(bright music) >> Hello, welcome to theCUBE's presentation of the Unstoppable Domains Partner Showcase. I'm John Furrier host of theCUBE. We're here talking about the metaverse and what it all means, what it brings to the table. We've got two pioneers here in the metaverse breaking it down, doing great stuff. Both co-founders of companies, Noah Gainer, co-founder and CEO Parcel. And CJ Hetherington Co-Founder of Atlantis World, digging deep and doing all the great stuff in the Midwest. Chill and thanks for coming on theCUBE. >> Thank you so much for having us. >> Thanks for having us. >> So, first of all, I want to say congratulations for the work you guys are doing. This is one of the biggest waves we've seen coming on. It's a changing user expectations, it's a changing architecture, it's real technology involved, there's a lot of action. 30% of people at University of California, Berkeley are dropping out of the Computer Science program to get into Web3. This is the biggest technological change, business model change, user experience change. And we've been seeing going back multiple inflection points. This is a big deal. So the metaverse is real. Some people say, "Well, you know, it's not com..." It's coming it's just a matter of time. So let's get into it. What are you guys doing? Tell us about your company's Parcel and Atlantis World. Noah, start with you. >> Sure, so Parcel is a marketplace for virtual real estate. So you can think of something like OpenSea, which everyone is familiar with, but we solely focus on virtual land and virtual real estate in a number of virtual world, maybe part of decent land or the sandbox. So we feature those on our platform and, you know, we take it the next level with the user experience. So we have fully interactive maps. We have price estimates. You can think of it like a estimate on Zillow and in general, we're building the fully verticalized solution for virtual real estate users. And that will extend into rentals, like Airbnbing out your virtual condo or getting a mortgage on your virtual home, as well as, you know, cultivating the community around it. And especially helping empower creators and architects and builders and getting them work and getting their work on display. >> I'm looking forward to digging into that, that sounds very cool. CJ what's Atlantis world doing? What do you got going on? >> Yeah, exactly. So at Atlantis world, we're building the Web3, social metaverse by connecting Web3 with social, gaming, and education in one light web virtual world, that's accessible to everybody. So by going with actually a light web first and a pixel approach so that you can play on mobile or a really old device, because the problem with existing metaverses is that they set an incredibly high cost barrier to entry and also tech isn't necessarily readily available globally in terms of things like VR headsets and gaming PCs. Like for example, when I was in Africa, I travel a lot. If my book would break, it's not even that I couldn't necessarily afford to buy anyone, it's actually not available there. So we're ruling out a lot of the global kind of population from a metaverse experience. And we're building something which is like 3D pixel and super light weight, to kind of bridge that gap and build something which is ready to be massive up till now and onboard billions of users into Web3. So they'll all basically be using Web3 applications in a gamified way and going really hard on connecting that with social features, like voice chat and talking, getting, and virtual events and vaulting and all of that stuff. >> You know, I love what you guys are doing, you're pioneering a whole another area, but what's great about the whole crypto area, is that, since you know, 2017 onwards you saw Ethereum set the developer market started coming in strong. So you start to see that development. And now we got the metaverse. So I got to ask you guys what's the current definition of the metaverse. I mean everyone's... I mean, since Facebook changed their name to Meta, it's been a hype cycle and everyone's like, "Woh..." First of all, you know why they did that. But they're actually putting a lot of DAO in this. This is a wave, we talked about that. But what is the metaverse? How do you describe it? Why is it relevant? Virtual real estate, that sounds cool. What does this all come together? Explain it for the people out there that might not be getting it right. >> Yeah, I feel like for me, the critical difference between an ordinary gamer, what one might think of as game and a metaverse is actually Web3. For me, Web3 is metaverse. And for me it's really because Web3 enables real world utility, but inside of a virtual environment. So for example, inside of Atlantis, you might run into a DeFi bank and understand by interacting with other game characters, which are programmed to teach you about DeFi and like, what is Avel, how to deposit. And so you're actually getting a real world utility out of doing something in a virtual environment. And for me, that's what really bridges the gap into metaverse. Yeah, I'm really kind of bullish on that. (chuckles) >> Noah, what's your take? Define the current state of the definition of the metaverse? What is the metaverse? >> Yeah, to me, it's the 3D internet. And I do agree with what CJ's saying, how, you know, what makes it the most compelling and will ultimately the most successful is that addition of a blockchain and essentialized, you know, tributed ledger technology. Because you can have the closed metaverse, which nobody wants that future. And I don't believe that will be the future. you know, versus the open metaverse, which is blockchain-based, the users are the owners of the assets and the land and everything around it. And it's really foreign by the people. But I see the metaverse as just an extension of the internet we're already using today but we're going to have hardware that makes it 3D and more immersive like AR and VR. >> Yeah, I think- >> Yeah, definitely- >> Go on CJ. >> Around kind of like eight or nine months ago when we started to build Atlantis, we decided that the metaverse was a virtual world where you could live, work, play, and earn, and that's what we've been building. It started off as like building the metaverse that has DeFi and over the kind of time it's gone on our community has grown, we've started to understand the future of our product and our mission and values. It started to become the Web3 metaverse, right? And then on top of that, the Web3 social metaverse, so it's a combination of what all these things. >> You know, it's interesting. And I'm a little bit older than you guys, I wish I was your age, but when the web came along, people were saying the same thing. That the web's terrible. It's a stupid thing. It's never going to be real. And yeah, there was problems. It was slow to dial up back in the day. But yeah, now with gaming, I got to say, I had to look at the gaming evolution being a gamer myself, old school, I guess, but the gaming culture is proxy to what I see kind of happening in the metaverse. And let me get your reaction to that. I'm not saying directly, but you saw what gaming did, right? In game currency, some, you know, pockets of the same kind of dynamic where a lot of value is happening, the expectations were different for users. So how does the metaverse... How does gaming cross over? What's the ecosystem of metaverse? Obviously it's a cultural shift, one. Infrastructure, two. But I can just see this new generation of thinking. It's a whole nother level. Can you guys share your thoughts on that riff? >> Absolutely. Yeah, absolutely. It's like for us, we really believe that we can enable a social revolution, where workers from impoverished and remote regions can actually be onboarded into these digital player to earn economies and also learn to earn economies. So it's about leveraging Web3 and blockchain gaming, whatever actually you want to call it, to enable this revolution and actually onboard new people into a completely new working and dynamic. One of the other things we envision for Atlantis, imagine like you run around this game world and you complete quests inside of the game. And these quests basically involve talking to the non-player characters, the NPCs, which are basically pre-programmed. I don't know if anyone's ever played an MMORPG before, but it can be super fun. And they'll actually teach you how to use different crypto applications. Whether that's a DeFi bank, NFT marketplace, kind of digital asset exchange. And once you all do that, the kind of end goal in vision is that you'll be rewarded with tokens. So users will earn crypto for learning about crypto. And if anybody wants to do that right now, they can actually go to rabbithole.gg. It's a different project to Atlantis, but they building learn to earn, and you go on you complete quests and interact with different crypto applications. And it's so crucial for onboarding. And yeah, it's going to be really powerful, the kind of revolution that play to earn and learn to earn will enable. >> I'll check out the rabbihole.gg sounds awesome. What's your take on the reaction to that riff on this convergence of culture tech, gaming, vibe that's kind of divine the metaverse what's your take on that, Noah? >> Yeah, I mean... I think gaming will be the on ramp for maybe the first billion people, you know, into blockchain. It's something people already do and are already paying for, and they now have the opportunity to get paid to play. So the incentives are extremely strong and I think that will be a great way to usher people in, teach 'em about blockchain without realizing that they're using blockchain. And then once they're already in it and have already used it, then it becomes much more natural to user than other applications. >> It's funny, people always talk about, "Oh, user experience!" You know, expectations drive experience, right? If you expect something and if they're used to gaming, I see the great, great call out there, good point. Well, let me ask you guys a question, 'cause I think this is comes out a lot in terms of like the market shifts and metaverse, as an old expression, "Great markets pull the products out of companies or out of the industry." What organic growth have you guys seen in the metaverse that's been either a surprise or a natural evolution of just success and just growth, because the market's hungry for this and it is relevant. It's new, what's pulling out? What's coming out of the organic aspect of the metaverse? >> I think a lot of art and architecture and design. And, you know, it's empowering a lot of independent creators and allowing 'em to stretch their skills in a way that they maybe couldn't do before, but now can do and get compensated for. Like, we see really see the rise of the creator coming in the next couple of years in the open metaverse and finally they will be the ruling class. They won't get the short end of the stick, which artists have for... I mean all the time. >> Yeah, some of the wall street bet skies in the same way, feel the same way. CJ, What's your take on... What's getting pulled out on the organic execution growth of the interactions and metaverse evolution? >> Of course, yeah. I would, first of all love to go back to the previous point on gaming and just kind of like, definitely agree with what Noah said. And the thing is that gaming is 3.4 billion user market, and they're typically an experimental by nature people and group of users, right? So it's definitely a huge onboarding opportunity for teaching users about Web3 and using Web3 in a gamified way and making that kind of inherently fun and engaging. And again, in terms of organic growth, Web3 is incredible for that. We place a huge emphasis on, I think, collaborate versus compete and try to enable network effects for everybody who is involved in Atlantis and becoming part of our fast growing ecosystem. Like we have eight blockchain, more than 10 DeFi apps, like Aave, Yearn, Balanced, 1inch, Perpetual. All of the DAOs like The Exile, MetaCartel, lobsterdao, PizzaDAO, all of the NFT communities. Like we're actually building a yacht for bought yacht club on the beach in Atlantis. So that's fun. But yeah, we grew our community. We're very early stage still. We've been building only for eight or nine months, but we grew our community to like 20 to 30,000 community members across social channels. And we recently raised over a million dollars from our community and we're fully bootstrapped and taken no private money. So the ability to actually do that and to coordinate both kind of community efforts and fundraising and resources is really testament to Web3 and what it's becoming in the community aspect of that. And also its future and the kind of dawn and domination of the Metaverse. >> Well, I got to say, I just got to give you props for that. I think that fundraising dynamic is a real entrepreneurial new thing, that's awesome. You've got active community vote with their contribution and whether it's money and or other value, right? You got social value. This is the whole thing about the metaverse, there's a new community culture going next level here. >> We believe in community and we believe in Web3. And we know we don't understand why most leading metaverses are focusing fully on huge IP and actually ignoring Web3. So we're actually trying to build the infrastructure layer for Web3 applications and for Web3 driven utility inside of the metaverse. And what we mean by that imagine that any developer or any project or any team or any company could occupy a plot for free inside of the metaverse, customize it by branding and then effectively set up shop, whether that's a Web3 integration, so it's a DeFi Bank, or it's an exchange. Or whether that's an NFT marketplace or a music venue or a coworking space. We're really excited about that. And we really believe we've designed the value capture mechanism for virtual land in the metaverse and we're approaching it in a different way to land in the real world. >> That's awesome. Well, let's get that infrastructure conversation, unstoppable domains obviously there having the partner showcase here. You guys are partners. This NFT kind of like access method is a huge... I love it by the way. I think it's phenomenal. I love the value there, but it's also digital identity and it's distributed naming. So you kind of got this enablement vibe, you got solve a problem. How is it working with you guys? Take us through what does unstoppable metaverse... Why does unstoppable matter to the metaverse? >> Yeah, unstoppable is very great mostly for identity and having a kind of crush chain identity inside of the metaverse and just kind of in Web3 in general. And unstoppable, we enable log in with unstoppable. So if you have, for example, an unstoppable domain which is like a human readable kind of crypto wallet address, but you can also do some incredible, stuff with it, and there is a lot of fun and exciting utility, effectively, like if you would have, I don't know, like unstoppable.dao you would be able to use that to log in to the Atlantis metaverse and it would represent some of your identity and social graph in game with your peers. >> Awesome, Noah, what's your take on the unstoppable angle on this? >> Yeah, I mean, it makes it social. So, instead of you can have a feed, you know, something we're thinking about at Parcel is like a feed of all the real estate transactions, and you could follow certain people, you can follow your friends and see a feed of everything that your friends are doing in English or human readable terms that are not just like a wallet address. So, that's obviously a big one and they're also giving people more options in terms of, naming and top level domains if you want to be something.wallet or .nft, or hopefully eventually .metaverse- >> John: Yes. >> Will help expand that ecosystem much more. In addition to like on our... Like backend being able to capture email when they login and to provide better marketing for our users. >> What would you guys say to other metaverse partners looking for work with unstoppable domains for their login and digital identity, what would you recommend? >> It doesn't make sense to- >> I believe- >> Connect with the best DAO and integrate that if you want to keep shipping stuff for your community and keeping it exciting and engaging and enabling user choice in how they choose to display their identity in virtual environments. >> Yeah, there's practically no downside and plenty of upside, again, having those users who are already using unstoppable domains quickly, you know, log into your site and plug in. >> All right. That's awesome. Good stuff with unstoppable. I got to ask you guys give an example of on your products, I love the metaverse progression. I love the pioneering work you guys are doing. And again, the funding things are different. The user expectations are different. The technology experience are different. Billions of people going to be in enabled for it. What are the cool things you guys got going on? CJ, we were talking before we came on camera about the tree thing you got going on. Take us through some of the things that are exciting that people may not know about or may know about. What should they pay attention to share, share some insight? >> Yeah, of course. So one of the fun things, actually that we're building on that on these sites together with our full team and also some outside contributors from the community and two kin protocol, which is a regenerative finance protocol. And I'll get into that a little bit in a minute. Effectively what we're actually doing is planting a carbon capturing virtual forest inside of the metaverse that will in future also be bio diverse. So how we're approaching that is imagine that you can plant NFT trees inside of the metaverse, providing that your will deposit X amount of kind of USD stablecoin or Ether or some digital asset. You can actually use that to deposit inside of the tree. And we will use some, probably something like super fluid, which is like a kind of smart projecting infrastructure platform. And we all essentially enable every single second funds being sent from the contract and actually purchasing real world carbon credits. So legitimate, you know, government bags to carbon credits from the voluntary kind of public market that have actually been bridged on chain, transformed into a crypto asset, and they will be locked away inside of these trees inside of game forever. And in future, we also hope to have like user on animals, roaming the great forest of Atlantis, which will have biodiversity and endangered species credit, locked inside. And we hope to support a variety of different kind of sustainable assets and things like that to really populate this ecosystem. >> So it's you're doing climate change good for real, as well as rendering it as an asset for everyone to see and enjoy. >> Absolutely. And for me, that's what makes the metaverse the metaverse, that's what I talked about. It's how Web3 enables the metaverse to cross over into our real world, ordinary life from URL to IRL and actually provide some incredible positive impact for all of humanity on the planet. >> And Noah, you have some action going on there. I mean, I would be like, "oh, virtual real estate, isn't it unlimited real estate?" But when you have users come together, this value, we've seen this in gaming, what are some of the cool things you got going on over there at Parcel? >> Yeah, I think one thing that stands out, which maybe not enough people are thinking about are AR virtual world. So, right now a lot of people are focused on the VR types, central and sandbox and, and Atlantis, but there very well may be a billion people using augmented reality before there are a billion using virtual reality just because of the nature of the hardware development and apple may come out with their AR headset by the end of the year. So there are a few projects there they've taken the real world to map and Parcel it out into hexagons, and you can actually buy that, and you own that, that piece and you can put your own custom content there. And on that social impact point, we have heard about a few projects that are trying to use it for good. And like one project is bought up some land in the Amazon rain forest and some of the proceeds go to conservation of the rain forest. So, you know, we're all about using blockchain for good and right, coming together as a globe. >> I can't wait to see the commercial real estate division of your group with all the work from, a remote coming on. Guys, great stuff you got going on, again, you guys are pioneering an area that is coming big. It's coming strong, its got a lot of... A momentum, vitality, and energy to it. Put a plug in for your companies. Noah, we'll start with you. What's going on with Parcel, share a plug for the company. What you're looking for, do some key highlights, news, take a minute to, to give a plug. >> Sure. Yeah, great. We are the destination for virtual real estate and that extends well beyond just the buyers and sellers. That's everyone across the whole chain with property managers and property developers, but then also the builders and creators and artists, and we are working right now on aggregating the best creator directory in the metaverse. So you can think of it as a place where artists can come showcase their work and get hired. As well as just generally like bridging this knowledge gap that is much wider than we even expected. So we have our Parcel learn product coming soon, which is a fully fledged, knowledge base with education, informational content and lots of rich data. >> Where can people get involved? What's the channels? Are all channels open? Where can we find you? >> Yeah, our websites Parcel.so on Twitter, you can find us at ParcelNFT and you can link to our discord from either one of those. It's the best way to get involved. >> All right, CJ, put a plug in for the last world, I know you got a lot of action to share. >> Yeah, of course. I would love to see everybody there. Thanks so much for having us. And thanks for listening. Like I said, at the start of the call, we're building the Web3 social metaverse and we're connecting Web3 with social gaming and education, in one light web virtual world that's accessible to everybody. We're also doing some crazy stuff like planting their cabin, capturing virtual forest and all of that, and trying to be the infrastructure layer for Web3 driven real world utility inside of the metaverse. And we believe that we have designed the critical value capture mechanism for virtual learn. I we'll be sharing more all of that very soon and continuing to integrate the best apps from across the Web3 ecosystem and showcasing them at the center of Atlantis. You can go to discord.gg/atlantisworld. If you would love to learn more about us, you can go to wiki.atlantis.world. And there is some documentation now, which includes back story and team and some of our milestones and achievements so far from winning hackathons to raising grants and launching our Alpha belt, soft launching it. And we all have the public free to play coming in March. And where most active, I would say on discord and Twitter. On Twitter you can find us atlantisOx, or just search Atlantis world. And it's the first one that come up. >> All right. CJ, thank you. Noah, thanks for coming out. I really appreciate you spending the time here, and unstoppable showcase and being a partner. Again they got the great digital identity, great plug there for them here. Thanks for sharing that and thanks for sharing the time. Appreciate you guys are pioneer of some good stuff. Appreciate it. >> Thanks so much man. >> I so appreciate that. >> All right, theCUBE's unstoppable domains partner showcase. Thanks for watching. (bright music)

