorn category. I think very few companies are going to have the level of growth that we have on the amount of ARR that we have with the net revenue retention and the churn and upsell. All those aspects continue to be very, very strong for us. >> I want to go back to the sort of enterprise conversation. So large enterprises would engage with you as a complement to their existing SOC, correct? Is that a fair statement or not necessarily? >> It's in some cases. In some cases, they're looking to not have a SOC. So we run into a lot of cases where they want to replace their SIEM, and they want a solution like Arctic Wolf to do that. And so there's a poll, I can't remember, I think it was Forrester, IDC, one of them did it a couple years ago, and they found out that 70% of large enterprises do not want to build the SOC, and it's not 'cause they don't need one, it's 'cause they can't afford it, they can't staff it, they don't have the expertise. And you think about if you're a tech company or a bank, or something like that, of course you can do it, but if you're an international plumbing distributor, you're not going to (chuckles), someone's not going to graduate from Stanford with a cybersecurity degree and go, "Cool, I want to go work for a plumbing distributor in their SOC," right? So they're going to have trouble kind of bringing in the right talent, and as a result, it's difficult to go make a multimillion-dollar investment into a SOC if you're not going to get the quality people to operate it, so they turn to companies like us. >> Got it, so, okay, so you're talking earlier about capabilities that large enterprises require that there might be some gaps, you might lack some features. A couple questions there. One is, when you do some of those, I inferred some of that is integrations. Are those integrations sort of one-off snowflakes or are you finding that you're able to scale those across the large enterprises? That's my first question. >> Yeah, so most of the integrations are pretty straightforward. I think where we run into things that are kind of enterprise-centric, they definitely want open APIs, they want access to our platform, which we don't do today, which we are going to be doing, but we don't do that yet today. They want to do more of a SIEM replacement. So we're really kind of what we call an open XDR platform, so there's things that we would need to build to kind of do raw log ingestion. I mean, we do this today. We have raw log ingestion, we have log storage, we have log searching, but there's like some of the compliance scenarios that they need out of their SIEM. We don't do those today. And so that's kind of holding them back from getting off their SIEM and going fully onto a solution like ours. Then the other one is kind of the level of customization, so the ability to create a whole bunch of custom rules, and that ties back to, "I want to get off my SIEM. I've built all these custom rules in my SIEM, and it's great that you guys do all this automatic AI stuff in the background, but I need these very specific things to be executed on." And so trying to build an interface for them to be able to do that and then also simulate it, again, because, no matter how big they are running their SIEM and their SOC... Like, we talked to one of the largest financial institutions in the world. As far as we were told, they have the largest individual company SOC in the world, and we operate almost 15 times their size. So we always have to be careful because this is a cloud-based native platform, but someone creates some rule that then just craters the performance of the whole platform, so we have to build kind of those guardrails around it. So those are the things primarily that the large enterprises are asking for. Most of those issues are not holding them back from coming. They want to know they're coming, and we're working on all of those. >> Cool, and see, just aside, I was talking to CISO the other day, said, "If it weren't for my compliance and audit group, I would chuck my SIEM." I mean, everybody wants to get rid of their SIEM. >> I've never met anyone who likes their SIEM. >> Do you feel like you've achieved product market fit in the larger enterprise or is that still something that you're sorting out? >> So I think we know, like, we're on a path to do that. We're on a provable path to do that, so I don't think there's any surprises left. I think everything that we know we need to do for that is someone's writing code for it today. It's just a matter of getting it through the system and getting into production. So I feel pretty good about it. I think that's why we are seeing such a high growth rate in our large enterprise business, 'cause we share that feedback with some of those key customers. We have a Customer Advisory Board that we share a lot of this information with. So yeah, I mean, I feel pretty good about what we need to do. We're certainly operate at large enterprise scales, so taking in the amount of the volume of data they're going to have and the types of integrations they need. We're comfortable with that. It's just more or less the interfaces that a large enterprise would want that some of the smaller companies don't ask for. >> Do you have enough tenure in the market to get a sense as to stickiness or even indicators that will lead toward retention? Have you been at it long enough in the enterprise or you still, again, figuring that out? >> Yeah, no, I think we've been at it long enough, and our retention rates are extremely high. If anything, kind of our net retention rates, well over 100% 'cause we have opportunities to upsell into new modules and expanding the coverage of what they have today. I think the areas that if you cornered enterprise that use us and things they would complain about are things I just told you about, right? There's still some things I want to do in my Splunk, and I need an API to pull my data out and put it in my Splunk and stuff like that, and those are the things we want to enable. >> Yeah, so I can't wait till you guys go public because you got Snowflake up here, and you got Veritas down here, and I'm very curious as to where you guys go. When's the IPO? You want to tell me that? (chuckling) >> Unfortunately, it's not up to us right now. You got to get the markets- >> Yeah, I hear you. Right, if the market were better. Well, if the market were better, you think you'd be out? >> Yeah, I mean, we'd certainly be a viable candidate to go. >> Yeah, there you go. I have a question for you because I don't have a SOC. I run a small business with my co-CEO. We're like 30, 40 people W-2s, we got another 50 or so contractors, and I'm always like have one eye, sleep with one eye open 'cause of security. What is your ideal SMB customer? Think S. >> Yeah. >> Would I fit? >> Yeah, I mean you're you're right in the sweet spot. I think where the company started and where we still have a lot of value proposition, which is companies like, like you said it, you sleep with one eye open, but you don't have necessarily the technical acumen to be able to do that security for yourself, and that's where we fit in. We bring kind of this whole security, we call it Security Operations Cloud, to bear, and we have some of the best professionals in the world who can basically be your SOC for less than it would cost you to hire somebody right out of college to do IT stuff. And so the value proposition's there. You're going to get the best of the best, providing you a kind of a security service that you couldn't possibly build on your own, and that way you can go to bed at night and close both eyes. >> So (chuckling) I'm sure something else would keep me up. But so in thinking about that, our Amazon bill keeps growing and growing and growing. What would it, and I presume I can engage with you on a monthly basis, right? As a consumption model, or how's the pricing work? >> Yeah, so there's two models that we have. So typically the kind of the monthly billing type of models would be through one of our MSP partners, where they have monthly billing capabilities. Usually direct with us is more of a longer term deal, could be one, two, or three, or it's up to the customer. And so we have both of those engagement models. Were doing more and more and more through MSPs today because of that model you just described, and they do kind of target the very S in the SMB as well. >> I mean, rough numbers, even ranges. If I wanted to go with the MSP monthly, I mean, what would a small company like mine be looking at a month? >> Honestly, I do not even know the answer to that. >> We're not talking hundreds of thousands of dollars a month? >> No. God, no. God, no. No, no, no. >> I mean, order of magnitude, we're talking thousands, tens of thousands? >> Thousands, on a monthly basis. Yeah. >> Yeah, yeah. Thousands per month. So if I were to budget between 20 and $50,000 a year, I'm definitely within the envelope. Is that fair? I mean, I'm giving a wide range >> That's fair. just to try to make- >> No, that's fair. >> And if I wanted to go direct with you, I would be signing up for a longer term agreement, correct, like I do with Salesforce? >> Yeah, yeah, a year. A year would, I think, be the minimum for that, and, yeah, I think the budget you set aside is kind of right in the sweet spot there. >> Yeah, I'm interested, I'm going to... Have a sales guy call me (chuckles) somehow. >> All right, will do. >> No, I'm serious. I want to start >> I will. >> investigating these things because we sell to very large organizations. I mean, name a tech company. That's our client base, except for Arctic Wolf. We should talk about that. And increasingly they're paranoid about data protection agreements, how you're protecting your data, our data. We write a lot of software and deliver it as part of our services, so it's something that's increasingly important. It's certainly a board level discussion and beyond, and most large organizations and small companies oftentimes don't think about it or try not to. They just put their head in the sand and, "We don't want to be doing that," so. >> Yeah, I will definitely have someone get in touch with you. >> Cool. Let's see. Anything else you can tell me on the product side? Are there things that you're doing that we talked about, the gaps at the high end that you're, some of the features that you're building in, which was super helpful. Anything in the SMB space that you want to share? >> Yeah, I think the biggest thing that we're doing technically now is really trying to drive more and more automation and efficiency through our operations, and that comes through really kind of a generous use of AI. So building models around more efficient detections based upon signal, but also automating the actions of our operators so we can start to learn through the interface. When they do A and B, they always do C. Well, let's just do C for them, stuff like that. Then also building more automation as far as the response back to third-party solutions as well so we can remediate more directly on third-party products without having to get into the consoles or having our customers do it. So that's really just trying to drive efficiency in the system, and that helps provide better security outcomes but also has a big impact on our margins as well. >> I know you got to go, but I want to show you something real quick. I have data. I do a weekly program called "Breaking Analysis," and I have a partner called ETR, Enterprise Technology Research, and they have a platform. I don't know if you can see this. They have a survey platform, and each quarter, they do a survey of about 1,500 IT decision makers. They also have a survey on, they call ETS, Emerging Technology Survey. So it's private companies. And I don't want to go into it too much, but this is a sentiment graph. This is net sentiment. >> Just so you know, all I see is a white- >> Yeah, just a white bar. >> Oh, that's weird. Oh, whiteboard. Oh, here we go. How about that? >> There you go. >> Yeah, so this is a sentiment graph. So this is net sentiment and this is mindshare. And if I go to Arctic Wolf... So it's typical security, right? The 8,000 companies. And when I go here, what impresses me about this is you got a decent mindshare, that's this axis, but you've also got an N in the survey. It's about 1,500 in the survey, It's 479 Arctic Wolf customers responded to this. 57% don't know you. Oh, sorry, they're aware of you, but no plan to evaluate; 19% plan to evaluate, 7% are evaluating; 11%, no plan to utilize even though they've evaluated you; and 1% say they've evaluated you and plan to utilize. It's a small percentage, but actually it's not bad in the random sample of the world about that. And so obviously you want to get that number up, but this is a really impressive position right here that I wanted to just share with you. I do a lot of analysis weekly, and this is a really, it's completely independent survey, and you're sort of separating from the pack, as you can see. So kind of- >> Well, it's good to see that. And I think that just is a further indicator of what I was telling you. We continue to have a strong financial performance. >> Yeah, in a good market. Okay, well, thanks you guys. And hey, if I can get this recording, Hannah, I may even figure out how to write it up. (chuckles) That would be super helpful. >> Yes. We'll get that up. >> And David or Hannah, if you can send me David's contact info so I can get a salesperson in touch with him. (Hannah chuckling) >> Yeah, great. >> Yeah, we'll work on that as well. Thanks so much for both your time. >> Thanks a lot. It was great talking with you. >> Thanks, you guys. Great to meet you. >> Thank you. >> Bye. >> Bye.

