(upbeat music) >> Hello, everyone; I'm John Furrier with SiliconANGLE NEWS and host of theCUBE, and welcome to our SiliconANGLE NEWS MWC NEWS UPDATE in Barcelona where MWC is the premier event for the cloud telecommunication industry, and in the news here is Red Hat, Red Hat announcing a collaboration with NVIDIA, Samsung and Arm on Efficient Open Networks. Red Hat announced updates across various fields including advanced 5G telecommunications cloud, industrial edge, artificial intelligence, and radio access networks, RAN, and Efficiency. Red Hat's enterprise Kubernetes platform, OpenShift, has added support for NVIDIA's converged accelerators and aerial SDK facilitating RAND deployments on industry standard service across hybrid and multicloud platforms. This composable infrastructure enables telecom firms to support heavier compute demands for edge computing, AI, private 5G, and more, and just also helps network operators adopt open architectures, allowing them to choose non-proprietary components from multiple suppliers. In addition to the NVIDIA collaboration, Red Hat is working with Samsung to offer a new vRAN solution for service providers to better manage their open RAN networks. They're also working with UK chip designer, Arm, to create new networking solutions for energy efficient Red Hat Open Source Kubernetes-based Efficient Power Level Exporter project, or Kepler, has been donated to the open Cloud Native Compute Foundation, allowing enterprise to better understand their cloud native workloads and power consumptions. Kepler can also help in the development of sustainable software by creating less power hungry applications. Again, Red Hat continuing to provide OpenSource, OpenRAN, and contributing an open source project to the CNCF, continuing to create innovation for developers, and, of course, Red Hat knows what, a lot about operating systems and the telco could be the next frontier. That's SiliconANGLE NEWS. I'm John Furrier; thanks for watching. (monotone music)

(electronic music) >> Hello, I'm John Furrier with SiliconANGLE News and host of theCUBE, and welcome to our news update for MWC in Barcelona, the premier event for cloud and to the telecommunication industry. News today, VMware in the news has lots of announcements, where it's expanding its line of products for communication service providers with Open RAND portfolio VMware's unveiled service management orchestration framework for simplifying and automating radio access networks and their applications. RANDs have traditionally been proprietary because of their need for low latency and speed and the Overran Alliance is championed open standard that would expand the number of players in the RAND ecosystem. According to Sanjay Oppai, senior vice president and general manager of the service provider and Edge Business Unit at VMware, VMware is the forefront of getting deployed in telcos both in the RAND as well as the core and VMware hopes they can extend their leadership from the enterprise data center and SD WAN and be the defacto standard in the RAND. VMware is also announcing a technical preview that'll allow communications service providers to run disaggregated and virtualized RAND functions directly on bare metal servers using VMware Tanzu. Project Hui is the initiative aimed at telecom providers that need flexibility in how they deploy edge devices. The VMware Telco cloud platform is also being improved to deliver carrier grade intelligent networking and lateral security features such as distributed firewall and intrusion detection and prevention, along with support for energy efficient use cases for 4G and 5G core load balancing. For enterprise customers, VMware is delivering new and enhanced remote worker device connectivity and intelligent wireless capabilities to its SD WAN and Secure Access Service Edge, or SASE Products, is also expanding its collaboration with Intel aimed at delivering new edge applications based on 5G connectivity that will support SD WAN use cases involving mobile and internet of things devices. Again, VMware spinning their portfolio in the news. Again, VMware is not stopping. Of course, theCUBE's, all the coverage of VMware Explorer will be coming up this year in 2023. Don't miss that. But at mwc, Dave Vellante and Lisa Martin, the entire Cube team are there for four days of live coverage. Of course, all the news and reporting is on SiliconANGLE.com. For all the action, go there. And of course theCUBE.net is where the broadcast is in Barcelona. This is theCUBE News. Thanks for watching.

(intense music) >> Hello, I'm John Furrier with "SiliconANGLE News" and the host of theCUBE here in Palo Alto, with coverage of MWC 2023. theCUBE is onsite in Barcelona, four days of wall to wall coverage. Here is a news update from MWC and in the news here is Google. Google Cloud targets cloud native network transformation for all the carriers or cloud service providers, and the communication service providers. They announced three new products to help communications service providers, also known as CSPs, build, deploy and operate hybrid cloud native networks, as well as collect and manage network data. The new products, when combined with Unified Cloud, enables the CSPs to improve customer experience, artificial intelligence, and data analytics. This is a big move, because 70% of communication service providers are expected to adopt cloud native network functions by the end of this year, making it a big, big wave. One of the key features of Google's products is the telecom network automation. This cloud service accelerates CSPs network and edge deployments through the use of Kubernetes based cloud native automation tools. It's managed by a cloud version of open source Nephio, project that Google founded in 2022. Of course, other key product announcements with Google, the Telecom Data Fabric, a tool that helps CSPs generate insights. That's the data driven piece, to target and optimize their network performance and reliability, works by simplifying the collection, normalization, correlation through an adaptive framework. This is kind of where AI shines. Finally, Google has telecom subscriber insights, a powerful AI tool that enables CSPs to extract insights from existing data sources in a privacy safe environment. Let's see if this is better than Bing search, we'll see. But CSPs are moving to the cloud across all channels. This is a really important trend, as cloud native scale, AI, data, configuration, automation all come to the edge of the network. That's an update from "SiliconANGLE News". Check out the coverage on siliconangle.com. Of course, thecube.net, four days, Dave Vellante and Lisa Martin are there. I'm here in Palo Alto. Thanks for watching. (slow music) (upbeat music)

(Introductory music) >> Hello everyone, welcome to theCUBE's coverage of Mobile World Congress (MWC) and also SiliconANGLEs news coverage. Welcome to SiliconANGLEs news update for MWC. I'm John Furrier, host of theCUBE and reporter with SiliconANGLE News Today. Google showcasing new updates for Android and wearables at MWC. Kind of going after the old Apple-like functionality. Google has announced some new updates for Android and wearables at MWC and Barcelona. The new features are aimed at enhancing user productivity, connectivity and overall enjoyment across various devices for Chromebooks and all their Android devices. This is their answer to be Apple-like. New features include updates to Google Keep, audio enhancements, instant pairing of Chromebooks, headphones, new emojis, smartphones, more wallet options, and greater accessibility options. These features designed to bridge the gap between different devices that people use together often such as watches and phones or laptops or headphones. Fast Pair, another feature which allows new Bluetooth headphones to be connected to a Chromebook with just one tap. If the headphones are already set up with Android phone, the Chromebook will automatically connect to them with no additional setup. And finally, Google Keep taking notes for you that app - very cool. New features include widgets for Android screens, making it easier for users to make to-do lists from their mobile devices and Smartwatches phones. So that's the big news there. And it's really about Apple-like functionality and they have added things to their meat, which is new backgrounds and then filters that's kind of a Zoom clone. So here you got Android, Google adding stuff to their wallet. They are really stepping up their game and they want to be more mobile in at a telecom conference like this. They can see them upping their game to try to compete with Apple. And that's the update from from Google, Android and Chromebook updates. Stay tuned for more coverage. Check out SiliconANGLE.com for our special report on Mobile World Congress and Barcelona. Got theCUBE team - Dave Vellante, Lisa Martin, the whole gang is there for four days of live coverage. Check that out on theCUBE.net (closing music)

