>>Mhm Yes. Hello and welcome back to the cube coverage of red hat summit 2021 Virtual. I'm john furrier host of the Q. We've got a great lineup here. We've got two great guests just bad padan, E. S. V. P. Of cloud platforms at red hat and Tracy ranking VP of open shift engineering at Red Hat folks. Thanks for coming on. Good to see. You got some big news, you guys have made some acquisitions. Uh stack rocks you guys bought into red hat was a really big deal. People want to know, what's the story? How's it going? What's the uptake? What's the integration, how's it going? >>Right, thanks john, thanks for having us on. Um so yeah, we're really excited with stack rocks acquisition being the team on board. Uh Well, the first thing to note before even why we did it uh was for for you and and then the beers have been following us closely. This is our first acquisition as red Hat being part of IBM. So, so, so quite big for us from that perspective as well. Right? Continue to maintain our independence um within uh IBM uh and I really appreciate that way of working together. Um but saying all of that aside, you know, as a company have always been focused on ensuring that were direct enterprise capabilities to just sort of doing that for two decades. With with Lennox, security has always been a big part of our story, right, ensuring that, you know, we're finding cbs updating uh and sending out patches to our customers and doing that in a reliable fashion running mission critical applications. We applied that same if you will um security mindset on the community side with the open ship platform. Um we've invested insecurity ourselves organically, right, you know, uh in various areas and making it more secure, all right, can't run containers uh as Root by default, uh investing in things like role based access control and so on. And we really felt like we want to deepen our commitment to security. Uh and so, you know, in conversations with stack rocks, we found just a great fit, just a great team building a really interesting approach to community security, right? You know, very declared of approach to it. Uh you know, focus on a vision around this notion of shift left. But you've probably been hearing from that because we're a little bit right. Which is this uh idea that, you know, we're in the world moving from devops to death setups. Uh and the approach that sack rocks were saying, so great team, great product, really great vision with regard to kind of weather going forward and finding a nice alignment between, you know what, you know, they've been thinking about the value that we want to bring >>Yeah, I want to dig into the depths cops, piece of it. But you brought up the IBM acquisition as part of now Red Hat bought IBM you know it's just you remember back in 2019 I interviewed Arvin on the cube when he was at IBM you guys were still independent and he had a smile on his face. He is pro cloud, he is all about cloud Native and even that interview I had no idea what was going on behind the scenes but I was kind of drilling him on some of the things that were important at that time which are now certainly relevant today which is cloud Native, Agile development Programmable infrastructure. I don't think we touched on security that much was kind of inherent in the conversation. He was like all smiling, he loves the cloud Native and and this is where it comes into the relevant, I have to ask you, what was it like to get this through? IBM where they're like girl green light or was it, was it different? What was different about this acquisition? >>John great, great question for you to ask. And you know, I will say that, uh, you know, everyone's heard the stories they're telling us. They get, you know, part of IBM, you know, it's definitely working on red hat jOHn the cube we've talked to you and several of your colleagues about that. Um, the great thing has been that, look, the redhead way of working, uh, are still pushing forward with regard to our commitment to open source, uh, and our culture, you know, is still the way it is. And I have to give huge credit not just to urban and his and his team, but definitely to orbit right. He's always champion, He's champion rather acquisition. He's champion kind of, you know, the independence that we've had and he takes very, very firm stance around it. Um, and look, IBM uh, story company uh, in the United States and really in the world, um, they have, there was working and you know, for redhead, they've kind of said, look, we'll give you a pass path, right? So, uh, getting the acquisition through, if you will, diarrhea processes, um, really was, was hugely supported by, you know, from mormon, but all the way down. Russian strategic >>strategic bet with the dollars involved trace, they want to get you in this because, you know, one of the things about shift left and getting security built in by default, which has always been part of red hat, that's never been an issue. It just extends as developers want to have native security built in. There's a technology angle to this as well. So, um, obviously cloud native is super important. What investments are you guys making with this acquisition and how does that translate to customer benefits? >>Yeah, I mean the one thing that is really important about the stock rocks acquisition and kind of, you know, key for us is, you know, this was a cube native solution and I think that's really, you know, was important piece as to why stock rocks might have been, you know, was a great fit for us. Um, and so you know, what we've been trying to do in the short time that that team has been on board with us is really, you know, taken a deep look and understanding where are the intersection points of some of the things that we have been trying to focus on, you know, just with inside of, you know, open shift in red hat in general and where do they have bring the additional value. Um, and really trying to make sure that when we create this solution and ultimately it is a solution that's cohesive across the board. Um, we don't add confusion too. You know what, some of the things that maybe we already do this team knows, you know, how to they know their customer base. They really know what the customers are looking for. And we are just trying to absorb, I would say so much of this information uh as we are trying to, you know, create what the right road map will be uh for stack rocks from a long term and infrared had ultimately in the security space. I mean, as the chef said, I mean we are red hats known for being, you know, security mind focus built on top of realm, you know, uh the leader and so we want to make sure that what we've got that actually serves, you know, the developers being able to not just secure the environment and the platform, but also the workloads, customers need that security from us. Um and build it in so that we have, you know, into the cube native >>controls. >>So stack rocks was known for reinventing and security enterprise security with cloud native. How is it complimentary? How does it fit in? Can you guys just quickly talk to that point because um like you said, you guys had security but as kubernetes and containers in general continue to rise up and and kubernetes continue to become a hybrid cloud kind of linchpin for applications. Um where's the synergy? Where's where does this connect? And what are some of the uh the part of the areas where it's it's fitting in nicely or or any overlaps that you can talk about as well? >>Yeah, I can start and then maybe Tracy if you want to add to that securities of it's a wide space. Right? So, you know, just saying security is like, well, you know what security you're talking about, you're talking about, you know, and use the security, like what your desktop are you talking about? You know, intrusion prevention? I mean, it's a huge, huge, you know, space. Uh you know, many companies devoted to the entire spectrum, you know, self has a very robust security business. We're very focused on uniting Tracy. Was talking about this, the Kubernetes Native security part of this. Right. You know, do we have the appropriate runtime uh, controls in place? Uh You know, our policies configured appropriately Well, if they're in one cluster, are they being applied consistently across, you know, every cluster? How do we make sure that, you know, we make security the domain, not just of the operators but also uh in in uh make it easier for it to be adopted at development time. So, you know, there's a, there's a, if you will, a very sort of uh a lot of surface area for security, we're trying to really think about the pieces that are most relevant for our enterprise customers and the ones that are deploying it at scale. And I'm sure we can build on it. Having said that, john what I do want to add also is that because expands even of Cuban any security is so large, there is a lot of room for our partners to play. Right? And so before you asked me that question, I want to say that there is space. Right? So you know, I've had conversations with you know, all the other folks in the cloud native security space. We know them well, we've been working with them over the years and we could do to look forward to ensure that they're building over and above the foundation of Berlin. >>So plenty of beachhead, what you're saying from a, from a security sample, you guys hit the table stakes added into the product, but there's so much surface area going on with this hybrid cloud and soon to be multi cloud that you're saying this room for partners to play. >>Exactly, right, >>okay. Tracy quick under the hood, you know, actually shift left. That's kind of the mindset for developers who are writing modern applications might not want to get under the hood, who just wanted all the program ability of security and not have to come back to it. I mean that seems to be the complaint that I hear. It's like okay I gotta come back and do a security, more security work. I just wrote the code that was last week or yesterday and that seems to be the developer productivity. Then there's also under the hood devops what how does this all fit? >>Yeah, so it's uh let's take a take a step back and this is how I kind of like to think about it. So we are trying to look at, you know, how do we just enable in some of the C. I. C. D. The tooling that we have? How do we actually take and enable some of the technology that was already available in stock rocks today and actually put it into those tools. Because if we can make it easy for you to not just develop your application and, you know, integrated in with what you're, the tooling is that you're trying to use for the entire life cycle of developing your application. It then becomes exactly what you didn't say, you know, what they're doing now is it's an after thought. We don't need it to be an afterthought. Um and I think, you know, we're seeing the changing from a customer mindset where um they're become customers are becoming a lot more aware of these things. So if we actually get this into, you know, some of the Argo and the ci cd pipe pipeline work, then it just becomes something natural and not a secondary thought because actually when it's a secondary thought, uh we have exposures and that's not what a customer wants when they're creating, you know, creating these workloads, they're trying to rapidly create the workloads, so we need to make it um to have those integration points in as quickly >>as possible. >>Totally nailed. I mean there's productivity issues and there's also the top line which is security. Great stuff. Congratulations on that acquisition. Security continues to be built in from the beginning. That's what people want. They want productivity want want security, great stuff, Great acquisition. Congratulations. Um Next next segment I want to get into is uh open shifts around telemetry. Tell us about telemetry for open shift. What is this about? >>Yeah, another big interesting topic for us. So over a year ago we released open Ship for and you know, we learned a lot of lessons, you know, shipping open ship three up and over the years and really getting feedback from hundreds of customers around the globe. One of the things obviously we heard from a lot was you know, make install the upgrade experience better. Right. But you know, we were thinking about how can we take that forward to the next level, which is is there a way for us to say, you know, let these clusters they connected up so we can get a better sense of cluster help and help with remote health monitoring will be able to proactively provide information back to our customers around, let's say, you know, if applications are healthy clusters healthy and how they're running and how we can help them um could figure them if they're not. Um And so that led us to introducing uh inflammatory remote health monitoring directly into open ship for as a value that we can provide to customers. Um And what that really starts doing is starts bringing this notion of a public cloud, like experience to customers with clusters run across the hybrid cloud. Right? So you have the expectation that, you know, your clusters are monitored and watched over in the public cloud and we want to make sure we can provide that to customers regardless of, you know, where they're running in. So, so that's just >>a quick question on that insights for open shit. That's what you're getting to. Is that on premise? And in the cloud? So it's hybrid environment, is that correct? >>Exactly. Right. So, the insights for open ship is all about that, Right? So how can be proactively, you know, uh identify risk helped remediated? How can we uh do things like, for example, give you recommendations, cost optimization, right insights around around around that. Uh and to your point, right? The goal is to make it completely hybrid. So, it's obviously a new area right for customers want Leslie used to that, you know, in an on premise environment, they're used to that in a public cloud or cloud native environment. And we're trying to make sure we bring that consistently across to our customers, you know, regardless of where they're running apart. >>Tracy. Talk about the the developer productivity involved because if you have telemetry and you have insight into what's going on in the infrastructure and the data, what's going on the application, you can be more proactive, You don't have to get pulled into these rabbit holes of troubleshooting. Oh, is a trace over here or something going on over here. Are clusters going down or should I could have caught that there's a lot of, you know, good intentions with with the code and then all of a sudden new code gets pushed and then also that triggers this to go off and you have all these kind of dependencies, day two