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM. Welcome to the cubes coverage of IBM Think 2021. I'm your host lisa martin today. Have a new guest new to the cube moderate Tabla, the director of strategic partnerships for enterprise application services is joining me moderate. It's nice to have you on the program. >>Thank you lisa. Very excited to be here and hello everyone. >>So different this year. Again Virtual like last year we're going to talk about digital transformation and we saw this huge acceleration in 2020. The massive adoption of SAS applications. We want to talk though about IBM managed services for S AP applications. So before we get into that I'd love for you to be able to describe what your role is to our audience. >>Absolutely lisa. So good day everyone. I've been with IBM for over 23 years and my current role, I run the strategic alliances for IBM basically in the E. R. P. Space S. A. P. Being our primary strategic partner, I have a global team of architects and we basically look at market requirements. Talk to a lot of customers, talk to our business partner S. A. P. Obviously um you know, try to help them would come up with a solution. Well the transformation journey to the cloud and hopefully today, you know, we'll elaborate a little bit more on the exact work that we do in this space. So very happy to be here. Thank you. >>Sure. So we're going to dissect the IBM s. A. P. Relationship. I think you even worked at S. A. P. Before your 23 year tenure at IBM. So we'll get to some of that as well. But help us understand customers have so much choice each day. There is more and more interest why should a customer choose IBM as their strategic partner for this digital transformation journey. >>Well really, IBM has been in this essay p business for many, many decades. As you know Um we have many many certified people in S. A. P. close to 40,000 people actually globally. Um And we can help the clients in various aspects of their journey. So you know the typical cloud journey has four different aspects to it. Um You need the advice so you need basically systems integration services to help customers actually define the scope on, you know what they actually want to either upgrade, bring it to current as well as you know what workloads they want to move to the cloud. We can help customers with our Systems integration services called the Global Business Business Services in IBM we can help them with their entire planning, we can help them with the actual move to the cloud. So IBM offers a whole different variety of services for migration or not only to see ASAP workloads. I mean ASAP typically ends up being the heart of the workloads that any of the major customers run but surrounding SCP, there's a lot of other applications so we can help plan that entire journey for advice and then move it as well as in the interim. You know, there's also another step which can be some customers. They need to build net new and you know upgrade their applications to the latest technologies so we can help them with that. And then once the building move is over, obviously customers need help with the actual steady state run state environment and that's where this key service that we have managed services for SCP applications helps them. So our certifications with S. A. P. And the fact that we have consultants that are certified and all these different aspects of the journey can really help your clients. The other part, I would say that IBM is really a hybrid cloud provider. So obviously we have our cloud service, the IBM cloud, but we can offer this service meeting the customer where they need to be. So we are a client centric service, so if the customer has a choice of AWS or Azure, uh we can meet them left. So this is how, you know, we can really help our customers with our expertise. I know the data point to note that, you know, 70 80 of the enterprise customers still have not moved their workloads to the cloud. So this is a space, especially with Covid, as you've seen what's happened, you know, customers now are really, really looking to accelerate the journey because it's become a necessity, It's no longer something that our Ceo and C I O can push to the right, right, this is something they have to act now. So I began with all these various services, you know, specifically good in the S A. P area. Um, and given that we've been managing these production workloads for a lot of these enterprise customers on our cloud services for many, many years, we have the experience, we can truly help them with their journey >>And as you said, that's so critical of these days. One of the things that I think we learned in 2020 is is there was no time like the present, it really became such a massive shift that for business survival, those that weren't digitized definitely were in some hot water. Talk to me. So you talked about the IBM s, a P relationship being longstanding, Can you talk to me about the different aspects of the alliance and how that helps you guys to meet customers where they are? >>Sure. Um so s. a. p. and idea, we've been strategic partners for over 46 years. That's a long time. The partnership obviously has evolved over the years and I'll talk about you know a few of the different aspects where we've been partners um you know, the alliance initially obviously started, you know, IBM is in multiple businesses as you know, we have our one of the largest systems integrators in the world from a global business services point of view as well as one of the largest application planet services providers. So that's uh you know part of the alliance then we have our server groups, the power systems that IBM has. So that's another dimension of the alliance where um you know 5 6000 plus ASAP clients even today are still running um there? S a the applications on the power systems, whether it's on premise or also in some of the cloud deployment models. Historically we also had obviously the Database DB two alliance, but now with the S. A. P. S moved to Hannah um that's kind of a little bit of a mute point. Obviously it still exists, but most of the clients are now obviously being encouraged really to adopt S. A. P. S latest S four hana from the services standpoint. The other facet, you know, is really around the cloud services. So that's really our topic today right. Um in the cloud services area we have alliances with S. A. P very very strong alliances that have existed for you know, almost a decade now. Um as I said we've been managing the production workloads for very very large customers in many different industries, their entire supply chains. HR financial systems are running on IBM either in the old traditional hosting models um or also in our cloud models for the past 10 plus years. Right as IBM has evolved, so we have made sure that we do a whole different types of certifications with S. A. P. To stay current. Um many of these certifications are done either you know every two years, some are done every year. And if anyone checks, you know, the S. A. P. Service marketplace website which is owned by S. A. P. You can see IBM listed in all these different angles as a certified provider. There isn't another provider that can claim this breath in terms of certifications that IBM has done and that's why customers can benefit either from one or two of these services that IBMS provides or obviously a combination is a single vendor if the customer needs. So, you know, we have the sex, we have the credibility, we have decades of, you know, Delivery excellence in these areas, servicing these clients. Lots of the Fortune, 100 customers actually are running. Um there? S a p workloads on the IBM systems, whether in traditional hosting or in a hybrid cloud deployment. Some cases were actually providing services for customers that run their SCP workloads on premise. So we cater to that, you know, sets of clients as well and then of course others that are purely on our cloud. Um IBM cloud as well as hyper scholars. Yeah, so long >>list of certifications, that seems to be one of the biggest differentiators that you talked about me a little bit about how things have evolved over the last 12 to 18 months. in terms of how is IBMS focus changed for hybrid cloud with S. A. P. >>Yeah, so the focus changed if you know, you know, until last year we will call the cloud and cognitive company. Um This year of course the whole company has changed and we're going through a major transformation at the moment. We are the hybrid cloud company now. And that that name change means a lot. It means a lot in the sense that it gives choices to the customer, that's what the whole mission is all about. We want to make sure that customers are consuming IBM services and the IBM wants to meet them where they want to be. So there's you know, flexibility of choices in terms of hybrid, another cloud deployment model. So most customers in the S. A. P. Area, you know, they're looking for either just a pure private cloud deployment or they're looking for public cloud deployment or a combination and some are because, you know, there? S A. P. S. Footprint sizes are so large. Think about the multinational global companies, you know, and then they operate in so many different regions of the world and their data sizes of their databases are so large. Perhaps, you know, the public cloud really isn't a good fit yet. These customers are looking to move some sort of their workloads to the cloud. So that's where this hybrid cloud helps them. Because customers, you know, 90 plus percent of the clients today are really not choosing one hyper Scaler as their deployment option. They're really looking at multiple. So because they're running their workloads not just ASAP, but everything else, you know, SCP always brings along a whole bunch of other applications like tax applications and other interfaces, homegrown applications analytics that the customers are using. So if you want to take advantage of the true hybrid cloud and the benefits of all the various um, deployments and hyper scale is available in that region. Really, the hybrid cloud strategy from IBM is a perfect fit because we give them choices of deployment. We're not saying that you have to deploy an IBM cloud. Um, we're saying you can deploy either on premise VWs as your idea of cloud. Really what makes sense? You know, best sense for the types of war clothes that the customer is looking at. So that's how the strategy for IBM has completely changed to meet the clients, you know, for what they're actually looking for. Talk to me a >>little bit about the go to market so I B M and S A P longstanding decades old relationship, A lot of certifications that you talked about. We're talking about business critical Applications, you mentioned supply chain a minute ago and I can't help but think it how supply chain has been affected in the last year. What is the good market approach with respect to providing consultation services to help customers determine? Should we migrate to what Hyper Scaler and how and when? >>Yeah, so we can help them with that? Um, so hyper hyper scale is obviously, you know, IBM has been listed for example, as the leader in Gartner 2020 and you know, there's lots of other stats that show them that IBM is a leader in application services, in consulting services, application management services as well as managed services. So these are all different, Right? And you can see us being listed as a leader either it's in Gartner or I. D. C. Or Horse or Wave. And for many reasons and you know, IBM actually has one series of pinnacle awards from S. A. P. Over the U. S. How this helps the clients really determined is that, you know, IBM obviously does a lot of studies externally. We have internal as well as external facing views of comparatives of the various hyper scholars, um you know, including Aws, Azure, G. C. P. And so on. So when a customer comes to us for asking for advice, um, and so on, we basically look at our own intellectual properties, all the analysis that has been done. And more importantly, we look at the full scope of services that the customer wants is doing. What sort of a business are there in. We have industry experts, there's E. R. P. Strategy, um, folks within IBM. So, you know, they go after a certain industry and when they, let's say, you know, they've gone after the oil and gas industry, for example, they will look at multiple customers in that particular space. So based on their experiences, we can actually define the right road map for the client to be able to help them to move their work clothes to this hybrid cloud strategy that I just mentioned. Right? So that's how we can help them because we have the expertise in that industry as well. >>And I'm curious moderate in the last year with so much flux and rapidly changing market conditions, Did you >>see any >>one or two industries in particular really leading the charge here and coming to IBM. S. A. P. For help on this transformation journey which has been accelerated by a couple of years. >>Suddenly the retail industry for sure, right. I mean in spite of the crisis, I think the retail industry did pretty well, right? Because people still have to buy stuff. Of course, the whole buying behavior change. No question. Um You and I don't know about two days of, but for me, you know, I was never a major online shopper. Oh yeah. You know, I just about everything. Um previously it used to be select things here and there, but now it's totally changed, right? So that industry certainly has accelerated. No question. We've had a lot of those coming. The other industries that I've seen. The change in the last 12, 18 months is really, for example, you know, the banking industry and so on. Um IBM basically, you know, launched a lot of services in the financial services sector for this reason. Um So those are of course transforming very fast to keep up with the market. Um and I'm sure there's others, right? But these are the two that come to mind. Yeah, >>two that have been most affected and needed to pivot so quickly. In addition to health care. Let me ask you one final question here. Before we wrap. Talk to me about the advantages of using the PMC partner managed cloud s a P license resale model. The advantages of using that and the benefits. >>Sure. Um so we, you know, so far our discussion was really focused around, you know, the various service capabilities that IBM has in terms of our capabilities for helping clients with hyper scholars and hybrid cloud. We also need to spend a little bit of time talking about the operations model. Right? So when they're running their production workloads on IBM PMC is yet another dimension. So what PMC partner managed cloud is really some very limited partnerships that s A P does And the IBM is the lead on that one in this base. What ASAP allows is the partner, which in this case is IBM to resell the ASAP software license to a customer. So IBM has the rights globally to resell the license and why is that beneficial to the client? Because now, um, IBM can actually turn around the S. A. P license and have the customer pay us in a SAS model. So it basically is now an apex model where the customer is basically paying, you know, a monthly fee as an example, so there's no upfront cost to the client and they basically pay IBM and IBM PS ASAP. So IBM is kind of holding the risk if you will on behalf of the customer, it gives customers more choices, more flexibilities, better pricing approach. So if the customer wants as an example to buy everything the full package, including systems implementation services, deployment models with choices on a cloud, whether it's IBM cloud or others as well as the license itself. IBM has this end to end capability today. We've been selling it to several clients for a few years in several geography is right. So that's the advantage behind it. >>Excellent. Thanks for breaking that down moderate and joining me today talking about what's new with I B M and S A P, the opportunities for customers to accelerate their digital transformation. We appreciate you stopping by. >>Thank you very much, lisa truly enjoyed it. Thank you. >>Good. Me too. For moderate Tabla. I'm lisa martin. You're watching the cubes coverage of IBM think 2021. >>Mm.