(suspenseful music) >> Welcome back everyone, this is the SiliconANGLE news report, news flash, news update. I'm John Furrier, host of theCUBE, SiliconANGLE founder and editor. Got our team in Mobile World Congress, MWC. But here's some news flash: the GSMA debuted API toolkit as AWS and Microsoft roll out their offerings to make the cloud part of the telco world. The GSMA association, which runs this program and is the most important organization in telecommunications, unveiled the GSMA Open Gateway. This is a toolkit designed for creating applications that integrate with multiple carrier networks. The technology debuted at MWC23. This is the largest trade show opened in the telco area. This Open Gateway allows carriers to support APIs created with the technology that'll interoperate with each other. That means interoperability and cloud is coming to the telecommunication carriers. That's your cell phone, that's wireless. This allows developers to move applications from one carrier to another without needing to port their code. This is a huge game-changer. This is big news, and, of course, Microsoft and AWS are pounding stories out there as well. They got 21 carriers worldwide adopted and it's created using an open-source API toolkit called CAMARA. And Amazon and AWS are jumping on the cloud bandwagon with this and driving it hard into telco. And that's the big story, and, of course, more actions happening, theCUBE is onsite for four days in Barcelona for MWC23 and keep the news flowing. Check out SiliconANGLE.com, you'll see all the news there, and, of course, theCUBE.net for the livestream. I'm John Furrier, that's the news brief. (atmospheric music)

(energetic music) >> Welcome to the Silicon Angle News update Mobile World Congress theCUBE coverage live on the floor for four days. I'm John Furrier, in the studio here. Dave Vellante, Lisa Martin onsite. Intel in the news, Intel accelerates 5G network virtualization with radio access network boost for Xeon processors. Intel, well known for power and computing, they today announced their integrated virtual radio access network into its latest fourth gen Intel Xeon system on a chip. This move will help network operators gear up their efforts to deliver Cloud native features for next generation 5G core and edge networks. This announcement came today at MWC, formerly knows Mobile World Congress. In Barcelona, Intel is taking the latest step in its mission to virtualize the world's networks, including Core, Open RAN and Edge. Network virtualization is the key capability for communication service providers as they migrate from fixed function hardware to programmable software defined platforms. This provides greater agility and greater cost efficiency. According to Intel, this is the demand for agile, high performance, scalable networks requiring adoption. Fully virtualized software based platforms run on general purpose processors. Intel believes that network operators need to accelerate network virtualization to get the most out of these new architectures, and that's where it can be made its mark. With Intel vRAN Boost, it delivers twice the capability and capacity gains over its previous generation of silicon with the same power envelope with 20% in power savings that results from an integrated acceleration. In addition, Intel announced new infrastructure power manager for 5G core reference software that's designed to work with vRAN Boost. Intel also showcased its new Intel Converged Edge media platform designed to deliver multiple video services from a shared multi-tenant architecture. The platform leverages Cloud native scalability to respond to the shifting demands. Lastly, Intel announced a range of Agilex 7 Field Programmable Gate Arrays and eASIC N5X structured applications specific integrated circuits designed for individual cloud communications and embedded applications. Intel is targeting the power consumption which is energy and more horsepower for chips, which is going to power the industrial internet edge. That's going to be Cloud native. Big news happening at Mobile World Congress. theCUBE is there. Go to siliconangle.com for all the news and special report and live feed on theCUBE.net. (energetic music)

(energetic instrumental music) >> Hey, everyone. Welcome to SiliconANGLE CUBE News here from Mobile World Congress. This is a Mobile World Congress news update. Dell in the news here partners with leading infrastructure companies, Dell Technologies, really setting up an ecosystem. Here, Dell, with leading telecom and infrastructure players accelerating the network adoption, announcing that it's launching the Dell's Open Telecom Ecosystem community. A community of multiple telecom partners and communication service providers aimed at becoming a unifying force in the telecom industry. This announcement comes just days after Dell introduced a host of new hardware, platforms designed to help the teleconference build cloud-native open radio network access, also called RAN architectures, using proprietary and sub-components for various suppliers. Dell's Open Telecom Ecosystem community has already partnered with Nokia, Qualcomm, Amdocs and Juniper Networks to create new offerings aimed at accelerating open RAN price performance for communication service providers. This includes creating a new virtual RAN offering using Open Telecom Ecosystem Labs, and as the center for testing and validation, building next-generation 5G virtualized distributed units and deploy and automated validated 5G-SA network with various partners across the ecosystem. Dell's promising that this is just the beginning of the collaboration with the telecom industry as it seeks to accelerate the adoption of 5G networking technologies and solve key industry challenges. More action's on the ground, go to thecube.net, theCUBE is broadcasting live for four days, Dave Vellante, Lisa Martin. I'm in the studios in Palo Alto bringing you the news. Lot of action happening, of course. Go to siliconangle.com to catch all the breaking news. We have a special report. We already got 10 plus stories already flowing. Probably have another 10 today. Day two tomorrow as MWC continues to power more news coverage for the edge and cloud-native technologies. (pensive ambient music)

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)