operations, many people call this kind of that phenomenon where everything looks good and then you start pushing more stuff more code and then the cluster goes down and then it's like wait, that could have been avoided. That was a dumb error, we could have fixed that this is kind of the basic what I call human software error kind of stuff that's not intended. The telemetry help this area. >>Yeah, it does. And actually one point that even to take it further, that I think it's important is our customers can learn from each other not even having to talk to each other, which is the beauty of what telemetry is and what redhead insights, rope and shift is. You know, what we have been able to see is you know, there are certain characteristics that happen even across, you know, certain groups of customers but they don't know that they don't talk to each other, but the telemetry is giving us a night into what some of those patterns are. And so when a customer in one site starts to have, we start to see telemetry, you know, you know, maybe a. T. D. Is going down for a certain reason and and we can determine that we then have the ability to take that telemetry and you know, be able to send alerts back to all the other customers and say, hey we recognize this might be becoming an issue, You know, here's how you might re mediate it or hey we've already put a fix out for this issue that we're starting to see you having an issue, you should probably take action on. So it's an increasing the the efficiency of customers without them necessarily having to, you know, constantly be understanding, monitoring, you know, watching everything like they had had to do from of the three perspective, we're now giving them some of the insights of what we know as developers back to them, >>you know, that's interesting. I think that's really key because it's talking to a friend last night we just talked about cybersecurity and we're talking about how a lot of these things are patterns that have that are the same and people just don't talk to each other. There's no shared insights. I think this is an interesting dynamic where you can get the collective intelligence of other patterns and then share that. So the question that I mean that's that's a game changer in my opinion. So that's awesome. The question I have is can you guys push alerts and recommendations to the customers? So from this data? So how does that work? Is that built into the product? Can I get some proactive notifications and saying, hey, you know, your cluster might go down and we've seen this before, we've seen this movie. I mean she is that built in. >>Yeah, so john you're keeping it exactly where we're taking this, right? And I think Tracy started putting out some breadcrumbs for you there. So uh, first get comfortable with the foundation was laid out, get clusters connected right. Then information starts going, reported, we start getting exactly to what you said, john write a set of patterns that we can see Tracy, start talking about what we can, if we see pattern on one end, we can go off and help customers on other end. Now, if you take this forward interest for your viewers today, um introduce a I you know, into this, right? And then we can start almost starting to proactive now of saying, look, you know, following actions are going to be committed or we expect them to be committed. You know, here's what the outcome is a result of that. Here's what we recommend for you to do, right? So start proactive remediation along that. So that is exactly, you know, the surface that we're trying to lay down here and I think this is a huge, >>huge game changer. Well, great stuff, want to move on the next we're getting go on for hours on that one topic. I think telemetry is a super important trend. Uh you guys are on top of a great, great job to bring in the Ai piece. I think that's super cool. Let's get back to the end of blocking and tackling Tracy. You know, one of the things that we're seeing with devops as it goes mainstream now, you've got def sec apps in there too, is you've got the infrastructure and you've got the modern application development, modern application developers, just wanna code, be productive, all that security shifting left, everyone's all happy that things are going great under the hood. You have a whole set of developers working on infrastructure. The end of the customers don't want to manage their own infrastructure. How is red hat focused on these two groups? Because you got this SRE like cloud Ops persona developing in the enterprise and you got the developers, it's kind of like almost two worlds coming together, how you, how you helping customers, you know, control their infrastructure and manage it better. >>Yeah, so great question. And you know, this really plays to the strength of what, you know, we have been trying to champion here at red hat for for many years now around the hybrid cloud and this, you know, hopefully everybody's recently heard about the announcement we've made with our new offering Rosa in partnership with amazon. Um you know, we've got different offerings that enables customers to really focus, as you mentioned on the key aspects that they are concerned about, which is how do they drive their businesses, how do they create their applications, their workloads that they need to and offload, you know, the need for having to understand all of the I. T. Infrastructure that's underneath. Um We want to red hat to reduce the operational complexity that customers are having um and give them the ability to really focus on what's important for them. Um how can they be able to scale out their applications, their businesses and continue to add value where they need to have and so um I think it's great we're seeing a huge uptake right now and we've got customers and they understand completely this hybrid cloud model where they're, you know, purchasing open shift um for certain, you know, applications and workloads that they want to run inside their own data centers. And then for those that they know that they don't, you know, don't have to be inside their own data centers. They don't want to have all of that operational complexity. They want to utilize some of the clouds. That's when they're starting to look at other things like rosa or open shift dedicated and and really starting to find the right mix that works well for their business. >>So are you saying that you guys are going to the next level because the previous, I won't say generation but the current situation was okay, you're born in the cloud or you lift and shift to the cloud, You do that manually, then you go on premise to build that cloud operations. Now you're in a hybrid environment. So you're saying if I get this right that you guys are providing automation around standing up in building services on AWS and cloud, public cloud and hybrid, is that kinda what you're getting at? >>Yeah. So the to go to the higher multi cloud world, right? You want platform consistency, right? Running my application running on a platform consistently, you know, where we go. Right. Tracy started talking about this idea of in some cases you say, well I've got the infrastructure team, I've got the ops team, johnny talked about this notion of, well the dwarves can be hard, sometimes right to some groups. Um, and so hey, red hat or hey redhead, plus, you know, my hyper scale of choice, you know, take that off of my hands, Right. Run that for me consistently yourself. Right. So I focused on my application uh and the management of infrastructure is something that's on you Tracy talked about rosa, that's our joint uh first party service that you know, we've got with amazon were directly available in amazon's console, you can go pull that down, right. You'll see red hat open shift on AWS, right on their uh we've got a similar one with Microsoft Azure Tracy mentioned open dedicated, we stand up the platform, we have our own sorry team that manages it with IBM as well as with google. So you pick your cloud of choice and we'll make sure, you know, we'll give you a platform that if you as a customer so choose to self manage. Great, go for it. If you'd like for us to manage it directly ourselves or in conjunction with the cloud provider and provided to you as a native service, you know, we can do that for you as well. Right? So that day to obsolete, you know, challenge that we're talking about. You know, it's something that we can get your hands if you want us to. >>That's really cool. You gotta manage service. They can do it themselves whatever they want. They can do it on public cloud and hybrid. Great stuff. Yeah, I think that's the key. Um, and that's, that's, that's killer. Now, the next question is my favorite. I want to ask you guys both pretend I'm a customer and I'm like, okay, Tracy shit, tell me what's in it for me. What is open shifts and red hat doing for me is the customer? What are you bringing to the table for me? What are you gonna do for me? What is red hat doing for me today? So if you have the kind of bottom line we were in the elevator or probably I ask you, I like what I'm hearing. Why? Why are you cool? Why are you relevant? What's in it for me? >>You >>already start? Okay. Yeah, so I mean I think it's a couple of things that we let's just tie it back to the first initial blend. I mean we've got, we're enabling the customers to choose like where do they want to work that run their workloads, what do they want to focus on? I think that's the first thing. Um we're enabling them to also determine like what workloads do they want to put on there. We continue to expand the workloads that we are providing um capabilities to customers. You know most, you know one of the more recent ones we've had is you know, enablement of Windows containers a huge plus for us. Um, you know, it's just kind of talked about, dropped the buzzword ai you know, recently, you know, we're looking at that, we're talking about, you know, moving workloads need to go to the edge now. It's not just about being in the data centers, so it's about enablement. That's really what open shift as you know, bread and butter is, is, you know, let us, you know, create the ability for you to drive your workloads, whichever, whatever your workloads is, modernize those workloads um, in place them wherever you want to. >>Yes, your your answer. How would you say to that? >>I'll build on what Tracy said, right. She obviously took the, you know, build up tribal Benjamin perspective and I'll sort of talk about a business thing you're introducing, actually add threat at summit. So, you know, we go up and acquire stock rocks, you know, further deepen investment in communities or containment of security. Uh if you recall, john, we've talked to you about, you know, advanced cluster management team that we actually got from IBM incorporate that within red hat, um, to start providing, you know, those capabilities are consistent, you know, cluster policy, immigration management. Um, and you know, in the past we've made an acquisition of Core West, we've got a lot of technology from that incorporated the platform and also things like the quake container registry. What we're introducing address had some it is a way for us to package all of that together. So a customer doesn't say, look, you know, let me pick out a container platform here, let me go find, you know, somebody manage it over there. You let me see, you know what security you adhere. We introduced something called open shift platform plus right. Which is the packaging of, you know, core Open shift contain a platform uh, capabilities within uh, stack rocks, which we're calling advanced cluster security capabilities of cluster management, which is called advanced cluster management. And the quake container registry always want to make it much easier for customers to consume that. And again, you know, the goal is, you know, run that consistently in your hybrid multi club >>chef Tracy. Great, great segment, great insight. Um, here on the cloud platform and open shift under the hood. Uh, you guys are well positioned and I was talking about Arvin and idea who acquired red hat. You know, it's pretty clear that cloud native hybrid is the new cloud operating environment. That's clear. You guys are well positioned. And congratulations. Final question Chef. Take a minute to quickly put the plug in for open shift. What's next? Um, looking forward, what do you guys building on? Um, what's on the roadmap if you can negative share the road map, but yeah, tell us what you're thinking about. I mean you're innovating out in the open, love your shirt by the way and that's the red hat way, looking ahead. What's coming for? Open shift? >>So john I will say this, our roadmap is out in the open every quarter. Our product managers host the session right open to anybody, right? You know, customers prospect, competitors, anybody can can come on. Um, and uh, you hear about our road map, lots of interesting things they're working on uh, as you can imagine investments on the edge front, right? So that's across our portfolio, right on the open shift side, but also on learning platform as well as on the open stack front, make it easier to have, you know, slim down open shift. we'll run that you won't be able to run uh open ship in remote locations and then manage it. Um So expect for us uh you know, just to show you more work there, drinking things like uh ai and more workloads directly onto the platform, but you'll see what they're doing to get more Alex on what we're doing to take uh technologies that we've got called Open data hub to make it easier to run more data intensive, more ai ml types of frameworks directly a platform. Um And so that's a great interest, more workloads Tracy, start talking about that. Right, so Windows containers, support has G eight, uh and what's really awesome about that is that we've done that with Microsoft, right, so that offering is jointly supported by both us and our partners over at Microsoft uh virtualization, which is taking much machines and being able to run them as dangerous orchestrated by communities Um, and and doing more work, you know, on that front as well. So just a lot of different areas uh, were investigated and really, really excited to bring more workloads on 2:00. >>Well, Chef Tracy, great segment with a lot of data in there. Thanks for spending time in and providing that insight and uh, sharing the information. A lot of flowers blooming um, here in the cloud native environment, a lot of action. A lot of new stuff going on. Love the shift left. I think that's super relevant. You guys do a great job. Thanks for coming on. I appreciate it. >>Okay. >>This the cubes coverage of red hat summit. I'm john for a host of the cube. Thank you for watching.