>>Yeah, >>yeah. Hello and welcome to this track to creating engaging analytics experiences for all. I'm Hannah Sinden Thought spots Omiya director of marketing on. I'm delighted to have you here today. A boy Have we got to show for you now? I might be a little bit biased as the host of this track, but in my humble opinion, you've come to a great place to start because this track is all about everything. Thought spot. We'll be talking about embedded search in a I thought spot one spot I. Q. We've got great speakers from both thoughts about andare customers as well as some cool product demos. But it's not all product talk. We'll be looking at how to leverage the tech to give your users a great experience. So first up is our thoughts about one deep dive. This session will be showing you how we've built on our already superb search experience to make it even easier for users across your company to get insight. We've got some great speakers who are going to be telling you about the cool stuff they've been working on to make it really fantastic and easy for non technical people to get the answers they need. So I'm really delighted to introduce Bob Baxley s VP of design and experience That thought spot on Vishal Kyocera Thought spots director of product management. So without further ado, I'll hand it over to Bob. Thanks, >>Hannah. It's great to be here with everybody today and really excited to be able to present to you thought spot one. We've been working on this for months and months and are super excited to share it before we get to the demo with Shawl, though, I just want to set things up a little bit to help people understand how we think about design here. A thought spot. The first thing is that we really try to think in terms of thought. Spot is a consumer grade product, terms what we wanted. Consumer grade you x for an analytics. And that means that for reference points rather than looking at other enterprise software companies, we tend to look at well known consumer brands like Google, YouTube and WhatsApp. We firmly believe that people are people, and it doesn't matter if they're using software for their own usage or thought are they're using software at work We wanted to have a great experience. The second piece that we were considering with thoughts about one is really what we call the desegregation of bundles. So instead of having all of your insights wraps strictly into dashboards, we want to allow users to get directly to individual answers. This is similar to what we saw in music. Were instead of you having to buy the entire album, of course, you could just buy individual songs. You see this in iTunes, Spotify and others course. Another key idea was really getting rid of gate keepers and curators and kind of changing people from owning the information, helping enable users to gather together the most important and interesting insights So you can follow curator rather than feeling like you're limited in the types of information you can get. And finally, we wanted to make search the primary way, for people are thinking about thought spot. As you'll see, we've extended search from beyond simply searching for your data toe, also searching to be able to find pin boards and answers that have been created by other people. So with that, I'll turn it over to my good friend Rachel Thio introduce more of thought, spot one and to show you a demo of the product. >>Thank you, Bob. It's a pleasure to be here to Hello, everyone. My name is Michelle and Andy, product management for Search. And I'm really, really excited to be here talking about thoughts about one our Consumer analytics experience in the Cloud. Now, for my part of the talk, we're gonna first to a high level overview of thoughts about one. Then we're going to dive into a demo, and then we're gonna close with just a few thoughts about what's coming next. So, without any today, let's get started now at thought spot. Our mission is to empower every user regardless of their expertise, to easily engage with data on make better data driven decisions. We want every user, the nurse, the neighborhood barista, the teacher, the sales person, everyone to be able to do their jobs better by using data now with thoughts about one. We've made it even more intuitive for all these business users to easily connect with the insights that are most relevant for them, and we've made it even easier for analysts to do their jobs more effectively and more efficiently. So what does thoughts about one have? There's a lot off cool new features, but they all fall into three main categories. The first main category is enhanced search capabilities. The second is a brand new homepage that's built entirely for you, and the third is powerful tools for the analysts that make them completely self service and boost their productivity. So let's see how these work Thought Spot is the pioneer for search driven analytics. We invented search so that business users can ask questions of data and create new insights. But over the years we realized that there was one key piece off functionality that was missing from our search, and that was the ability to discover insights and content that had already been created. So to clarify, our search did allow users to create new content, but we until now did not have the ability to search existing content. Now, why does that matter? Let's take an example. I am a product manager and I am always in thought spot, asking questions to better understand how are users are using the product so we can improve it now. Like me, A lot of my colleagues are doing the same thing. Ah, lot of questions that I asked have already been answered either completely are almost completely by many of my colleagues, but until now there's been no easy way for me to benefit from their work. And so I end up recreating insights that already exists, leading to redundant work that is not good for the productivity off the organization. In addition, even though our search technology is really intuitive, it does require a little bit of familiarity with the underlying data. You do need to know what metric you care about and what grouping you care about so that you can articulate your questions and create new insights. Now, if I consider in New employees product manager who joins Hotspot today and wants to ask questions, then the first time they use thought spot, they may not have that data familiarity. So we went back to the drawing board and asked ourselves, Well, how can we augment our search so that we get rid off or reduced the redundant work that I described? And in addition, empower users, even new users with very little expertise, maybe with no data familiarity, to succeed in getting answers to their questions the first time they used Hot Spot, and we're really proud and excited to announce search answers. Search answers allows users to search across existing content to get answers to their questions, and its a great compliment to search data, which allows them to search the underlying data directly to create new content. Now, with search answers were shipping in number of cool features like Answer Explainer, Personalized search Results, Answer Explorer, etcetera that make it really intuitive and powerful. And we'll see how all of these work in action in the demo. Our brand new homepage makes it easier than ever for all these business users to connect with the insights that are most relevant to them. These insights could be insights that these users already know about and want to track regularly. For example, as you can see, the monitor section at the top center of the screen thes air, the KP eyes that I may care most about, and I may want to look at them every day, and I can see them every day right here on my home page. By the way, there's a monitoring these metrics in the bankrupt these insights that I want to connect with could also be insights that I want to know more about the search experience that I just spoke about ISS seamlessly integrated into the home page. So right here from the home page, I can fire my searchers and ask whatever questions I want. Finally, and most interestingly, the homepage also allows me to connect with insights that I should know about, even if I didn't explicitly ask for them. So what's an example? If you look at the panel on the right, I can discover insights that are trending in my organization. If I look at the panel on the left, I can discover insights based on my social graph based on the people that I'm following. Now you might wonder, How do we create this personalized home page? Well, our brand new, personalized on boarding experience makes it a piece of cake as a new business user. The very first time I log into thought spot, I pay three people I want to follow and three metrics that I want to follow, and I picked these from a pool of suggestions that Ai has generated. And just like that, the new home page gets created. And let's not forget about analysts. We have a personalized on boarding experience specifically for analysts that's optimized for their needs. Now, speaking of analysts, I do want to talk about the tools that I spoke off earlier that made the analysts completely self service and greatly boost their productivity's. We want analysts to go from zero to search in less than 30 minutes, and with our with our new augmented data modeling features and thoughts about one, they can do just that. They get a guided experience where they can connect, model and visualize their data. With just a few clicks, our AI engine takes care off a number of tasks, including figuring out joints and, you know, cleaning up column names. In fact, our AI engine also helps them create a number of answers to get started quickly so that these analysts can spend their time and energy on what matters most answering the most complicated and challenging and impactful questions for the business. So I spoke about a number of different capabilities off thoughts about one, but let's not forget that they are all packaged in a delightful user experience designed by Bob and his team, and it powers really, really intuitive and powerful user flows, from personalized on boarding to searching to discover insights that already exist on that are ranked based on personalized algorithms to making refinements to these insights with a assistance to searching, to create brand new insights from scratch. And finally sharing all the insights that you find interesting with your colleagues so that it drives conversations, decisions and, most importantly, actions so that your business can improve. With that said, let's drive right into the demo for this demo. We're going to use sales data set for a company that runs a chain off retail stores selling apparel. Our user is a business user. Her name is Charlotte. She's a merchandiser, She's new to this company, and she is going to be leading the genes broader category. She's really excited about job. She wants to use data to make better decisions, so she comes to thought spot, and this is what she sees. There are three main sections on the home page that she comes to. The central section allows you to browse through items that she has access to and filter them in various ways. Based for example, on author or on tags or based on what she has favorited. The second section is this panel on the right hand side, which allows her to discover insights that are trending within her company. This is based on what other people within her company are viewing and also personalized to her. Finally, there's this search box that seamlessly integrated into the home page. Now Charlotte is really curious to learn how the business is doing. She wants to learn more about sales for the business, so she goes to the search box and searches for sales, and you can see that she's taken to a page with search results. Charlotte start scanning the search results, and she sees the first result is very relevant. It shows her what the quarterly results were for the last year, but the result that really catches her attention is regional sales. She'd love to better understand how sales are broken down by regions. Now she's interested in the search result, but she doesn't yet want to commit to clicking on it and going to that result. She wants to learn more about this result before she does that, and she could do that very easily simply by clicking anywhere on the search result card. Doing that reveals our answer. Explain our technology and you can see this information panel on the right side. It shows more details about the search results that she selected, and it also gives her an easy to