(upbeat music) >> Hello everyone. Welcome to Silicon Angle news breaking story here. Amazon Web Services, expanding their relationship with Hugging Face, breaking news here on Silicon Angle. I'm John Furrier, Silicon Angle reporter, founder and also co-host of theCUBE. And I have with me Swami from Amazon Web Services, vice president of database analytics machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on, taking the time. >> Hey John, pleasure to be here. >> We've had many conversations on theCUBE over the years. We've watched Amazon really move fast into the large data modeling. You SageMaker became a very smashing success. Obviously you've been on this for a while, now with Chat GPT, open AI, a lot of buzz going mainstream, takes it from behind the curtain, inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment I think in the industry. I want to get your perspective because your news with Hugging Face, I think is a is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware application centric, more programmable, more API access. What's the big news about with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah, first of all, they're very excited to announce our expanded collaboration with Hugging Face because with this partnership, our goal, as you all know, I mean Hugging Face I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS will be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this we can accelerate the training, fine tuning, and deployment of these large language models and vision models from Hugging Face in the cloud. So, and the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these Chat GPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models. They need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale, so And unlike search, web search style applications where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where a Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale. I'll deep dive on it and by training teaming up on the SageMaker front now the time it takes to build these models and fine tune them as also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the, to the time savings and the cost savings as well on the on the training and inference. It's a huge issue. But before we get into that, just how long have you guys been working with Hugging Face? I know this is a previous relationship. This is an expansion of that relationship. Can you comment on the what's different about what's happened before and then now? >> Yeah, so Hugging Face, we have had an great relationship in the past few years as well where they have actually made their models available to run on AWS in a fashion, even inspect their Bloom project was something many of our customers even used. Bloom Project for context is their open source project, which builds a GPT three style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation of this generative AI model, building on their highly successful Bloom project as well. And the nice thing is now by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way. Now for instance, tier 1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs and Forex more higher throughput. Now these models, especially as they train that next generation generated AI model, it is going to be not only more accessible to all the developers who use it in open. So it'll be a lot cheaper as well. And that's what makes this moment really exciting because yeah, we can't democratize AI unless we make it broadly accessible and cost efficient, and easy to program and use as well. >> Okay, thanks Swami. We really appreciate. Swami's a Cube alumni, but also vice President, database analyst machine learning web services breaking down the Hugging Face announcement. Obviously the relationship he called it the GitHub of machine learning. This is the beginning of what we will see, a continuing competitive battle with Microsoft. Microsoft launching OpenAI. Amazon's been doing it for years. They got Alexa, they know what they're doing. It's going to be very interesting to see how this all plays out. You're watching Silicon Angle News, breaking here. I'm John Furrier, host of the Cube. Thanks for watching. (ethereal music)

(bright upbeat music) >> Hello, everyone. Welcome to SiliconANGLE News breaking story here. Amazon Web Services expanding their relationship with Hugging Face, breaking news here on SiliconANGLE. I'm John Furrier, SiliconANGLE reporter, founder, and also co-host of theCUBE. And I have with me, Swami, from Amazon Web Services, vice president of database, analytics, machine learning with AWS. Swami, great to have you on for this breaking news segment on AWS's big news. Thanks for coming on and taking the time. >> Hey, John, pleasure to be here. >> You know- >> Looking forward to it. >> We've had many conversations on theCUBE over the years, we've watched Amazon really move fast into the large data modeling, SageMaker became a very smashing success, obviously you've been on this for a while. Now with ChatGPT OpenAI, a lot of buzz going mainstream, takes it from behind the curtain inside the ropes, if you will, in the industry to a mainstream. And so this is a big moment, I think, in the industry, I want to get your perspective, because your news with Hugging Face, I think is another tell sign that we're about to tip over into a new accelerated growth around making AI now application aware, application centric, more programmable, more API access. What's the big news about, with AWS Hugging Face, you know, what's going on with this announcement? >> Yeah. First of all, they're very excited to announce our expanded collaboration with Hugging Face, because with this partnership, our goal, as you all know, I mean, Hugging Face, I consider them like the GitHub for machine learning. And with this partnership, Hugging Face and AWS, we'll be able to democratize AI for a broad range of developers, not just specific deep AI startups. And now with this, we can accelerate the training, fine tuning and deployment of these large language models, and vision models from Hugging Face in the cloud. And the broader context, when you step back and see what customer problem we are trying to solve with this announcement, essentially if you see these foundational models, are used to now create like a huge number of applications, suggest like tech summarization, question answering, or search image generation, creative, other things. And these are all stuff we are seeing in the likes of these ChatGPT style applications. But there is a broad range of enterprise use cases that we don't even talk about. And it's because these kind of transformative, generative AI capabilities and models are not available to, I mean, millions of developers. And because either training these elements from scratch can be very expensive or time consuming and need deep expertise, or more importantly, they don't need these generic models, they need them to be fine tuned for the specific use cases. And one of the biggest complaints we hear is that these models, when they try to use it for real production use cases, they are incredibly expensive to train and incredibly expensive to run inference on, to use it at a production scale. So, and unlike web search style applications, where the margins can be really huge, here in production use cases and enterprises, you want efficiency at scale. That's where Hugging Face and AWS share our mission. And by integrating with Trainium and Inferentia, we're able to handle the cost efficient training and inference at scale, I'll deep dive on it. And by teaming up on the SageMaker front, now the time it takes to build these models and fine tune them is also coming down. So that's what makes this partnership very unique as well. So I'm very excited. >> I want to get into the time savings and the cost savings as well on the training and inference, it's a huge issue, but before we get into that, just how long have you guys been working with Hugging Face? I know there's a previous relationship, this is an expansion of that relationship, can you comment on what's different about what's happened before and then now? >> Yeah. So, Hugging Face, we have had a great relationship in the past few years as well, where they have actually made their models available to run on AWS, you know, fashion. Even in fact, their Bloom Project was something many of our customers even used. Bloom Project, for context, is their open source project which builds a GPT-3 style model. And now with this expanded collaboration, now Hugging Face selected AWS for that next generation office generative AI model, building on their highly successful Bloom Project as well. And the nice thing is, now, by direct integration with Trainium and Inferentia, where you get cost savings in a really significant way, now, for instance, Trn1 can provide up to 50% cost to train savings, and Inferentia can deliver up to 60% better costs, and four x more higher throughput than (indistinct). Now, these models, especially as they train that next generation generative AI models, it is going to be, not only more accessible to all the developers, who use it in open, so it'll be a lot cheaper as well. And that's what makes this moment really exciting, because we can't democratize AI unless we make it broadly accessible and cost