>> Narrator: From around the globe, it's theCUBE presenting enterprise digital resilience on hybrid and multicloud brought to you by Io-Tahoe. >> Okay, now we're going to go into the demo and we want to get a better understanding of how you can leverage OpenShift and Io-Tahoe to facilitate faster application deployment. Let me pass the mic to Sabita, take it away. >> Thanks, Dave. Happy to be here again. >> Guys as Dave mentioned my name's Sabita Davis. I'm the Enterprise Account Executive here at Io-Tahoe. So today we just wanted to give you guys a general overview of how we're using OpenShift. >> Yeah, hey, I'm Noah, Io-Tahoe's Data Operations Engineer working with OpenShift and I've been learning the ins and outs of OpenShift for like the past few months. And I'm here to share what I've learned. >> Okay so before we begin I'm sure everybody wants to know Noah. What are the benefits of using OpenShift? >> Well, there's five that I can think of, faster time to operations, simplicity, automation, control and digital resilience. >> Okay, so that's really interesting because those are the exact same benefits that we at Io-Tahoe deliver to our customers. But let's start with faster time to operation, by running Io-Tahoe on OpenShift is it faster than let's say using Kubernetes and other platforms? >> Well, our objective at Io-Tahoe is to be accessible across multiple cloud platforms, right? And so by hosting our application in containers we're able to achieve this. So to answer your question it's faster to create end user application images using container tools like Kubernetes with OpenShift as compared to like Kubernetes with Docker, Kryo >> or Containerd. >> Okay, so we got a bit technical there. Can you explain that in a bit more detail? >> Yeah, there's a bit of vocabulary involved. So basically containers are used in developing things like databases, web servers or applications such as Io-Tahoe. What's great about containers is that they split the workload. So developers can select the libraries without breaking anything. And CIS admins can update the host without interrupting the programmers. Now OpenShift works hand-in-hand with Kubernetes to provide a way to build those containers for applications. >> Okay, got it. So basically containers make life easier for developers and system admins. So how does OpenShift differ from other platforms? >> Well, this kind of leads into the second benefit I want to talk about which is simplicity. Basically there's a lot of steps involved with when using Kubernetes with Docker but OpenShift simplifies this with their source to image process that takes the source code and turns it into a container image but that's not all. OpenShift has a lot of automation and features that simplify working with containers an important one being its web console. So here I've set up a light version of OpenShift called CodeReady Containers. And I was able to set up for application right from the web console. And I was able to set up this entire thing in Windows, Mac and Linux. So it's environment agnostic in that sense. >> Okay, so I think I see in the top left that this is a developer's view. What would a systems admin view look like? >> That's a good question. So here's the administrator view and this kind of ties into the benefit of control. This view gives insights into each one of the applications and containers that are running and you can make changes without affecting deployment. And you can also within this view set up each layer of security and there's multiple that you can prop up but I haven't fully messed around with it because since with my luck, I'd probably lock myself out. >> Okay, so that seems pretty secure. Is there a single point security such as you user login or are there multiple layers of security? >> Yeah, there are multiple layers of security. There's your user login, security groups and general role based access controls but there's also a ton of layers of security surrounding like the containers themselves. But for the sake of time, I won't get too far into it. >> Okay, so you mentioned simplicity and time to operation as being two of the benefits. You also briefly mentioned automation and as you know automation is the backbone of our platform here at Io-Tahoe. So that certainly grabbed my attention. Can you go a bit more in depth in terms of automation? >> OpenShift provides extensive automation that speeds up that time to operation, right? So the latest versions of OpenShift come with a built-in cryo container engine which basically means that you get to skip that container engine installation step. And you don't have to like log into each individual container hosts and configure networking, configure registry servers, storage, et cetera. So I'd say it automates the more boring kind of tedious processes. >> Okay, so I see the Io-Tahoe template there. What does it allow me to do? >> In terms of automation in application development. So we've created an OpenShift template which contains our application. This allows developers to instantly like set up a product within that template or within that, yeah. >> Okay, so Noah, last question. Speaking of vocabulary, you mentioned earlier digital resilience is a term we're hearing especially in the banking and finance world. It seems from what you described industries like banking and finance would be more resilient using OpenShift, correct? >> Yeah, in terms of digital resilience, OpenShift will give you better control over the consumption of resources each container is using. In addition, the benefit of containers is that like I mentioned earlier sysadmins can troubleshoot the servers without bringing down the application. And if the application does go down it's easy to bring it back up using the templates and like the other automation features that OpenShift provides. >> Okay, so thanks so much Noah. So any final thoughts you want to share? >> Yeah, I just want to give a quick recap of like the five benefits that you gain by using OpenShift. The five are time to operation, automation, control, security and simplicity. You can deploy applications faster, you can simplify the workload, you can automate a lot of the otherwise tedious processes, and maintain full control over your workflow and you can assert digital resilience within your environment. >> So guys, thanks for that appreciate the demo. I wonder you guys have been talking about the combination of Io-Tahoe and Red Hat. Can you tie that in Sabita to digital resilience specifically? >> Yeah, sure Dave. So when we speak to the benefits of security controls in terms of digital resilience at Io-Tahoe we automated detection and apply controls at the data level. So this would provide for more enhanced security. >> Okay, but so if you were to try to do all these things manually I mean, what does that do? How much time can I compress? What's the time to value? >> So with our latest versions of Io-Tahoe we're taking advantage of faster deployment time associated with containerization and Kubernetes. So this kind of speeds up the time it takes for customers start using our softwares. They'd be able to quickly spin up Io-Tahoe in their own on-premise environment or otherwise in their own cloud environment like including AWS, Azure, Oracle GCP and IBM cloud. Our quick start templates allow flexibility to deploy into multicloud environments all just using like a few clicks. >> Okay, so now I'll just quickly add, so what we've done Io-Tahoe here is we've really moved our customers away from the whole idea of needing a team of engineers to apply controls to data as compared to other manually driven workflows. So with templates, automation, pre-built policies and data controls one person can be fully operational within a few hours and achieve results straight out of the box on any cloud. >> Yeah, we've been talking about this theme of abstracting the complexity that's really what we're seeing is a major trend in this coming decade. Okay, great. Thanks Sabita, Noah. How can people get more information or if they have any follow up questions, where should they go? >> Yeah, sure Dave I mean if you guys are interested in learning more reach out to us @infoatiotahoe.com to speak with one of our sales engineers. I mean, we'd love to hear from you. So book a meeting as soon as you can. >> All right, thanks guys. Keep it right there for more cube content with Io-Tahoe. (gentle music)

>>From around the globe. It's the cube presenting enterprise digital resilience on hybrid and multicloud brought to you by IO Tahoe. Okay. Now we're going to go into the demo and we want to get a better understanding of how you can leverage OpenShift and IO Tahoe to facilitate faster application deployment. Let me pass the mic to Savita, take it away. >>Uh, thanks Dave. Happy to be here again. Um, guys, as they've mentioned, my name is to be the Davis. I'm the enterprise account executive here at IO Tahoe. Uh, so today we just wanted to give you guys a general overview of how we're using open shift. >>Yeah. Hey, I'm Noah IO. Tahoe's data operations engineer working with OpenShift, and I've been learning the ins and outs of OpenShift for like the past few months and I'm here to share it up line. >>Okay. So, so before we begin, I'm sure everybody wants to know Noah. What are the benefits of using OpenShift? >>Well, um, there's five that I can think of a faster time to operations, simplicity, automation control, and digital resilience. >>Okay. So, so that, that's really interesting because those are the exact same benefits that we at Aja Tahoe delivered to our customers. But, uh, let's start with faster time to operation by running IO Tahoe on OpenShift. Is it faster than let's say using Kubernetes and other platforms? >>Well, um, our objective at IO Tahoe has to be accessible across multiple cloud platforms, right? And so by hosting our application and containers, uh, we're able to achieve this. So to answer your question, it's faster to create end user application images, using container tools like Kubernetes with OpenShift as compared to like Kubernetes with Docker cryo or container D. >>Okay. So, so we got a bit technical there. Um, can you explain that in a bit more detail? >>Yeah, there's a bit of vocabulary involved. Uh, so basically containers are used in developing things like databases, web servers, or applications such as I've taught. What's great about containers is that they split the workload. So developers can select a libraries without breaking anything. And CIS admins can update the host without interrupting the programmers. Uh, now OpenShift works hand-in-hand with Kubernetes to provide a way to build those containers for applications. >>Okay, got it. Uh, so basically containers make life easier for developers and system admins. So how does OpenShift differ from other platforms? >>Um, well this kind of leads into the second benefit I want to talk about, which is simplicity. Basically. There's a lot of steps involved with when you're using Kubernetes with a Docker, but OpenShift simplifies this with their source to image process that takes the source code and turns it into a container image, but that's not all, uh, OpenShift has a lot of automation and features that simplify working with containers and important one being its web console. Um, so here I've set up a light version of OpenShift code ready containers. And I was able to set up our application right from the web console. And I was able to set up this entire thing in windows, Mac, and Linux. So it's environment agnostic in that sense. >>Okay. So I think I seen the top left. This is a developer's view. What would a systems admin view look like? >>That's a good question. So, uh, here's the, uh, administrator view and this kind of ties into the benefit of control. Um, this view gives insights into each one of the applications and containers that are running and you can make changes without affecting deployment. Um, and you can also within this view, set up each layer of security and there's multiple that you can prop up, but I haven't fully messed around with it because since with my look, I'd probably locked myself out. >>Okay. Um, so, so that seems pretty secure. Um, is there a single point security such as you use a login or are there multiple layers of security? Yeah. >>Um, there are multiple layers of security. There's your user login security groups and general role based access controls. Um, but there's also a ton of layers of security surrounding like the containers themselves. But for the sake of time, I won't get too far into it. >>Okay. Uh, so you mentioned simplicity and time to operation as being two of the benefits. You also briefly mentioned automation and as you know, automation is the backbone of our platform here at IO Tahoe. So that's certainly grabbed my attention. Can you go a bit more in depth in terms of automation? >>Yeah, sure. I'd say that automation is important benefit. Uh, OpenShift provides extensive automation that speeds up that time to operation, right? So the latest versions of open should come with a built-in cryo container engine, which basically means that you get to skip that container engine installation step. And you don't have to like log into each individual container hosts and configure networking, configure the registered servers, storage, et cetera. So I'd say, uh, it automates the more boring kind of tedious processes. >>Okay. So I see the iota template there. What does it allow me to do >>In terms of automation in application development? So we've created an OpenShift template, which contains our application. This allows developers to instantly like, um, set up a product within that template or within that. Yeah. >>Okay. Um, so Noah, last question. Speaking of vocabulary, you mentioned earlier digital resilience is a term we're hearing, especially in the banking and finance world. Um, it seems from what you described industries like banking and finance would be more resilient using OpenShift, correct? >>Yeah. In terms of digital resilience, OpenShift will give you better control over the consumption of resources each container is using. In addition, the benefit of containers is that, uh, like I mentioned earlier, CIS admins can troubleshoot the servers about like bringing down the application. And if the application does go down, it's easy to bring it back up using the templates and like the other automation features that OpenShift provides. >>Okay. So thanks so much. So any final thoughts you want to share? >>Yeah. Just want to give a quick recap of like the five benefits that you gain by using OpenShift. Uh, the five are time to operation automation, control, security and simplicity. Uh, you can deploy applications faster. You can simplify the workload. You can automate a lot of the otherwise tedious processes can maintain full control over your workflow and you can assert digital resilience within your environment. >>So guys, thanks for that. Appreciate the demo. Um, I wonder you guys have been talking about the combination of IO Tahoe and red hat. Can you tie that in Sabita to digital resilience specifically? >>Yeah, sure. Dave, um, so why don't we speak to the benefits of security controls in terms of digital resilience at Iowa hope? Uh, we automated detection and applied controls at the data level. So this would provide for more enhanced security. >>Okay. But so if you were to try to do all these things manually, I mean, what's, what does that do? How, how much time can I compress? What's the time to value? >>So, um, with our latest versions via Tahoe, we're taking advantage of faster deployment time, um, associated with containerization and Kubernetes. So this kind of speeds up the time it takes for customers to start using our software as they be able to quickly spin up a hotel and their own on-premise environment or otherwise in their own cloud environment, like including AWS or shore Oracle GCP and IBM cloud. Um, our quick start templates allow flexibility to deploy into multicloud environments, all just using like a few clicks. >>Okay. Um, so, so now I'll just quickly add, so what we've done, I Tahoe here is we've really moved our customers away from the whole idea of needing a team of engineers to apply controls to data as compared to other manually driven workflows. Uh, so with templates, automation, pre-built policies and data controls, one person can be fully operational within a few hours and achieve results straight out of the box, uh, on any cloud. >>Yeah. We've been talking about this theme of abstracting, the complexity that's really what we're seeing is a major trend in this coming decade. Okay, great. Thanks Savita Noah. Uh, ho how can people get more information or if they have any follow-up questions, where should they go? >>Yeah, sure. They've I mean, if you guys are interested in learning more, you know, reach out to us at info at dot com to speak with one of our sales engineers. I mean, we'd love to hear from you. So book a meeting as soon as you can. >>All right. Thanks guys. Keep it right there for more cube content with IO Tahoe.

from Las Vegas it's the queue covering VMworld 2018 brought to you by VMware and its ecosystem partners welcome back to the cube here in Las Vegas at VMworld 2018 I'm Stu moon with a co-host John we're happy to welcome to the program believes the first time guests know woz Murr who is the senior vice president and general manager of EUC or end-user tutoring at VMware thanks so much for joining us yeah absolutely thrilled to be here great show this year ya know we do a lot of interviews but we don't have enough room for every single GM but we're excited we got a lot going on at this show I mean we've been watching since Sanjay got put in charge of that group of years ago big acquisitions like AirWatch so tell us the the big news yeah I mean there's there's several things that we know a great a great opportunity for us to showcase some of the big big opera leases with work space one you know that we're finding that customers you know really have loved our product for ios and android we've had a lot of customers doing virtual desktops virtual apps now with workspace one we've brought all of it together seamless where they can now manage iOS Android Windows 10 obviously huge in the market both physical and virtual all with one tool and now even Mac right one of the big initiatives we've seen as Mac is a choice right where where employees say hey you know I really want to use a Mac but you know obviously there's one or two windows apps that we have to bring to Mac to make it successful in the enterprise and so obviously workspace one really bringing that together I mean know what you were early in VMware and left for a while came back you've been kind of the art one of the architects of this thing I was at VMware and it's doing with history and EMC you know on the server storage side there was this explosive excitement around virtualization and then desktop virtualization VDI came in that's right and I don't you know we were joking before about the earth video your VDI but it's been there its I went there it's a it's been more of a slow burn but it's it's it's crazy now and it's working and it's here what has been I'm just kind of curious what has been your philosophy and where you want to take VMware now that these you know but with all these technologies super useful super valuable kind of and trance I mean to use some buzzwords transforming the workspace right and it's real so yeah you know the first and foremost you know I think one of the things that we've done is we've matured virtual desktops virtual applications is is really look at what are the right use cases that they come in right you know I think for a while it was every PC is gonna be replaced with virtual and and I think we've you know now seen where it makes sense it's a phenomenal technology right where we have you know folks working from home in sensitive data can we deliver that secure you know real-time experience so I think we've become a lot smarter the second thing is that heterogeneity is now everywhere right people want to work on all these different devices you know there's there's there's Windows and Mac and Chromebooks and and people really want to have that that ability to work anywhere on any platform that they choose you know CIOs are telling us that that they're having a hard time recruiting key talent if they don't give a you know users choice right and so virtualization now helps us it helps us do that a little bit more in a more sophisticated way the other thing is that now people can start to run these workloads a little simpler in the cloud right we introduced Verizon cloud now and SoftLayer and you know on VMC as well now with this you're right so now you're seeing all the tech titans come together say you know run it on your local laptop run it in the cloud so we really see a lot of synergies again bring it back to workspace born yeah I like that the discussion choice you mentioned a whole bunch of cloud tech I made a joke that you know they have both Coke and Pepsi in the solutions Expo you know you can choose your containerized beverage of choice that's right there but at the same time sometimes people don't understand is that when Dells in the mix with VMware Dell has you know some really good history with everything down to the desktop I think back to the wise acquisition absolutely like so what is that whole stack you know if you will look like when you put it together how does that fit yeah it Dell has been a fantastic partner you know we you know as passed out on stage you know we announced a partnership with HP last year Dell this year Dell has done a phenomenal job now with what's called Dell provisioning for workspace one where out of the box you can take a physical Dell PC power it up and go directly into that that local management you know that is managed over over-the-air that you deliver the right applications the right services the right security patch and one of the really interesting things as you know del command tools underlying the OS now can be all managed by workspace one you know you tie that to you know the solutions like del complete where you can get VDI in a whole stack with Dell now you can start to say you know bring together that that whole solution of physical laptops virtual you know really make sense to tie it all together with Dell as an overall provider of the complete solution for enterprise you know one of the interesting things in the cloud evolution last few years is the is the rise of GPUs right we know it's not just a box of x86 and your 616 I've got all these GPUs in the cloud that kind of boomerang straight back to the desktops and how how important is that and how can the workspace you know horizon horizon and workspace view is one of those things I wish we can have the one a couple of customers I talk to you today said you know I said how's it going you know just flat out you tell us the goods the bads and they said I have to say the horizon experience is amazing right and part of that I think is because we have that back-end GPU power that we've never had before where you know there's it literally is difficult to tell the difference between physical and virtual you know we have a lot of our customers some in an auto and anytime people are using CAD or healthcare where they're trying to do rendering of imagery they can now use these back-end GPUs to actually get that full fidelity experience so it's really been opening up the use cases and really making this a real solution for especially highly regulated environments that's super nice so I mean a lot of news product news right that came out anything that you're particularly excited about I want to highlight you know one of the the biggest things is what we call workspace one intelligence I mean every software company here is saying you know analytics and and the machine learning and you know and I'd love to bring it back to you some real-world scenarios you know one of the areas that we all know app compatibility right when we're going for that latest upgrade now with Windows 10 upgrading every six months or so we've been able to look at that and say you know which apps are going to be incompatible how do we go fix them before we do the rollout and that also comes back to user experience right guaranteeing that the users are going to have a great experience making sure that we get those patches down but doing it in a smart way so that we don't break the user experience at the end of the day I really do think that that is going to be a major thrust you know for much of the industry as we get you know bigger and better one of the the facts that I know it's a it's interesting to note just six months in for 150 billion events ingest at a month on this cloud service right and we're just at the very beginning so you're gonna see some numbers over the next coming quarters and months and just how we're able to improve experience really remediates security almost instantly you know be able to do things like you know get rid of the mundane tasks and start to automate out you know some of these these trivial things alright so no I talking to some of the community members and security came up and and specifically around to you see it was like okay NSX I understand but security should s be table stakes in this environment shouldn't be something else it seemed to be a little bit of frustration with how it how it is today you know what's your feet I think Pat really said it well is that that security has to be built in right has to be intrinsic into into what we're building you know one of the things that you've seen we have this solution called trust network where we're what we're trying to do is take the information that we're ingesting all these data points of mobile devices Mac Winton and now start to share that in a way that that partners like CrowdStrike carbon black Symantec McAfee checkpoint Palo Alto you know 11 different providers all looking at that and saying if I correlate your data with my data we are getting insights that we've never seen before right and the the interesting thing about it is that the difference is real-time remediation right you see an event and so for example think about it from from your iPhone right if you jailbreak your iPhone within 30 milliseconds we can say hey you know let's let's eliminate enterprise data leave your personal stuff alone right we don't we don't care we don't want to know but let's get enterprise data off now how about on Windows 10 the same same opportunity right something looks strange listen well you know you're authenticating on this laptop and somebody else is authenticating over in you know Europe let's just pump for a multi-factor right like hey something looks wrong let's take a real-time remediation that's the difference that's the new game-changer that we see in this new modern era is is this ability to see something and just start to go into a normal escalation path of something might be wrong let's let's actually taking that and take an action no want to give you the final takeaway you know you've you've been in this part of the market for a while it's gone through a lot of changes for people that hadn't looked at a little bit what's what's the takeaway you want them to have no I think first and foremost is that this is a journey right this isn't like ESX where you pop a CD into the ROM and hit power on and like all right we're ready to go this is one that we say you know every three months can we say how we're either improving user experience improving security or radically changing the cost paradigm of management right and that's where we say hey you want to roll it office 365 let's make that you know a goal for the next three months hey you want to you know you want to figure out how to improve access to every SAS application in your environment great that's next hey do you want to figure out you know how are you gonna get better insight to where cost is or you want to move workloads out to the cloud here's how we can help you do that that makes our or our partners our customers heroes every three months right getting out in front of that CIO and saying here's what we're delivering for the business there's real business value okay and just in case for our audience a a CD was a thing before he had that's right driver we could have been it was this physical world that we lived in as opposed to today it's more virtual and the clouds that's right thanks so much there's a pleasure to work with you John Troyer I'm Stu minimun stay with us more coverage here from VM roll 2018 thanks for watching the Q thanks a lot [Music]

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