understand explanation off the data that it contains. You can see that it tells her that the metrics sales it's grouped by region and splitter on last year. She can also click on this preview button to see a preview off the chart that she would see if she went to that result. It shows her that region is going to be on the X axis and sales on the Y axis. All of this seems interesting to her, and she wants to learn more. So she clicks on this result, and she's brought to this chart now. This contains the most up to date data, and she can interact with this data. Now, as she's looking at this data, she learns that Midwest is the region with the highest sales, and it has a little over $23 million in sales, and South is the region with the lowest sales, and it has about $4.24 million in sales. Now, as Charlotte is looking at this chart, she's reminded off a conversation she had with Suresh, another new hire at the company who she met at orientation just that morning. Suresh is responsible for leading a few different product categories for the Western region off the business, and she thinks that he would find this chart really useful Now she can share this chart with Suresh really easily from right here by clicking the share button. As Charlotte continues to look at this chart and understand the data, she thinks, uh, that would be great for her to understand. How do these sales numbers across regions look for just the genes product category, since that's the product category that she is going to be leading? And she can easily narrow this data to just the genes category by using her answer Explorer technology. This panel on the right hand side allows her to make the necessary refinements. Now she can do that simply by typing in the search box, or she can pick from one off the AI generated suggestions that are personalized for her now. In this case, the AI has already suggested genes as a prototype for her. So with just a single click, she can narrow the data to show sales data for just jeans broken down by region. And she can see that Midwest is still the region with the highest sales for jeans, with $1.35 million in sales. Now let's spend a minute thinking about what we just saw. This is the first time that Charlotte is using Thought spot. She does not know anything about the data sources. She doesn't know anything about measures or attributes. She doesn't know the names of the columns. And yet she could get to insights that are relevant for her really easily using a search interface that's very much like Google. And as she started interacting with search results, she started building a slightly better understanding off the underlying data. When she found an insight that she thought would be useful to a colleague offers, it was really seamless for her to share it with that colleague from where she Waas. Also, even though she's searching over content that has already been created by her colleagues in search answers. She was in no way restricted to exactly that data as we just saw. She could refine the data in an insight that she found by narrowing it. And there's other things you can do so she could interact with the data for the inside that she finds using search answers. Let's take a slightly more complex question that Charlotte may have. Let's assume she wanted to learn about sales broken down by, um, by category so that she can compare her vertical, which is jeans toe other verticals within the company. Again, she can see that the very first result that she gets is very relevant. It shows her search Sorry, sales by category for last year. But what really catches her attention about this result is the name of the author. She's thrilled to note that John, who is the author of this result, was also an instructor for one off for orientation sessions and clearly someone who has a lot of insight into the sales data at this company. Now she would love to see mawr results by John, and to do that, all she has to do is to click on his name now all of the search results are only those that have been authored by John. In fact, this whole panel at the top of the results allow her to filter her search results or sort them in different ways. By clicking on these authors filter, she can discover other authors who are reputed for the topic that she's searching for. She can also filter by tags, and she can sort these results in different ways. This whole experience off doing a search and then filtering search results easily is similar to how we use e commerce search engines in the consumer world. For example, Amazon, where you may search for a product and then filter by price range or filter by brand. For example, Let's also spend a minute talking about how do we determine relevance for these results and how they're ranked. Um, when considering relevance for these results, we consider three main categories of things. We want to first make sure that the result is in fact relevant to the question that the user is asking, and for that we look at various fields within the result. We look at the title, the author, the description, but also the technical query underpinning that result. We also want to make sure that the results are trustworthy, because we want users to be able to make business decisions based on the results that they find. And for that we look at a number of signals as well. For example, how popular that result is is one of those signals. And finally, we want to make sure the results are relevant to the users themselves. So we look at signals to personalize the result for that user. So those are all the different categories of signals that we used to determine overall ranking for a search result. You may be wondering what happens if if Charlotte asks a question for which nobody has created any answer, so no answers exist. Let's say she wants to know what the total sales of genes for last year and no one's created that well. It's really easy for her to switch from searching for answers, which is searching for content that has already been created to searching the data directly so she can create a new insight from scratch. Let's see how that works. She could just click here, and now she's in the search data in her face and for the question that I just talked about. She can just type genes sales last year. And just like that, she could get an answer to her question. The total sales for jeans last year were almost $4.6 million. As you can see, the two modes off search searching for answers and searching, the data are complementary, and it's really easy to switch from one to the other. Now we understand that some business users may not be motivated to create their own insights from scratch. Or sometimes some of these business users may have questions that are too complicated, and so they may struggle to create their own inside from scratch. Now what happens usually in these circumstances is that these users will open a ticket, which would go to the analyst team. The analyst team is usually overrun with these tickets and have trouble prioritizing them. And so we started thinking, How can we make that entire feedback loop really efficient so that analysts can have a massive impact with as little work as possible? Let me show you what we came up with. Search answers comes with this system generated dashboard that analysts can see to see analytics on the queries that business users are asking in search answers so it contains high level K P. I is like, You know how many searches there are and how many users there are. It also contains one of the most popular queries that users are asking. But most importantly, it contains information about what are popular queries where users are failing. So the number on the top right tells you that about 10% off queries in this case ended with no results. So the user clearly failed because there were no results on the table. Right below it shows you here are the top search queries for original results exist. So, for example, the highlighted row there says jean sales with the number three, which tells the analysts that last week there were three searches for the query jean sales and the resulted in no results on search answers. Now, when an analyst sees a report like this, they can use it to prioritize what kind of content they could be creating or optimizing. Now, in addition to giving them inside into queries which led to no results or zero results. This dashboard also contains reports on creatives that lead to poor results because the user did get some results but didn't click on anything, meaning that they didn't get the answer that they were looking for. Taking all these insights, analysts can better prioritize and either create or optimize their content to have maximum impact for their business users with the least amount of for. So that was the demo. As you can see with search answers, we've created a very consumer search interface that any business user can use to get the answers to their questions by leveraging data or answers that have already been created in the system by other users in their organization. In addition, we're creating tools that allow analysts toe create or optimized content that can have the highest impact for these business users. All right, so that was the demo or thoughts about one and hope you guys liked it. We're really excited about it. Now Let me just spend a minute talking about what's coming next. As I've mentioned before, we want to connect every business user with the insights that are most relevant for them, and for that we will continue to invest in Advanced AI and personalization, and some of the ways you will see it is improved relevance in ranking in recommendations in how we understand your questions across the product within search within the home page everywhere. The second team that will continue to invest in is powerful analyst tools. We talked about tools and, I assure you, tools that make the analysts more self service. We are committed to improving the analyst experience so that they can make the most off their time. An example of a tool that we're really excited about is one that allows them to bridge the vocabulary difference that this even business user asks questions. A user asked a question like revenue, but the column name for the metric in the data set its sales. Now analysts can get insights into what are the words that users air using in their questions that aren't matching anything in the data set and easily create synonyms so that that vocabulary difference gets breached. But that's just one example of how we're thinking about empowering the analysts so that with minimal work, they can amplify their impact and help their business users succeed. So there's a lot coming, and we're really excited about how we're planning to evolve thoughts about one. With all that said, Um, there's just, well, one more thing that my friend Bob wants to talk to you guys about. So back to you, Bob. >>Thanks, Michelle. It's such a great demo and so fun to see all the new work that's going on with thought. Spot one. All the happenings for the new features coming out that will be under the hood. But of course, on the design side, we're going to continue to evolve the front end as well, and this is what we're hoping to move towards. So here you'll see a new log in screen and then the new homepage. So compared to the material that you saw just a few minutes ago, you'll notice this look is much lighter. A little bit nicer use of color up in the top bar with search the features over here to allow you to switch between searching against answers at versus creating new answers, the settings and user profile controls down here and then on the search results page itself also lighter look and feel again. Mork color up in the search bar up the top. A little bit nicer treatments here. We'll continue to evolve the look and feel the product in coming months and quarters and look forward to continue to constantly improving thoughts about one Hannah back to you. >>Thanks, Bob, and thank you both for showing us the next generation of thought spot. I'd love to go a bit deeper on some of the points you touched on there. I've got a couple of questions here. Bob, how do you think about designing for consumer experience versus designing for enterprise solutions? >>Yes, I mentioned Hannah. We don't >>really try to distinguish so much between enterprise users and consumer users. It's really kind of two different context of use. But we still always think that users want some product and feature and experience that's easy to use and makes sense to them. So instead of trying to think about those is two completely different design processes I think about it may be the way Frank Lloyd Wright would approached architecture. >>Er I >>mean, in his career, he fluidly moved between residential architecture like falling water and the Robie House. But he also designed marquis buildings like the Johnson wax building. In each case, he simply looked at the requirements, thought about what was necessary for those users and designed accordingly. And that's really what we do. A thought spot. We spend time talking to customers. We spend time talking to users, and we spent a lot of time thinking through the problem and trying to solve it holistically. And it's simply a possible >>thanks, Bob. That's a beautiful analogy on one last question for you. Bischel. How frequently will you be adding features to this new experience, >>But I'm glad you asked that, Hannah, because this is something that we are really really excited about with thoughts about one being in the cloud. We want to go really, really fast. So we expect to eventually get to releasing new innovations every day. We expect that in the near future, we'll get to, you know, every month and every week, and we hope to get to everyday eventually fingers crossed on housing. That can happen. Great. Thanks, >>Michelle. And thank you, Bob. I'm so glad you could all join us this morning to hear more about thoughts about one. Stay close and get ready for the next session. which will be beginning in a few minutes. In it will be introduced to thoughts for >>everywhere are >>embedded analytics product on. We'll be hearing directly from our customers at Hayes about how they're using embedded analytics to help healthcare providers across billing compliance on revenue integrity functions. To make more informed decisions on make effective actions to avoid risk and maximize revenue. See you there.