efficient and easy to program and use as well. >> Yeah. >> So very exciting. >> I'll get into the SageMaker and CodeWhisperer angle in a second, but you hit on some good points there. One, accessibility, which is, I call the democratization, which is getting this in the hands of developers, and/or AI to develop, we'll get into that in a second. So, access to coding and Git reasoning is a whole nother wave. But the three things I know you've been working on, I want to put in the buckets here and comment, one, I know you've, over the years, been working on saving time to train, that's a big point, you mentioned some of those stats, also cost, 'cause now cost is an equation on, you know, bundling whether you're uncoupling with hardware and software, that's a big issue. Where do I find the GPUs? Where's the horsepower cost? And then also sustainability. You've mentioned that in the past, is there a sustainability angle here? Can you talk about those three things, time, cost, and sustainability? >> Certainly. So if you look at it from the AWS perspective, we have been supporting customers doing machine learning for the past years. Just for broader context, Amazon has been doing ML the past two decades right from the early days of ML powered recommendation to actually also supporting all kinds of generative AI applications. If you look at even generative AI application within Amazon, Amazon search, when you go search for a product and so forth, we have a team called MFi within Amazon search that helps bring these large language models into creating highly accurate search results. And these are created with models, really large models with tens of billions of parameters, scales to thousands of training jobs every month and trained on large model of hardware. And this is an example of a really good large language foundation model application running at production scale, and also, of course, Alexa, which uses a large generator model as well. And they actually even had a research paper that showed that they are more, and do better in accuracy than other systems like GPT-3 and whatnot. So, and we also touched on things like CodeWhisperer, which uses generative AI to improve developer productivity, but in a responsible manner, because 40% of some of the studies show 40% of this generated code had serious security flaws in it. This is where we didn't just do generative AI, we combined with automated reasoning capabilities, which is a very, very useful technique to identify these issues and couple them so that it produces highly secure code as well. Now, all these learnings taught us few things, and which is what you put in these three buckets. And yeah, like more than 100,000 customers using ML and AI services, including leading startups in the generative AI space, like stability AI, AI21 Labs, or Hugging Face, or even Alexa, for that matter. They care about, I put them in three dimension, one is around cost, which we touched on with Trainium and Inferentia, where we actually, the Trainium, you provide to 50% better cost savings, but the other aspect is, Trainium is a lot more power efficient as well compared to traditional one. And Inferentia is also better in terms of throughput, when it comes to what it is capable of. Like it is able to deliver up to three x higher compute performance and four x higher throughput, compared to it's previous generation, and it is extremely cost efficient and power efficient as well. >> Well. >> Now, the second element that really is important is in a day, developers deeply value the time it takes to build these models, and they don't want to build models from scratch. And this is where SageMaker, which is, even going to Kaggle uses, this is what it is, number one, enterprise ML platform. What it did to traditional machine learning, where tens of thousands of customers use StageMaker today, including the ones I mentioned, is that what used to take like months to build these models have dropped down to now a matter of days, if not less. Now, a generative AI, the cost of building these models, if you look at the landscape, the model parameter size had jumped by more than thousand X in the past three years, thousand x. And that means the training is like a really big distributed systems problem. How do you actually scale these model training? How do you actually ensure that you utilize these efficiently? Because these machines are very expensive, let alone they consume a lot of power. So, this is where SageMaker capability to build, automatically train, tune, and deploy models really concern this, especially with this distributor training infrastructure, and those are some of the reasons why some of the leading generative AI startups are actually leveraging it, because they do not want a giant infrastructure team, which is constantly tuning and fine tuning, and keeping these clusters alive. >> It sounds like a lot like what startups are doing with the cloud early days, no data center, you move to the cloud. So, this is the trend we're seeing, right? You guys are making it easier for developers with Hugging Face, I get that. I love that GitHub for machine learning, large language models are complex and expensive to build, but not anymore, you got Trainium and Inferentia, developers can get faster time to value, but then you got the transformers data sets, token libraries, all that optimized for generator. This is a perfect storm for startups. Jon Turow, a former AWS person, who used to work, I think for you, is now a VC at Madrona Venture, he and I were talking about the generator AI landscape, it's exploding with startups. Every alpha entrepreneur out there is seeing this as the next frontier, that's the 20 mile stairs, next 10 years is going to be huge. What is the big thing that's happened? 'Cause some people were saying, the founder of Yquem said, "Oh, the start ups won't be real, because they don't all have AI experience." John Markoff, former New York Times writer told me that, AI, there's so much work done, this is going to explode, accelerate really fast, because it's almost like it's been waiting for this moment. What's your reaction? >> I actually think there is going to be an explosion of startups, not because they need to be AI startups, but now finally AI is really accessible or going to be accessible, so that they can create remarkable applications, either for enterprises or for disrupting actually how customer service is being done or how creative tools are being built. And I mean, this is going to change in many ways. When we think about generative AI, we always like to think of how it generates like school homework or arts or music or whatnot, but when you look at it on the practical side, generative AI is being actually used across various industries. I'll give an example of like Autodesk. Autodesk is a customer who runs an AWS and SageMaker. They already have an offering that enables generated design, where designers can generate many structural designs for products, whereby you give a specific set of constraints and they actually can generate a structure accordingly. And we see similar kind of trend across various industries, where it can be around creative media editing or various others. I have the strong sense that literally, in the next few years, just like now, conventional machine learning is embedded in every application, every mobile app that we see, it is pervasive, and we don't even think twice about it, same way, like almost all apps are built on cloud. Generative AI is going to be part of every startup, and they are going to create remarkable experiences without needing actually, these deep generative AI scientists. But you won't get that until you actually make these models accessible. And I also don't think one model is going to rule the world, then you want these developers to have access to broad range of models. Just like, go back to the early days of deep learning. Everybody thought it is going to be one framework that will rule the world, and it has been changing, from Caffe to TensorFlow to PyTorch to various other things. And I have a suspicion, we had to enable developers where they are, so. >> You know, Dave Vellante and I have been riffing on this