>> Live from Washington DC, it's theCUBE. Covering AWS Public Sector Summit 2018. Brought to you by Amazon Web Services and it's ecosystem partners. >> Hey, welcome back everyone. This is theCUBE's exclusive coverage live in Washington DC at Amazon Web Services AWS Public Sector Summit. I mean, it's so jam-packed you can't even move. This is like the re:Invent for Public Sector even though it's a summit for Amazon Web Services. I'm here with Dave Vellante, my co-host. Our next guest is John Wood, Chairman and CEO of Telos, and Rick Tracy, Chief Security Officer and the co-inventor of Xacta, it's hot technology. John, great to see you, welcome to theCUBE. >> Thanks guys. >> Thanks for having us. >> I love to get the brain trust here, John you're, like, probably one of the most experienced cyber security gurus in the DC area still standing. (laughing) As we said last time on theCUBE. >> Always, always. >> Okay. (laughing) And you've got some patents here, with some core technology, so first of all, I want to, before we get into some of the cool features of the products, talk about the dynamic of public sector, because Amazon has these summits, and they're kind of like a recycled re:Invent. Small scale, still packed. Talk about what Public Sector Summit is, because this is a completely different ballgame in this world. >> Sure, it's a perfect age for the cloud, and what this summit does, is it provides a great venue for people to come, learn about what works, get best practices, find use cases and just see what the ecosystem's all about in terms of how to make it work with the cloud. >> Rick, so what's your take? >> Well, if there's any doubt about it, what, is it double the size of last year? I think there were 7,000 people here last year and Teresa said today 14,500. So, yeah, I mean, it suits us perfectly because this is our sweet spot. >> So, Dave and I are always amazed by Amazon in general, the slew of announcements, Teresa Carlson picking the reins up where Andy Jassy does that Amazon re:Invent which is just tons of content, so many new announcements. What's your guys take on the hot news for you guys, because you guys are a major sponsor and you're in the ecosystem, you've been doing a lot of business with Amazon. >> Sure. >> What's going on in the business? What's happening with Telos? Why is it so booming right now for you guys? >> Well, I think people realize that there is a way to use automation where security can help drive cloud adoption. So, Rick and I co-authored an article back in 2011 that talked about why the cloud was more secure and it went over kind of like a lead ballon. And then back in 2014 the agency made the decision, the CIA made the decision, arguably the most security conscious organization in the world, to go to the cloud. And so that was a big, big, big, deal. But what we do is we help drive the security automation and orchestration stuff so you can reduce the time it takes to get what's called your authority to operate. And so I think that's a big deal now. The use of automation is being used to enhance the mission, so that the mission owners can get to their mission using the cloud, much more quickly. >> And we heard from the most powerful sentence in the keynote this morning was, "The cloud on it's weakest day is more secure than Client Service Solutions." This is a practitioner saying that, a leader of an agency saying that, not Amazon or not Telos. >> Absolutely. >> And it's because of that automation, right? I mean, that's really a key factor. >> It's because of the automation. It's also because the cloud providers are making sure that they lock down their physical infrastructure. Guards, gates, guns. All of the physical infrastructure and the virtual infrastructure, they do a really good job of that. If you think about it, the US government, unfortunately, 80% of their spend is around maintaining old systems. Well, the cloud providers are keeping modern. Those old systems have a lot of weaknesses from a standpoint of cyber security flaws. So, with a modern technology like the cloud, there's a lot more you can do around automation to lock down much more quickly. >> And the standardization that you get with a cloud makes it's easier as well, because there's not so many variations of things that you have to figure out how to protect. So, the standardized services that everything's built on really helps. >> Yeah, and people are adopting cloud in kind of different ways, which makes it harder, too. But you get the benefits of scale and speed, certainly. But I got to just pick up on some big news that's happened just last night and today. Microsoft Azure suffered an 11 hour downtime across Europe. 11 hours Azure's down, Microsoft Azure. This is a huge concern. Downtime, security, these are issues, I mean, this is just like, so, what's going on with this? >> Well, the truth of the matter is, if you think about where Amazon is today, Amazon is light years ahead of the rest of the cloud guys. The reason for that is they made the decision early on to take the risk around cloud. As a result of that, they have so many lessons learned that are beyond all of the other cloud providers, that that wouldn't happen to Amazon today, because they'll be able to back up, replication and duplication if they have, and their environments. >> How big do you think that lead is? You know, there's a lot of debate in the industry that other guys are catching up. The other side of the coin is, no, actually the flywheel effect is a lot like Secretariat in the stretch run of the Belmont, you were talking about racing before. What's your sense of that lead, even subjectively. >> I think it's between 5 and 10 years. There was a, it was crickets in this world, in the public sector world for cloud up until, literally, the agency decided to adopt. So the CIA made that decision, that was, sort of, the shot heard around the world as it relates to cloud adoption. Not just for public sector but for commercial as well, 'cause if you look at Amazon's ramp up, right after that decision was made, their ramp up has been amazing. >> That was a watershed event, for sure. >> It was, and it was very well documented, I mean, I read the judges ruling on that when IBM tried to stop them and the judge eviscerated IBM. And of course IBM had no cloud at the time, they had to go out and spend two billion dollars on software. John has lots of opinions on that, but okay, so that leaves-- >> I'm on the right side of history on that call. >> I think you are, it was a pretty good call. What about, what should be practitioners be thinking about? You talked about the standardization. Where should they be focused? Is it on response, is it on analytics, is it on training? What should it be? >> Well, from our perspective it is, a lot of the focus is on analytics, right? So, a lot of data that we've helped our customers collect over time for this ATO process that John previously mentioned, our goal with IO, Xacta IO, is to help organizations leverage that data to do more through analytics, so there's this dashboard with ad hoc reporting and analytic capability that's going to allow them to blend asset data with risk-to-threat data, with other sorts of data that they're collecting for ATO, specifically for the ATO process, that they can use now for more robust cyber risk management. So, for me, analytics is huge moving forward. >> And that's a prioritization tool so they can focus on the things that matter, or maybe double-click on that? >> It could be, it could be a prioritization tool, but it could also be a tool that you use to anticipate what might happen, right? So, some analytics will help you determine this asset is vulnerable for these variety of reasons, therefore it has to go to the top of the sack for remediation. But also, using that data over time might help you understand that this plus this plus this is an indication that this bad thing is going to happen. And so, analytics, I think, falls into both categories. Probably it's more the forecasting and predictive is something that's going to come later but as you unmask more data and understand how to apply rules to that data, it will naturally come. So, Rick and I have worked together for many, many years and, over a quarter of a century, so the way I would say it is like this. Xacta 360 helps you to accelerate your authority to operate, but that's a point in time. The holy grail for us as security practitioners is all around continuous monitoring of your underlying risk. So, the data analytics that he's talking about, is where we come about and looking at Xacta IO. So, Xacta IO helps fulfill that mission of continuous compliance, which means that the ATO is no longer just relevant at that moment in time because we can do continuous monitoring now at scale, in hybrid environments, in the cloud, on prem. 'Cause our clients are huge, so they're going to be a combination of environments that they're sitting in, and they need to understand their underlying risk posture. They need to have, they're going to have all kinds of scanners, so we don't really care, we can ingest any kind of scanner that you have with Exact IO. As a result of that, the security professional can spend their time on the analysis and not the pedestrian stuff that's just kind of wasting time, like documentation and all that stuff. >> Yeah, for us, data's a means to an end, right? It's either to get an ATO or to help you understand where you need to be focusing your resources to remediate issues. So, for us, leveraging the data that's produced by many companies that are at this show. Their data is a means to help us get our job done. >> Were you able to have, one follow up, if I may, were you able to have an impact, to me, even, again, subjectively, on that number, whatever that number is, that we get infiltrated, the customer gets infiltrated, it's 300 days before they even realize it. Are you seeing an impact on that as a result of analytics, or is it too early days? >> I would say it's still early. But it's reasonable to expect that there will be benefits in terms of faster detection. And maybe it's not even detection at some point, hopefully, it's anticipating so that you're not detecting something bad already happened, it's avoiding it before it happens. >> Yeah, and let me say it this way, too. You know, if you listen to John Edwards, the CIO from the CIA, he talks about how the reason he loves the cloud is because it used to take the agency about a year to provision a server, now it's a few minutes, right? Well that's great, but if you can't get your authority to operate, 'cause that can take another 18 months, you're not going to get the benefit of the cloud, right? So what we do, is we help accelerate how fast you can get to that ATO so that guys like the agency and anybody else that wants to use the cloud can use it much more quickly, right? >> Yeah, and the continuous integration and all that monitoring is great for security but I've got to ask you a question. Analytics are super important, we all know data analysis now is in the center of the value proposition across the board, horizontally. Not just data warehousing, analytics that are used as instrumentation and variables into critical things like security. So, with that being said, if you believe that, the question is, how does that shape the architecture, if I'm in an agency or I'm a customer, I want to build a cloud architecture that's going to scale and do all those things, be up, not go down, and have security. How does the architecture change with the cloud formula for the decision maker? Because right now they're like, "Oh, should I do multi-cloud, should I just Amazon" So, the data is a critical architectural decision point. How do you guys see