>>Hi and thanks for joining us today. I'm Arwa Qadoura, vice president of Goto Market for HP Green Lake. In this session, we're going to explore a few of the ways we're bringing the cloud to your data center and co locations, especially for your most demanding workloads. We'll show a few examples of how we do this and how we can help you with HP. Green Lake with HP Green Lake were leading the market for on premises and hybrid cloud. With a decade of experience and over 1000 customers, we've been able to continue enriching our portfolio of services, leveraging the vast input from our customers. And what we're hearing now is they want us to take on the apse and data that are most critical to run their business on our customers. Love the cloud experience and wanted available everywhere, including their data center and Coehlo H. P E. Green Lake is the cloud that comes to you. We deliver a cloud experience for your >>infrastructure and workloads in your data center or co location and at the edge. HP Greenlee Cloud Services offer consumption based economics and scalability for a wide range of platforms. All managed for you by HP or by a rich ecosystem of partners. In June, we brought the Self service point and click experience of the cloud to our new services for containers, virtual machines and ml apps, and dramatically sped up the delivery of our infrastructure services with standardized building blocks T shirt sized that you can get in ASL. It'll us 14 days and a few weeks ago we added V. D. I as a service to meet the strong demand to help your employees around the globe work securely wherever they may be. Today we will look at four examples of how we provide the cloud experience for the workloads that are most critical to run your business, and we'll give a few industry examples. First, we'll talk about helping financial institutions manage risk and compliance. We'll talk about improving health care with a secure, flexible electronic health records platform, optimizing production and delivery for manufacturing with S A P Hana and answering your biggest questions with high performance computing. When we talk about thes demanding workloads, whether we're talking about inventory management, payment processing, medical imaging or any additional ones you see here, two things typically hold true. First, they're very difficult to move to the public cloud due to the challenges around Leighton See and Performance data Gravity I P. And Privacy Protection and the data entanglement with many other APS. And secondly, they require app specific expertise to implement and integrate continual performance optimization, strong resiliency, security and compliance management. And container is a shin to achieve mobility. These air tough to meet but essential toe have. If you're betting your business on these workloads, we've helped our customers meet these challenges and requirements in the data center. Let's start our discussion about these workloads with managing risk and compliance. Risk and compliance management require analyzing huge amounts of data streaming in real time through the organization, and Splunk is widely used for this as the scales. We have found that often infrastructure is the bottleneck and organizations develop blind spots. Due to this, this means they could only see some of the data. Scaling and making changes is also a slow process with such a complex set of infrastructure, and I T resources often don't have the skills to manage new platforms such as container based implementations. We've looked at the situation and built a differentiated architecture er to solve this challenge. The solution is container based, using the HP as moral container platform. It's an infrastructure that is tuned for Splunk and resulted in a big reduction in the total servers needed. It's delivered as a service through HP Green Lake on premises fully managed to make adoption fast and to cover the skill gaps, I t may have the outcomes. We tested our approach and found the dramatic improvements you see here. Infrastructure efficiency improved dramatically, with 17 times increase in throughput and 12 Splunk indexers per host, up from one. Compliance and insights into risks improved from removing the blind spots with a 10 times reduction and infrastructure needed to ingest up to 8.7 terabytes per host per day. And customers have a greatly simplified I T operating model by moving to HP Green Lake fully managed so that HP takes care of the container and infrastructure management. Next, let's talk about improving health >>care with a secure, flexible e HR platform. The global pandemic is putting an extraordinary burden on an industry whose budgets and resources are already stretched to the limits and H P can help health systems in medical research institutions around the globe recognize the value of HP Green Lake for our infrastructure as a service needs scalable storage for high resolution medical imaging, high performance compute for medical research and v. D. I. For the digital workplace. Today we are pleased to introduce the platform for epics E H R System. This is a full platform. As a service offering for Elektronik Health Records, the service supports the epic software stack with validated HP infrastructure and epic certified expertise to run the full environment for you. This enables health care institutions toe offload the complexities of moving to and operating a modern epic platform, reducing cost risk and time with a fully managed paper use cloud service in their own data center or cola facility. Now our customers could focus on delivering life affecting healthcare outcomes and not on the nuances of daily technical operations and upgrades. So how is HP qualified? Think back to the requirements we talked about for expertise. We have a 25 year partnership with EPIC, and over 65% of epic customers use RHP infrastructure, including storage servers, software and networking. We know epic and are trusted by epic customers. We have a dedicated program management office with focused epic resources to help health care systems make the most of their epic platform improving their quality of care, financial performance, work, low efficiency and, most importantly, their patient outcomes. The next workload I'd like to cover is S a P Hana s A P Hannah runs many if not most manufacturing organizations, including our very own. Here in h P s A P finds that 70% of customers are looking to remain on premises with S A P Hana as they migrate toe s four For the reasons we discussed earlier performance, resiliency, security, I protection and control. And we're proud to be one of Aesop's most critical technology partners, running approximately 40% of the on Prem s a p customer base. Thes customers trust HP infrastructure to run their critical s a p environment and we're excited to extend the value into a fully managed on Prem Cloud service. Today we bring the cloud benefits of HP Green Lake toe s a P Hannah customers on premises in two ways. Standard hp Green Lake uses S a P certified technology from HP with the scalable paper use model with H P's outstanding support and management services ready to meet the demanding requirements of S A P. Hana. And now we are working with S a P for the S A P Hana Enterprise Cloud Customer Edition which is powered by HP Green Lake and fully managed by S A P for you, which is the sap cloud in your data center. HPD point next services are essential to our customers. One of the reasons that customers choose HP for workloads such as SAP is our expertise from strategy all the way to operation with advisory and professional services specific to your application. We help you succeed. HP understands migration toe s A. P s four hana and as the leading technology vendor of S a P Hannah Infrastructure and a large s a p Hannah customer ourself, we have the expertise within our advisory and professional services. To ensure your success as you move to s four, HP has delivered over 1500 s, a p Hana consulting projects and HP point Next services has the expertise globally to accelerate time to value and mitigate your risk. And lastly, HP offers a center of excellence Experience for S a P. Hannah providing specialized support from our experts Toe optimize operations for S a p environments The last and maybe the most demanding workload that will cover today is HPC high performance computing. Today we are announcing H p e Green Lake for HPC. This is an exciting time as we bring our cloud services to HPC wherever you need it. As the leader in HPC, we have significant i p To give HPC customers. We offer the speed and scalability that you need with components such as high speed interconnect, high density compute platforms and software to manage HPC operations and performance. And unlike other technology companies, thes are all from HP, fully integrated, fully supported and can be fully managed by HP. And we've built an ecosystem of I S V applications that we closely collaborate with to make HPC run seamlessly high. Performance computing can get complex with HP. Green Lake for HPC will simplify the approach without taking away any of the power. Pick the starting point that fits your use case small, medium or large, and get started. These building blocks are HPC optimized, meaning you could bring the technology that we use to predict weather or decode the human genome to your everyday APS. No capital up front, pay for what you use and the implementation is managed for you. With our building block approach, we can eliminate the long design and implementation phase, which could take months or even a year over time as your clusters grow, modernize and change H p e Green Lake Capacity management helps you always have capacity ready ahead of your needs. What is the experience with H. P Green Lake for HPC, you order, we deliver in as little as 14 days. We install your systems and you can quickly deploy your HPC APS. With the new point and click service experience, researchers and analysts can get access to their HPC cluster resources from the self service portal without putting. I t in the middle of every request we manage the clusters for you. Take care of upgrades, performance and growth, and you pay based on what you use. Simplifying HPC economics and operations. This is how we bring a cloud to your most demanding workloads. So we've covered a lot, and the big question is, so what? How do you benefit analysts have found that with HP Green Lake, you save 30 to 40% on total cost of ownership by eliminating over provisioning, which on its own is huge. But the additional benefits are equally important to our customers. You can speed deployments of projects by 75% cut your risk with 85% less unplanned downtime and improve ICTY productivity by 40% due to the services, including that greatly simplify I t operations. What's next? If you want to learn more about how we bring cloud services for your most demanding workloads, whether they're for risk management, E H. R s, a, p or HPC, or for other workloads you depend on us for Please engage your HP account team or your HP partner. If you're already are a customer for HP Green Lake, thank you. And we're ready to globally help you with your next project. And, of course, please visit us at p e dot com. Backslash Green Lake Thanks for joining me today.