concept called super cloud, and a lot of people have co-opted to be multicloud, but we really were getting at this whole next layer on top of say, AWS. You guys are the most comprehensive cloud, you guys are a super cloud, and even Adam and I are talking about ISVs evolving to ecosystem partners. I mean, your top customers have ecosystems building on top of it. This feels like a whole nother AWS. How are you guys leveraging the history of AWS, which by the way, had the same trajectory, startups came in, they didn't want to provision a data center, the heavy lifting, all the things that have made Amazon successful culturally. And day one thinking is, provide the heavy lifting, undifferentiated heavy lifting, and make it faster for developers to program code. AI's got the same thing. How are you guys taking this to the next level, because now, this is an opportunity for the competition to change the game and take it over? This is, I'm sure, a conversation, you guys have a lot of things going on in AWS that makes you unique. What's the internal and external positioning around how you take it to the next level? >> I mean, so I agree with you that generative AI has a very, very strong potential in terms of what it can enable in terms of next generation application. But this is where Amazon's experience and expertise in putting these foundation models to work internally really has helped us quite a bit. If you look at it, like amazon.com search is like a very, very important application in terms of what is the customer impact on number of customers who use that application openly, and the amount of dollar impact it does for an organization. And we have been doing it silently for a while now. And the same thing is true for like Alexa too, which actually not only uses it for natural language understanding other city, even national leverages is set for creating stories and various other examples. And now, our approach to it from AWS is we actually look at it as in terms of the same three tiers like we did in machine learning, because when you look at generative AI, we genuinely see three sets of customers. One is, like really deep technical expert practitioner startups. These are the startups that are creating the next generation models like the likes of stability AIs or Hugging Face with Bloom or AI21. And they generally want to build their own models, and they want the best price performance of their infrastructure for training and inference. That's where our investments in silicon and hardware and networking innovations, where Trainium and Inferentia really plays a big role. And we can nearly do that, and that is one. The second middle tier is where I do think developers don't want to spend time building their own models, let alone, they actually want the model to be useful to that data. They don't need their models to create like high school homeworks or various other things. What they generally want is, hey, I had this data from my enterprises that I want to fine tune and make it really work only for this, and make it work remarkable, can be for tech summarization, to generate a report, or it can be for better Q&A, and so forth. This is where we are. Our investments in the middle tier with SageMaker, and our partnership with Hugging Face and AI21 and co here are all going to very meaningful. And you'll see us investing, I mean, you already talked about CodeWhisperer, which is an open preview, but we are also partnering with a whole lot of top ISVs, and you'll see more on this front to enable the next wave of generated AI apps too, because this is an area where we do think lot of innovation is yet to be done. It's like day one for us in this space, and we want to enable that huge ecosystem to flourish. >> You know, one of the things Dave Vellante and I were talking about in our first podcast we just did on Friday, we're going to do weekly, is we highlighted the AI ChatGPT example as a horizontal use case, because everyone loves it, people are using it in all their different verticals, and horizontal scalable cloud plays perfectly into it. So I have to ask you, as you look at what AWS is going to bring to the table, a lot's changed over the past 13 years with AWS, a lot more services are available, how should someone rebuild or re-platform and refactor their application of business with AI, with AWS? What are some of the tools that you see and recommend? Is it Serverless, is it SageMaker, CodeWhisperer? What do you think's going to shine brightly within the AWS stack, if you will, or service list, that's going to be part of this? As you mentioned, CodeWhisperer and SageMaker, what else should people be looking at as they start tinkering and getting all these benefits, and scale up their ups? >> You know, if we were a startup, first, I would really work backwards from the customer problem I try to solve, and pick and choose, bar, I don't need to deal with the undifferentiated heavy lifting, so. And that's where the answer is going to change. If you look at it then, the answer is not going to be like a one size fits all, so you need a very strong, I mean, granted on the compute front, if you can actually completely accurate it, so unless, I will always recommend it, instead of running compute for running your ups, because it takes care of all the undifferentiated heavy lifting, but on the data, and that's where we provide a whole variety of databases, right from like relational data, or non-relational, or dynamo, and so forth. And of course, we also have a deep analytical stack, where data directly flows from our relational databases into data lakes and data virus. And you can get value along with partnership with various analytical providers. The area where I do think fundamentally things are changing on what people can do is like, with CodeWhisperer, I was literally trying to actually program a code on sending a message through Twilio, and I was going to pull up to read a documentation, and in my ID, I was actually saying like, let's try sending a message to Twilio, or let's actually update a Route 53 error code. All I had to do was type in just a comment, and it actually started generating the sub-routine. And it is going to be a huge time saver, if I were a developer. And the goal is for us not to actually do it just for AWS developers, and not to just generate the code, but make sure the code is actually highly secure and follows the best practices. So, it's not always about machine learning, it's augmenting with automated reasoning as well. And generative AI is going to be changing, and not just in how people write code, but also how it actually gets built and used as well. You'll see a lot more stuff coming on this front. >> Swami, thank you for your time. I know you're super busy. Thank you for sharing on the news and giving commentary. Again, I think this is a AWS moment and industry moment, heavy lifting, accelerated value, agility. AIOps is going to be probably redefined here. Thanks for sharing your commentary. And we'll see you next time, I'm looking forward to doing more follow up on this. It's going to be a big wave. Thanks. >> Okay. Thanks again, John, always a pleasure. >> Okay. This is SiliconANGLE's breaking news commentary. I'm John Furrier with SiliconANGLE News, as well as host of theCUBE. Swami, who's a leader in AWS, has been on theCUBE multiple times. We've been tracking the growth of how Amazon's journey has just been exploding past five years, in particular, past three. You heard the numbers, great performance, great reviews. This is a watershed moment, I think, for the industry, and it's going to be a lot of fun for the next 10 years. Thanks for watching. (bright music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