that shaping, what's your advice to practitioners around designing the cloud architecture for data in mind. Just use Amazon? (laughs) >> Well, yes. (laughs) Just use Amazon. I mean, all the tools that you need exist here, right, and so-- >> If all the tools you need in the cloud exist here. >> Alright, so rephrase another way. >> But John, the issue is you're not going to have all your stuff in the cloud if you're the air force or if you're the army, because you have 75 years of data that you got to push in. So over the next 10 years there's going to be this "hybrid" environment where you'll have some stuff in the cloud, some stuff in a hybrid world, some stuff on prem, right? >> How I secured that, so that's a great point. So, data's everywhere, so that means you're going to need to collect it and then measure certain things. What's the best way to secure it and then is that where Xacta fits in? I'm trying to put that together if I'm going to design my architecture and then go to procurement, whether it's on premise or multi-cloud. >> Well, there are lots of security products that people use to secure, whether you're on prem or whether you're in the cloud and our platform leverages that information to determine whether things are secure enough. So there's a distinction between cyber risk management and actually securing a database, right? So, there's so many granular point products that exist for different points along the security chain, lifecycle chain, if you will, that our objective is to ingest as much of that information and purpose it in a way that allows someone to understand whether they're actually secure or not. And so it's understanding your security posture, transforming that security information to risk so that you can prioritize, as you were talking about before. >> You're taking a platform mentality as opposed to a point product. >> We're taking an enterprise view of risk. So, the enterprise is, remember, it's on prem, and hybrid and cloud. If all your stuff is in the cloud, Amazon has the answer for you. None of our customers are in that situation. If you're a start up, Amazon's the way to go, period. But all of our customers have legacy. As a result of that it's an enterprise view of risk. That's why companies like Telos partner so well with Amazon because they're all about being close to the customer, they're all about using automation. We are as well. >> Alright, talk about the news you guys have, Xacta IO, you're the co-inventor of it, Jack. Talk about this product. What's the keys, what does it do, where's it applied to, you mentioned a little bit of getting past the authority time point there. What's the product about? The product is about ingesting massive amounts of information to facilitate the ATO process, one, but managing cyber risk more generically because not everybody has an ATO requirement. So, you asked a few seconds ago about, so you're taking a platform approach. Yes, we're blending three separate products that we currently have, taking that functionality and putting it on a very, very, robust platform that can exist on prem, it can exist in the cloud. To enable organizations to manage their cyber risk and if they choose, or they have a requirement, to deal with things like FedRAMP and risk management framework and cyber security framework and iso certification and things of that nature. The point is, not everyone has an ATO requirement but everyone has a need to manage their risk posture. So we're using our ability to ingest lots and lots of data from lots and lots of different sources. We're organizing that data in ways that allow an organization to understand compliance and/or risk and/or security, and visualize all that through some dashboard with ad hoc reporting that let's them blend that data across each other to get better insights about risk posture. >> And to visualize it in a way that makes sense to the user. >> Yes, so, if you're the CEO, you're going to want to see it a certain way. If you're the IT manager, you're going to want to see it a certain way. If you're a risk assessor, you're going to want to see it a different way. So that's kind of what we're talking about. >> I got to ask you one question, I know we got to go, but, a hardcore security practitioner once said to me that hardcore security practitioners, like you guys, when they were kids they used to dream about saving the world. So, I want to know, who's your favorite superhero? >> Superman. >> Superman? >> Spiderman. >> Alright, awesome. (laughing) >> That was a basic question for you guys. >> Thank you very much >> Yeah, that's the hardest question, see they're fast, they know. Star Trek or Star Wars? (laughing) >> Depends on the generation. >> We won't go there. theCUBE have 15 more minutes today. Okay, final question, what's this going to do for your business now you have new, opened up new windows with the new product integration. How's that going to change Telos, what does it do for you guys from a capabilities standpoint? >> Well, the big thing I'd suggest your listeners and your watchers to consider is, there's a new case study that just came out, it's published jointly by the CIA, Amazon and Telos, talking about why working together is really, really, really groundbreaking in terms of this movement to the cloud. 'Cause your public sector listeners and viewers are going to want to know about that because this ATO thing is really a problem. So this addresses a massive issue inside of the public sector. >> And final question, while you're here, just to get your thoughts, obviously there's a big change of the guard, if you will, from old guard to new guard, that's an Amazon term Andy Jassy uses. Also, we all saw the DOD deal, JEDI's right there on the table, a lot of people jockeying, kind of old school policy, lobbying, sales is changing. How is the landscape, from a vendor-supplies to the agencies changed and/or changing with this notion of how things were done in the past and the new school? So, three points, legislatively there's top cover, they understand the need to modernize, which is great. The executive branch understands the need to modernize through the IT modernization act as well as the cyber security executive order. And then lastly, there are use cases now that can show the way forward. Here's the problem. The IT infrastructure out there, the IT guys out there that do business in the government, many of them are not paid to be efficient, they're paid cost plus, they're paid time and material, that's no way to modernize. So, fundamentally, I think our customers understand that and they're going to revolutionize the move forward. >> And the rules are changing big time. Sole source, multi-source, I mean, Amazon's on record, I've got Teresa on record saying, "Look, if we don't want a sole source requirement, let everyone bid fairly." Let's see who wins. Who can bring a secret cloud to the table? No one else has that. >> In terms of past performance and customer use cases they're pretty much in the head, for sure. >> Great, Amazon kicking butt here, Telos, congratulations for a great event, thanks for coming on. >> Thanks a lot guys. >> I appreciate it. >> Alright, CUBE coverage here in DC, this is theCUBE, I'm John Furrier with Dave Vellante. Stay with us, we have more great interviews stacked up all day and all day tomorrow. Actually you have half day tomorrow until two 'o clock Eastern. Stay with us for more, we'll be right back. (upbeat music)

>> Announcer: Live, from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. Okay, welcome back everyone, this is theCUBE, live here in Las Vegas at The Venetian, this is Informatica Worlds exclusive coverage with theCUBE, Informatica World 2018, I'm John Furrier, with my co-host Jim Kobielus, analyst at Wikibon, SiliconANGLE, and theCUBE, our next guest is Tracy Ring, Vice President at Deloitte Consulting, great to see you again. >> You as well! >> So, love havin' you on, last year, you know, we go through all the interviews and, you know it always comes up, and this is important, you know we are passionate about women in tech, inclusion and diversity, huge topic, the job's never done, in fact, I was in New York last week for a blockchain event, and I wore a shirt that said: Satoshi's Female. (Tracy laughing) And I literally was getting so many high fives and, but it's not just women in tech, there's a role that men play, this is, sort of an ongoing conversation so. What's the state of the industry, from your perspective, how do you see it? Obviously the data world is, indiscriminate data is data, >> Tracy: Absolutely. >> It should be 50/50. >> Yeah, you know I think that the, the opportunity is multi-faceted, right? So we're in a place where technology is changing unbelievably fast, we're graduating nearly as many men as women in, fields of science, data analytics, computer engineering, etc. But what we're not seeing, a combination of women in leadership roles as much as we would expect, we're not seeing the retention of women in those roles. And for me, I'm really passionate about the fact that supporting, attracting, and keeping women in those roles, is really critical, right? There's an interesting facet to how this all really, really plays together, Deloitte for 20 years has a women initiative, right? 20 years of supporting women, embracing them, helping them support leadership roles and, and I think that the time is now. If not, it's long overdue, to really support them within this field. I also think that women in data, an initiative that we're launching this year, and having our launch event today, is sort of super timely because women in data is not women who only become CIOs, or will only become CDOs, these are women that will be the Chief Marketing Officers, the CHROs, and using data to tell their stories. >> You know, we had a guest on earlier, who was a man, but he was the head of the CDO for the Ireland Bank, and Peter Burris asked the question, said hey, where did you come from technical? No, he came from the business side, who knows technology, this is what you're getting at, and I think this is something that we've been seeing as a pattern that you don't rise up through the ranks and be super nerdy, although that's cool too, and there's a lot more STEM action but there's also multiple vectors into the field. You can come from business, and know tech, and a lot more tech is consumable, and learnable, either online, or through some sort of other proficiency so, this is a big story and so, how do you guys, looking at that, at Deloitte, I know Deloitte's got the track record, but this all scales beyond Deloitte, right? It's an industry thing. >> Tracy: Absolutely. >> How are you guys seeing this? How are you looking at helping people, either connect the dots, or support each other? What's some of the latest and greatest? >> Yeah, I mean I think Informatica is part of what has created the case for change, right? We've democratized data integration, we have, you know, made self-service analytics, we've put data in the cloud in everyone's hands, right? So technology is out there more, every single day, and I think the unique part is, is that, when we think about diversity wholistically, and I think of diversity from ages, and geographic, and gender, etc. I think really being able to take all of that diverse experience, and be able to listen to business user's requirements in a way that they can hear it! And listen for something different, right? And brings skills to bare, that aren't necessarily there. I think if we can build better technology, that's more future-proofed, based on having a diverse crowd listening, and trying to build something that's far more compelling than, you know, I asked for X, build me X. I think when we