(upbeat music) >> Welcome back. We're here at Supercomputing 22 in Dallas. My name's Paul Gill, I'm your host. With me, Dave Nicholson, my co-host. And one thing that struck me about this conference arriving here, was the number of universities that are exhibiting here. I mean, big, big exhibits from universities. Never seen that at a conference before. And one of those universities is Indiana University. Our two guests, Dave Jent, who's the AVP of Networks at Indiana University, Aaron Neal, Deputy CIO at Indiana University. Welcome, thanks for joining us. >> Thank you for having us. >> Thank you. >> I've always thought that the CIO job at a university has got to be the toughest CIO job there is, because you're managing this sprawling network, people are doing all kinds of different things on it. You've got to secure it. You've got to make it performant. And it just seems to be a big challenge. Talk about the network at Indiana University and what you have done particularly since the pandemic, how that has affected the architecture of your network. And what you do to maintain the levels of performance and security that you need. >> On the network side one of the things we've done is, kept in close contact with what the incoming students are looking for. It's a different environment than it was then 10 years ago when a student would come, maybe they had a phone, maybe they had one laptop. Today they're coming with multiple phones, multiple laptops, gaming devices. And the expectation that they have to come on a campus and plug all that stuff in causes lots of problems for us, in managing just the security aspect of it, the capacity, the IP space required to manage six, seven devices per student when you have 35,000 students on campus, has always been a challenge. And keeping ahead of that knowing what students are going to come in with, has been interesting. During the pandemic the campus was closed for a bit of time. What we found was our biggest challenge was keeping up with the number of people who wanted to VPN to campus. We had to buy additional VPN licenses so they could do their work, authenticate to the network. We doubled, maybe even tripled our our VPN license count. And that has settled down now that we're back on campus. But again, they came back with a vengeance. More gaming devices, more things to be connected, and into an environment that was a couple years old, that we hadn't done much with. We had gone through a pretty good size network deployment of new hardware to try to get ready for them. And it's worked well, but it's always challenging to keep up with students. >> Aaron, I want to ask you about security because that really is one of your key areas of focus. And you're collaborating with counties, local municipalities, as well as other educational institutions. How's your security strategy evolving in light of some of the vulnerabilities of VPNs that became obvious during the pandemic, and this kind of perfusion of new devices that that Dave was talking about? >> Yeah, so one of the things that we we did several years ago was establish what we call OmniSOC, which is a shared security operations center in collaboration with other institutions as well as research centers across the United States and in Indiana. And really what that is, is we took the lessons that we've learned and the capabilities that we've had within the institution and looked to partner with those key institutions to bring that data in-house, utilize our staff such that we can look for security threats and share that information across the the other institutions so that we can give each of those areas a heads up and work with those institutions to address any kind of vulnerabilities that might be out there. One of the other things that you mentioned is, we're partnering with Purdue in the Indiana Office of Technology on a grant to actually work with municipalities, county governments, to really assess their posture as it relates to security in those areas. It's a great opportunity for us to work together as institutions as well as work with the state in general to increase our posture as it relates to security. >> Dave, what brings IU to Supercomputing 2022? >> We've been here for a long time. And I think one of the things that we're always interested in is, what's next? What's new? There's so many, there's network vendors, software vendors, hardware vendors, high performance computing suppliers. What is out there that we're interested in? IU runs a large Cray system in Indiana called Big Red 200. And with any system you procure it, you get it running, you operate it, and your next goal is to upgrade it. And what's out there that we might be interested? That I think why we come to IU. We also like to showcase what we do at IU. If you come by the booth you'll see the OmniSOC, there's some video on that. The GlobalNOC, which I manage, which supports a lot of the RNE institutions in the country. We talk about that. Being able to have a place for people to come and see us. If you stand by the booth long enough people come and find you, and want to talk about a project they have, or a collaboration they'd like to partner with. We had a guy come by a while ago wanting a job. Those are all good things having a big booth can do for you. >> Well, so on that subject, in each of your areas of expertise and your purview are you kind of interleaved with the academic side of things on campus? Do you include students? I mean, I would think it would be a great source of cheap labor for you at least. Or is there kind of a wall between what you guys are responsible for and what students? >> Absolutely we try to support faculty and students as much as we can. And just to go back a little bit on the OmniSOC discussion. One of the things that we provide is internships for each of the universities that we work with. They have to sponsor at least three students every year and make that financial commitment. We bring them on site for three weeks. They learn us alongside the other analysts, information security analysts and work in a real world environment and gain those skills to be able to go back to their institutions and do an additional work there. So it's a great program for us to work with students. I think the other thing that we do is we provide obviously the infrastructure that enable our faculty members to do the research that they need to do. Whether that's through Big Red 200, our Supercomputer or just kind of the everyday infrastructure that allows them to do what they need to do. We have an environment on premise called our Intelligent Infrastructure, that we provide managed access to hardware and storage resources in a way that we know it's secure and they can utilize that environment to do virtually anything that they need in a server environment. >> Dave, I want to get back to the GigaPOP, which you mentioned earlier you're the managing director of the Indiana GigaPOP. What exactly is it? >> Well, the GigaPOP and there are a number of GigaPOP around the country. It was really the aggregation facility for Indiana and all of the universities in Indiana to connect to outside resources. GigaPOP has connections to internet too, the commodity internet, Esnet, the Big Ten or the BTAA a network in Chicago. It's a way for all universities in Indiana to connect to a single source to allow them to connect nationally to research organizations. >> And what are the benefits of having this collaboration of university. >> If you could think of a researcher at Indiana wants to do something with a researcher in Wisconsin, they both connect to their research networks in Wisconsin and Indiana, and they have essentially direct connection. There's no commodity internet, there's no throttling of of capacity. Both networks and the interconnects because we use internet too, are essentially UNT throttled access for the researchers to do anything they need to do. It's secure, it's fast, easy to use, in fact, so easy they don't even know that they're using it. It just we manage the networks and organize the networks in a way configure them that's the path of least resistance and that's the path traffic will take. And that's nationally. There are lots of these that are interconnected in various ways. I do want to get back to the labor point, just for a moment. (laughs) Because... >> You're here to claim you're not violating any labor laws. Is that what you're going to be? >> I'm here to hopefully hire, get more people to be interested to coming to IU. >> Stop by the booth. >> It's a great place to work. >> Exactly. >> We hire lots of interns and in the network space hiring really experienced network engineers, really hard to do, hard to attract people. And these days when you can work from anywhere, you don't have to be any place to work for anybody. We try to attract as many students as we can. And really we're exposing 'em to an environment that exists in very few places. Tens of thousands of wireless access points, big fast networks, interconnections and national international networks. We support the Noah network which supports satellite systems and secure traffic. It really is a very unique experience and you can come to IU, spend lots of years there and never see the same thing twice. We think we have an environment that's really a good way for people to come out of college, graduate school, work for some number of years and hopefully stay at IU, but if not, leave and get a good job and talk well about IU. In fact, the wireless network today here at SC was installed and is managed by a person who manages our campus network wireless, James Dickerson. That's the kind of opportunity we can provide people at IU. >> Aaron, I'd like to ask, you hear a lot about everything moving to the cloud these days, but in the HPC world I don't think that move is happening as quickly as it is in some areas. In fact, there's a good argument some workloads should never move to the cloud. You're having to balance these decisions. Where are you on the thinking of what belongs in the data center and what belongs in the cloud? >> I think our approach has really been specific to what the needs are. As an institution, we've not pushed all our chips in on the cloud, whether it be for high performance computing or otherwise. It's really looking at what the specific need is and addressing it with the proper solution. We made an investment several years ago in a data center internally, and we're leveraging that through the intelligent infrastructure that I spoke about. But really it's addressing what the specific need is and finding the specific solution, rather than going all in in one direction or another. I dunno if Jet Stream is something that you would like to bring up as well. >> By having our own data center and having our own facilities we're able to compete for NSF grants and work on projects that provide shared resources for the research community. Just dream is a project that does that. Without a data center and without the ability to work on large projects, we don't have any of that. If you don't have that then you're dependent on someone else. We like to say that, what we are proud of is the people come to IU and ask us if they can partner on our projects. Without a data center and those resources we are the ones who have to go out and say can we partner on your project? We'd like to be the leaders of that in that space. >> I wanted to kind of double click on something you mentioned. Couple of things. Historically IU has been I'm sure closely associated with Chicago. You think of what are students thinking of doing when they graduate? Maybe they're going to go home, but the sort of center of gravity it's like Chicago. You mentioned talking about, especially post pandemic, the idea that you can live anywhere. Not everybody wants to live in Manhattan or Santa Clara. And of course, technology over decades has given us the ability to do things remotely and IU is plugged into the globe, doesn't matter where you are. But have you seen either during or post pandemic 'cause we're really in the early stages of this. Are you seeing that? Are you seeing people say, Hey, thinking about their family, where do I want to live? Where do I want to raise my family? I'm in academia and no, I don't want to live in Manhattan. Hey, we can go to IU and we're plugged into the globe. And then students in California we see this, there's some schools on the central coast where people loved living there when they were in college but there was no economic opportunity there. Are you seeing a shift, are basically houses in Bloomington becoming unaffordable because people are saying, you know what, I'm going to stay here. What does that look like? >> I mean, for our group there are a lot of people who do work from home, have chosen to stay in Bloomington. We have had some people who for various reasons want to leave. We want to retain them, so we allow them to work remotely. And that has turned into a tool for recruiting. The kid that graduates from Caltech. Doesn't want to stay in Caltech in California, we have an opportunity now he can move to wherever between here and there and we can hire him do work. We love to have people come to Indiana. We think it is a unique experience, Bloomington, Indianapolis are great places. But I think the reality is, we're not going to get everybody to come live, be a Hoosier, how do we get them to come and work at IU? In some ways disappointing when we don't have buildings full of people, but 40 paying Zoom or teams window, not kind the same thing. But I think this is what we're going to have to figure out, how do we make this kind of environment work. >> Last question here, give you a chance to put in a plug for Indiana University. For those those data scientists those researchers who may be open to working somewhere else, why would they come to Indiana University? What's different about what you do from what every other academic institution does, Aaron? >> Yeah, I think a lot of what we just talked about today in terms of from a network's perspective, that were plugged in globally. I think if you look beyond the networks I think there are tremendous opportunities for folks to come to Bloomington and experience some bleeding edge technology and to work with some very talented people. I've been amazed, I've been at IU for 20 years and as I look at our peers across higher ed, well, I don't want to say they're not doing as well I do want brag at how well we're doing in terms of organizationally addressing things like security in a centralized way that really puts us in a better position. We're just doing a lot of things that I think some of our peers are catching up to and have been catching up to over the last 10, 12 years. >> And I think to sure scale of IU goes unnoticed at times. IU has the largest medical school in the country. One of the largest nursing schools in the country. And people just kind of overlook some of that. Maybe we need to do a better job of talking about it. But for those who are aware there are a lot of opportunities in life sciences, healthcare, the social sciences. IU has the largest logistics program in the world. We teach more languages than anybody else in the world. The varying kinds of things you can get involved with at IU including networks, I think pretty unparalleled. >> Well, making the case for high performance computing in the Hoosier State. Aaron, Dave, thanks very much for joining you making a great case. >> Thank you. >> Thank you. >> We'll be back right after this short message. This is theCUBE. (upbeat music)

>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

(bright upbeat music) >> Greetings, brilliant community and thank you so much for tuning in to theCUBE here for the last three days where we've been live from Detroit, Michigan. I've had the pleasure of spending this week with Lisa Martin and John Furrier. Thank you both so much for hanging out, for inviting me into the CUBE family. It's our first show together, it's been wonderful. >> Thank you. >> You nailed it. >> Oh thanks, sweetheart. >> Great job. Great job team, well done. Free wall to wall coverage, it's what we do. We stay till everyone else-- >> Savannah: 100 percent. >> Everyone else leaves, till they pull the plug. >> Lisa: Till they turn the lights out. We're still there. >> Literally. >> Literally last night. >> Still broadcasting. >> Whatever takes to get the stories and get 'em out there at scale. >> Yeah. >> Great time. >> 33. 33 different segments too. Very impressive. John, I'm curious, you're a trend watcher and you've been at every single KubeCon. >> Yep. >> What are the trends this year? Give us the breakdown. >> I think CNCF does this, it's a hard job to balance all the stakeholders. So one, congratulations to the CNCF for another great KubeCon and CloudNativeCon. It is really hard to balance bringing in the experts who, as time goes by, seven years we've been all of, as you said, you get experts, you get seniority, and people who can be mentors, 60% new people. You have vendors who are sponsoring and there's always people complaining and bitching and moaning. They want this, they want that. It's always hard and they always do a good job of balancing it. We're lucky that we get to scale the stories with CUBE and that's been great. We had some great stories here, but it's a great community and again, they're inclusive. As I've said before, we've talked about it. This year though is an inflection point in my opinion, because you're seeing the developer ecosystem growing so fast. It's global. You're seeing events pop up, you're seeing derivative events. CNCF is at the center point and they have to maintain the culture of developer experts, maintainers, while balancing the newbies. And that's going to be >> Savannah: Mm-hmm. really hard. And they've done a great job. We had a great conversation with them. So great job. And I think it's going to continue. I think the attendance metric is a little bit of a false positive. There's a lot of online people who didn't come to Detroit this year. And I think maybe the combination of the venue, the city, or just Covid preferences may not look good on paper, on the numbers 'cause it's not a major step up in attendance. It's still bigger, but the community, I think, is going to continue to grow. I'm bullish on it. >> Yeah, I mean at least we did see double the number of people that we had in Los Angeles. Very curious. I think Amsterdam, where we'll be next with CNCF in the spring, in April. I think that's actually going to be a better pulse check. We'll be in Europe, we'll see what's going on. >> John: Totally. >> I mean, who doesn't like Amsterdam in the springtime? Lisa, what have been some of your observations? >> Oh, so many observations. The evolution of the conference, the hallway track conversations really shifting towards adjusting to the enterprise. The enterprise momentum that we saw here as well. We had on the show, Ford. >> Savannah: Yes. We had MassMutual, we had ING, that was today. Home Depot is here. We are seeing all these big companies that we know and love, become software companies right before our eyes. >> Yeah. Well, and I think we forget that software powers our entire world. And so of course they're going to have to be here. So much running on Kubernetes. It's on-prem, it's at the edge, it's everywhere. It's exciting. Woo, I'm excited. John, what do you think is the number one story? This is your question. I love asking you this question. What is the number one story out KubeCon? >> Well, I think the top story is a combination of two things. One is the evolution of Cloud Native. We're starting to see web assembly. That's a big hyped up area. It got a lot of attention. >> Savannah: Yeah. That's kind of teething out the future. >> Savannah: Rightfully so. The future of this kind of lightweight. You got the heavy duty VMs, you got Kubernetes and containers, and now this web assembly, shows a trajectory of apps, server-like environment. And then the big story is security. Software supply chain is, to me, was the number one consistent theme. At almost all the interviews, in the containers, and the workflows, >> Savannah: Very hot. software supply chain is real. The CD Foundation mentioned >> Savannah: Mm-hmm. >> they had 16,000 vulnerabilities identified in their code base. They were going to automate that. So again, >> Savannah: That was wild. >> That's the top story. The growth of open source exposes potential vulnerabilities with security. So software supply chain gets my vote. >> Did you hear anything that surprised you? You guys did this great preview of what you thought we were going to hear and see and feel and touch at KubeCon, CloudNativeCon 2022. You talked about, for example, the, you know, healthcare financial services being early adopters of this. Anything surprise either one of you in terms of what you predicted versus what we saw? Savannah, let's start with you. >> You know what really surprised me, and this is ironic, so I'm a community gal by trade. But I was really just impressed by the energy that everyone brought here and the desire to help. The thing about the open source community that always strikes me is, I mean 187 different countries participating. You've got, I believe it's something like 175,000 people contributing to the 140 projects plus that CNCF is working on. But that culture of collaboration extends far beyond just the CNCF projects. Everyone here is keen to help each other. We had the conversation just before about the teaching and the learnings that are going on here. They brought in Detroit's students to come and learn, which is just the most heartwarming story out of this entire thing. And I think it's just the authenticity of everyone in this community and their passion. Even though I know it's here, it still surprises me to see it in the flesh. Especially in a place like Detroit. >> It's nice. >> Yeah. >> It's so nice to see it. And you bring up a good point. It's very authentic. >> Savannah: It's super authentic. >> I mean, what surprised me is one, the Wasm, or web assembly. I didn't see that coming at the scale of the conversation. It sucked a lot of options out of the room in my opinion, still hyped up. But this looks like it's got a good trajectory. I like that. The other thing that surprised me that was a learning was my interview with Solo.io, Idit, and Brian Gracely, because he's a CUBE alumni and former host of theCUBE, and analyst at Wikibon, was how their go-to-market was an example of a modern company in Covid with a clean sheet of paper and smart people, they're just doing things different. They're in Slack with their customers. And I walked away with, "Wow that's like a playbook that's not, was never, in the go-to-market VC-backed company playbook." I thought that was, for me, a personal walk away saying that's important. I like how they did that. And there's a lot of companies I think could learn from that. Especially as the recession comes where partnering with customers has always been a top priority. And how they did that was very clever, very effective, very efficient. So I walked away with that saying, "I think that's going to be a standard." So that was a pleasant surprise. >> That was a great surprise. Also, that's a female-founded company, which is obviously not super common. And the growth that they've experienced, to your point, really being catalyzed by Covid, is incredibly impressive. I mean they have some massive brand name customers, Amex, BMW for example. >> Savannah: Yeah. >> Great point. >> And I interviewed her years ago and I remember saying to myself, "Wow, she's impressive." I liked her. She's a player. A player for sure. And she's got confidence. Even on the interview she said, "We're just better, we have better product." And I just like the point of view. Very customer-focused but confident. And I just took, that's again, a great company. And again, I'm not surprised that Brian Gracely left Red Hat to go work there. So yeah, great, great call there. And of course other things that weren't surprising that I predicted, Red Hat continued to invest. They continue to bring people on theCUBE, they support theCUBE but more importantly they have a good strategy. They're in that multicloud positioning. They're going to have an opportunity to get a bite at the apple. And I what I call the supercloud. As enterprises try to go and be mainstream, Cloud Native, they're going to need some help. And Red Hat is always has the large enterprise customers. >> Savannah: What surprised you, Lisa? >> Oh my gosh, so many things. I think some of the memorable conversations that we had. I love talking with some of the enterprises that we mentioned, ING Bank for example. You know, or institutions that have been around for 100 plus years. >> Savannah: Oh, yeah. To see not only how much they've innovated and stayed relevant to meet the demands of the consumer, which are only increasing, but they're doing so while fostering a culture of innovation and a culture that allows these technology leaders to really grow within the organization. That was a really refreshing conversation that I think we had. 