>> TheCUBE presents Ignite 22 brought to you by Palo Alto Networks. >> Welcome back to Las Vegas, everyone. We're glad you're with us. This is theCUBE live at Palo Alto Ignite 22 at the MGM Grant in Las Vegas. Lisa Martin here with Dave Vellante, day one of our coverage. We've had great conversations. The cybersecurity landscape is so interesting Dave, it's such a challenging problem to solve but it's so diverse and dynamic at the same time. >> You know, Lisa theCUBE started in May of 2010 in Boston. We called it the chowder event, chowder and Lobster. It was a EMC world, 2010. BJ Jenkins, who's here, of course, was a longtime friend of theCUBE and made the, made the transition into from, well, it's still data, data to, to cyber. So >> True. And BJ is back with us. BJ Jenkins, president Palo Alto Networks great to have you back on theCUBE. >> It is great to be here in person on theCube >> Isn't it great? >> In Vegas. It's awesome. >> And we can tell by your voice will be, will be gentle. You, you've been in Vegas typical Vegas occupational hazard of losing the voice. >> Yeah. It was one of the benefits of Covid. I didn't lose my voice at home sitting talking to a TV. You lose it when you come to Vegas. >> Exactly. >> But it's a small price to pay. >> So things kick off yesterday with the partner summit. You had a keynote then, you had a customer, a CISO on stage. You had a keynote today, which we didn't get to see. But talk to us a little bit about the lay of the land. What are you hearing from CISOs, from CIOs as we know security is a board level conversation. >> Yeah, I, you know it's been an interesting three or four months here. Let me start with that. I think, cybersecurity in general is still front and center on CIOs and CISO's minds. It has to be, if you saw Wendy's presentation today and the threats out there companies have to have it front and center. I do think it's been interesting though with the macro uncertainty. We've taken to calling this year the revenge of the CFO and you know these deals in cybersecurity are still a top priority but they're getting finance and procurements, scrutiny which I think in this environment is a necessity but it's still a, you know, number one number two imperative no matter who you talked to, in my mind >> It was interesting what Nikesh was saying in the last conference call that, hey we just have to get more approvals. We know this. We're, we're bringing more go-to-market people on board. We, we have, we're filling the pipeline 'cause we know they're going to split up deals big deals go into smaller chunks. So the question I have for you is is how are you able to successfully integrate those people so that you can get ahead of that sort of macro transition? >> Yeah I, you know, I think there's two things I'd say about uncertain macro situations and Dave, you know how old I am. I'm pretty old. I've been through a lot of cycles. And in those cycles I've always found stronger companies with stronger value proposition separate themselves actually in uncertain, economic times. And so I think there's actually an opportunity here. The message tilts a little bit though where it's been about innovation and new threat vectors to one of you have 20, 30, 40 vendors you can consolidate become more effective in your security posture and save money on your TCOs. So one of the things as we bring people on board it's training them on that business value proposition. How do you take a customer who's got 20 or 30 tools take 'em down to 5 or 10 where Palo is more central and strategic and be able to demonstrate that value. So we do that through, we're making a huge investment in our people but macroeconomic times also puts some stronger people back on the market and we're able to incorporate them into the business. >> What are the conditions that are necessary for that consolidation? Like I would imagine if you're, if you're a big customer of a big, you know, competitor of yours that that migration is going to be harder than if you're dealing with lots of little point tools. Do those, do those point tools, are they sort of is it the end of the subscription? Is it just stuff that's off the books now? What's, the condition that is ripe for that kind of consolidation? >> Look, I think the challenge coming into this year was skills. And so customers had all of these point products. It required a lot more human intervention as Nikesh was talking about to integrate them or make them work. And as all of us know finding people with cybersecurity skills over the last 12 months has been incredibly hard. That drove, if you know, if you think about that a CIO and a CISO sitting there going, I have all all this investment in tools. I don't have the people to operate 'em. What do I need to do? What we tried to do is elevate that conversation because in a customer, everybody who's bought one of those, they they bought it to solve a problem. And there's people with affinity for that tool. They're not just going to say I want to get consolidated and give up my tool. They're going to wrap their arms around it. And so what we needed to do and this changed our ecosystem strategy too how we leverage partners. We needed to get into the CIO and CISO and say look at this chaos you have here and the challenges around people that it's, it's presenting you. We can help solve that by, by standardizing, consolidating taking that integration away from you as Nikesh talked about, and making it easier for your your high skill people to work on high skill, you know high challenges in there. >> Let chaos reign, and then reign in the chaos. >> Yes. >> Andy Grove. >> I was looking at some stats that there's 26 million developers but less than 3 million cybersecurity professionals. >> Talked about that skills gap and what CISOs and CIOs are facing is do you consider from a value prop perspective Palo Alto Networks to be a, a facilitator of helping organizations deal with that skills gap? >> I think there's a short term and a long term. I think Nikesh today talked about the long term that we'll never win this battle with human beings. We're going to have to win it with automation. That, that's the long term the short term right here and now is that people need people with cybersecurity skills. Now what we're trying to do, you know, is multifaceted. We work with universities to standardize programs to develop skills that people can come into the marketplace with. We run our own programs inside the company. We have a cloud academy program now where we take people high aptitude for sales and technical aptitude and we will put them through a six month boot camp on cloud and they'll come out of that ready to really work with the leading experts in cloud security. The third angle is partners, right, there are partners in the marketplace who want to drive their business into high services areas. They have people, they know how to train. We give them, we partner with them to give them training. Hopefully that helps solve some of the short-term gaps that are out there today. >> So you made the jump from data storage to security and >> Yeah. >> You know, network security, all kinds of security. What was that like? What you must have learned a lot in the last better part of a decade? >> Yeah. >> Take us through that. >> You know, so the first jump was from EMC. I was 15 years there to be CEO of Barracuda. And you know, it was interesting because EMC was, you know large enterprise for the most part. At Barracuda we had, you know 250,000 small and mid-size enterprises. And it was, it's interesting to get into security in small and mid-size businesses because, you know Wendy today was talking about nation states. For small and mid-size business, it's common thievery right? It's ransomware, it's, and, those customers don't have, you know, the human and financial resources to keep up with the threat factor. So, you know, Nikesh talked about how it's taken 'em four and a half years to get into cybersecurity. I remember my first week at Barracuda, I was talking with a customer who had, you know, breached data shut down. There wasn't much bitcoin back then so it was just a pure ransom. And I'm like, wow, this is, you know, incredible industry. So it's been a good, you know, transition for me. I still think data is at the heart of all of this. Right? And I have always believed there's a strong connection between the things I learned growing up at EMC and what I put into practice today at Palo Alto Networks. >> And how about a culture because I, you know I know have observed the EMC culture >> Yeah. >> And you were there in really the heyday. >> Yeah. >> Right? Which was an awesome place. And it seems