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network. Hello, everyone. This is Dave Balanta. And welcome to the cubes Virtual coverage of AWS reinvent 2020 with a special focus on the A p N partner experience. I'm excited to have two great guests on the program. Fernando Castillo is the head s a p on AWS Partner Network and s A P Alliance and AWS and Stephen Jones is the general manager s a p E c to enterprise that aws Gentlemen, welcome to the Cube. Great to see you. >>I'm here. >>So guys ASAP on AWS. It's a core workload for customers. I call it the poster child for mission Critical workloads and applications. Now a lot has happened since we last talked to you guys. So So tell us it. Maybe start with Stephen. What's going on with Sapna Ws? Give us the update. >>I appreciate the question Day. Look, a lot of customers continue to migrate. These mission critical workloads State of us on a good example is the U. S. Navy right? Who moved their entire recipe landscape European workload AWS. This is a very large system of support. Over 72,000 users across 66 different navy commands. They estimate that 70 billion worth of parts and goods actually transact through the system every year. Just just massive. Right? And this this type of adoptions continued to accelerate a very rapid clip. And today, over 5000 customers now are running SFP workloads. I need to be us on there really trusting us, uh, to to manage and run these workloads. And another interesting stat here is that more than half of these customers are actually running asap, Hana, which is a safe He's flagship in memory database. >>Right, Fernando, can you add to that? >>Sure. So definitely about, you know, the customs are also SCP themselves continue to lose a dollar less to run their own offerings. Right? So think about conquer SCP platform. SCP analytics were when new offers like Hannah Cloud. In addition to that, we continue to see the P and L despondent network to grow at an accelerated pace. Today we have over 60 SNP company partners all over the world helping SFP customers s O that customers are my green. There s appeal asking CW's. They only look for reduced costs, improved performance but also toe again access to new capabilities. So innovate around their core business systems and transform their businesses. >>So for now, I wonder if I could stay with you for a minute. I mean, the numbers that Steve was putting out there, it's just massive scale. So you obviously have a lot of data. So I'm wondering when you talk to these customers, Are you discerning any common patterns that are emerging? What are some of the things that you're hearing or seeing when you analyze the data? >>Sure. So just to give a couple example right. Our biggest customers are doing complete ASAP. Transformations on Toe s four Hana. Their chance they're going to these new S a p r p code nine All customers have immediate needs, and they're taking their existing assets to AWS, so looking to reduce costs and improve performance, but also to sell them apart for innovation. This innovation is something that operation or something that they can wait. They need it right now. It's they This time to innovate is now right on some of these customers saying that while s and P has nice apart. So that is a multi year process on most organizations and have a look from waiting for this just before they start innovating. So instead of that, they focus on bringing what they have on start innovating right away on Steve has some great stories around here, so maybe Steve can share with that. Goes with that? >>Yeah, that'd be great, Steve. >>Yeah. Look, I think a good example here on and Fernando touched it, touched on it. Well, right. So customers coming from all kind of different places in their journey aws as it relates to this this critical workload and some are looking to really reap the benefits of the investments they made over the last couple decades sometimes. And Vista is a really good example Here, um there a subsidiary of Cook Industries, they migrated and moved their existing S a P r P solution called E c C. To AWS. They estimate that this migration alone from an infrastructure cost savings perspective, has netted them about two million per year. Additionally, you know, they started to bring some of the other issues they were trying to solve from a business perspective, together now that they were on the on the on the business on the AWS platform. And one thing that recognizes they had different data silos, that they had been operating in an on premises world. Right? So massive factories solution and bringing all of that data together on a single platform on AWS and enriching that with the SCP data has allowed them to actually improve their forecasting supply chain processes across multiple data sources and the estimate that that is saving them additional millions per year. So again, customers are not necessarily waiting to innovate. Um, but actually moving forward now. >>All right, so I gotta ask, you don't hate me for asking this question, but but everybody talks about how great they are. Supporting s a P is It's one of the top, of course, because s a p, you know, huge player in the in the application space. So I want you guys to address how aws specifically compares Thio some of your competitors that are, you know, the hyper scaler specifically as it relates to supporting S a P workloads. What's the rial differential value that you guys bring? Maybe Steve, you could start >>Sure, you're probably getting to know us a little bit. Way don't focus a lot on competition, Aziz mentioned week We continue to see customers adopt AWS for S a p a really rapid clip. And that alone actually brings a lot of feedback back into how we consider our own service offerings as it relates to this particular workload on that, that's it. That's important signal right for what we're building. But customers do tell us the security performance availability matters, especially for this workload, which, you know, to be honest, is the backbone of many, many organizations. Right? And we understand why. And there was a study that was done recently about a. D. C. Where they found that even a single hour of unplanned downtime as a released this particular workload could cost millions. And so it's it's super important. And if you look at, um, you know, publicly available data from an average perspective, um, it has considerably less downtime than the other hyper scale is out there way. Take the performance and availability of oh, our entire global footprint and in this workload in particular, super important. >>Well, you know, that's a great point, Steve. I mean, if you got critical mission critical applications like ASAP supporting the business, that's driving revenue. It's driving productivity. The higher the value of the application, the greater the impact when it's down, I wonder, Fernando, you know, Steve said, You guys don't focus on the competition. Well, is an analyst. You know, I always focused on the competition, So I wonder if you're gonna add anything to that. >>Sure. So again, as you can imagine, multiple analyst called Space right. And, uh, everybody shares information. And analysts have agreed that Italy's clean infrastructure services, including the three quite a for CP across the globe. So we feel very humble and honor about this recognition on this encourages to continue to improve ourselves to give you a couple examples for a 10 year in a row. Italy's US evaluated as a leader in the century Gardner Magic Quadrant, right for cloud infrastructure from services. And, as you know, the measure to access right they measure very execute on complete, insufficient were the highest, both of them. Another third party, just not keep with one is icy, right? You know, technology research dreamers, you already you might know advice for famous Well, the reason they publisher s a p on infrastructure service provider lands reports long name which, basically, the analyzers providers were best suited to host s a. P s four hana workloads on more broadly s a p Hannah landscapes, you know, very large scape ASAP 100 landscapes. So they recognize it, at least for the third year in a row. And conservative right, the best class enterprise. Great infrastructure towards security performances, Steve mentioned, but also making the panic community secure. Differentiation. Andi, they posted. They mentioned it all us as a little position in quadrant for the U. S. U K France, Germany, the Nordics in Brazil. So again, really honor and humble on discontinued in court just to continue to improve. >>You know, Steve, I just wrote a piece on Cloud 2030 trying to project what the next 10 years is gonna look like in one of the I listed a lot of things, but one of the things I talked about was some of the technical factors like alternative processors, specialized networks, and you guys have have have really, always done a good job of sort of looking at purpose built, you know, stuff that that can run workloads faster. How relevant is that in the the S A P community? >>Oh, that's a great question, David. It's It's absolutely relevant. You take a look at what? What we've done over the years with nitro and how we've actually brought the ability for customers to run on environmental infrastructure but still have that integrated, uh, native cloud experience. Uh, that is absolutely applicable to Unless if you workload and we're actually able toe with that technology, bring the capability to customers to run thes mission critical workloads on instances with up to 24 terabytes of brand, albeit bare metal, but fully integrated into the AWS network fabric, >>right? I mean, a lot of people, you know, need that bare metal raw performance on, and that makes sense that you've been, you know, prioritize such an important class of workload. I'm not surprised that that I mean, the numbers that you threw out a pretty impressive eso. It's clear you're leading the charge here. Maybe you could share a little glimpse of what's coming in the future. Show us a little leg, Steve. >>Yeah, well, look, uh, we know that infrastructure is super important. Thio. Our customers and in particular the customers are running these mission critical workloads. But there's a lot of heavy lifting, uh, that that we also want to simplify. And so you've seen some indications of what we've done here over the years, uh, ice G that Fernando mentioned actually called out. AWS is differentiating here, right? So for for many years, we've actually been leading in releasing tools for customers to actually orchestrate and automate the deployment of these types of worthless so ASAP in particular. I mean, if you think about it a customer who is coming to a to a hyper scale platforms like AWS and having to learn what that means, Plus understand all the best practices from S, A, P and AWS to make that thing really shine from a performance and availability perspective, that's a heavy asked. Right? So we put a lot of work from a tooling perspective into into automating this and making this super simple not just for customers, but also partners. >>Anything you wanna chime in on that particular the partner side, Fernando. >>Sure. So this is super important for public community, right? As you can imagine, the tooling that we're bringing together toe. The market is helping the Spanish to move quicker, right? So they don't have to reinvent. They will all the time. They will just take this and move and take it and move forward. Give an example. One of our parents in New York, three hosts. Thanks for lunch. We start with Steve just reference right. They want to create work clothes in an automated way. Speeding up the delivery time. 75% corporation is every environments. So it just imagine the the impact of these eso a thing here that is important is our goal is to help customers and partners move quicker, removing any undifferentiated heavy lifting, right, Andi, that's kind of the mantra of this group. >>You know, when you think about what Doug Young was saying is in the keynote, um, the importance of partners and I've been on this kick about we've moved in this industry from products to platforms, and the next 10 years is gonna be about leveraging ecosystems. The power of many versus the resource is of a few or even one is large is a W s so so partners air critical on I wonder if you could talk toe the role that that the network partners air playing in affecting S a p customer outcomes and strategies. Maybe Steve, you could take that first. >>Yeah, but look, we recognize that the migration on the management of these systems it's complex, right? And for years, we've invested in a global community of partners many partners who have been fundamental to s a p customer success over over a couple decades, Right? And so, um, that there are some nuances that that need to be realized when it comes to running ASAP on on a hyper scale platforms like AWS. And so we put a lot of work into making sure these partners are equipped to ensure customers have have a really good experience. And I mean, in a recent conversation I had with a CEO of a large, uh, CPG company, he told me he reflected that the partners really are the glue. That kind of brings it all together for them. And, uh, you know, just to share something with you today, our partners, our partner