'Cause you can kind of >> Savannah: Absolutely. think about these old stodgy companies. Nah, of course they're going to digitize. >> Thinking about working for the bank, I think it's boring. >> Right? >> Yeah. And they were talking about, in fact, those great t-shirts that they had on, >> Yeah, yeah, yeah, yeah. were all about getting more people to understand how fun it is to work in tech for ING Bank in different industries. You don't just have to work for the big tech companies to be doing really cool stuff in technology. >> What I really liked about this show is we had two female hosts. >> Savannah: Yeah. >> How about that? Come on. >> Hey, well done, well done on your recruitment there, champ. >> Yes, thank you boss. (John laughs) >> And not to mention we have a really all-star production team. I do just want to give them a little shout out. To all the wonderful folks behind the lines here. (people clapping) >> John: Brendan. Good job. >> Yeah. Without Brendan, Anderson, Noah, and Andrew, we would be-- >> Of course Frank Faye holding it back there too. >> Yeah, >> Of course, Frank. >> I mean, without the business development wheels on the ship we'd really be in an unfortunate spot. I almost just swore on television. We're not going to do that. >> It's okay. No one's regulating. >> Yeah. (all laugh) >> Elon Musk just took over Twitter. >> It was a close call. >> That's right! >> It's going to be a hellscape. >> Yeah, I mean it's, shit's on fire. So we'll just see what happens next. I do, I really want to talk about this because I think it's really special. It's an ethos and some magic has happened here. Let's talk about Detroit. Let's talk about what it means to be here. We saw so many, and I can't stress this enough, but I think it really matters. There was a commitment to celebrating place here. Lisa, did you notice this too? >> Absolutely. And it surprised me because we just don't see that at conferences. >> Yeah. We're so used to going to the same places. >> Right. >> Vegas. Vegas, Vegas. More Vegas. >> Your tone-- >> San Francisco >> (both laugh) sums up my feelings. Yes. >> Right? >> Yeah. And, well, it's almost robotic but, and the fact that we're like, oh Detroit, really? But there was so much love for this city and recognizing and supporting its residents that we just don't see at conferences. You uncovered a lot of that with your swag-savvy segments, >> Savannah: Yeah. >> And you got more of that to talk about today. >> Don't worry, it's coming. Yeah. (laughs) >> What about you? Have you enjoyed Detroit? I know you hadn't been here in a long time, when we did our intro session. >> I think it's a bold move for the CNCF to come here and celebrate. What they did, from teaching the kids in the city some tech, they had a session. I thought that was good. >> Savannah: Loved that. I think it was a risky move because a lot of people, like, weren't sure if they were going to fly to Detroit. So some say it might impact the attendance. I thought they did a good job. Their theme, Road Ahead. Nice tie in. >> Savannah: Yeah. And so I think I enjoyed Detroit. The weather was great. It didn't rain. Nice breeze outside. >> Yeah. >> The weather was great, the restaurants are phenomenal. So Detroit's a good city. I missed some hockey games. I'd love to see the Red Wings play. Missed that game. But we always come back. >> I think it's really special. I mean, every time I talked to a company about their swag, that had sourced it locally, there was a real reason for this story. I mean even with Kasten in that last segment when I noticed that they had done Carhartt beanies, Carhartt being a Michigan company. They said, "I'm so glad you noticed. That's why we did it." And I think that type of, the community commitment to place, it all comes back to community. One of the bigger themes of the show. But that passion and that support, we need more of that. >> Lisa: Yeah. >> And the thing about the guests we've had this past three days have been phenomenal. We had a diverse set of companies, individuals come on theCUBE, you know, from Scott Johnston at Docker. A really one on one. We had a great intense conversation. >> Savannah: Great way to kick it off. >> We shared a lot of inside baseball, about Docker, super important company. You know, impressed with companies like Platform9 it's been around since the OpenStack days who are now in a relevant position. Rafi Systems, hot startup, they don't have a lot of resources, a lot of guerilla marketing going on. So I love to see the mix of startups really contributing. The big players are here. So it's a real great mix of companies. And I thought the interviews were phenomenal, like you said, Ford. We had, Kubia launched on theCUBE. >> Savannah: Yes. >> That's-- >> We snooped the location for KubeCon North America. >> You did? >> Chicago, everyone. In case you missed it, Bianca was nice enough to share that with us. >> We had Sarbjeet Johal, CUBE analyst came on, Keith Townsend, yesterday with you guys. >> We had like analyst speed dating last night. (all laugh) >> How'd that go? (laughs) >> It was actually great. One of the things that they-- >> Did they hug and kiss at the end? >> Here's the funny thing is that they were debating the size of the CNC app. One thinks it's too big, one thinks it's too small. And I thought, is John Goldilocks? (John laughs) >> Savannah: Yeah. >> What is John going to think about that? >> Well I loved that segment. I thought, 'cause Keith and Sarbjeet argue with each other on Twitter all the time. And I heard Keith say before, he went, "Yeah let's have it out on theCUBE." So that was fun to watch. >> Thank you for creating this forum for us to have that kind of discourse. >> Lisa: Yes, thank you. >> Well, it wouldn't be possible without the sponsors. Want to thank the CNCF. >> Absolutely. >> And all the ecosystem partners and sponsors that make theCUBE possible. We love doing this. We love getting the stories. No story's too small for theCUBE. We'll go with it. Do whatever it takes. And if it wasn't for the sponsors, the community wouldn't get all the great knowledge. So, and thank you guys. >> Hey. Yeah, we're, we're happy to be here. Speaking of sponsors and vendors, should we talk a little swag? >> Yeah. >> What do you guys think? All right. Okay. So now this is becoming a tradition on theCUBE so I'm very delighted, the savvy swag segment. I do think it's interesting though. I mean, it's not, this isn't just me shouting out folks and showing off t-shirts and socks. It's about standing out from the noise. There's a lot of players in this space. We got a lot of CNCF projects and one of the ways to catch the attention of people walking the show floor is to have interesting swag. So we looked for the most unique swag on Wednesday and I hadn't found this yet, but I do just want to bring it up. Oops, I think I might have just dropped it. This is cute. Is, most random swag of the entire show goes to this toothbrush. I don't really have more in terms of the pitch there because this is just random. (Lisa laughs) >> But so, everyone needs that. >> John: So what's their tagline? >> And you forget these. >> Yeah, so the idea was to brush your cloud bills. So I think they're reducing the cost of-- >> Kind of a hygiene angle. >> Yeah, yeah. Very much a hygiene angle, which I found a little ironic in this crowd to be completely honest with you. >> John: Don't leave the lights on theCUBE. That's what they say. >> Yeah. >> I mean we are theCUBE so it would be unjust of me not to show you a Rubik's cube. This is actually one of those speed cubes. I'm not going to be able to solve this for you with one hand on camera, but apparently someone did it in 17 seconds at the booth. Knowing this audience, not surprising to me at all. Today we are, and yesterday, was the t-shirt contest. Best t-shirt contest. Today we really dove into the socks. So this is, I noticed this trend at KubeCon in Los Angeles last year. Lots of different socks, clouds obviously a theme for the cloud. I'm just going to lay these out. Lots of gamers in the house. Not surprising. Here on this one. >> John: Level up. >> Got to level up. I love these 'cause they say, "It's not a bug." And anyone who's coded has obviously had to deal with that. We've got, so Star Wars is a huge theme here. There's Lego sets. >> John: I think it's Star Trek. But. >> That's Star Trek? >> John: That's okay. >> Could be both. (Lisa laughs) >> John: Nevermind, I don't want to. >> You can flex your nerd and geek with us anytime you want, John. I don't mind getting corrected. I'm all about, I'm all about the truth. >> Star Trek. Star Wars. Okay, we're all the same. Okay, go ahead. >> Yeah, no, no, this is great. Slim.ai was nice enough to host us for dinner on Tuesday night. These are their lovely cloud socks. You can see Cloud Native, obviously Cloud Native Foundation, cloud socks, whole theme here. But if we're going to narrow it down to some champions, I love these little bee elephants from Raft. And when I went up to these guys, I actually probably would've called these my personal winner. They said, again, so community focused and humble here at CNCF, they said that Wiz was actually the champion according to the community. These unicorn socks are pretty excellent. And I have to say the branding is flawless. So we'll go ahead and give Wiz the win on the best sock contest. >> John: For the win. >> Yeah, Wiz for the win. However, the thing that I am probably going to use the most is this really dope Detroit snapback from Kasten. So I'm going to be rocking this from now on for the rest of the segment as well. And I feel great about this snapback. >> Looks great. Looks good on you. >> Yeah. >> Thanks John. (John laughs) >> So what are we expecting between now and KubeCon in Amsterdam? >> Well, I think it's going to be great to see how they, the European side, it's a chill show. It's great. Brings in the European audience from the global perspective. I always love the EU shows because one, it's a great destination. Amsterdam's going to be a great location. >> Savannah: I'm pumped. >> The American crowd loves going over there. All the event cities that they choose are always awesome. I missed Valencia cause I got Covid. I'm really bummed about that. But I love the European shows. It's just a little bit, it's high intensity, but it's the European chill. They got a little bit more of that siesta vibe going on. >> Yeah. >> And it's just awesome. >> Yeah, >> And I think that the mojo that carried throughout this week, it's really challenging to not only have a show that's five days, >> but to go through all week, >> Savannah: Seriously. >> to a Friday at 4:00 PM Eastern Time, and still have the people here, the energy and all the collaboration. >> Savannah: Yeah. >> The conversations that are still happening. I think we're going to see a lot more innovation come spring 2023. >> Savannah: Mm-hmm. >> Yeah. >> So should we do a bet, somebody's got to buy dinner? Who, well, I guess the folks who lose this will buy dinner for the other one. How many attendees do you think we'll see in Amsterdam? So we had 4,000, >> Oh, I'm going to lose this one. >> roughly in Los Angeles. Priyanka was nice enough to share with us, there was 8,000 here in Detroit. And I'm talking in person, we're not going to meddle this with the online. >> 6500. >> Lisa: I was going to say six, six K. >> I'm going 12,000. >> Ooh! >> I'm going to go ahead and go big I'm going to go opposite Price Is Right. >> One dollar. >> Yeah. (all laugh) That's exactly where I was driving with it. I'm going, I'm going absolutely all in. I think the momentum here is building. I think if we look at the numbers from-- >> John: You could go Family Feud >> Yeah, yeah, exactly. And they mentioned that they had 11,000 people who have taken their Kubernetes course in that first year. If that's a benchmark and an indicator, we've got the veteran players here. But I do think that, I personally think that the hype of Kubernetes has actually preceded adoption. If you look at the data and now we're finally tipping over. I think the last two years we were on the fringe and right now we're there. It's great. (voice blares loudly on loudspeaker) >> Well, on that note (all laugh) On that note, actually, on that note, as we are talking, so I got to give cred to my cohosts. We deal with a lot of background noise here on theCUBE. It is a live show floor. There's literally someone on an e-scooter behind me. There's been Pong going on in the background. The sound will haunt the three of us for the rest of our lives, as well as the production crew. (Lisa laughs) And, and just as we're sitting here doing this segment last night, they turned the lights off on us, today they're letting everyone know that the event is over. So on that note, I just want to say, Lisa, thank you so much. Such a warm welcome to the team. >> Thank you. >> John, what would we do without you? >> You did an amazing job. First CUBE, three days. It's a big show. You got staying power, I got to say. >> Lisa: Absolutely. >> Look at that. Not bad. >> You said it on camera now. >> Not bad. >> So you all are stuck with me. (all laugh) >> A plus. Great job to the team. Again, we do so much flow here. Brandon, Team, Andrew, Noah, Anderson, Frank. >> They're doing our hair, they're touching up makeup. They're helping me clean my teeth, staying hydrated. >> We look good because of you. >> And the guests. Thanks for coming on and spending time with us. And of course the sponsors, again, we can't do it without the sponsors. If you're watching this and you're a sponsor, support theCUBE, it helps people get what they need. And also we're do a lot more segments around community and a lot more educational stuff. >> Savannah: Yeah. So we're going to do a lot more in the EU and beyond. So thank you. >> Yeah, thank you. And thank you to everyone. Thank you to the community, thank you to theCUBE community and thank you for tuning in, making it possible for us to have somebody to talk to on the other side of the camera. My name is Savannah Peterson for the last time in Detroit, Michigan. Thanks for tuning into theCUBE. >> Okay, we're done. (bright upbeat music)

>>Okay, welcome back. Everyone. Live cube coverage here in Las Vegas for Amazon re Mars hot event, machine learning, automation, robotics, and space. Two days of live coverage. We're talking to all the hot technologists. We got all the action startups and segment on sustainability and F pan hero for vet global lead, Amazon sustainability data initiative. Thanks for coming on the cube. Can I get that right? Can >>You, you, you did. >>Absolutely. Okay, great. <laugh> thank >>You. >>Great to see you. We met at the analyst, um, mixer and, um, blown away by the story going on at Amazon around sustainability data initiative, because we were joking. Everything's a data problem now, cuz that's cliche. But in this case you're using data in your program and it's really kind of got a bigger picture. Take a minute to explain what your project is, scope of it on the sustainability. >>Yeah, absolutely. And thank you for the opportunity to be here. Yeah. Um, okay. So, um, I, I lead this program that we launched several years back in 2018 more specifically, and it's a tech for good program. And when I say the tech for good, what that means is that we're trying to bring our technology and our infrastructure and lend that to the world specifically to solve the problems related to sustainability. And as you said, sustainability, uh, inherently needs data. You need, we need data to understand the baseline of where we are and also to understand the progress that we are making towards our goals. Right? But one of the big challenges that the data that we need is spread everywhere. Some of it is too large for most people to be able to, um, access and analyze. And so, uh, what we're trying to tackle is really the data problem in the sustainability space. >>Um, what we do more specifically is focus on Democrat democratizing access to data. So we work with a broader community and we try to understand what are those foundational data sets that most people need to use in the space to solve problems like climate change or food security or think about sustainable development goals, right? Yeah. Yeah. Like all the broad space. Um, and, and we basically then work with the data providers, bring the data to the cloud, make it free and open to everybody in the world. Um, I don't know how deep you want me to go into it. There's many other layers into that. So >>The perspective is zooming out. You're, you're, you're looking at creating a system where the democratizing data means making it freely available so that practitioners or citizens, data, Wrangler, people interested in helping the world could get access to it and then maybe collaborate with people around the world. Is that right? >>Absolutely. So one of the advantages of using the cloud for this kind of, uh, effort is that, you know, cloud is virtually accessible from anywhere where you have, you know, internet or bandwidth, right? So, uh, when, when you put data in the cloud in a centralized place next to compute, it really, uh, removes the, the need for everybody to have their own copy. Right. And to bring it into that, the traditional way is that you bring the data next to your compute. And so we have this multiple copies of data. Some of them are on the petabyte scale. There's obviously the, the carbon footprint associated with the storage, but there's also the complexity that not everybody's able to actually analyze and have that kind of storage. So by putting it in the cloud, now anyone in the world independent of where of their computer capabilities can have access to the same type of data to solve >>The problems. You don't remember doing a report on this in 2018 or 2017. I forget what year it was, but it was around public sector where it was a movement with universities and academia, where they were doing some really deep compute where Amazon had big customers. And there was a movement towards a open commons of data, almost like a national data set like a national park kind of vibe that seems to be getting momentum. In fact, this kind of sounds like what you're doing some similar where it's open to everybody. It's kinda like open source meets data. >>Uh, exactly. And, and the truth is that these data, the majority of it's and we primarily work with what we call authoritative data providers. So think of like NASA Noah, you came me office organizations whose mission is to create the data. So they, their mandate is actually to make the data public. Right. But in practice, that's not really the case. Right. A lot of the data is stored like in servers or tapes or not accessible. Um, so yes, you bring the data to the cloud. And in this model that we use, Amazon never actually touches the data and that's very intentional so that we preserve the integrity of the data. The data provider owns the data in the cloud. We cover all the costs, but they commit to making it public in free to anybody. Um, and obviously the computer is next to it. So that's, uh, evaluated. >>Okay. Anna. So give me some examples of, um, some successes. You've had some of the challenges and opportunities you've overcome, take me through some of the activities because, um, this is really needed, right? And we gotta, sustainability is top line conversation, even here at the conference, re Mars, they're talking about saving climate change with space mm-hmm <affirmative>, which is legitimate. And they're talking about all these new things. So it's only gonna get bigger. Yeah. This data, what are some of the things you're working on right now that you can share? >>Yeah. So what, for me, honestly, the most exciting part of all of this is, is when I see the impact that's creating on customers and the community in general, uh, and those are the stories that really bring it home, the value of opening access to data. And, and I would just say, um, the program actually offers in addition to the data, um, access to free compute, which is very important as well. Right? You put the data in the cloud. It's great. But then if you wanna analyze that, there's the cost and we want to offset that. So we have a, basically an open call for proposals. Anybody can apply and we subsidize that. But so what we see by putting the data in the cloud, making it free and putting the compute accessible is that like we see a lot, for instance, startups, startups jump on it very easily because they're very nimble. They, we basically remove all the cost of investing in the acquisition and storage of the data. The data is connected directly to the source and they don't have to do anything. So they easily build their applications on top of it and workloads and turn it on and off if you know, >>So they don't have to pay for it. >>They have to pay, they basically just pay for the computes whenever they need it. Right. So all the data is covered. So that makes it very visible for, for a lot of startups. And then we see anything like from academia and nonprofits and governments working extensively on the data, what >>Are some of the coolest things you've seen come out of the woodwork in terms of, you know, things that built on top of the, the data, the builders out there are creative, all that heavy, lifting's gone, they're being creative. I'm sure there's been some surprises, um, or obvious verticals that jump healthcare jumps out at me. I'm not sure if FinTech has a lot of data in there, but it's healthcare. I can see, uh, a big air vertical, obviously, you know, um, oil and gas, probably concern. Um, >>So we see it all over the space, honestly. But for instance, one of the things that is very, uh, common for people to use this, uh, Noah data like weather data, because no, basically weather impacts almost anything we do, right? So you have this forecast of data coming into the cloud directly streamed from Noah. And, um, a lot of applications are built on top of that. Like, um, forecasting radiation, for instance, for the solar industry or helping with navigation. But I would say some of the stories I love to mention because are very impactful are when we take data to remote places that traditionally did not have access to any data. Yeah. And for instance, we collaborate with a, with a program, a nonprofit called digital earth Africa where they, this is a basically philanthropically supported program to bring earth observations to the African continents in making it available to communities and governments and things like illegal mining fighting, illegal mining are the forestation, you know, for mangroves to deep forest. Um, it's really amazing what they are doing. And, uh, they are managing >>The low cost nature of it makes it a great use case there >>Yes. Cloud. So it makes it feasible for them to actually do this work. >>Yeah. You mentioned the Noah data making me think of the sale drone. Mm-hmm <affirmative> my favorite, um, use case. Yes. Those sales drones go around many them twice on the queue at reinvent over the years. Yeah. Um, really good innovation. That vibe is here too at the show at Remar this week at the robotics showcases you have startups and growing companies in the ML AI areas. And you have that convergence of not obvious to many, but here, this culture is like, Hey, we have, it's all coming together. Mm-hmm <affirmative>, you know, physical, industrial space is a function of the new O T landscape. Mm-hmm <affirmative>. I mean, there's no edge in space as they say, right. So the it's unlimited edge. So this kind of points to the major trend. It's not stopping this innovation, but sustainability has limits on earth. We have issues. >>We do have issues. And, uh, and I, I think that's one of my hopes is that when we come to the table with the resources and the skills we have and others do as well, we try to remove some of these big barriers, um, that make it things harder for us to move forward as fast as we need to. Right. We don't have time to spend that. Uh, you know, I've been accounted that 80% of the effort to generate new knowledge is spent on finding the data you need and cleaning it. Uh, we, we don't have time for that. Right. So can we remove that UN differentiated, heavy lifting and allow people to start at a different place and generate knowledge and insights faster. >>So that's key, that's the key point having them innovate on top of it, right. What are some things that you wanna see happen over the next year or two, as you look out, um, hopes, dreams, KPIs, performance metrics, what are you, what are you driving to? What's your north star? What are some of those milestones? >>Yeah, so some, we are investing heavily in some areas. Uh, we support, um, you know, we support broadly sustainability, which as, you know, it's like, it's all over, <laugh> the space, but, uh, there's an area that is, uh, becoming more and more critical, which is climate risk. Um, climate risk, you know, for obvious reasons we are experienced, but also there's more regulatory pressures on, uh, business and companies in general to disclose their risks, not only the physical, but also to transition risks. And that's a very, uh, data heavy and compute heavy space. Right. And so we are very focusing in trying to bring the right data and the right services to support that kind of, of activity. >>What kind of break was you looking for? >>Um, so I think, again, it goes back to this concept that there's all that effort that needs to be done equally by so many people that we are all repeating the effort. So I'll put a plug here actually for a project we are supporting, which is called OS climates. Um, I don't know if you're familiar with it, but it's the Linux foundation effort to create an open source platform for climate risk. And so they, they bought the SMP global Airbus, you know, Alliance all these big companies together. And we are one of the funding partners to basically do that basic line work. What are the data that is needed? What are the basic tools let's put it there and do the pre-competitive work. So then you can do the build the, the, the competitive part on top of it. So >>It's kinda like a data clean room. >>It kind of is right. But we need to do those things, right. So >>Are they worried about comp competitive data or is it more anonymized out? How do you, >>It has both actually. So we are primarily contributing, contributing with the open data part, but there's a lot of proprietary data that needs to be behind the whole, the walls. So, yeah, >>You're on the cutting edge of data engineering because, you know, web and ad tech technologies used to be where all that data sharing was done. Mm-hmm <affirmative> for the commercial reasons, you know, the best minds in our industry quoted by a cube alumni are working on how to place ads better. Yeah. Jeff Acker, founder of Cloudera said that on the cube. Okay. And he was like embarrassed, but the best minds are working on how to make ads get more efficient. Right. But that tech is coming to problem solving and you're dealing with data exchange data analysis from different sources, third parties. This is a hard problem. >>Well, it is a hard problem. And I'll, I'll my perspective is that the hardest problem with sustainability is that it goes across all kinds of domains. Right. We traditionally been very comfortable working in our little, you know, swimming lanes yeah. Where we don't need to deal with interoperability and, uh, extracting knowledge. But sustainability, you, you know, you touch the economic side, it touches this social or the environmental, it's all connected. Right. And you cannot just work in the little space and then go sets the impact in the other one. So it's going to force us to work in a different way. Right. It's, uh, big data complex data yeah. From different domains. And we need to somehow make sense of all of it. And there's the potential of AI and ML and things like that that can really help us right. To go beyond the, the modeling approaches we've been done so >>Far. And trust is a huge factor in all this trust. >>Absolutely. And, and just going back to what I said before, that's one of the main reasons why, when we bring data to the cloud, we don't touch it. We wanna make sure that anybody can trust that the data is nowhere data or NASA data, but not Amazon data. >>Yes. Like we always say in the cube, you should own your data plane. Don't give it up. <laugh> well, that's cool. Great. Great. To hear the update. Is there any other projects that you're working on you think might be cool for people that are watching that you wanna plug or point out because this is an area people are, are leaning into yeah. And learning more young, younger talents coming in. Um, I, whether it's university students to people on side hustles want to play with data, >>So we have plenty of data. So we have, uh, we have over a hundred data sets, uh, petabytes and petabytes of data all free. You don't even need an AWS account to access the data and take it out if you want to. Uh, but I, I would say a few things that are exciting that are happening at Mars. One is that we are actually got integrated into ADX. So the AWS that exchange and what that means is that now you can find the open data, free data from a STI in the same searching capability and service as the paid data, right. License data. So hopefully we'll make it easier if I, if you wanna play with data, we have actually something great. We just announced a hackathon this week, uh, in partnership with UNESCO, uh, focus on sustainable development goals, uh, a hundred K in prices and, uh, so much data <laugh> you >>Too years, they get the world is your oyster to go check that out at URL at website, I'll see it's on Amazon. It use our website or a project that can join, or how do people get in touch with you? >>Yeah. So, uh, Amazon SDI, like for Amazon sustainability, that initiative, so Amazon sdi.com and you'll find, um, all the data, a lot of examples of customer stories that are using the data for impactful solutions, um, and much more >>So, and these are, there's a, there's a, a new kind of hustle going out there, seeing entrepreneurs do this. And very successfully, they pick a narrow domain and they, they own it. Something really obscure that could be off the big player's reservation. Mm-hmm <affirmative> and they just become fluent in the data. And it's a big white space for them, right. This market opportunities. And at the minimum you're playing with data. So this is becoming kind of like a long tail domain expertise, data opportunity. Yeah, absolutely. This really hot. So yes. Yeah. Go play around with the data, check it outs for good cause too. And it's free. >>It's all free. >>Almost free. It's not always free. Is it >>Always free? Well, if you, a friend of mine said is only free if your time is worth nothing. <laugh>. Yeah, >>Exactly. Well, Anna, great to have you on the cube. Thanks for sharing the stories. Sustainability is super important. Thanks for coming on. Thank you for the opportunity. Okay. Cube coverage here in Las Vegas. I'm Sean. Furier, we've be back with more day one. After this short break.