really do our clients, and the world of justice is when we, you know, someone asks for X, and you ask them 10 more questions, and heavy--what about this? And what, and what, and what? And I think really being much more inquisitive, giving people the ability to be inquisitive, and bringing more opinions to the table to be inquisitive. >> And bringing more diversity of practice, makes the applications better, so that's clear. We see that in some of the conversations we have, but I got to ask about the question of roles, what are you seeing, kind of, you look at the trends, are there certain roles that are, that are being adopted with women in tech more than others? Less, trending down, up? What are some of the trend lines on, either roles in tech, for women? >> Yeah, you know, I think that over all, when I had the opportunity, so when we decided, we're going to launch a program within Informatica. We want the women who are going to be the Chief Data Officers of tomorrow. And it was a great question because, actually what we ended up saying is, the Chief Data Officers of tomorrow could be so many different current roles right now, right? And how do we really, kind of, attract the right women into this cohort, support them for a long year and, provide them the forum to network, connect with others, understand different career paths. You know, looking at what we're seeing, you know, with GDPR, and regulations, and all these other things happening, you know, the concepts and roles that didn't even exist years ago, right, so data governance leads and, Chief Analytic Officers, and all of these-- >> James: Or Chief AI Officers! >> Exac--(laughing) >> How do we bring women into the hottest fields like AI, deep learning? If you look at the research literature, out of, both the commercial and the academic world, many of the authors of the papers are men, I mean, more than the standard ratio of men to women in the corporate space, near as I can tell, from my deep reading. How do you break women into AI, for example, when they haven't been part of that overall research community? That's just a, almost like a rhetorical question. >> Yeah, how do you not, you know, it's just impossible to not bring them to bear, the skills, the talent, the ingenuity, I think it's absolutely mandatory, and someone said to me, they said well, why are the men not invited to this event? Why are they not in the cohort? And I said, you know, because there's a component of all this, that we want to grow and foster and support, and create opportunities. You know, one of the women that sat on our board today said, you know, I'm not somebody who's going to golf, I'm not someone who's going to go to a sports game, I'm going to meet you in the board room, and we're going to talk about compelling topics there. And so I think it's about, encouraging and fostering a new way of networking that's more aligned with what women are interested in, and what, you know, sometimes we do best and, I think creating an opportunity for a different type of everything, in the way that we operate is important. >> I think self-awareness, for men, and this also, creating a good vibe, right? Having a good vibe is critical, in my opinion, and also, you know, not judging people right, you know, based upon, you have some women say, hey I like to get dressed up and that's what I am, some people who don't want to go to sports and, some guys want this, so I think generally, there needs to be, kind of a reset, like hey, let's just have an open mind and a good vibe. >> It's like lunch and learns, you know, lunch and learns are, are a great enabler for centers of confidence, to get together on a regular basis, to talk about business and technical-related things, but also it's a social environment. How can you build more of those kinds of opportunities into the corporate culture, where, they're not skewing, the actual socializing, to traditionally male-dominated hobbies or interests, or traditionally female-dominated hobbies or interests? How can you have, sort of a balance, of those kinds of socialization opportunities in a professionally appropriate environment that also involve a fair amount of shop talk? 'cause that's what gets people bonding, promoted in their careers is that they do deep shop talk in the appropriate settings. >> Yeah, it's interesting, one of the women that I personally consider a mentor, she said if it wasn't for data, I wouldn't be where I am today. And she said, you know, I grew up in and industry where, unfortunately, I really didn't have a voice at the table, and my voice at the table came from data, it came from my ability to see connections, patterns, and detect things, and also for my ability to create networks of people, and make connections and pull things together in a way that my colleagues weren't doing. And, you know, when she tells that story I think that's, that's the template, right? >> John: That's the empowerment. >> We want to say, use everything at your bevy to bring the best value to your business end-users, and she's connecting the dots in a way that no one else had, and is using data as really, the impetis to really, solidify everything that she's saying, it's inarguable. >> That's a great story, it's a phenomenal story. >> It's just amazing. >> Once she got into power she really drove that hard, that's awesome. Well, let's take that to the next level, so, you know, I have a daughter as a junior at UCAL Berkeley, and she's a STEM girl, and so she's got a good vibe in there >> James: STEM girl, I have a stem girl too, mines 28 now. >> You know, and so, kind of aside, but she, turned away from computer science because, at, you know, in middle school the vibe wasn't there, right? And it was kind of a social thing, we mentioned social. You're advice to young women now? Because we're seeing people with the democratization, you see YouTube, you see all these tools, you got robots, you got makers, of course, you got data, you've seen a lot more touch points where people can, you know, ingratiate in unthreatened, un, you know, just, getting immersed in tech. So you have, you're starting to get people the taste of not being tracked into it. So, what's the advice for young folks trying to navigate? And is it networking groups, is it mentoring? What's the playbook in your mind? >> Yeah, I think it's a combination of everything that you've mentioned, right? I absolutely think that your network, and what one of my mentors calls your sleeper network, right? The network that's out there, the people that I worked with five years ago, and we worked, and were in a war room til two a.m. and you know, then I, I just got busy, right? And reactivating your sleeper networks, you know, having the courage to kind of, keep people apprised, using social media, in a way that people, you know, the number of people that say, oh I didn't know you were up to this, that, or the other, thank goodness you posted. And so, I think using all of the technology to your advantage. And I also think there's a component of someone, I mean, I had an MIS degree for undergrad, and I started out as a developer. >> You might have to explain what this is for the younger generation. (laughing) >> Oh, I know, how crazy is that! Oh my gosh, >> Is that in the DP department, was that in the DP department? >> Can you imagine. But I wasn't interested in technology that much, it was what was going to get me a job and, and I thought I would become a business analyst, I've stayed with it, and now really passionate about tech, but, I think there's a component of all this that, every job, you know, the CHROs, the CAOs, all of the roles that roll up, you know, every finance person I know that's exceptional, is phenomenal with data! Right? And so, I think, not only creating a network of people that are in the industry, but I think it's about telling the stories outside the industry, and telling the oh my gosh, you'll never believed what we learned today. And I think that's the magic of the stories, and being transparent. >> Well Tracy, you're an inspiration, thanks so much for coming on theCUBE, really love the story. I got to ask, what are you up to now? Tell us what's up with you, obviously you've moved on from MIS, Management Information Systems, part of the DP, Data Processing department, that's many computer days. >> Tracy: Oh my. >> Oh my God, we're goin' throwback there. >> Tracy: Absolutely. >> What're you up to now? What are you havin' fun with? >> Yeah, so my day job, I have the luxury of working across our cognitive analytic, and our PA alliances, which is an insane mouthful, but it means I get to work with some of our most exciting alliance partners that Deloitte is building solutions, and going to market, and getting really great customer stories under our belt. And I think really kind of blowing the doors off of, of what we did three years ago, five years ago, and 20 years ago, when MIS degrees were still being handed out, so. >> A lot more exciting now, isn't it. >> (laughing) It's way better now! So. >> I wish I was 23 again, you know, havin' a good time. (Tracy laughing) >> Yeah, so, really wholistically, seeing what we consider ecosystems and alliances, is, that's my day job. >> Tracy Ring, Vice President at Deloitte, great story, fun to have on theCUBE, also doing some great work, super exciting time, you got cloud, you got data, it really is probably one of the most creative times in the tech industry, it's super fun to get involved. This is theCUBE, here out in the open, at Informatica World in Las Vegas. I'm John Furrier with Jim Kobielus, be back with more, stay with us! From Vegas, we'll be right back. >> Tracy: Thank you. (bubbly music)

>>live from San Francisco. It's the Q covering in dramatic. A World 2017 brought to you by Inform Attica. Welcome >>back, everyone. We live here in San Francisco at the Mosconi West with In From Attica. World 2017. This is Cubes Exclusive coverage. I'm John Furry with the Cube and Peter Barris with vicky bond dot com General manager we have on research. Our next guest is Tracy Ring, specialist leader at Deloitte Consulting in the trenches. Put it all together. Welcome to the Cube. Thanks for joining us today. Appreciate it. >>Thank you for having me. I'm excited to be here. >>So your specialist, But in the system global system, integrated world, that means you basically globally look at the solutions. And And what's interesting is why I'm excited. Conversation with you is that, you know, point solutions can come and go. But now we're in this compose herbal world of cloud data, etcetera, where ah, holistic view has to be looked at. So what? I want to get your thoughts on in from Attica and what you guys are doing because we've heard it's the heartbeat. But yet there's also a hygiene issue. So you got this heart surgeon and the hygienist, and you have all kinds of specialty rolls of and data. It's pretty broad, but yet supercritical. How do you look at the holistic big picture? >>Absolutely. I mean, we're seeing the view of ecosystems being so much more important. Were so Maney technology disruptors. I mean, three years ago, we weren't even hearing about Kafka, and Duke was really new, and and so I think demystifying, simplifying, helping customers understand the art of the possible what can be done? What are leading practice organizations doing and then really making it real? How do you so this complex story together, how do you best leverage and get your investment out of technologies like in from Attica in their complimentary tools >>is interesting. IBM has Watson in from Attica. Has Claire ASAP has Leonardo s A P has Einstein. >>It would be >>great to get them all together >>and have dinner, right? So I mean, but this speaks >>well, You got Alexa