like Palo Alto obviously, different times but you know, similar like laser focus on solving problems, you know, obviously great, you know value sellers, you know, you guys aren't the commodity >> Yeah. For Product. But there seemed to be some similarities from afar. I don't know Palo Alto as well as I know EMC. >> I think there's a lot. When I joined EMC, it was about, it was 2 billion in in revenue and I think when I left it was over 20, 20, 21. And, you know, we're at, you know hopefully 5, 5 5 in revenue. I feel like it's this very similar, there's a sense of urgency, there's an incredible focus on the customer. you know, Near and Moche are definitely different individuals but the both same kind of disruptive, Israeli force out there driving the business. There are a lot of similarities. I, you know, the passion, I feel privileged as a, you know go to market person that I have this incredible portfolio to go, you know, work with customers on. It's a lucky position to be in, but very I feel like it is a movie I've seen before. >> Yeah. And but, and the course, the challenges from the, the target that you're disrupting is different. It was, you know, EMC had a lot of big, you know IBM obviously was, you know, bigger target whereas you got thousands of, you know, smaller companies. >> Yes. >> And, and so that's a different dynamic but that's why the consolidation play is so important. >> Look at, that's why I joined Palo Alto Networks when I was at Barracuda for nine years. It just fascinated me, that there was 3000 plus players in security and why didn't security evolve like the storage market did or the server market or network where working >> Yeah, right. >> You know, two or three big gorillas came to, to dominate those markets. And it's, I think it's what Nikesh talked about today. There was a new problem in best of breed. It was always best of breed. You can never in security go in and, you know, say, Hey it's good I saved us some money but I got the third best product in the marketplace. And there was that kind of gap between products. I, believe in why I joined here I think this is my last gig is we have a chance to change that. And this is the first company as I look from the outside in that had best of breed as, you know Nikesh said 13 categories. >> Yeah. >> And you know, we're in the leaders quadrant and it's a conversation I have with customers. You don't have to sacrifice best of breed but get the benefits of a platform. And I, think that resonates today. I think we have a chance to change the industry from that viewpoint. >> Give us a little view of the voice of the customer. You had, was it Sabre? >> Yeah. >> That was on >> Scott Moser, The CISO from Sabre. >> Give us a view, what are you hearing from the voice of the customer? Obviously they're quite a successful customer but challenges, concerns, the partnership. >> Yeah. Look, I think security is similar to industries where we come up with magic marketing phrases and, you know, things to you know, make you want to procure our solutions. You know, zero trust is one. And you know, you'll talk to customers and they're like, okay, yes. And you know, the government, right? Joe, Joe Biden's putting out zero trust executive orders. And the, the problem is if you talk to customers, it's a journey. They have legacy infrastructure they have business drivers that you know they just don't deal with us. They've got to deal with the business side who's trying to make the money that keeps the, the company going. it's really helped them draw a map from where they're at today to zero trust or to a better security architecture. Or, you know, they're moving their apps into the cloud. How am I going to migrate? Right? Again, that discussion three years ago was around lift and shift, right? Today it's about, well, no I need cloud native developed apps to service the business the way I want to, I want to service it. How do I, so I, I think there's this element of a trusted partner and relationship. And again, I think this is why you can't have 40 or 50 of those. You got to start narrowing it down if you want to be able to meet and beat the threats that are out there for you. So I, you know, the customers, I see a lot of 'em. It's, here's where I'm at help me get here to a better position. And they know it's, you know Scott said in our keynote today, you don't just, you know have layer three firewall policies and decide, okay tomorrow I'm going to go to layer seven. That, that's not how it works. Right? There's, and, and by the way these things are a mission critical type areas. So there's got to be a game plan that you help customers go through to get there. >> Definitely. Last question, my last question for you is, is security being a board level conversation I was reading some stats from a survey I think it was the what's new in Cypress survey that that Palo Alto released today that showed that while significant numbers of organizations think they've got a cyber resiliency playbook, there's a lot of disconnect or lack of alignment at the boardroom. Are you in those conversations? How can you help facilitate that alignment between the executive team and the board when it comes to security being so foundational to any business? >> Yeah, it's, I've been on three, four public company boards. I'm on, I'm on two today. I would say four years ago, this was a almost a taboo topic. It was a, put your head in the sand and pray to God nothing happened. And you know, the world has changed significantly. And because of the number of breaches the impact it's had on brand, boards have to think about this in duty of care and their fiduciary duty. Okay. So then you start with a board that may not have the technical skills. The first problem the security industry had is how do I explain your risk profile in a way you can understand it. I'm, I'm on the board of Generac that makes home generators. It's a manufacturing, you know, company but they put Wifi modules in their boxes so that the dealers could help do the maintenance on 'em. And all of a sudden these things were getting attacked. Right? And they're being used for bot attacks. >> Yeah. >> Everybody on their board had a manufacturing background. >> Ah. >> So how do you help that board understand the risk they have that's what's changed over the last four years. It's a constant discussion. It's one I have with CISOs where they're like help us put it in layman's terms so they understand they know what we're doing and they feel confident but at the same time understand the marketplace better. And that's a journey for us. >> That Generac example is a great one because, you know, think about IOT Technologies. They've historically been air gaped >> Yes. >> By design. And all of a sudden the business comes in and says, "Hey we can put wifi in there", you know >> Connect it to a home Wifi system that >> Make our lives so much easier. Next thing you know, it's being used to attack. >> Yeah. >> So that's why, as you go around the world are you discerning, I know you were just in Japan are you discerning significant differences in sort of attitudes toward, towards cyber? Whether it's public policy, you know things like regulation where you, they don't want you sharing data, but as as a cyber company, you want to share that data with you know, public and private? >> Look it, I, I think around the world we see incredible government activity first of all. And I think given the position we're in we get to have some unique conversations there. I would say worldwide security is an imperative. I, no matter where I go, you know it's in front of everybody's mind. The, on the, the governance side, it's really what do we need to adapt to make sure we meet local regulations. And I, and I would just tell you Dave there's ways when you do that, and we talk with governments that because of how they want to do it reduce our ability to give them full insight into all the threats and how we can help them. And I do think over time governments understand that we can anonymize the data. There's, but that, that's a work in process. Definitely there is a balance. We need to have privacy, we need to have, you know personal security for people. But there's