(gentle music) >> We all know that virtually every organization is using the cloud in some way, shape or form. But those same organizations are building or maybe buying abstraction layers that attempt to hide the underlying complexity of these clouds. Which are now connected to on-prem workloads, they're in hybrid models, spanning across multiple clouds, and bleeding out to the edge. Now, while such an approach is extensively simplifies technology, provisioning and management, it brings challenges. And these challenges are fundamentally data problems. For example, with the sprawling clouds, how do you track sensitive data and know where it lives? How do you ensure compliance and privacy protections in a world of ever-changing regulations? How can you securely share data in an increasingly decentralized environment? How can you identify gaps in security policies and how can organizations identify and stop exfiltration in this complex environment? And, oh, by the way, very importantly, how can this all be automated? Because the number one challenges that CSOs face is a lack of talent. Hello everyone, this is Dave Vellante, and welcome to this CUBE conversation where we profile emerging technologies, innovative startups and disruptive trends in enterprise tech. And today we're pleased to welcome two guests from a really interesting firm trying to solve many of these problems. With us are Dr. Noah Johnson, who was the co-founder and CTO. And April Mitchell, head of engineering, both from Dasera. Folks, welcome April. Great to see you again. >> It's a pleasure to be here. Thanks, Dave. >> Okay Noah, let me start with you. I got to ask you, is security in your mind a do-over? >> Hey Dave. Thanks for having us. Great to be here. So yeah, you hear the adage a lot today, security is broken. And certainly if you look at the number of data breaches and misuses of data in the last few years, clearly something isn't working, right? Now, our view actually is that data security needs to be rethought, and kind of designed from the ground up for the modern way that the data is used. And that's exactly what we're doing. So we don't say, do over, so much as data security re-imagined, especially for the cloud. >> Yeah, you can't just rip and replace, but it's a little tongue in cheek there. But tell me more about the background of the company. Why did you and your co-founders start the firm? Are those challenges that I laid out upfront, the ones that you're directly attacking? >> Yeah, we're attacking all of them. So the background of the company, our technology originally came from PhD work that I did while I was studying at UC Berkeley. So I've spent most of the last decade or so looking at different cybersecurity problems. And my dissertation specifically focused on, how do you secure sensitive data while still allowing people to access it in a flexible way? As part of that work, I was able to collaborate with a big tech company, Fortune 500 company, who were facing very similar problems internally. They needed to get a handle on their data. And so through that kind of research collaboration, we built a platform that was able to track data and monitor how the data was used to better protect it while still allowing the company to be data-driven. They ended up deploying the system at scale. And so this was really strong, at least initial validation. The approach that we're using at Dasera actually is quite effective and sound. So since then, we've talked to hundreds of other CSOs and security teams, and really sort of gotten a deeper appreciation for the magnitude of the problem today. No person that we've spoken to has high confidence in their data security. And we can dig into the reasons for that. It's not for lack of effort, it's that this is a very hard problem, especially with the moving to the cloud. >> Yeah, I mean, trust is popping up on the NPS surveys. It's like the number one factor today. April, let's bring you into the discussion. You and I met early last decade and we've followed your career since then. What attracted you to Dasera? >> Yeah, that's a great question, Dave. I've spent my career at Fortune 10 companies with 15,000 plus employees. What made me take this step to go to all the magnitude, a smaller company and team? And I would say it was an easy choice and I was driven by a bold vision, the right team and an innovators heart. When I had a conversation with Ani, the CEO and Noah's co-founder, Ani and I crossed paths back at HP, and he had the opportunity to work with myself and one of my collaborators. And I'd say at the time we were the two co-founders running our own little two-person startup within HP labs, delivering consumer web services. And Ani and I connected then. And we knew we wanted to have that chance to work together in the future. And I was blessed with the opportunity to go from analytics, to programmability at HP at Cisco. And when Ani called me up just a little over two months ago, and he told me about Dasera, immediately I was interested. Data security is a wonderful hot space with so many challenges, and that innovation and the challenge from a real research perspective is what drew me to Dasera. And I had the conversation with Noah. And we went deep into differential privacy and the cracks of his PhD research. And I understood there, this company is built on very strong bonds. And really, to be successful, it's about the team. You have to have a diverse team with great experience. And when I talked to every single one of the team members, they shared a vision and they shared a passion. And you know me, I love being a part of a strong team and I love building strong teams. And that's exactly what we're doing here at Dasera. >> Thank you for that April. So Noah, give us the north star. Like early on, you guys got to focus in on where you're headed. What is that north star? >> Our goal is to really solve data security. You know, we touched on earlier, clearly current solutions aren't working. We think we have a very innovative solution that is designed specifically for where data lives today, which is the cloud. We see ourselves as being the kind of gold standard for tracking and managing and securing data in the cloud across the entire life cycle. You know, from the point the data is created to all of the different ways that data is used, to when the data is deleted, we want to build a system that lets companies for the first time, get that visibility, create that feedback loop between the data users, the different security stakeholders, the legal teams. Help them make better, more informed security decisions by providing that visibility. >> So April, I use this chart sometimes when I do segments on security. I think it's from Optiv and it's this, it shows all the different segments and this is a very fragmented market space. So I'm wondering, like for first of all, like who's the enemy, I mean, who you're trying to attack? But it's so fragmented, maybe there isn't one. But from an engineering standpoint, part two of the question is, what are the really gnarly problems that you're focusing on? But talk about part A first, if you would. Who are you targeting here? >> Absolutely. I would say the best defense is a really good offense. And how are we approaching this problem differently? And there are many data security tools out there. Many processes, from access control to DLP, but we still had 4,000 events, 4,000 breaches in the last year alone. So we can't continue to expect different outcomes by using these same approaches. So that's where we are changing the story. And we have a bold purpose. We don't want to be a typical existing cybersecurity company. We want to take the approach of treating data security as sacred, we want to make the world a safer place, and we want to do that by securing data across its life cycle. Creation to deletion. You asked about the gnarly challenges that are out there. To do that right, you have to do it at speed. You've got to do it in real time and you have to do it at scale. And those are definitely the challenges that we're running into right now from an engineering perspective. >> So Noah, when you looked at the landscape, you saw, as April said, it was just so many different tools out there. How do you describe your difference in the marketplace? And April, please chime in as well. >> Sure. Yeah. So everyone has a slightly different approach. April touched on this earlier. We want to fix data security. So in some sense, we're all on the same team. We have different views of the most effective way of solving this problem, but ultimately everyone wants to solve the same problem. I would say, we're the only ones that give a comprehensive look at the entire data life cycle. So if you look at other similar security offerings, a lot of players are focused on just access control, right? Or data loss prevention, or specific features like encryption. And these are all really important technologies, but they're not sufficient, right? These are technologies that have been in use for the last decade and yet we still see data breaches on a daily basis. And the reason for that is, even if you have those systems in place, there's a lot that can go wrong between when someone is granted access to all of the different ways they consume and share the data. And so where we're unique is we give this holistic picture of the data end to end. And we don't necessarily replace those other solutions. Actually, we compliment them. Our system can tell you, if you have an encryption solution in place, are you encrypting the right data, right? Are you using it the right way? So you get more value out of those tools. Or if you have access control, our platform can be a set of guard rails or kind of a backstop that can let you know, are those access control rules properly configured, are certain users over privileged, and so forth. So really providing that context, like I said earlier, to make better security decisions. That's where we're differentiated. That's kind of our unique view of how to solve the problem. >> April, anything you'd add to that? It sounds like you're a platform for all these tools. I feel like I need that for my apps. But what's the secret sauce there? >> Yeah. I think the secret sauce is that we've learned from the challenges that our customers are facing. We have an approach where we want to rapidly innovate and rapidly validate. And our team is doing that. Noah mentioned a couple of the key features. I'm going to add a few more, because really when you're making a choice, what should I use, you've got to start with, what do you want to protect? Your data and your people? How can we help you protect that? Well, we can help you manage data sprawl. You'd be surprised by how many customers on the cloud are really interested, or use our product for the first time and go, "Oh my gosh, I did not know that that was there. When did that get there? How did it get copied there? Why is it there?" You know, and they're asking these questions. So we want to help you track that sprawl of your data. We want to monitor the data when it's in use. How are people growing it? How are your employees accessing it? How are they using it? Are they using it in the right way? Are they using it in the right way today? Are they using it on the right way tomorrow based the permissions? And we can give you that risk analysis and that perspective. We also want to let you know that when the data's sprawls, when there's a new copy that's stored in the new data store, is it configured the right way? Are you protecting it the right way? We can analyze that for you as well. So really the completeness of the features from the end to end solution, you can't protect across the entire data life cycle from creation to deletion, unless you're truly connecting and understanding how the data is being used. >> Great. Thank you. Noah, what's the ideal customer look like? Big, small, different industries? Will you give us the ICP? >> So as far as industries, our view is, a data breaches is a data breach. So any company that collects data and needs to protect it would benefit from our solution. I will say specifically, organizations that are cloud first and data-driven. Meaning they collect a lot of data and need to use that data, especially if that data is sensitive. So think B to C companies, retail, e-commerce, social media, finance, any company that collects consumer data, there are legal obligations, security obligations, kind of a higher standard of care that's required for that data. And that's where we can really help. So we're seeing traction actually from all of these industries. As far as the ideal user profile, we are targeting data security professionals. But we are a platform. We are a collaboration platform. Our system is designed to let different stakeholders within the organization, work together. From the security team, to the legal team, to the different data custodians, they can all collaborate seamlessly within the platform using that context that we're stitching together about the data flow. >> That last point is important because it used to be, it was the SecOps team. It was their problem. And now it's IT, it's security, it's legal, it's the line of business. And then the first point you made about cloud first and data-driven, that's good news for your term. Because if you're not cloud first and data-driven, you're probably not going to be in business by the end of the decade. So, how about the business case? You know, your startup, the ideal startup situation is you're 10X the value at 1/10 the price. Now, maybe in your case it's a little different 'cause you're taking that holistic view as opposed to one narrow view. But what's the justification? Lay out the ROI. >> Yeah. So we've designed the platform actually to be very quick time to value and easy to deploy. The platform is fully automated, has built in policies and machine learning. So you spin it up and it will automatically discover the data stores, it will go and crawl the data to automatically classify it. And so now you've already solved the problem of just data sprawl, knowing what data is out there. And then we can show customers, here's how the databases are configured, is the data sufficiently protected, here's how employees are interacting with the data. And then finally optionally to write policies and workflows to make sure that there's a process in place to protect the data across its entire life cycle. So there's sort of an evolution of different features. So there's kind of a maturity evolution from just, number one, identifying the data, or like we say, you can't protect what you don't know exists, to protecting it and identifying whether there are any security risks and compliance gaps. And then finally automatic proactive protection and remediation by security policies. >> So where are you guys in terms of the maturity? Obviously it's early days, but where are you in terms of product market fit? Have you nailed that? Still trying to figure that out? I know you've raised around 9 million, you're out of stealth. You give us a sense of the maturity curve. >> I can jump in on that one and speak a bit about our first customers. And then Noah can add more detail as well. But we're seeing these cloud first organizations, the CSOs, the chief security or privacy officers coming to us because they know that traditional approaches aren't working. We are here, we are ready to engage. We aren't just grabbing, what's coming, we're talking about what we have now. And we will sit arm to arm with you and made sure that we are solving the challenges that your team is facing right now. And that's where we're getting early feedback. That's where we've really been able to showcase some new innovations and to validate and move from there. But I would say, if you're interested in talking to us, please call, please visit the website and make that connection. Because we're not stealth, we're not hiding. We're engaging and definitely have a offering that is ready to be used. >> So okay, so you're in market with that offering. What do I buy from you? Is it a SAS, is it a subscription, is it a service? >> Yeah, so we have a few different product offerings and deployment models, depending on where the data's stored and the environment that the company wants to run the software. So we support on-prem, we also have a SAS offering as well. >> Okay, and that runs in the cloud, obviously, the SAS offering, or you can sort of put it as in a require on appliance? How do I deploy it on on-prem? >> No appliance runs. Runs purely in the cloud. And within an hour to onboard, to connect to the environment and to get a scan up and running. >> And it's status of the company, am I right, I think you've raised like $9 million. Head count, anything you can share in that regard? Are you hiring? I'm sure you are. >> We're growing very quickly. There's been tremendous traction as April mentioned earlier, and we're super excited about the opportunity ahead of us. It's clear we've tackled the very big problem that is still unsolved. So we have big plans and we fortunately have been able to raise some capital to help us build out the team, to add the capabilities that we need to fully solve this problem end to end. So we're well on our way, but it's a journey. This is an unsolved problem for a reason, right? It's quite complex. And we've got a great headstart, we've got a great approach, we've got some great early customers, but there's a way to go still. >> And I'll use that opportunity to say, yes, we are hiring. And if you're interested in this space, if you want to learn from a team of experts, but also grow your skills and take on some new challenges, then please go to the website and check out the current positions that we have. Dropped me ping through any of the social media networks, 'cause we'd love to hear from you. >> Great. Website is Dasera, D-A-S-E-R-A. All right. So check it out. Guys. great to have you on. Thanks so much. Best of luck. We'll be tracking you, and really congratulations on getting to this point. And I know you have a lot more work to do, but really exciting times, I'm sure, for you. >> Thanks Dave. >> It's a pleasure to see you this way and hopefully in person soon. >> Hopefully. Yeah, absolutely. Hopefully in '21. We'll see. We'll see. Thank you for watching everybody. This is Dave Vellante for the CUBE, we'll see you next time. (gentle music)

(upbeat music) >> Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, Fast Data, a retail industry business imperative. My name is Brent Biddulph, Global Managing Director of Retail and Super Bids here at Cloudera and today's hosts. Joining me today is our feature speaker Brian Kilcourse, Managing Partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in empirical segments. At the end of today's session I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data ingest, analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Cloudera is supporting retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases, primarily across four key business pillars and these will be very familiar to those in the industry. Personalize interactions of course plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the omni-channel journey, moving into the merchandising line of business, focused on localizing sorbet, promotional planning, forecasting, demand forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization, right now as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, which is pretty exciting to me as a former store operator, what's happening with physical brick and mortar right now, especially for traditional retailers. The whole re-imagining of stores right now is on fire in a lot of focus because frankly this is where fulfillment is happening, this is where customers steal 80% of revenue is driven through retail through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had in decades and a lot of it has to do of course with IoT data and analytics in the new technologies that really help drive benefits for retailers from a brick and mortars standpoint. And then finally, to wrap up before handing off to Brian, as you'll see, all of these lines of businesses are rogue, really experiencing the need for speed, fast data. So we're moving beyond just discovery analytics, things that happened five, six years ago with big data, et cetera and we're really moving into real time capabilities because that's really where the difference makers are, that's where the competitive differentiation is across all of these lines of business and these four key pillars within retail. The dependency on fast data is evident, it's something that we all read in terms of those that are students of the industry if you will, that we're all focused on in terms of bringing value to the individual lines of business but more importantly to the overall enterprise. So without further ado, I really want to have Brian speak here as a third party analyst. He's close in touch with what's going on retail talking to all the solution providers, all the key retailers about what's important, what's on their plate, what are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward. So Brian off to you. >> Well, thanks, Brent. I appreciate the introduction. And I was thinking as you were talking, what is fast data? Well, fast data is fast data, it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday. Fast data is data that's coming at you in something approaching real time and we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years and what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry. I was a retail technologist my entire working life and so we started this company. So I have a built-in bias of course, and that is that the difference between the winners in the retail world and in fact in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, one other thing about RSR Research, our research is free to the entire world. We don't have a paywall that you have to get behind, all you have to do is sign into our website, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you and we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and is being driven by the real world. We saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one, do I redirect my replenishments to store B because store A is impacted by the pandemic, those kinds of things. These two drawings are actually from a book that came out in 1997 and it was a really important book for me personally is by a guy named Steven Hegel and the name of the book was "The Adaptive Enterprise." When you think about your business model and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all, it changes once in a generation or maybe once in a lifetime, but it's established quite early. And then from that point on, it's basically a wash, rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out, season in and season out and the most important pieces of information that you have is the transaction data from the last cycle. So Brent knows this from his experience as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature, it's typically pulled from your ERP or from your best of breed solution set. On the right is where the world is really going, and before we get into the details of this, I'll just use a real example. I'm sure like me, you've watched the path of hurricanes as they go up to the Florida Coast. And one of the things