and Amazon and Google. I mean, this is an interface issues you're talking about. Ah, cognitive. A real time new user interface and machine interface into data that is completely out of the possible. It's what's happening in the world is changing. Developers is changing. Practitioners, architects. Everyone's impacted your reaction to all this. >>You know, I think it's probably the most exciting time that we've seen in so long, and I think you so well articulated all of the players that air there. I think when you add in I, O. T. And Device Management, you know it's really an exciting time. And I think it's really driving some amazing things with regard to how organizations are literally transforming themselves. And in both our clients as well as the ecosystem of technologies, companies air are literally shifting their entire business model. It's it's very exciting. >>So one of the things that the typified system integrator types behavior like to elect a lawyer big consulting firm was big application. Let's deploy the big application for accounting for finance for HR whatever. Also culminating in New York, which was the Grand pa of everything. Right now we're talking about analytics where we have to focus on the outcome's not just a big package for a function, but really a complex, ideally strategic differentiating outcome. Yeah, typically using a whole bunch of smaller tools that have to be bought together similar. What John was talking about as a specialist who looks at these tools take us through kind of a new thought process, outcome, capability to tool in the entire journey to get there. >>Absolutely. I think one of the things that delight does that is really, really unique is having conversations that start with art of the possible, what could be done? What are leading practice organizations doing Help me set a strategy? Yeah, and I think the real answer is there's less about sort of benchmarking what everyone else is doing and more about >>really, You got it, You got >>it. It's really about revolutionizing, you know, and and going into a new angle of what is truly, truly possible. And I think, ah, lot of the things that were sort of table stakes and in the way that we would look at success totally turned on its head. And we're looking at organizations monetizing their data and, you know, creating new business ventures because of the insights that they're deriving and a lot of times will use. Delight has an insight studio and a greenhouse, and a couple of really highly collaborative spaces that we take clients to. Ah, well, you know, plan 123 day workshops, depending on how difficult of problem they're trying to solve and help them charter road map. And take that road map, which is in many cases, business oriented business results driven and help them so in and layer in the technologies that are gonna make that reality possible. What's >>the opportunities for cognitive? I mean, you guys talk a lot of Deloitte about a Friday different things, but specifically there's some key opportunity around. Call the cognitive or you guys call the cognitive. IBM also used that word cognition, but really a I artificial augmented intelligence are signs of a new kind of opportunity landscape. Whether you see for customer opportunities out there, >>absolutely, we talk a lot about what we consider the inside driven advantage. And that's really about using all of the tools in the toolkit to make that insight driven, data driven, better decisions around what organizations conduce. Oh, and kind of. It is a huge component of that, you know, it's we've been hearing stories for years about companies sort of predicting the next best offer and you know, we're seeing this move so much further, removing into robotics process automation. You know, the space is getting, I think, even more complex. But I think what's interesting is when we talk to organizations about, you know, they're not hiring tons of people to go out and do data integration through wonderful organizations. Confirm Attica. That's really been solved. So companies were able to both take their technical resource is and shift them into solving Maur difficult problems, hairier technology opportunities and use that to help shape their business. >>That's like compose abilities. So in dramatic, a world's got a set of solutions and technologies. Some sass ified someone fram. But here it is. But you're deluded you. That's just one element to your mix of things composed for clients. You mention those three years opportunities. Digital transformation is kind of the categorical wave >>Iran, but the end of >>the day it's business transformation. You mentioned changing the business model. >>How do >>customers take advantage of those business opportunities in whether it's robotics or industrial i ot or insights and analytics? What What is the customer impact and how did they get those business benefits? >>Yeah, I mean, I think again like I said, a lot of times it starts with, you know, what is their goal? What do they want to be known for in the marketplace and that value branding of Of what is it that they see themselves differentiating amongst their competitors and using a pretty solid process and rigorous approach to that strategy? Tea set? You know, what are the pillars to achieve? That is, I think, a big piece of it. I think the other component is we see a lot of organizations sort of challenging themselves to do more. And we'll have organizations say I believe that I can doom or what? What could I do? And I think that's interesting that >>we'll just fall upon that because Pete and I were talking earlier before we came on about what gets customers excited when the iPad came out. That was the first kind of visual of >>I gotta have my analytics on the dashboard. Let's start. I >>call the dashboard wave now with bots and aye aye. You're seeing another reaction. >>Yeah, I gotta have that. Automated. Do you see it the same way? And how does that >>translate to the custom when they see these this eye candy and the visualization stuff. How does that impact your world and the impact of the customer? Your customer? >>Absolutely. I mean, we used to live in a world where if I needed to have my data extracted, I would, you know, submit a request. And it was this very long, lengthy process. And, you know, when you think about the robotic single and and process automation, you know, automated data pools are are there. And I think the interesting part is is that it's not about just cost out of i t. It's not about, you know, getting off of on premise hardware. It's about driving better customer satisfaction, driving better business outcomes. You know, the implications. I think whether you're in life sciences or you're in retail, you can touch your customer in a way that is. You know what I would say? Sort of delighting them versus just giving them what they asked for. >>So I wanna I wanna test of theory on you and see how live and see how this seals lines up with thinking and where you see your customers going. So we have this notion that wicked bond, our research of what we call systems of agency. And by that we mean effectively that historically we did we create systems that recorded action big t p e r p. More recently, as you said, we're now creating systems that suggest action predictive analytics, those types of things. And now we're moving in the world were actually going to have systems that take action. Yeah, where authority and data have to move together so that the system is acting as an agent on behalf of the brand now in from Attica has done some really interesting things here with some of their new tooling, some of the metadata tooling to ensure that that type of meeting can move with the data. So if you think about where Deloitte and customers are going, are they starting to move into this new realm where we're building systems, take action on behalf of the brand and what does that mean for the types of tooling? But we're gonna have to find for customers so they can make it, you >>know? I mean, this morning we were delighted to hear the latest announcement around how metadata is really such a core component, and and I think of it is metadata is in many cases where most organizations do see the monetization of their data payoff. Right? We're not only do I have highest golden record like we talked about 10 years ago, I have data lineage. I have data traceability. I have the whole entire story. So it's really much more cost justified. Uh, you know, hearing the announcement today of Claire, and you know how we now have the Aye Aye of our clairvoyance is really exciting. And, you know, I I don't know that we're completely there. And I think we'll continue to innovate as in from Attica. Always does. But we certainly are a whole lot closer. And I would say, you know, your concept is you know, certainly we're all going to the park for >>good. My final question. Let's get your thoughts on because you have a global perspective. You work with the ecosystem partners. You heard all the stories. You've heard all the raps and all the Kool Aid injectors from the different suppliers. But there's two things going on that that's interesting. One is we're kind of going back to the end to end solution. Absolutely. I'm seeing five g with Intel Smart cities I ot So everyone wants to get back to that end to an accountability with data and packets moving. All that could step with applications over the top. But yet there's not one single vendor owning it, so it's kind of a multi vendor world, yet it's gotta be in tow end and bulletproof secure. I mean, >>that's your world. It's not derailed. I mean, you got to be busy, your reaction to that. And what's that? What's that >>mean to the industry? And how should customers? I'd look at that Say okay, Want to get some stability? I want great SL ways, but I want a flexibility for compose ability I want and empower my app developers Dr Top Line Revenue. This is the Holy Grail. We're kind of in the wheelhouse right now. >>Yeah, 100%. I think it's a very exciting time and the like, I said, the fabric of what organizations need to sew together two really achieve their analytic insights and, uh, you know, leveraging their data. I think data is just becoming more and more important, and it's a phenomenal place toe to be in both for where I sit on the consulting side helping all of our customers and certainly where globally we're seeing our client's going >>and your and your message to the client is what we got your back on. This >>has to look, that's what you guys do. You sew it together. It's got to be more than that. It's got ideas for you could see. I think it's a >>lot. I think it's that it's not just about bolting in a technology or 10 technologies. It's about solving the most difficulty technology problems with, you know, with data helping. >>You gotta be savvy to, as they say in the swim lanes of the different firms and got to bring your expertise to the table with some of your own tech. >>Absolutely. And and I think for us we never sort of a ra missed that there is a huge business, and if you if you don't take the business aspect of it, what business problem are we solving? What value are regenerating? How are we ultimately impacting our customers customers, you know? Then you know you're sort of missing the what we consider the most important piece of the pie. >>Tracey Ring with the Lloyd. Great to have you on. Thanks for your insight. Very insightful. That all the data's right there. We're gonna make sense of it here in the Cube. Thanks for sharing, Dee Lloyd. Really put it all together. Composing the future Cloud Data Mobile. It's all here. Social is the que bringing all the live action from San Francisco. I'm John for Peter Burst more after this short break.