community network for S. If he is actually helping over 90% of net new customers who are coming toe migrate as if you were close to AWS, so they're just absolutely critical. >>So, Fernando, there's the m word, the migration, you know, it's you don't want to unless you have to, but people have to move to the cloud. So So what can you add to this conversation? >>Sure, they So again, just to echo what Steve mentioned, right? Uh, migration. Super important. We have ah group of partners that are right now specializing in migration projects. And they have built migration factories. You may have seen some of them. They have been doing press releases through the whole year saying that they're part of these and their special cells they're bringing to the helping customers adopt AWS. So they go through the next, you know, very detailed process. We call them map for ASAP partners. So they have these incremental value on top of being SCP competent funds, which I referred earlier on. This group has, as mentioned, you know, show additional capability to safeguard these migrations on. Of course, we appreciate and respect and we have put investment programs for them to help them support their own customers right in those in these migrations. But because the SNP ecosystem on it. But it's not about only migrations, right? One important topic that we need technologies as you as Steve mentioned, we have these great set of partner of customers have trusted us or 5000 through a year on these, uh, these customers asking for innovation right there, asking us how come the ecosystem help us innovate faster? So these partners are using a dollars a plan off innovation, creating new solutions that are relevant for SCP. So basically helping customers modernize their business processes so you can take an example like Accenture Data Accelerator writers taking SCP information and data legs Really harm is the power of data there or the Lloyd you know, kinetic finances helping, you know, deploy Central finance, which is a key component of SCP, or customer like partners like syntax that has created our industrial i o. T. Offering that connects with the SNP core. So more and more you will see thes ecosystem partners innovating on AWS to support SNP customers. >>You know, I think that's such an important point because for for decades have been around for a while. It's the migrations air like this. Oftentimes there's forced March because maybe a vendor is not going to support it anymore. Or you're just trying to, you know, squeeze Mawr costs out of the lemon. What you guys are talking about is leveraging an ecosystem for innovation and again that ties into the themes that we're talking about about Cloud 2030 in the next decade of innovation. Let's close, guys. What can customers ASAP customers AWS customers expect from reinvent this year? Um, you know, maybe more broadly, what can they expect from A W S in the coming 12 months? Maybe, Steve, you could give us a sense, and then Fernando could bring us home. >>You bet. Look, um, this year we've really tried to focus on customer stories, right? So we've we've optimized. There's a number of sessions here agreement this year. We want customers and partners to learn from other from other customer experiences, so customers will be able to listen to Bristol Myers Squibb talk about their performance, their their experiences, Alando Newmont's and Volkswagen. And I'll be talking about kind of different places where they are on this, this journey to cloud and this innovation life cycle, right, because it really is about choice and what's right for their business. So we're pretty excited about that. >>Yeah. Nice mix of representative Industries there. I Fernando bring us home, please. >>Sure. So, again, we think about 21 in the future. Rest assured, we'll continue to invest heavily to make sure it values remains the platform innovation. Right on choice for recipe customers where a customer wants to move their existing investments on continue to add value. So what they have already done for years or goto export transformation. We're here to support their choice. Right? And we're committed to that as part of our customers Asian culture. So we're super excited about the future. And we're thankful for you to spend time with us today. >>Great, guys, Look, these are the most demanding workloads we're seeing that that rapid movement to the cloud is just gonna accelerate over the coming years. Thanks so much for coming on The Cube. Really appreciate it. >>Our pleasure. Thank >>you. All >>right. Thank you for watching everyone keep it right there from or great content. You're watching the cube aws reinvent 2020

>>from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue. Here's your host Still, Minutemen >>Hi and welcome to the Boston area studio. Happy to welcome back two of our Cube alumni, both from Active e o Brian Regan, the C M O of the company. And it took Rommel. Who's the vice president and general manager of Cloud? Gentlemen, thanks so much for joining us. >>Happy New Year's too great to be here. >>Yeah, 2020 way we're talking about. We don't all have flying cars and some of these things, but there are a lot of exciting things and ever changing in the tech world. We're gonna talk a lot about N. C. Which, of course, is active use announcement. If I heard the sea, it's about clouds, about containers and about copy data management. With course, you know we know act as always quite well, Brian. Let's start with a company update first. Of course, you know, copy data management is where activity really created a category, but all of these new waves of technology that activity is fitting into Well, 2000 >>19 was an incredible year for us, you know, continued accelerating our growth in the market in the enterprise particularly, You know that the secular trends around hybrid and multi cloud really played well to our existing strengths. And 10 c really builds on those strengths will talk more about that. I know in a moment we also saw continued, you know, as digital transformation as as application modernization initiatives to cold. In just about every enterprise, our database capabilities really played again a cz a strength that we could capitalize on to land significant enterprise accounts, get started with them and then really start to expand overall data platform data management platform in those accounts >>s Oh, sure, before we get into the 10 see stuff specifically. But Brian, Brian teed up some of those cloud trends and how I think about data protection. Data management absolutely has changed. You know, I remember a couple years ago we said, Oh, well, you know, people are adopting all these clouds. All of these concerns still exist. You know. It doesn't go away. It's not magically Oh, I did office 3 65 I don't need to think about all the things that I thought about without. Look, when I do public cloud and build new applications. Oh, wait. You know, somebody needs to take care of that data. So bring us inside your customers. The team that's building these products and some of those big trends should >>happen. You're still so happy to be back in the Cube. So 2019 really defined. There were a lot of for enterprises really started moving. Production will look to the cloud multi cloud become a reality for active field way. We're running production workloads on seven o'clock platforms. So the key elements off being infrastructure agnostic wherein active you can do everything in all clark platforms. Basically, infrastructure neutral was a key element. On the other element was a single pane of glass. You could have an Oracle worker running on prime with the logic application running in azure and not know the difference. S o. The seamless mobility of data was the key element. That lot of our enterprises took advantage from elective standpoint on a lot of the 10 see capabilities adds onto those capabilities and you see more of these adoptions happening in 2020. So I think 10 seat eases up absolutely perfectly for that market. >>Yeah, let's talk a little bit about activities, place in the market, that differentiation there, that direct connection with the application and the partner's eyes. Real big piece of it. >>It's a huge piece and something we really not just double triple down on in 2019. Certainly for us our database capabilities, which we believe are really second to none in the industry, we continue to expand and enrich the capabilities, including ASAP Hana obviously already Oracle and sequel server D B two, as well as the linen space databases, the new and no sequel databases. We also understood, and as our customers were talking to us about their application modernization, they were moving Maur of their front and capabilities two containers, and they wanted that the data to come with it a t east temporarily on. So that was a big focus for us as well was making sure that we could bring the data whether it was into a V M, into a container into a physical server into any number of clouds in order to support that application. At that time, it was a critical part of our differentiation. For two dozen 1 19 >>I'd love just a little more on the database piece because you go to Amazon, reinvent and you know, the migrations of databases to the cloud, of course, is a major conversation. You look at Amazon, they have a whole number of their offerings as well, as if you want to use any database out there, they'll let you use it. Course Oracle might charge him or if you're doing it on the Amazon, the Amazon partner. The azure partnership with Oracle was big news in the back and 1/2 of 2019. So when you're working with their customers, you know, databases still central to you know how they run their business and one of the bigger expenses on the books, they're So you know what we look at 2020. You know, what is the landscape specifically from a database? Well, we continue >>to see and in most of our large enterprise accounts that Oracle and sequel servers continue to dominate the majority of the payload of databases. We don't see that changing, although we do see net new applications being built on new database platforms. Thio complement the oracle and sequel server back end. So we are seeing a rise of the bongos and the new and no Sequels out there. We're also seeing Maur consideration of building in the cloud, as opposed to starting on Prem and then potentially leveraging the cloud sort of post facto and in terms of the application architecture's. So our ability to support both the the legacy big iron database platforms as well as the new generation platforms, regardless of application architectural, regardless of the geometry of the application, is a big part of our differentiation >>going forward. >>All right, so let let's Wave hinted about it. But 10 c major announcement. Let's get into how that extends what we've been talking about. >>Absolutely so you know, we've made a lot of the new databases, particularly the no sequel databases, the Mongols and Hannah's first class citizens intensity, which means we understand not just the database. He also he also the ecosystem that the database lives. We all know Hannah's a fairly big database in terms of the number of machines that consumes number off, you know, applications that you use it and toe capture and actually provide value for Hannah. You need to understand where the Honda database lifts and so some of the capabilities we've added in 10 C's to kind of figure out this ecosystem, and when you migrate, you might need the ecosystem, not just the holiday. The peace because you know that is that is a key element. On the second aspect is the containers that that Brian touched on. Now we're seeing legacy data being presented into containers, and there's a bridge too quiet for that. Now. How do you present that bridge containers could be brought up, but they're lifeless unless you give them data. So the actors of bridge ready and you bring up the container using communities of whatever framework you have and be married the data into the container framework. So most organizations, you know, as they evolved from yesterday's architecture to today's architect. And they need this bridge, which helps them navigate that that my creation process and an active field being the data normalization platform is helping them live on