you might've noticed is that there are several different possible paths. These are models and you'll hear a lot about models when you talk to people in the AI world. These are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane. Based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the real hurricane progresses, they will see if it's following that path or if it's varying, it's going down a different path and based on that they will adapt to a new model. And that is what I'm talking about here. Not everything is of course is life and death as a hurricane but it's basically the same concept. What's happening is you have your internal data that you've had since this command and control model that we've mentioned on the left and you're taking an external data from the world around you and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside. Back to my COVID example, when people were tracking the path of the pandemic through communities, they learned that customers or consumers would favor certain stores to pick up what they needed to get. So they would avoid some stores and they would favor other stores and that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees, they wanted to know where they could get their employees to service these customers, how far away were they, were they in a community that was impacted or were they relatively safe. These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So first of all, there's a context for these decisions, there's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time and that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view and based on those two inputs what's happening internally, where your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary and this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. If you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see and then because it's an intrinsically more complicated model to automate decision-making where it makes sense to do so. That's pretty complicated and I talk about new data and as I said earlier, the old data is all transactional in nature, mostly about sales. Retail has been a wash in sales data for as long as I can remember, they throw most of it away but they do keep enough to create the forecast for the next business cycle. But there's all kinds of new information that they need to be thinking about and a lot of this is from the outside world and a lot of this is non-transactional in nature. So let's just take a look at some of them. Competitive information. Retailers are always interested in what the competitor is up to, what are they promoting? How well are they doing? Where are they? What kind of traffic are they generating? Sudden and significant changes in customer behaviors and sentiment, COVID is a perfect example of something that would cause this, consumers changing their behaviors very quickly. And we have the ability to observe this because in a great majority of cases nowadays, retailers have observed that customers start their shopping journey in the digital space. As a matter of fact, Google recently came out and said that 63% of all sales transactions begin in the digital domain, even if many of them end up in the store. So we have the ability to observe changes in consumer behavior, what are they looking at? When are they looking at it? How long do they spend looking at it? What else are they looking at while they're doing that? What is the outcome of them looking? Market metrics certainly, what's going on in the marketplace around you? A good example of this might be something related to a sporting event. If you've planned based on normal demand and for your store and there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand, so understanding what's going on in the market is really important. Location, demographics and psychographics. Demographics have always been important to retailers, but now we're talking about dynamic demographics. What customers or what consumers are in your market in something approaching real time. Psychographics has more to do with their attitudes, what kind of folks are in a particular marketplace, what do they think about, what do they favor, and all those kinds of interesting details. Real time environmental and social incidents, of course, I mentioned hurricanes and so that's fairly self-evident. Disruptive events, sporting events, et cetera, these are all real. And then we get the real time Internet-of-Things, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where it really gets interesting, this is where the supply chain people will start talking about the digital twin to their physical world. If you can't say something you can't manage it and retailers want to be able to manage things in real time. So IoT along with AI analytics and the data that's generated is really, really important for them going forward. Community health, we've been talking a lot about that, the progression of the flu, et cetera, et cetera. Business schedules, commute patterns, school schedules, and weather, these are all external data that are interesting to retailers and can help them to make better operational decisions in something approaching real time. I mentioned the automation of decision-making, this is a chart from Gardner and I'd love to share with you. It's a really good one because it describes very simply what we're talking about and it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data, we're getting more data all the time. Retailers for a long time now since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Sometime in the not-too-distant past this data was started to be used to make diagnostic decisions, not only what happened but why did it happen? And we might think of this as, for example, if sales were depressed and for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics and this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or the cannibalization effect of your category plans if you happen to be a grocer. And based on that, the human will make a decision as to what they need to do next. Then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data. AI if I could simplify it to the next maximum extent, it essentially is a data tool that allows you to see patterns in data which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that of course, because it uses math instead of rules. So instead of an if then or else statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data and based on those we can make some models. For example, my guy in my (chuckles) just turned 70. My 70 year old man, I'm a white guy, I live in California, I have a certain income and a certain educational level. I'm likely to behave in this way based on a model, that's pretty simplistic but based on that, you can see that when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, they might be expected to make a certain action. And so this is where prescriptive really comes into play. AI makes that possible. And then finally, when you start to think about moving closer to the customer or something approaching a personalized level, a one-to-one level, you suddenly find yourself in the situation of having to make not thousands of decisions but tens of millions of decisions and that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting and it's new, it's just the latest turn of the technology screw and it allows us to use this new data to basically automate decision-making in the business in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. This happens to be from a location analytics study we just published last week, and we asked retailers, what are the big challenges? What's been going on in the last 12 months for them, and what's likely to be happening for them in the next few years and it's just fascinating because it speaks to the need for faster decision-making. The challenges in the last 12 months are all related to COVID. First of all, fulfilling growing online demand, this is a very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand and this is one of those areas where retailers are now finding themselves needing to look at that exogenous or that external data that I mentioned to you. Last year sales were not a good predictor of next year sales, they needed to look at sentiment, they needed to look at the path of the disease, they needed to look at the availability of products, alternate sourcing, global political issues, all of these things get to be pretty important and they affect the forecast. And then finally, managing the movement of the supply through the supply chain so that they could identify bottlenecks. Now, point to one of them which we can all laugh at now because it's kind of funny, it wasn't funny at the time. We ran out of toilet paper (laughs) toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population and yet we ran out. And the thing we didn't expect when the COVID pandemic hit was that people would panic and when people panic they do funny things. One of the things I do is buy up all the available toilet paper, I'm not quite sure why that happen but it did happen and it drained the supply chain. So retailers needed to be able to see that, they needed to be able to find alternative sources, they needed to be able to do those kinds of things. This gets to the issue of visibility, real-time data, fast data. Tomorrow's challenge is kind of interesting because one of the things that retailers put at the top of their list is improve inventory productivity. The reason that they are interested in this is because they will never spend as much money on anything as they will on inventory and they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big. But in this complex, fast moving world that we live in today is this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCs and the warehouses and their picking capacity. We're talking about Echo's, we're talking about Echo's level of decision-making about what's flowing through the supply chain all the way from the manufacturing door to the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it. You'll hear retailers and people like me talk about the digital twin, this is where this really becomes important. And again, the digital twin is enabled by IoT and AI analytics. And finally, they need to increase their profitability for online fulfillment. This is a huge issue, for some grocers the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020 what they needed to do to fulfill those customer orders in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those features to be available to them all the time and many people really like them. Now retailers need to find out how to do it profitably and one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. Now when we think about the hard one wisdom that retailers have come up with we think about these things, better visibility has led to better understanding which increases their reaction time which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common and in our research, we separate over-performers, who we call retail winners from everybody else, average and under-performers. And we've noticed throughout the life of our company that retail winners don't just do all the same things that others do, they tend to do other things and this shows up in this particular graph. This again is from the same study. So what are the opportunities to address these challenges I mentioned to you in the last slide? First of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand on the consumer side. A better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase, observing things that are happening in the marketplace around the retailer so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase as they engage with us. One of the things we all know about consumers now is that they are in control and literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs. Optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in-store, buy online pick up at a locker, a direct to consumer, all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important and never goes away is the reduction of waste, shrink within the supply chain. I'm embarrassed to say that when I was a retail executive in the nineties, we were no more certain of consumer demand than anybody else was but we wanted to commit to very high service levels for some of our key categories somewhere approaching 95% and we found the best way to do that was to flood the supply chain with inventory. It sounds irresponsible now, but in those days that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world, money is too tight and we can't have that inventory sitting around and move to the right places once we discover what the right places. We have to be able to predict, observe, and respond in something much closer to real time. Onto the next slide, the simple message here, again a difference between winners and everybody else. The messages, if you can't see it you can't manage it. And so we asked retailers to identify to what extent an AI enabled supply chain can help their company address some issues. Look at the differences here, they're shocking. Identifying network bottlenecks, this is the toilet paper story I told you about. Over half of retail winners feel that that's very important, only 19% of average and under-performers, no surprise that they're average and under-performers. Visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list but you get the just, retail winners understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today and in order to do that you need to be able to number one, see it and number two, you need to be able to analyze it, and number three, you have to be able to make decisions based on what you saw. Just some closing observations and I hope this was interesting for you. I love talking about this stuff, you can probably tell I'm very passionate about it. But the rapid pace of change in the world today is really underscoring the importance, for example, of location intelligence as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes and how products are brought to the market. And in order to do that they need to be able to see people, they need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it and based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in, it's a real-time world, it's a sense and respond world and it's the way forward. So Brent, I hope that was interesting for you. I really enjoyed talking about this as I said, we'd love to hear a little bit more. >> Hey, Brian, that was excellent. I always love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. One of the higher level research articles around fast data frankly, is the whole notion of IoT, right? Now many does a lot of work in this space. What I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year between now and 2025. Now, how's that possible? Well, part of it is because of the Kinsey captures not only traditional retail but also QSRs and entertainment venues, et cetera, that's considered all of retail. But it's a staggering number and it really plays to the effect that real time can have on individual enterprises, in this case we're talking of course about retail. So a staggering number and if you think about it, from streaming video to sensors, to beacons, RFID, robotics, autonomous vehicles retailers are asking today, even pizza delivery and autonomous vehicles. If you think about it, it shouldn't be that shocking, but when they were looking at 12 different industries, retail became like the number three out of 12 and there's a lot of other big industries that will be leveraging IoT in the next four years. So retailers in the past have been traditionally a little stodgy about their spend in data and analytics. I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy and IoT really is the next frontier, which is kind of the definition of fast data. So I just wanted to share just a few examples or exemplars of retailers that are leveraging the Cloudera technology today. So now they pay for advertisement at the end of this, right? So what is Cloudera bringing to market here? So across all retail verticals, if we look at, for example, a well-known global mass virtual retailer, they're leveraging Cloudera data flow which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So it is best to class movement of data from an ingest standpoint, but we're also able to help the round trip. So we'll pull up sensor data off all the refrigeration units for this particular retailer, they'll hit it up against the product lifecycle table, they'll understand temperature fluctuations of 10, 20 degrees based on fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration, they'll know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. So this particular customer leverages further data flow to understand temperature fluctuations, the impact on the product life cycle and the roundtrip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager. Hey, you had a 20 degree drop in temperature, we suggest you lower the price on these products that we know are in that cooler for the next couple of days by 20%. So you don't have to worry about freshness issues and or potential shrink. The grocery with fresh product, if you don't sell it, you smell it, you throw it away, it's cost to the bottom line. So critically important and tremendous ROI opportunity that we're helping to enable there. From a leading global drugstore retailer, so this is more about data processing and we're excited of the recent partnership with the Nvidia. So fast data isn't always at the edge with IoT, it's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will never achieve personalization, you will never achieve one-on-one communications with retailers or with customers, and why is that? Because customers in many cases are touching your brand several times a week. So if taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization, in fact, you may offend customers by offers you might push out based on what they just bought yesterday you had no idea of it. So that's what we're really excited about, again with the computation speed that Nvidia brings to Cloudera. We're already doing this today, we've already been providing levels of exponential speed and processing data, but when Nvidia brings to the party is course GPUs right, which is another exponential improvement to processing workloads like demand forecast, customer profiles. These things need to happen behind the scenes in the back office much faster than retailers have been doing in the past. That's just the world we all live in today. And then finally, from a proximity marketing standpoint or just from an in-store operations standpoint, retailers are leveraging Cloudera today, not only data flow but also of course our compute and storage platform and ML, et cetera, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line, you can now start to understand how to better merchandise the store, where the hotspots are, how to in real time improve your customer service. And from a proximity marketing standpoint, understand how to engage with the customer for right at the moment of truth, right, when they're right there in front of the particular department or category, upward leveraging mobile device. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. From an overall platform standpoint of course, Cloudera is an enterprise data platform, right? So we're helping to enable the entire data life cycle, so we're not a data warehouse, we're much more than that. So we have solutions to ingest data from the Edge, from IoT, leading practice solutions to bring it in. We also have experiences to help leverage the analytic capabilities of data engineering, data science, analytics and reporting. We're not encroaching upon the legacy solutions that many retailers have today, we're providing a platform that's open source that helps weave all this mess together that existed retail today from legacy systems because no retailer frankly is going to rip and replace a lot of stuff that they have today. Right. And the other thing the Cloudera brings to market is this whole notion of on-prem hybrid cloud and multicloud, right. So our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. We're kind of religious about open source and lack of vendor lock-in, maybe to our fault, but as a company we pull that together from a data platform standpoint so it's not a rip or replace situation. It's like helping to connect legacy systems, data and analytics, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. And then finally, I want to thank everyone for joining today's session, I hope you found it informative. I can't thank Brian Kilcourse enough, like he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course in talking to a lot of our partners in other technology companies out there as well. But I really appreciate everyone joining the session, and Brian, I'm going to kind of leave it open to you to any closing comments that you might have based on what we're talking about today in terms of fast data and retail. >> First of all, thank you, Brent. And this is an exciting time to be in this industry. And I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that and it frankly wasn't even usable. But what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that make us a trusted provider of their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community and I'm glad to be a part of it and I'm glad to be working with you. So thank you, Brent. >> Yeah, of course, Brian. And one of the exciting things for me too, I've being in the industry as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and IT for a change, right? (Brian laughs) They've all kind of come to this final pinnacle of this is what it's going to take to compete. You and I talked to a lot of colleagues, even salespeople within Cloudera, like, oh, retail, very stodgy, slow to move. That's not the case anymore. >> No. >> Everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry that we couldn't see years ago, Brian. I mean, retailers are like pulling people from some of the greatest tech companies out there, right? From a data science, data engineering standpoint, application developers. Retail is really getting its legs right now in terms of go to market and the leverage of data and analytics, which to me is very exciting. >> Well, you're right. I mean, I became a CIO around the time that point of sale and data warehouses were starting to happen, data cubes and all those kinds of things. And I never thought I would see a change that dramatic as the industry experience back in those days, 1989, 1990, this changed doors that, but the good news is again, as the technology is capable, we're talking about making technology and information available to retail decision-makers that consumers carry around in their purses and pockets as they're right now today. So the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. >> Yeah, for sure. Hey, thanks everyone. We'll wrap up, I know we ran a little bit long, but appreciate everyone hanging in here with us. We hope you enjoyed the session. Our contact information is right there on the screen, feel free to reach out to either Brian and I. You can go to cloudera.com, we even have joint sponsored papers with RSR, you can download there as well as other eBooks, other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time today.