>> Live from Las Vegas it's The Cube covering NAB 2017. Brought to you by HGST. >> Welcome back to The Cube. We are live at NAB 2017. I'm Lisa Martin. Very, very excited, kind of geeking out right now to be joined by our next two guests. Sam Blackman, the co-founder and CEO of AWS Elemental, welcome to The Cube. >> Sam: Thank you so much. >> And we have NASA astronaut, Tracy Caldwell Dyson. Both of you, welcome to The Cube. >> Thank you. >> Today has been a very historic day for technology and space. This was the first ever live 4k video stream that happened between you on Earth, Sam, and Doctor Peggy Whitson, aboard the International Space Station. >> Sam: Yes. >> Wow. Tell us about that. >> It was truly amazing to be part of history and the amount of technology that came into play to make this possible. You know, sitting in the conference room in NAB in the middle of Las Vegas, seeing astronauts 250 miles ahead, going around the Earth, 17,000 miles an hour and a seamless, beautiful 4k picture. It was mind blowing. Hard to believe it's happened still. >> I can't even imagine. I'm getting goosebumps for you. Tell us some of the things that Dr. Whitson shared about her experiences. What was the interaction like? >> Well, Commander Whitson and Colonel Fisher was also in the interview and that guy is hilarious, by the way. >> Yeah, he is. >> He is hilarious. They talked about how advanced imaging technology really helps NASA perform experiments and bring experiments that are happening on the space station down to Earth for researchers to use that data and discover how the world works inside the universe. Some of the really interesting examples revolved around some experiments they showed. With thin film technology they had a very small, metallic structure that they could pull water out of and then corral that water, convert it into a spherical shape and in the 4k resolution, you could just see every element of that thin film in a way that looked like it was right next to us. I mean, it was transformative. >> Tracy: Yeah. >> I bet it was. Well, speaking of transformative, this was, I mentioned, a really historic event for a number of reasons. Obviously, for those of us on the ground, for AWS Elemental. But, Tracy, from your perspective, you've been in space for 188... I had it here somewhere, hours. >> Yeah, days. >> You've been on STS118, you've been on the Soyuz to the station on expeditions 23 and 24. What does this capability now mean in the life of an astronaut? >> I think what it does is it helps us bring the experience to everybody here on Earth. It is so hard to capture what we are not just seeing, but experiencing. The richness, the detail, the vividness of the colors and how they're changing are all a part of looking at our beautiful planet. And just from that alone, being able to bring that to the American people, the world, really, is, I think to me a great relief. Because it grieves me to think about how in the world I would describe this beautiful, magnificent view to everybody back home. >> I can imagine. You've done extra-vehicular space walks. >> Tracy: Yes. >> And I can imagine it's indescribable. >> It is. And from the fact you're looking at our planet from 250 miles above, you see the curvature of the Earth, you see it moving at a super high speed, you don't feel the wind in your face, but there's no doubt you're traveling very fast. Just the fact that you are out in the vacuum of space. If you could bring parts of that experience to people back home ... I'm excited to think about how that would transform just the way people think, not to mention the way that they act towards our planet. >> I also think inspiration ... We were talking before we went on that you were about 14 when the Challenger incident happened, we all kind of remember exactly where we were, and that really, a teacher being in space was so inspirational to you. Can you imagine shifting the conversation and what this technology is able to do inspiring the next generation of people that want to be the next Tracy Caldwell Dyson? >> Well, I think what the technology does today, especially in imaging capabilities, is it provides so much more detail than I could even describe. That a young person today watching that, and our generation today is so visual, that they're going to pick up on things that I wouldn't even think to describe to them. And it's going to capture their imagination in ways that are astounding. Compared to I, who, just the sheer knowledge of knowing there was a teacher that was going into space, propelled me to work really hard. I can only imagine what this generation's going to be capable of because of the images that we're bringing to them. >> It's so exciting. Sam, this is really kind of the tip of the iceberg. From AWS Elemental's perspective, first of all, you just had a rebrand. But what does this mean for the future of the video ecosystem? >> Well, I think it really shows you how the technology components came come together to create unbelievable pictures no matter where you are on the planet or in space. We had a live 4k encoder on the space station itself sending down signals to Johnson Space Center, then Johnson Space Center sending redundant links to Las Vegas, here, and the convention center. And then processing the video, the interview with Tracy, here in the space center-- or, here in NAB and then using the cloud to distribute that all over the world. So these 4k images, which take a significant amount of bandwidth, can be created in space, delivered here, produced and delivered anywhere in the world using the power of the cloud and advanced networking technology. And that's pretty amazing, when you think about it. >> Lisa: It really is. I don't think the three of us are smiling big enough. >> I know. It hurts! >> There's so much relief in this face. >> Lisa: I can imagine >> I bet. >> I absolutely can imagine, I think. One of the cool things about-- This is our first time at NAB with The Cube, but we're here: Media, entertainment, Hollywood. What this shows is this transcendence of technology to space. And there's so much interest in space. In fact, Tracy, you were an advisor to Jessica Chastain on "The Martian," which is probably pretty exciting. >> Oh, absolutely. It is. >> But just the transcendence of that and how this technology can be used to power things that everybody can understand, movies and things. But also the future of space exploration, which I can imagine, right now in the era of the space shuttle being retired now, depending on Soyuz rockets to get to the space station as the next vehicle is delivered, this must be quite inspirational for you as an astronaut, as not only is the next vehicle in development, but also, the exploration of Mars. In fact, you were just last month with President Trump. >> Tracy: Yes. >> As they signed a bill. What are your thoughts about that and how do you see imaging technology being an instrumental part of Mars exploration? >> In so many ways, but at the top is the momentum. Like you said, with Hollywood has captured space in some real endearing ways. And the images from NASA, from the human space flight program to Hubble to deep space, it is propelling ... it's momentum. And I think we need that momentum, especially with our young folks because they're going to be the ones, let's face it, who are going to be in the best condition to be on the planet of Mars. So, if we can continue to feed them the images as lifelike as we can, so that they feel they're there, I think we are heading in the right direction to actually being there. >> Wow, fantastic! Congratulations to both of you. Thank you both so much for joining us on The Cube. We can't wait to see what's next. >> Sam: Thank you so much. >> Tracy: Thank you. Thank you. >> Well, for Tracy and Sam, I'm Lisa Martin. You've been watching The Cube live from NAB 2017. Stick around, we'll be right back. (funky music)

(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

(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)

(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat 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.