both segments, Right? Nobody does us turn the switch off of the old one and move to the new That'll be co exist. That is the key element >>way spent a lot of time over the last couple of years hearing about cloud native architectures and that discussion of data, it is kind of something you need to kind of dig in to understand. I'm glad to hear you talking about, You know, when you talk about storage and container ization, you know where that fits today? Because originally it was only stateless. But now we know we could do state full environment here. But while container ization is, you know, growing at huge leaps and bounds, customers aren't taking their Oracle database and shoving Brian A lot of discussion about the partnerships. I think it was seven. You know, major cloud providers. That activity is there talk a little bit about the common native. The relationships with some >>of those partners? Absolutely. I mean, way made great strides from a go to market standpoint with our cloud partners this past year. Google Cloud is probably our most significant go to market partner. From a cloud standpoint, we've done a lot of joint engineering works in order to support both our existing, uh, software platform as well as our SAS control plane in the Google Cloud. We have landed many significant deals with with Google this past year on dhe. They have been as they continue to really increase their focus on enterprise accounts and both hybrid as well as public cloud sort of architectures. We are hand in glove with them as their backup in D R partner for those club >>workloads. >>Great eso We talked quite a bit about the database peace, but in general, back into the cloud archive in the cloud. What is 10 see specifically an active you, in general, enhance in those environments >>so tense he bring It brings in you know, the key elements of the recovery orchestration. So if I have to bring up, let's say, 500 machines in any club platform, how did I do it? Well, I can go and bring up one machine at a time and take two days to bring it up or with active fuels. Resiliency. Director. You can create a recovery plan and a push pardon Recovery happens, so we've seen a lot of customers adopt that, particularly customers that want to leverage the Google platform for its infrastructure capabilities. Wants an orchestration, that is, that is, that understands the applications that are coming up, so there is a significant benefit from a PR standpoint of the recovery orchestrations will be invested a lot of time and tuning the performance and understanding Google and Amazon and Azure to make sure this was built, right. The other big push we're seeing for the clock platforms ASAP, ASAP, as an enterprise has taken a mission to say, there's no more data centers. Everything is going to the cloud. So an escapee workloads are not the easiest were close to manage. And so they did the the intersection point of S A P and the cloud is very active. Field becomes really valuable because, though, did this data sets by definition or large, their complex and there were distributed. And the D artists of paramount importance because these air crown jewels So so those segments of the R orchestration forward with, you know ASAP and Hannah, which is to get our strength of databases. It's kind of their tense. He really hits, hits, hits a home run >>when we're talking to users in the discussion of multi Cloud in general, one of the challenges is Yoon hee. Different skill sets across. One of those powerful things I've heard from active use really is a normalization across any cloud or even in a cloud. Oh, wait. I was gonna stuck six up again in an archive. That means I'm never going to touch it again. Ingress and egress fees. You know, I have to figure these out or I need toe dedicated engineer to those kind of environments. So it seems that just fundamentally the architecture that you built it active eo is toe help customers really get their arms around those multi cloud >>environments? Absolutely. And I think there are two additional components that really one of which has lived with activity from the very beginning of the company, which is a p a p I. First, the cloud is very much an AP I centric type of operating model on with active fio We don't change the management system were operating model. But in fact we incorporate in eso all of this orchestration that it shook talked about can be actuated via a P I. The second piece, which we really started in 2017 with our eight Dato platform release, is the the consumption and the intelligent consumption of object with 10 see, we've continued to advance our object capabilities. In fact, we published a paper with the SG in late 2019 that talked about mounting 50 terabyte Oracle databases directly out of object with actually increased performance versus the production block >>storage behind it. >>So we have really with 10 C, actually added cashing to even further performance optimized object workloads, which speaks to both the flexibility but also the economic flexibility of being able. Thio contemplate running workloads in the cloud out of object at a lower cost platform without necessarily the compromise of performance that you would normally expect >>absolutely. And like you said, the skill set required. Do I need to put it in object to any reported in block? We can eliminate that right. Be neutralized that to say you want to leverage the cloud, give us your cost point and you can dial the cost up or down, depending on what you see for performance, and we will be the day that back and forth, so that flexibility is enormous for customers. >>That's greater if you talk to anybody that's been in the storage industry for a while, and you want to make them squirm, say the word migration s O. We know how painful it has been if you go talk to any of the triple vendors, they have so many tools and so many service is to help do that in a cloud era. It should be a little bit easier, but it sounds like that's another key piece. Intensity? >>Absolutely, absolutely. I mean, 10 See, you know, hits the home. I think with the A P. I integration. So the other element 2019 Saul, was the scale of deployment effective. You know, when you have to manage hundreds of thousands of machines across different geo's, that is a scale that comes to the data protection that you know, people. Really? You have a seat to actually build for it and and work with it and be sorry in 2019 and 10 See, incorporates a lot of that capabilities as well, making it ask Cloud needed as possible. So basically, around these applications globally. All >>right, uh, I was wondering if you might have a customer example toe really highlight the impact that NBC's having understand if you can't name them specifically, but, uh, yeah, >>well, actually, shook has already talked about 11 customer slash partner. Who is I think still the world's largest software company in the world based out of Germany. And they are powering their enterprise cloud on the data management data protection. Beneath that enterprise cloud across four different hyper scale er's using, active you on. I think they're on record in a weapon. Our earlier in December, talking about their evaluation of pretty much every technology out there on the one that could really deliver on performance at scale across clouds was activity >>on. The key element was they wanted a single platform with a single pane of glass across all platforms, and an active feel was the solution to each other. So >>and certainly I think we credit them and are the rest of our enterprise customers for pushing us to make 10 see more powerful and more a capable across any clout, you know, Ultimately, an inter enterprise is going to make a decision that they've probably already made the decision to incorporate cloud into their enterprise architecture. What we give them is the freedom and the flexibility to choose any cloud. And, by the way, any cloud today that might change tomorrow and having the ability to seamlessly migrate and or convert from cloud eight o'clock be. Is something that active powers as well? >>Yeah, just make sure we're clear as to what's happening there. It's great that you've got flexibility there when we're talking about data and data gravity. Of course, we're not talking about just lifting an entire database land, you know, ignoring the laws of physics there. But it's the flexibility of using a ll These various things, any way Talk about A S, A P, of course, needs to live across all these clouds. But when you talk about an enterprise, you know what is kind of that? That killer use case? Because we said we're not at a point where cloud is not a utility. I don't wake up in the morning and look at the sheet and say, Oh, I'm gonna, you know, use Cloud a versus cloud be s o. You know what is? You know the importance of that flexibility for us >>today. The majority of our business starts with company saying I need to deliver my data faster to my developers or my tester's, or even increasingly, my data scientists and analysts and my data sets have become so large that it's becoming increasingly difficult for me to do that with regularity. So the currency of the data is starting to suffer. That is the first use case for us and that that powering that enterprise transformational initiative around a new application or an updated application based on a historical app using those enterprise databases delivering that seamlessly quickly, regardless of how big the data is still remains our first use case. And then, increasingly, those customers air realizing that they can start to achieve the other benefits of active eo, including I can start to back that up to the cloud. Aiken actually orchestrate recoveries in the cloud. Not just bulk sort of transfer, but actually the entire application stack. And bring that up in the cloud. I can start Thio, take those those data sets and actually amount them into containers for my next generation application. So that starting point of give me my data as quickly as possible, regardless of how big it is, starts to become universal in terms of its applicability for all use cases. >>Yeah, I guess I shook. The last thing I wanna understand from you is in 2019. We saw a lot of large providers putting out their vision for how I manage in this multi cloud environment. You were at the Google Cloud event where Anthros was unveiled. I was at Microsoft ignite when as your ark was unveiled. VM wear has things like tans you out there. So this moldy cloud environment how do I manage across these disperse environments? What? What What are all those move mean to active you on how you look at things. >>And I think you know, the Tennessee release and with the core architecture that if you had in place, which was multiple already and a P I ready. So those are the two elements that are kind of building blocks that you can tie into any one of those construct you talked about. All right, so we've had we have customers, innovated us with Antos. If customers get up service now we have customers doing Vieira with us, right? So there are many, many integration platforms. The latest I saw was an Alexa app, but we were mounting an oracle database on a voice command. So So you know, there's endless possibilities as thes equal systems evolve because active feel stays behind the cowards powering the data delivering the data available if needed on the target. So that is the key element in the neighbor that we see that helps all these other platforms become super successful. >>So, Brian, it sounds very much a hell wind. The big trends that we're seeing here keep partnerships and, you know, meeting your customers where they need to >>pay. Absolutely. We continue Thio play in the enterprise market, where these thes are absolutely top of mind of every CEO and top of their agenda. Onda, we are working hand in glove with them to make sure that our platform not only anticipates their needs but delivers on their current state of needs as well. >>Brian, thank you so much. Congratulations on the 10 sea launch Cloud containers. Copy data management. Look forward to watching your customers and your continued Thanks. As always, Very much. All right, I'm still Minutemen. Lots more coverage here in 2020. Check out the cube dot net for all of it. And thank you for watching the Cube