>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

>>from >>Around the globe, it's the cube with digital coverage of IBM think 2021 brought to you by IBM. >>Welcome back to IBM Think 2021 we're gonna dig into the intersection of finance and business strategy. My name is Dave Volonte and you're watching the cubes continuous coverage of IBM thinking with me is brian Hoffman is the chief operating officer of IBM Global financing, brian, thanks for coming on the cube today. >>Good morning, Great to be here. >>Hey, good morning. So I think we've heard a lot about the impact of hybrid cloud ai digital transformation and I wonder as a finance person in a former CFO, what do you see? And how do you think about some of the key considerations and financials and strategies that are supporting these major projects? Right? We got to come to the CFO and say, hey, we want to spend some money and here's the benefit, here is the cost. How can see IOS and their teams work with CFOs to try to really accelerate that digital transformation. >>Great question. And actually that question, I think I might have answered it a little bit differently, like two years ago, a year ago before the pandemic, I think it's actually changed a little bit with pandemic in my experience is the CFO people would come into me for projects and there's three ways you can justify it, but you can justify short term immediate, quick payback kind of hitters, you can justify it with, you know, improving our efficiency or effectiveness, um you know, reducing costs in the long run, making the client experience better or more from a strategic point of view, um you know, growing revenue getting to new clients, improving margins right? And so the the hybrid cloud transformation journey really still addresses those three things and when we come in, a lot of people focus like I said, on that third strategic point, but but all three of those come into play, and what's really interesting now is is as I'm dealing with it, I'm talking to other Cfos. The pandemic is really, if you will throw in a wrinkle in here, right? So the clients that I'm talking to, the IBM clients, they have to operate their business very differently and and their business models, some of them are changing clearly. Their clients, their business models are changing their operating differently as well. Um So, so our clients have to react to that and Hybrid Cloud and that that that type of of a structure really can support that. So there's really an emphasis here now to act with much more speed on this journey to get moving on it to get there because you have to make these changes and doing those two things in concert really has a ton of business value. >>Yeah I mean the cfos that I've talked to in the C. I. O. S. It's really kind of industry dependent, right? If you're in airlines or hospitality was like uh we got to cut costs. A lot of organizations said okay we're gonna support remote workers put in V. D. I. Or deal with endpoint security or whatever it was. But we're actually gonna double down on our digital transformation. This is we're gonna lean into an opportunity for us to come out stronger. How did you guys approach it in terms of your own internal digital >>transformation? Yeah. We we we were working on our digital transformation uh a little bit before the pandemic and it kind of followed those those three uh those three items when they when they first started implementing it, they came in and said hey if we can if we can move to a cloud platform, our infrastructure savings will be pretty significant. You know the I. T. Infrastructure savings will be 30 to 40%. So you know, quick payback CFO types love that. So you know, we went forward with that. Um but then quickly we saw the real benefits of moving to a hybrid cloud strategy. So just as an example as we were making some of these changes, we found a workflow tool in one of our markets in europe, that was a great tool and uh if we wanted to implement that across the business um in the old days, You know, we're in 40 countries, we've got 2500 employees, three lines of business. It would have been very complex because our operating structure is is very robust, very complex. Um Probably have taken a year, two years to do that. But since we are now on a cloud platform we got that rolled out that workflow tool rolled out across our business in months, Saving 20-30 of of workload. Being much more um efficiently getting to our clients and reacting quickly to them. And in fact that tool got adopted across IBM because that cloud platform enabled that to happen. And then the great thing which I didn't even realize at the time but now thinking more strategically um are my I. T. Resource earlier was running at about 50 50 50 people working on maintenance. The kind of things with 50 on development as we've now transition to this cloud. My I. T. Resources now 70 plus percent dedicated to new development. So now we can go attack new things that really provide customer value in the pandemic. You know the first thing to look at is can we get into more um you know electronic contracts, E signatures, things that would provide value to customers anyway. But in the pandemic is like really a significant, you know differentiator for us. So all those things were enabled by that journey that we've been taking, >>interesting. I mean most of the CF I uh in fact every CFO I know of a public company took advantage of cheap debt and improving their balance sheets. And liquidity is not the problem today, especially in the tech industry at the same time. You know I'm interested in how companies are using financing. They don't want to necessarily build out data centers but they do want to fund their digital transformation. So what are you seeing in terms of how your customers are using financing? You know, what's the conversation like? What advice are you giving? >>Yeah. So um you know, it depends a little bit on the type of customer, like you said, you know, we we deal with a lot of the biggest, strongest customers in the world. And, and as we deal with them, financing really helps the return on their investment, right, aligning the payments of those cash flows for when they're getting the benefits. Uh And and we see a real good value in improving the return on those investments in helping, you know, if it's something that's going to go to the board that really makes a difference to them. Uh So, you know, that that's always been a value proposition. It continues to be. Um The other thing that's helping now, like you said, is even in this environment, people want to accelerate this transition. Um but it's a, it's a, it's a big time of uncertainty. So, you know, some of the smaller clients, some of the more uh you know, the industries that are a little more cash constrained airlines, et cetera, you know, they're looking for the the immediate cash flow benefits. Um But many of the cfos are saying, hey, listen, you know, I can I want to go as fast as I can help me put together a structure that lets me, you know, get this in place as quick as possible, but not below my budget is not make me take too much risk in this time of uncertainty, but keeps me moving and I think that's where financing really comes in as well. Um And we're kind of talking much more about that value proposition than just if you will be improved ri proposition that we've had all along. >>I want to talk a little bit more about IBM global financing. I mean, people, a lot of times people misunderstand it. You know when you look at I. B. M. S. Debt, you gotta you gotta take out the piece that IBM global financing because that's a significant portion and that's sort of self self fulfilling. But what do people need to know about IBM global financing, >>We actually run three different businesses and we've been transforming our strategy over time. So you know right now with with IBM being all in on hybrid, we are very focused on helping IBM and IBM clients on this digital journey on IBM growing their revenue. Um you know, we we in the past have been more of if you're really full service. It finance are doing a lot more than just IBM but we are really focused now on on helping IBM. So I think the best thing for for IBM clients to know is as you're talking to IBM about the total solution, the total value profit IBM brings that financing, that cash flow solution should be embedded in what they're looking at and can provide a lot of value. Um You know, the second thing I think most people know is we provide financing for IBM s channel, so you know, distributors, resellers etcetera, if you're an IBM distributor or reseller, you know about us, because just about 100% of IBM partners use us to provide that working capital financing, uh you know, we have a state of the art platforms were just so integrated with them. Again, I don't have to I don't have to do a sales pitch on that because they don't know us. Um and the third one just because people might not realize this is, we do haven't we call it an asset recovery business, um it's a pretty small business, you know, it's bringing back equipment that comes off lease, so that uh is used by IBM internally. Um and while, you know, it's not, it's not as well known, I'm pretty proud of it because it really does help with the focus that the world that IBM has on sustainability and reuse and um and making sure that, you know, we're treating the planet fairly here, so that that's a small but powerful piece of our business well, >>You're quite broader than leasing mainframes in the 80s, >>that's for sure. >>Talking more about give, you can double click on that sort of hybrid cloud and obviously machine intelligence is a big piece of those digital transformation. So, so how specifically are you, are you helping clients really take advantage of things like hybrid cloud? >>So yeah, so um what we have typically had been doing and I can give you a couple different examples if you will, you know, for larger clients. What we tend to be doing is helping them like I said, accelerate their business. So um, you know, they're looking to modernize their applications but they still have a big infrastructure in place and so they'll run into uh you know, budget constraints and and you know, cash is still be careful to managed. So for them we are much more typically focused on, you know, if you will project based financing that allows those cash flows to line up with the savings. Again, those are tend to be bigger projects that often go to boards that return benefit is very important. Ah a little bit different value proposition for more mid market customers. So, you know, as I was kind of just looking recently, we have a couple of different customers like form engineering um or or Novi still there to smaller uh compared to some of the other customers we use uh they are again much more focused on how do I, how do I conserve and best use my cash immediately? But they want to get this, they want to get this transformation going. So you know we provide flexible payment plans to them so they can go at the rate and pace that they need to, they can align up those cash deals with their budgets for their business cycles etcetera. So again, where smaller customers where timing of the cash flow in their business cycle is very important. We provide that benefit as well. >>You know, I wonder if I could ask you. So you remember of course the early days of public cloud, one of the first tail winds for public cloud was the pen was not the pandemic, the for the financial crisis of 2007. And a lot of CFO said, Okay let's shift to uh to an apex model. And now you can always provide financial solutions to customers. But it seems like today when I talk to clients, it's it's much more integrated, it's not just the public cloud, you can do that for on premises and again you always could do that. But it seems like there's much more simpatico uh in the way in which you provide that that that solution is that >>fair? Absolutely. And this might be a little to finance geeky, I don't know. But if you go back, well if you go back to the financial crisis and all that and at that time um a lot of people were looking to financing for you call that ah please. But you know if if I was talking about off balance sheet transactions right? Um and and you know between regulation etcetera etcetera, that that off balance sheet thing. First of all, people are seeing through it that much more clearly. But second, you know the the uh financial disclosure say you kind of have to show that stuff so that that if you will, window dressing benefit has gone away. So now which is great for me, we really get to talk about what's the real benefit, what is the, you know, what is the real benefit of? You know, you want to make sure that you have known timed expenditures. You know that if your business grows that your your expenses can grow evenly with those with that business growth, you don't have to take big chunky things and so you know uh financing under the covers of an integrated solution and IBM has a lot of those integrated solutions allows businesses to have that, you know, known timing known quantities. Most of the benefits that people were looking for from that affects cloud model. Um without, you know, some of the problems that you have, when you try to have to go straight to a public cloud for very, you know, big sensitive businesses, confidential confidential data etcetera. >>Thanks for that. So, okay, we're basically out of time. But I wonder if you could give us the bumper sticker and key takeaways, maybe you could summarize for our audience. >>Yeah. For those that noah global financing or dealing with IBM, my view would be in the past we might have been a little more, you know, out there with our own with our own banner etcetera. In the future. I think that you should expect to see us very well integrated into anything you're doing. I think our value proper is clear and compelling and and and will be included uh in these hybrid con transformations to the benefit of our clients. So that's that's our objective and we're well on our way there. >>Great. Anywhere, anywhere I'm gonna go for more, more familiar, obviously IBM dot com. You got some resources there. But there is >>there any Absolutely dot com? There's there's a thank you. Just probably a slash financing. But yeah, there's >>were >>loaded with information of people. >>Excellent brian thanks so much for coming to the cube. Really great to have you today. >>I appreciate the time. >>My pleasure. Thank you for watching everybody's day. Volonte for the Cuban. Our coverage of IBM think 2021, the virtual edition right back.

>>from around the globe. It's the Cube presenting enterprise. Digital resilience on hybrid and multi cloud Brought to You by Iota Ho. Hello, everyone, and welcome to our continuing Siri's covering data automation brought to you by Io Tahoe. Today we're gonna look at how to ensure enterprise resilience for hybrid and multi cloud. Let's welcome in age. Eva Hora, who is the CEO of Iota A J. Always good to see you again. Thanks for coming on. >>Great to be back. David Pleasure. >>And he's joined by Fozzy Coons, who is a global principal architect for financial services. The vertical of financial services. That red hat. He's got deep experiences in that sector. Welcome, Fozzie. Good to see you. >>Thank you very much. Happy to be here. >>Fancy. Let's start with you. Look, there are a lot of views on cloud and what it is. I wonder if you could explain to us how you think about what is a hybrid cloud and and how it works. >>Sure, yes. So the hybrid cloud is a 90 architecture that incorporates some degree off workload, possibility, orchestration and management across multiple clouds. Those clouds could be private cloud or public cloud or even your own data centers. And how does it all work? It's all about secure interconnectivity and on demand. Allocation of resources across clouds and separate clouds can become hydrate when they're similarly >>interconnected. And >>it is that interconnectivity that allows the workloads workers to be moved and how management can be unified in off the street. You can work and how well you have. These interconnections has a direct impact on how well your hybrid cloud will work. >>Okay, so we'll fancy staying with you for a minute. So in the early days of Cloud that turned private Cloud was thrown a lot around a lot, but often just meant virtualization of an on PREM system and a network connection to the public cloud. Let's bring it forward. What, in your view, does a modern hybrid cloud architecture look like? >>Sure. So for modern public clouds, we see that, um, teams organizations need to focus on the portability off applications across clouds. That's very important, right? And when organizations build applications, they need to build and deploy these applications as small collections off independently, loosely coupled services, and then have those things run on the same operating system which means, in other words, running it on Lenox everywhere and building cloud native applications and being able to manage and orchestrate thes applications with platforms like KUBERNETES or read it open shit, for example. >>Okay, so that Z, that's definitely different from building a monolithic application that's fossilized and and doesn't move. So what are the challenges for customers, you know, to get to that modern cloud? Aziz, you've just described it. Is it skill sets? Is that the ability to leverage things like containers? What's your view there? >>So, I mean, from what we've seen around around the industry, especially around financial services, where I spent most of my time, we see that the first thing that we see is management right now because you have all these clouds and all these applications, you have a massive array off connections off interconnections. You also have massive array off integrations, possibility and resource allocations as well, and then orchestrating all those different moving pieces. Things like storage networks and things like those are really difficult to manage, right? That's one. What s O Management is the first challenge. The second one is workload, placement, placement. Where do you place this? How do you place this cloud? Native applications. Do you or do you keep on site on Prem? And what do you put in the cloud? That is the the the other challenge. The major one. The third one is security. Security now becomes the key challenge and concern for most customers. And we could talk about how hundreds? Yeah, >>we're definitely gonna dig into that. Let's bring a J into the conversation. A J. You know, you and I have talked about this in the past. One of the big problems that virtually every companies face is data fragmentation. Um, talk a little bit about how I owe Tahoe unifies data across both traditional systems legacy systems. And it connects to these modern I t environments. >>Yeah, sure, Dave. I mean, fancy just nailed it. There used to be about data of the volume of data on the different types of data. But as applications become or connected and interconnected at the location of that data really matters how we serve that data up to those those app. So working with red hat in our partnership with Red Hat being able Thio, inject our data Discovery machine learning into these multiple different locations. Would it be in AWS on IBM Cloud or A D. C p R. On Prem being able thio Automate that discovery? I'm pulling that. That single view of where is all my data then allows the CEO to manage cast that can do things like one. I keep the data where it is on premise or in my Oracle Cloud or in my IBM cloud on Connect. The application that needs to feed off that data on the way in which you do that is machine learning. That learns over time is it recognizes different types of data, applies policies to declassify that data. Andi and brings it all together with automation. >>Right? And that's one of the big themes and we've talked about this on earlier episodes. Is really simplification really abstracting a lot of that heavy lifting away so we can focus on things A. J A. Z. You just mentioned e nifaz e. One of the big challenges that, of course, we all talk about his governance across thes disparity data sets. I'm curious as your thoughts. How does Red Hat really think about helping customers adhere to corporate edicts and compliance regulations, which, of course, are are particularly acute within financial services. >>Oh, yeah, Yes. So for banks and the payment providers, like you've just mentioned their insurers and many other financial services firms, Um, you know, they have to adhere Thio standards such as a PC. I. D. S s in Europe. You've got the G g d p g d p r, which requires strange and tracking, reporting documentation. And you know, for them to to remain in compliance and the way we recommend our customers to address these challenges is by having an automation strategy. Right. And that type of strategy can help you to improve the security on compliance off the organization and reduce the risk after the business. Right. And we help organizations build security and compliance from the start without consulting services residencies. We also offer courses that help customers to understand how to address some of these challenges. And that's also we help organizations build security into their applications without open sources. Mueller, where, um, middle offerings and even using a platform like open shift because it allows you to run legacy applications and also continue rights applications in a unified platform right And also that provides you with, you know, with the automation and the truly that you need to continuously monitor, manage and automate the systems for security and compliance >>purposes. Hey, >>Jay, anything. Any color you could add to this conversation? >>Yeah, I'm pleased. Badly brought up Open shift. I mean, we're using open shift to be able. Thio, take that security application of controls to to the data level. It's all about context. So, understanding what data is there being able to assess it to say who should have access to it. Which application permission should be applied to it. Um, that za great combination of Red Hat tonight. Tahoe. >>But what about multi Cloud? Doesn't that complicate the situation even even further? Maybe you could talk about some of the best practices to apply automation across not only hybrid cloud, but multi >>cloud a swell. Yeah, sure. >>Yeah. So the right automation solution, you know, can be the difference between, you know, cultivating an automated enterprise or automation caress. And some of the recommendations we give our clients is to look for an automation platform that can offer the first thing is complete support. So that means have an automation solution that provides that provides, um, you know, promotes I t availability and reliability with your platform so that you can provide, you know, enterprise great support, including security and testing, integration and clear roadmaps. The second thing is vendor interoperability interoperability in that you are going to be integrating multiple clouds. So you're going to need a solution that can connect to multiple clouds. Simples lee, right? And with that comes the challenge off maintain ability. So you you you're going to need to look into a automation Ah, solution that that is easy to learn or has an easy learning curve. And then the fourth idea that we tell our customers is scalability in the in the hybrid cloud space scale is >>is >>a big, big deal here, and you need a to deploy an automation solution that can span across the whole enterprise in a constituent, consistent manner, right? And then also, that allows you finally to, uh, integrate the multiple data centers that you have, >>So A J I mean, this is a complicated situation, for if a customer has toe, make sure things work on AWS or azure or Google. Uh, they're gonna spend all their time doing that, huh? What can you add really? To simplify that that multi cloud and hybrid cloud equation? >>Yeah. I could give a few customer examples here Warming a manufacturer that we've worked with to drive that simplification Onda riel bonuses for them is has been a reduction cost. We worked with them late last year to bring the cost bend down by $10 million in 2021 so they could hit that reduced budget. Andre, What we brought to that was the ability thio deploy using open shift templates into their different environments. Where there is on premise on bond or in as you mentioned, a W s. They had G cps well, for their marketing team on a cross, those different platforms being out Thio use a template, use pre built scripts to get up and running in catalog and discover that data within minutes. It takes away the legacy of having teams of people having Thio to jump on workshop cause and I know we're all on a lot of teens. The zoom cause, um, in these current times, they just sent me is in in of hours in the day Thio manually perform all of this. So yeah, working with red hat applying machine learning into those templates those little recipes that we can put that automation toe work, regardless of which location the data is in allows us thio pull that unified view together. Right? >>Thank you, Fozzie. I wanna come back to you. So the early days of cloud, you're in the big apple, you know, financial services. Really well. Cloud was like an evil word within financial services, and obviously that's changed. It's evolved. We talked about the pandemic, has even accelerated that, Um And when you really, you know, dug into it when you talk to customers about their experiences with security in the cloud it was it was not that it wasn't good. It was great, whatever. But it was different. And there's always this issue of skill, lack of skills and multiple tools suck up teams, they're really overburdened. But in the cloud requires new thinking. You've got the shared responsibility model you've got obviously have specific corporate requirements and compliance. So this is even more complicated when you introduce multiple clouds. So what are the differences that you can share from your experience is running on a sort of either on Prem or on a mono cloud, um, or, you know, and versus across clouds. What? What? What do you suggest there? >>Yeah, you know, because of these complexities that you have explained here, Miss Configurations and the inadequate change control the top security threats. So human error is what we want to avoid because is, you know, as your clouds grow with complexity and you put humans in the mix, then the rate off eras is going to increase, and that is going to exposure to security threat. So this is where automation comes in because automation will streamline and increase the consistency off your infrastructure management. Also application development and even security operations to improve in your protection, compliance and change control. So you want to consistently configure resources according to a pre approved um, you know, pre approved policies and you want to proactively maintain a to them in a repeatable fashion over the whole life cycle. And then you also want to rapid the identified system that require patches and and reconfiguration and automate that process off patching and reconfiguring so that you don't have humans doing this type of thing, right? And you want to be able to easily apply patches and change assistant settings. According Thio, Pre defined, based on like explained before, you know, with the pre approved policies and also you want is off auditing and troubleshooting, right? And from a rate of perspective, we provide tools that enable you to do this. We have, for example, a tool called danceable that enables you to automate data center operations and security and also deployment of applications and also obvious shit yourself, you know, automates most of these things and obstruct the human beings from putting their fingers on, causing, uh, potentially introducing errors right now in looking into the new world off multiple clouds and so forth. The difference is that we're seeing here between running a single cloud or on prem is three main areas which is control security and compliance. Right control here it means if your on premise or you have one cloud, um, you know, in most cases you have control over your data and your applications, especially if you're on Prem. However, if you're in the public cloud, there is a difference there. The ownership, it is still yours. But your resources are running on somebody else's or the public clouds. You know, e w s and so forth infrastructure. So people that are going to do this need to really especially banks and governments need to be aware off the regulatory constraints off running, uh, those applications in the public cloud. And we also help customers regionalize some of these choices and also on security. You will see that if you're running on premises or in a single cloud, you have more control, especially if you're on Prem. You can control this sensitive information that you have, however, in the cloud. That's a different situation, especially from personal information of employees and things like that. You need to be really careful off that. And also again, we help you rationalize some of those choices. And then the last one is compliant. Aziz. Well, you see that if you're running on Prem or a single cloud, um, regulations come into play again, right? And if you're running a problem, you have control over that. You can document everything you have access to everything that you need. But if you're gonna go to the public cloud again, you need to think about that. We have automation, and we have standards that can help you, uh, you know, address some of these challenges for security and compliance. >>So that's really strong insights, Potsie. I mean, first of all, answerable has a lot of market momentum. Red hats in a really good job with that acquisition, your point about repeatability is critical because you can't scale otherwise. And then that idea you're you're putting forth about control, security compliance It's so true is I called it the shared responsibility model. And there was a lot of misunderstanding in the early days of cloud. I mean, yeah, maybe a W s is gonna physically secure the, you know, s three, but in the bucket. But we saw so many Miss configurations early on. And so it's key to have partners that really understand this stuff and can share