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(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)

>>We're back at your storage service. Emil Stan is here. He's the chief commercial officer and chief marketing officer of open line. Thank you, Emil, for coming on the cube. Appreciate your time. >>Thank you, David. Nice. Uh, glad to be here. >>Yeah. So tell us about open line. You're a managed service provider. What's your focus? >>Yeah, we're actually a cloud managed service provider and I do put cloud in front of the managed services because it's not just only the scripts that we manage. We have to manage the clouds as well nowadays. And then unfortunately, everybody only thinks there's one cloud. But's always multiple layers in the cloud. So we have a lot of work in integrating, uh, it where a cloud manages provider in the Netherlands, focusing on, uh, companies who have a head office in the Netherlands, mainly in the, uh, healthcare local government, social housing logistics department. And then in the midsize companies between say 250 to 10,000 office employees. Uh, and that's what we do. We provide them with excellent cloud managed services, uh, as it should be >>Interesting, you know, lot early on in the cloud days, highly regulated industries like healthcare government were somewhat afraid of the cloud. So I'm sure that's one of the ways in which you provide value to your customers is helping them become cloud proficient. Maybe you could talk a little bit more about the value prop to customers. Why do they do business with you? >>Yeah, I think, uh, there are a number of reasons why they do business with us or choose to choose for our managed services provider. Tracy, of course, are looking for stability and continuity, uh, and, and from a cost perspective, predict predictable costs, but nowadays you also have a shortage in personnel and knowledge. So, and it's not always very easy for them to access, uh, those skill sets because most it, people just want to have, uh, a great variety in work, what they are doing, uh, towards, towards the local government, uh, healthcare, social housing. They actually, uh, a sector that, uh, that are really in between embracing the public cloud, but also have a lot of legacy and, and bringing together best of all, worlds is what we do. So we also bring them comfort. We do understand what legacy, uh, needs from a manager's perspective. We also know how to leverage the benefits in the public cloud. Uh, and, uh, I'd say from a marketing perspective, actually, we focus on using an ideal cloud, being a mix of traditional and future based cloud. >>Thank you. I, you know, I'd like to get your perspective on this idea of as a service and the, as a service economy that we often talk about on the cube. I mean, you work with a lot of different companies. We talked about some of the industries and, and increasingly it seems like organizations are focused more on outcomes, continuous value delivery via, you know, suites of services and, and they're leaning into platforms versus one off product offerings, you know, do you see that? How do you see your customers reacting to this as a service trend? >>Yeah. Uh, to be honest, sometimes it makes it more complex because services like, look at your Android or iPhone, you can buy apps, uh, and download apps the way you went to. So they have a lot of apps, but how do you integrate it into one excellent workflow, something that works for you, David or works for me? Uh, so the difficulty, some sometimes lies in, uh, the easy accessibility that you have to those solutions, but nobody takes into account that they're all part of a chain or workflow supply chain, uh, and, and, uh, they're being hyped as well. So what, we also have a lot of time in, in, in, in, in managing our customers, is that the tremendous feature push feature push that there is from technology providers, SaaS providers. Whereas if you provide 10 features, you only need one or two, uh, but the other eight are very distracting from your prime core business. Uh, so there's a natural way in that people are embracing, uh, SA solutions, embracing cloud solutions. Uh, but what's not taken into account as much is that we love to see it the way that you integrate all those solutions to it's something that's workable for the person that's actually using them. And it's seldomly that somebody is only using one solution. There's always a chain of solutions. Um, so yeah, there are a lot of opportunities, but also a lot of challenges for us, but also for our customers. >>Do you see that trend toward, as a service continuing, or do you actually see based on what you're just saying that pendulum, you know, swinging back and forth, somebody comes out with a new sort of feature product and that, you know, changes the dynamic or do you see as a service really having legs? >>Ah, that's very, very good question, David, because that's something that's keeps our busy all the time. We do see a trend in as a service looking at, uh, talk about pure later on. We also use pure as a service more or less. Yeah. And it really helps us. Uh, but you see, uh, um, that sometimes people make a step too, too fast, too quick, not well thought of, and then you see what they call sort of cloud repatriation, tend that people go back to what they're doing and then they stop innovating or stop leveraging. The possibilities are actually there. Uh, so from a consultancy guidance and architecture point of view, we try to help them as much possible to think in a SA thought, but just don't use the, cloud's just another data center. Eh, and so it's all about managing the maturity on our side, but on our customer side as well. >>So I'm interested in how you're sort of your philosophy and it relates, I think, in, in, in terms of how you work with pure, but how do you stay tightly in lockstep with, with your customers so that you don't over rotate so that you don't them to over rotate, but then you're not also, you don't wanna be too late to the game. How, how do you manage all that? >>Oh, there's, there's, there's a world of interactions between us and our customers. And so I think a well known, uh, uh, uh, thing that people, the most customer intimacy, that's very important for us to get to know our customers and get to predict which way they're moving. But the, the thing that we add to it is also the ecosystem intimacy. So no, the application and services landscape, our customers know the primary providers and work with them, uh, to, to, to create something that, that really fits the customers to just not look at from our own silo where a cloud managed service provider that we actually work in the ecosystem with, with, with, with the primary providers. And we have, I think where the average customers, I think we have, uh, uh, uh, in a month we have so much interactions on our operational level and technical levels, strategic level. >>We do bring together our customers also, and to jointly think about what we can do together, what we independently can never reach, but we also involve our customers in defining our own strategy. So we have something we call a customer involvement board. So we present a strategy and today, does it make sense? Eh, this is actually what you need also. So we take a lot of our efforts into our customers and we do also, uh, understand the significant moments of truth. We are now in this, in this broadcast, David there. So you can imagine that at this moment, not thinking go wrong. Uh, if, if, if the internet stops, we have a problem. And now, so we, we actually know that this broadcast is going on for our customers. And we manage that. It's always on, uh, uh, where in the other moments in the week, we might have a little less attention, but this moment we should be there in these moments of truth that we really embraced. We got them well described. Everybody working out line knows what the moment of truth is for our customers. Uh, uh, so we have a big logistics provider. For instance, you does not have to ask us to, uh, have, uh, a higher availability on black Friday or cyber Monday. We know that's the most important part in the year for him or her. Does it answer your question, David? >>Yes. We know as well. You know, when these big, the big game moments you have to be on your top, uh, top of your game. Yeah. Uh, you know, the other thing, a Emil about this as a service approach that I really like is, is it's a lot of it is consumption based and the data doesn't lie, you can see adoption, you know, D daily, weekly, monthly. And so I wonder how you're leveraging pure as a service specifically in what kind of patterns you're seeing in, in, in the adoption, >>Uh, pure as a service for our customers. It's mainly never visible. Uh, we provide storage services, provide storage solutions, storage job is part of a bigger thing of a server of application. Uh, so the real benefits to be honest of, of course, towards our customer, it's all flash, uh, uh, and they have the fast, fastest storage is available. But for ourself, we, uh, we use less resources to manage our storage. We have far more that we have a near to maintenance free storage solution now because we have it as a service and we work closely together with pure. Uh, so, uh, actually the way we treat our customers is the way pure treats us as well. And that's why there's a used click. So the real benefits, uh, uh, how we leverage is it normally we had a bunch of guys managing us storage. Now we only have one and knowing that's a shortage of it, personnel, the other persons can well be, uh, involved in other parts of our services or in other parts of an innovation. So, uh, that's simply great. >>You know, um, my takeaway Emil is that you've made infrastructure, at least, least the storage infrastructure, invisible to your customers, which is the way it should be. You didn't have to worry about it. And you've, you've also attacked the, the labor problem. You're not, you know, provisioning lungs anymore, or, you know, tuning the storage, you know, with, with arms and legs. So that's huge. So that gets me into the next topic, which is business transformation. That, that means that I can now start to attack the operational model. So I've got a different it model. Now I'm not managing infrastructure same way. So I have to shift those resources. And I'm presuming that it's a bus now becomes a business transformation discussion. How are you seeing your customers shift those resources and focus more on their business as a result of this sort of as a service trend? >>I think I do not know if they, they transform their business. Thanks to us. I think that they can more leverage their own business. They have less problems, less maintenance, et cetera, et cetera. But we also add new, uh, certainties to it, like, uh, uh, the, the latest service we we released was imutable storage being the first in the Netherlands offering this thanks to, uh, thanks to the pure technology, but for customers, it takes them to give them a good night rest because, you know, we have some, uh, geopolitical issues in the world. Uh, there's a lot of hacking. People have a lot of ransomware attacks and, and we just give them a good night rest. So from a business transformation, doesn't transform their business. I think that gives them a comfort in running your business, knowing that certain things are well arranged. You don't have to worry about that. We will do that. We'll take it out of your hands and you just go ahead and run your business. Um, so to me, it's not really transformation. It's just using the right opportunities at the right moment. >>The imutable piece is interesting because of course, but speaking of as a service, you know, anybody can go on the dark web and buy ransomware as a service. I mean, as it's, he was seeing the, as a service economy hit, hit everywhere, the good and the, and the not so good. Um, and so I presume that your customers are, are looking at, I imutability as another service capability of the service offering and really rethinking, maybe because of the recent, you know, ransomware attacks, rethinking how they, they approach, uh, business continuance, business resilience, disaster recovery. Do you see that? >>Yep, definitely. Definitely. I, not all of them yet. Imutable storage. So it's like an insurance as well. Yeah. Which you have when you have imutable storage and you have, you have a ransomware attack, at least if you part the data, which never, if data is corrupted, you cannot restore it. If your hardware is broken, you can order new hardware. Every data is corrupted. You cannot order new data. Now we got that safe and well. And so we offer them the possibility to, to do the forensics and free up their, uh, the data without a tremendous loss of time. Uh, but you also see that you raise the new, uh, how do you say, uh, the new baseline for other providers as well? Eh, so there's security of the corporate information security officer, the CIO, they're all fairly happy with that. And they, they, they raise the baseline for others as well. So they can look at other security topics and look from, say a security operation center that now we can really focus on our prime business risks, because from a technical perspective, we got it covered. How can we manage the business risk, uh, which is a combination of people, processes and technology. >>Right. Makes sense. Okay. I'll give you the last word. Uh, talk about your relationship with pure, where you wanna see that, that going in the future. >>Uh, I hope we've be working together for a long time. Uh, I, I ex experienced them very involved. Uh, it's not, we have done the sell and now it's all up to you now. We really closely working together. I know if I talk to my prime marketing, Marcel height is very happy and it looks a little more or less if we work with pure, like we're working with colleagues, not with a supplier, uh, and a customer, uh, and, uh, the whole pure concept is quite fascinating. Uh, I, uh, I had the opportunity to visit San Francisco head office, and they told me to fish in how they launched pure being, if you want to implement it, it had to be on one credit card. The, the, the menu had to be on one credit card, just a simple thought of put that as your big hair, audacious goal to make the simplest, uh, implementable storage available. But for, uh, it gives me the expectation that there will be a lot of more surprises with puring in near future. Uh, and for us as a provider, what we, uh, literally really look forward to is that, that for us, these new developments will not be new migrations. It will be a gradual growth of our services on storage services. Uh, so that's what I expect, and that was what I, and we look forward to. >>Yeah, that's great. Uh, thank you so much, Emil, for coming on the, the cube and, and sharing your thoughts and best of luck to you in the future. >>Thank you. >>You're welcome. Thanks for having me. You're very welcome. Okay. In a moment, I'll be back to give you some closing thoughts on at your storage service. You're watching the cube, the leader in high tech enterprise coverage.

(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)