the experiences of other clients. So this all sounds great. A j. You're sharp, you know, financial background. What about the economics? >>You >>know, our survey data shows that security it's at the top of the spending priority list, but budgets are stretched thin. E especially when you think about the work from home pivot and and all the areas that they had toe the holes that they had to fill their, whether it was laptops, you know, new security models, etcetera. So how do organizations pay for this? What's the business case look like in terms of maybe reducing infrastructure costs so I could, you know, pay it forward or there's a There's a risk reduction angle. What can you share >>their? Yeah. I mean, the perspective I'd like to give here is, um, not being multi cloud is multi copies of an application or data. When I think about 20 years, a lot of the work in financial services I was looking at with managing copies of data that we're feeding different pipelines, different applications. Now what we're saying I talk a lot of the work that we're doing is reducing the number of copies of that data so that if I've got a product lifecycle management set of data, if I'm a manufacturer, I'm just gonna keep that in one location. But across my different clouds, I'm gonna have best of breed applications developed in house third parties in collaboration with my supply chain connecting securely to that. That single version of the truth. What I'm not going to do is to copy that data. So ah, lot of what we're seeing now is that interconnectivity using applications built on kubernetes. Um, that decoupled from the data source that allows us to reduce those copies of data within that you're gaining from the security capability and resilience because you're not leaving yourself open to those multiple copies of data on with that. Couldn't come. Cost, cost of storage on duh cost of compute. So what we're seeing is using multi cloud to leverage the best of what each cloud platform has to offer That goes all the way to Snowflake and Hiroko on Cloud manage databases, too. >>Well, and the people cost to a swell when you think about yes, the copy creep. But then you know when something goes wrong, a human has to come in and figured out um, you brought up snowflake, get this vision of the data cloud, which is, you know, data data. I think this we're gonna be rethinking a j, uh, data architectures in the coming decade where data stays where it belongs. It's distributed, and you're providing access. Like you said, you're separating the data from the applications applications as we talked about with Fozzie. Much more portable. So it Z really the last 10 years will be different than the next 10 years. A. >>J Definitely. I think the people cast election is used. Gone are the days where you needed thio have a dozen people governing managing black policies to data. Ah, lot of that repetitive work. Those tests can be in power automated. We've seen examples in insurance were reduced teams of 15 people working in the the back office China apply security controls compliance down to just a couple of people who are looking at the exceptions that don't fit. And that's really important because maybe two years ago the emphasis was on regulatory compliance of data with policies such as GDP are in CCP a last year, very much the economic effect of reduce headcounts on on enterprises of running lean looking to reduce that cost. This year, we can see that already some of the more proactive cos they're looking at initiatives such as net zero emissions how they use data toe under understand how cape how they can become more have a better social impact. Um, and using data to drive that, and that's across all of their operations and supply chain. So those regulatory compliance issues that may have been external we see similar patterns emerging for internal initiatives that benefiting the environment, social impact and and, of course, course, >>great perspectives. Yeah, Jeff Hammer, Bucker once famously said, The best minds of my generation are trying to get people to click on ads and a J. Those examples that you just gave of, you know, social good and moving. Uh, things forward are really critical. And I think that's where Data is gonna have the biggest societal impact. Okay, guys, great conversation. Thanks so much for coming on the program. Really appreciate your time. Keep it right there from, or insight and conversation around, creating a resilient digital business model. You're watching the >>Cube digital resilience, automated compliance, privacy and security for your multi cloud. Congratulations. You're on the journey. You have successfully transformed your organization by moving to a cloud based platform to ensure business continuity in these challenging times. But as you scale your digital activities, there is an inevitable influx of users that outpaces traditional methods of cybersecurity, exposing your data toe underlying threats on making your company susceptible toe ever greater risk to become digitally resilient. Have you applied controls your data continuously throughout the data Lifecycle? What are you doing to keep your customer on supply data private and secure? I owe Tahoe's automated, sensitive data. Discovery is pre programmed with over 300 existing policies that meet government mandated risk and compliance standards. Thes automate the process of applying policies and controls to your data. Our algorithm driven recommendation engine alerts you to risk exposure at the data level and suggests the appropriate next steps to remain compliant on ensure sensitive data is secure. Unsure about where your organization stands In terms of digital resilience, Sign up for a minimal cost commitment. Free data Health check. Let us run our sensitive data discovery on key unmapped data silos and sources to give you a clear understanding of what's in your environment. Book time within Iot. Tahoe Engineer Now >>Okay, let's now get into the next segment where we'll explore data automation. But from the angle of digital resilience within and as a service consumption model, we're now joined by Yusuf Khan, who heads data services for Iot, Tahoe and Shirish County up in. Who's the vice president and head of U. S. Sales at happiest Minds? Gents, welcome to the program. Great to have you in the Cube. >>Thank you, David. >>Trust you guys talk about happiest minds. This notion of born digital, foreign agile. I like that. But talk about your mission at the company. >>Sure. >>A former in 2011 Happiest Mind is a born digital born a child company. The reason is that we are focused on customers. Our customer centric approach on delivering digitals and seamless solutions have helped us be in the race. Along with the Tier one providers, Our mission, happiest people, happiest customers is focused to enable customer happiness through people happiness. We have Bean ranked among the top 25 i t services company in the great places to work serving hour glass to ratings off 41 against the rating off. Five is among the job in the Indian nineties services company that >>shows the >>mission on the culture. What we have built on the values right sharing, mindful, integrity, learning and social on social responsibilities are the core values off our company on. That's where the entire culture of the company has been built. >>That's great. That sounds like a happy place to be. Now you said you had up data services for Iot Tahoe. We've talked in the past. Of course you're out of London. What >>do you what? Your >>day to day focus with customers and partners. What you focused >>on? Well, David, my team work daily with customers and partners to help them better understand their data, improve their data quality, their data governance on help them make that data more accessible in a self service kind of way. To the stakeholders within those businesses on dis is all a key part of digital resilience that will will come on to talk about but later. You're >>right, e mean, that self service theme is something that we're gonna we're gonna really accelerate this decade, Yussef and so. But I wonder before we get into that, maybe you could talk about the nature of the partnership with happiest minds, you know? Why do you guys choose toe work closely together? >>Very good question. Um, we see Hyo Tahoe on happiest minds as a great mutual fit. A Suresh has said, uh, happiest minds are very agile organization um, I think that's one of the key things that attracts their customers on Io. Tahoe is all about automation. Uh, we're using machine learning algorithms to make data discovery data cataloging, understanding, data done. See, uh, much easier on. We're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation with the emphasis on agility that happiest minds have that that's a really nice combination work works very well together, very powerful. I think the other things that a key are both businesses, a serious have said, are really innovative digital native type type companies. Um, very focused on newer technologies, the cloud etcetera on. Then finally, I think they're both Challenger brands on happiest minds have a really positive, fresh ethical approach to people and customers that really resonates with us at Ideo Tahoe to >>great thank you for that. So Russia, let's get into the whole notion of digital resilience. I wanna I wanna sort of set it up with what I see, and maybe you can comment be prior to the pandemic. A lot of customers that kind of equated disaster recovery with their business continuance or business resilient strategy, and that's changed almost overnight. How have you seen your clients respond to that? What? I sometimes called the forced march to become a digital business. And maybe you could talk about some of the challenges that they faced along the way. >>Absolutely. So, uh, especially during this pandemic, times when you say Dave, customers have been having tough times managing their business. So happiest minds. Being a digital Brazilian company, we were able to react much faster in the industry, apart from the other services company. So one of the key things is the organisation's trying to adopt onto the digital technologies. Right there has bean lot off data which has been to manage by these customers on There have been lot off threats and risk, which has been to manage by the CEO Seo's so happiest minds digital resilient technology, right where we bring in the data. Complaints as a service were ableto manage the resilience much ahead off other competitors in the market. We were ableto bring in our business continuity processes from day one, where we were ableto deliver our services without any interruption to the services. What we were delivered to our customers So that is where the digital resilience with business community process enabled was very helpful for us. Toe enable our customers continue their business without any interruptions during pandemics. >>So I mean, some of the challenges that customers tell me they obviously they had to figure out how to get laptops to remote workers and that that whole remote work from home pivot figure out how to secure the end points. And, you know, those were kind of looking back there kind of table stakes, But it sounds like you've got a digital business. Means a data business putting data at the core, I like to say, but so I wonder if you could talk a little bit more about maybe the philosophy you have toward digital resilience in the specific approach you take with clients? >>Absolutely. They seen any organization data becomes. The key on that, for the first step is to identify the critical data. Right. So we this is a six step process. What we following happiest minds. First of all, we take stock off the current state, though the customers think that they have a clear visibility off their data. How are we do more often assessment from an external point off view on see how critical their data is, then we help the customers to strategies that right. The most important thing is to identify the most important critical herself. Data being the most critical assert for any organization. Identification off the data's key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure on monitor dearly so that they are consumed well as well as protected from external threats. Then, as 1/4 step, we try to bring in awareness, toe the people we train them >>at >>all levels in the organization. That is a P for people to understand the importance off the digital ourselves and then as 1/5 step, we work as a back up plan in terms of bringing in a very comprehensive and a holistic testing approach on people process as well as in technology. We'll see how the organization can withstand during a crisis time, and finally we do a continuous governance off this data, which is a key right. It is not just a one step process. We set up the environment, we do the initial analysis and set up the strategy on continuously govern this data to ensure that they are not only know managed will secure as well as they also have to meet the compliance requirements off the organization's right. That is where we help organizations toe secure on Meet the regulations off the organizations. As for the privacy laws, so this is a constant process. It's not on one time effort. We do a constant process because every organization goes towards their digital journey on. They have to face all these as part off the evolving environment on digital journey. And that's where they should be kept ready in terms off. No recovering, rebounding on moving forward if things goes wrong. >>So let's stick on that for a minute, and then I wanna bring yourself into the conversation. So you mentioned compliance and governance when when your digital business, you're, as you say, you're a data business, so that brings up issues. Data sovereignty. Uh, there's governance, this compliance. There's things like right to be forgotten. There's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these air built in on, and it's not a one shot deal. So do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there, >>so some of way have offered multiple services. Tow our customers on digital against. On one of the key service is the data complaints. As a service here we help organizations toe map the key data against the data compliance requirements. Some of the features includes in terms off the continuous discovery off data right, because organizations keep adding on data when they move more digital on helping the helping and understanding the actual data in terms off the residents of data, it could be a heterogeneous data soldiers. It could be on data basis, or it could be even on the data legs. Or it could be a no even on compromise all the cloud environment. So identifying the data across the various no heterogeneous environment is very key. Feature off our solution. Once we identify classify this sensitive data, the data privacy regulations on the traveling laws have to be map based on the business rules So we define those rules on help map those data so that organizations know how critical their digital assets are. Then we work on a continuous marching off data for anomalies because that's one of the key teachers off the solution, which needs to be implemented on the day to day operational basis. So we're helping monitoring those anomalies off data for data quality management on an ongoing basis. On finally, we also bringing the automated data governance where we can manage the sensory data policies on their later relationships in terms off mapping on manage their business roots on we drive reputations toe Also suggest appropriate actions to the customers. Take on those specific data sets. >>Great. Thank you, Yousef. Thanks for being patient. I want to bring in Iota ho thio discussion and understand where your customers and happiest minds can leverage your data automation capability that you and I have talked about in the past. I'm gonna be great if you had an example is well, but maybe you could pick it up from there, >>John. I mean, at a high level, assertions are clearly articulated. Really? Um, Hyoty, who delivers business agility. So that's by, um accelerating the time to operationalize data, automating, putting in place controls and actually putting helping put in place digital resilience. I mean way if we step back a little bit in time, um, traditional resilience in relation to data often met manually, making multiple copies of the same data. So you have a d b A. They would copy the data to various different places, and then business users would access it in those functional style owes. And of course, what happened was you ended up with lots of different copies off the same data around the enterprise. Very inefficient. ONDA course ultimately, uh, increases your risk profile. Your risk of a data breach. Um, it's very hard to know where everything is. And I realized that expression. They used David the idea of the forced march to digital. So with enterprises that are going on this forced march, what they're finding is they don't have a single version of the truth, and almost nobody has an accurate view of where their critical data is. Then you have containers bond with containers that enables a big leap forward so you could break applications down into micro services. Updates are available via a p I s on. So you don't have the same need thio to build and to manage multiple copies of the data. So you have an opportunity to just have a single version of the truth. Then your challenge is, how do you deal with these large legacy data states that the service has been referring Thio, where you you have toe consolidate and that's really where I attack comes in. Um, we massively accelerate that process of putting in a single version of the truth into place. So by automatically discovering the data, discovering what's dubica? What's redundant? Uh, that means you can consolidate it down to a single trusted version much more quickly. We've seen many customers have tried to do this manually, and it's literally taken years using manual methods to cover even a small percentage of their I T estates. With our tire, you could do it really very quickly on you can have tangible results within weeks and months on Ben, you can apply controls to the data based on context. So who's the user? What's the content? What's the use case? Things like data quality validations or access permissions on. Then, once you've done there. Your applications and your enterprise are much more secure, much more resilient. As a result, you've got to do these things whilst retaining agility, though. So coming full circle. This is where the partnership with happiest minds really comes in as well. You've got to be agile. You've gotta have controls. Um, on you've got a drug toward the business outcomes. Uh, and it's doing those three things together that really deliver for the customer. >>Thank you. Use f. I mean you and I. In previous episodes, we've looked in detail at the business case. You were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time. Thio get to the next step in terms of ultimately getting to the outcome. And we talked to a number of customers in the Cube, and the conclusion is, it's really consistent that if you could accelerate the time to value, that's the key driver reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean, how can they take advantage of some of these opportunities that we've discussed today. >>Well, we've tried to make that easy for customers. So with our Tahoe and happiest minds, you can very quickly do what we call a data health check. Um, this is a is a 2 to 3 week process, uh, to really quickly start to understand on deliver value from your data. Um, so, iota, who deploys into the customer environment? Data doesn't go anywhere. Um, we would look at a few data sources on a sample of data. Onda. We can very rapidly demonstrate how they discovery those catalog e on understanding Jupiter data and redundant data can be done. Um, using machine learning, um, on how those problems can be solved. Um, And so what we tend to find is that we can very quickly, as I say in the matter of a few weeks, show a customer how they could get toe, um, or Brazilian outcome on then how they can scale that up, take it into production on, then really understand their data state? Better on build. Um, Brasiliense into the enterprise. >>Excellent. There you have it. We'll leave it right there. Guys, great conversation. Thanks so much for coming on the program. Best of luck to you and the partnership Be well, >>Thank you, David Suresh. Thank you. Thank >>you for watching everybody, This is Dave Volonte for the Cuban are ongoing Siris on data automation without >>Tahoe, digital resilience, automated compliance, privacy and security for your multi cloud. Congratulations. You're on the journey. You have successfully transformed your organization by moving to a cloud based platform to ensure business continuity in these challenging times. But as you scale your digital activities, there is an inevitable influx of users that outpaces traditional methods of cybersecurity, exposing your data toe underlying threats on making your company susceptible toe ever greater risk to become digitally resilient. Have you applied controls your data continuously throughout the data lifecycle? What are you doing to keep your customer on supply data private and secure? I owe Tahoe's automated sensitive data. Discovery is pre programmed with over 300 existing policies that meet government mandated risk and compliance standards. Thes automate the process of applying policies and controls to your data. Our algorithm driven recommendation engine alerts you to risk exposure at the data level and suggests the appropriate next steps to remain compliant on ensure sensitive data is secure. Unsure about where your organization stands in terms of digital resilience. Sign up for our minimal cost commitment. Free data health check. Let us run our sensitive data discovery on key unmapped data silos and sources to give you a clear understanding of what's in your environment. Book time within Iot. Tahoe Engineer. Now. >>Okay, now we're >>gonna go into the demo. We want to get a better understanding of how you can leverage open shift. And I owe Tahoe to facilitate faster application deployment. Let me pass the mic to Sabetta. Take it away. >>Uh, thanks, Dave. Happy to be here again, Guys, uh, they've mentioned names to be the Davis. I'm the enterprise account executive here. Toyota ho eso Today we just wanted to give you guys a general overview of how we're using open shift. Yeah. Hey, I'm Noah Iota host data operations engineer, working with open ship. And I've been learning the Internets of open shift for, like, the past few months, and I'm here to share. What a plan. Okay, so So before we begin, I'm sure everybody wants to know. Noel, what are the benefits of using open shift. Well, there's five that I can think of a faster time, the operation simplicity, automation control and digital resilience. Okay, so that that's really interesting, because there's an exact same benefits that we had a Tahoe delivered to our customers. But let's start with faster time the operation by running iota. Who on open shift? Is it faster than, let's say, using kubernetes and other platforms >>are >>objective iota. Who is to be accessible across multiple cloud platforms, right? And so by hosting our application and containers were able to achieve this. So to answer your question, it's faster to create and use your application images using container tools like kubernetes with open shift as compared to, like kubernetes with docker cry over container D. Okay, so we got a bit technical there. Can you explain that in a bit more detail? Yeah, there's a bit of vocabulary involved, uh, so basically, containers are used in developing things like databases, Web servers or applications such as I have top. What's great about containers is that they split the workload so developers can select the libraries without breaking anything. And since Hammond's can update the host without interrupting the programmers. Uh, now, open shift works hand in hand with kubernetes to provide a way to build those containers for applications. Okay, got It s basically containers make life easier for developers and system happens. How does open shift differ from other platforms? Well, this kind of leads into the second benefit I want to talk about, which is simplicity. Basically, there's a lot of steps involved with when using kubernetes with docker. But open shift simplifies this with their source to image process that takes the source code and turns it into a container image. But that's not all. Open shift has a lot of automation and features that simplify working with containers, an important one being its Web console. Here. I've set up a light version of open ship called Code Ready Containers, and I was able to set up her application right from the Web console. And I was able to set up this entire thing in Windows, Mac and Lennox. So its environment agnostic in that sense. Okay, so I think I've seen the top left that this is a developers view. What would a systems admin view look like? It's a good question. So here's the administrator view and this kind of ties into the benefit of control. Um, this view gives insights into each one of the applications and containers that are running, and you could make changes without affecting deployment. Andi can also, within this view, set up each layer of security, and there's multiple that you can prop up. But I haven't fully messed around with it because with my luck, I'd probably locked myself out. So that seems pretty secure. Is there a single point security such as you use a log in? Or are there multiple layers of security? Yeah, there are multiple layers of security. There's your user login security groups and general role based access controls. Um, but there's also a ton of layers of security surrounding like the containers themselves. But for the sake of time, I won't get too far into it. Okay, eso you mentioned simplicity In time. The operation is being two of the benefits. You also briefly mention automation. And as you know, automation is the backbone of our platform here, Toyota Ho. So that's certainly grabbed my attention. Can you go a bit more in depth in terms of automation? Open shift provides extensive automation that speeds up that time the operation. Right. So the latest versions of open should come with a built in cryo container engine, which basically means that you get to skip that container engine insulation step and you don't have to, like, log into each individual container host and configure networking, configure registry servers, storage, etcetera. So I'd say, uh, it automates the more boring kind of tedious process is Okay, so I see the iota ho template there. What does it allow me to do? Um, in terms of automation in application development. So we've created an open shift template which contains our application. This allows developers thio instantly, like set up our product within that template. So, Noah Last question. Speaking of vocabulary, you mentioned earlier digital resilience of the term we're hearing, especially in the banking and finance world. Um, it seems from what you described, industries like banking and finance would be more resilient using open shift, Correct. Yeah, In terms of digital resilience, open shift will give you better control over the consumption of resource is each container is using. In addition, the benefit of containers is that, like I mentioned earlier since Hammond's can troubleshoot servers about bringing down the application and if the application does go down is easy to bring it back up using templates and, like the other automation features that open ship provides. Okay, so thanks so much. Know us? So any final thoughts you want to share? Yeah. I just want to give a quick recap with, like, the five benefits that you gained by using open shift. Uh, the five are timeto operation automation, control, security and simplicity. You could deploy applications faster. You could simplify the workload you could automate. A lot of the otherwise tedious processes can maintain full control over your workflow. And you could assert digital resilience within your environment. Guys, >>Thanks for that. Appreciate the demo. Um, I wonder you guys have been talking about the combination of a Iot Tahoe and red hat. Can you tie that in subito Digital resilience >>Specifically? Yeah, sure, Dave eso when we speak to the benefits of security controls in terms of digital resilience at Io Tahoe, we automated detection and apply controls at the data level, so this would provide for more enhanced security. >>Okay, But so if you were trying to do all these things manually. I mean, what what does that do? How much time can I compress? What's the time to value? >>So with our latest versions, Biota we're taking advantage of faster deployment time associated with container ization and kubernetes. So this kind of speeds up the time it takes for customers. Start using our software as they be ableto quickly spin up io towel on their own on premise environment are otherwise in their own cloud environment, like including aws. Assure or call GP on IBM Cloud a quick start templates allow flexibility deploy into multi cloud environments all just using, like, a few clicks. Okay, so so now just quickly add So what we've done iota, Who here is We've really moved our customers away from the whole idea of needing a team of engineers to apply controls to data as compared to other manually driven work flows. Eso with templates, automation, previous policies and data controls. One person can be fully operational within a few hours and achieve results straight out of the box on any cloud. >>Yeah, we've been talking about this theme of abstracting the complexity. That's really what we're seeing is a major trend in in this coming decade. Okay, great. Thanks, Sabina. Noah, How could people get more information or if they have any follow up questions? Where should they go? >>Yeah, sure. They've. I mean, if you guys are interested in learning more, you know, reach out to us at info at iata ho dot com to speak with one of our sales engineers. I mean, we love to hear from you, so book a meeting as soon as you can. All >>right. Thanks, guys. Keep it right there from or cube content with.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then