ways to collect that data in an anonymous way and give better security insight back into the architectures that are out there. >> All right. A little shift the gears here. A little sports question. We've had some great Boston's sports guests on theCUBE right? I mean, Randy Seidel, we were talking about him. Peter McKay, Snyk, I guess he's a competitor now but you know, there's no question got >> He got a little funding today. I saw that. >> Down round. But they still got a lot of money. Not of a down round, but they were, but yeah, but actually, you know, he was on several years ago and it was around the time they were talking about trading Brady. He said Never trade Brady. And he got that right. We, I think we can agree Brady's the goat. >> Yes. >> The big question I have for you is, Belichick. Do you ever question Has your belief in him as the greatest coach of all time wavered, you know, now that- No. Okay. >> Never. >> Weigh in on that. >> Never, he says >> Still the Goat. >> I'll give you my best. You know, never In Bill we trust. >> Okay. Still. >> All right >> I, you know, the NFL is a unique property that's designed for parody and is designed, I mean actively designed to not let Mr. Craft and Bill Belichick do what they do every year. I feel privileged as a Boston sports fan that in our worst years we're in the seventh playoff spot. And I have a lot of family in Chicago who would kill for that position, by the way. And you know, they're in perpetual rebuilding. And so look, and I think he, you know the way he's been able to manage the cap and the skill levels, I think we have a top five defense. There's different ways to win titles. And if I, you know, remember in Brady's last title with Boston, the defense won us that Super Bowl. >> Well thanks for weighing in on that because there's a lot of crazy talk going on. Like, 'Hey, if he doesn't beat Arizona, he's got to go.' I'm like, what? So, okay, I'm sometimes it takes a good good loyal fan who's maybe, you know, has >> The good news in Boston is we're emotional fans too so I understand you got to keep the long term long term in mind. And we're, we're in a privileged position in Boston. We've got Celtics, we've got Bruins we've got the Patriots right on the edge of the playoffs and we need the Red Sox to get to work. >> Yeah, no, you know they were last, last year so maybe they're going to win it all like they usually do. So >> Fingers crossed. >> Crazy worst to first. >> Exactly. Well you said, in Bill we trust it sounds like from our conversation in BJ we trust from the customers, the partners. >> I hope so. >> Thank you so much BJ, for coming back on theCUBE giving us the lay of the land, what's new, the voice of the customer and how Palo Alto was really differentiated in the market. We always appreciate your, coming on the show you >> Honor and privilege seeing you here. Thanks. >> You may be thinking that you were watching ESPN just now but you know, we call ourselves the ESPN at Tech News. This is Lisa Martin for Dave Vellante and our guest. You're watching theCUBE, the Leader and live emerging in enterprise tech coverage. (upbeat music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

>>Welcome back to Vegas. Lisa Martin here covering snowflake summit 22. We are live at Caesar's forum. A lot of guests here about 10,000 attendees, actually 10,000 plus a lot of folks here at the momentum and the buzz. I gotta tell you the last day and a half we've been covering this event is huge. It's probably some of the biggest we've seen in a long time. We're very pleased to welcome back. One of our cube alumni to the program, Ron Tayo principal and chief data officer at Deloitte one. It's great to have you joining us. >>Yeah, no, thank you. Super excited to be here with you today. >>Isn't it great to be back in person? Oh, >>I love it. I mean the, the energy, the, you know, connections that we're making definitely, definitely loving and loving the experience. >>Good experience, but the opportunity to connect with customers. Yes. I'm hearing a lot of conversations from snowflake folks from their partners like Deloitte from customers themselves. Like it's so great to be back in person. And they're really talking about some of the current challenges that are being faced by so many industries. >>That's right. Oh, that, that is, you know, I would say as a consultant, you know, it all comes down to that personal connection and that relationship. And so I am, I'm all for this and love, you know, being able to connect with our customers. >>Yeah. Talk to me about the Deloitte snowflake partnership. Obviously a ton of news announced from snowflake yesterday. Snowflake is a rocket ship. Talk to us about the partnership, what you guys do together, maybe some joint customer examples. >>Yeah. I mean, so snowflake is a strategic Alliance partner. We won the, you know, SI partner of the year award and for us, the, the shift and the opportunity to help our clients modernize and achieve a level of data maturity in their journey is, is strategically it's super important. And it's really about how do we help them leverage, you know, snowflake has underlying data platform to ultimately achieve, you know, broader goals around, you know, their business strategy. And our approach is always very much connected to overarching business strategies and sense of, is it a finance transformation, a supply chain transformation, a customer transformation, and what are the goals of those transformations and how do we ensure that data is a critical component to enabling that and with, you know, technologies and vendors and partners like snowflake, allowing us to even do that at a faster, better, cheaper pace only increases the overall business case and the value and the impact that it generates. >>And so we are super, super excited about our partnership with snowflake and we believe, you know, the journey is very, very bright. You know, we, this is the future, you know, often tell folks that, you know, data has and will continue to be more valuable than sort of the systems that own it and manage it. And I think we're starting to see that. I think the topic that I discussed today around data collaboration and data sharing is an example of how we're starting to see, you know, the importance and the value of data, you know, become way more important and more of the focus around the strategy for, for organizations >>As the chief data officer, what do data sharing and data collaboration mean to somebody in your position and what are some of the conversations you have with customer other CDOs at customer organizations? >>Yeah, so, so my role is, is sort of twofold. I, I am responsible for our internal data strategy. So when you think about Deloitte as a professional service organization, across four unique businesses, I am a customer of snowflake in our own data modernization journey, and we have our own strategy on how and what we share, not only internally across our businesses, but also externally across, you know, our partners. So, so I bring that perspective, but then I also am a client service professional and serve our clients in their own journey. So I often feel very privileged in, in the opportunity to be able to sort of not only share my own experience from a Deloitte perspective, but also in how we help our clients >>Talk about data maturation. You mentioned, you know, the volume of data just only continues to grow. We've seen so much growth in the last two years alone of data. We've seen all of us be so dependent on things like media and entertainment and retail, eCommerce, healthcare, and life sciences. What, how do you define data maturation and how does Deloitte and snowflake help companies create a pathway to get there? >>Yeah. Yeah. So I would say step one for us is all about the overarching business strategy. And when you sort of double click on the big, broad business strategy and what that means from a data strategy perspective, we have to develop business models where there is an economical construct to the value of data. And it's extremely important specifically when we talk about sharing and collaborating data, I would say the, the, the, the assumption or the, or, or, or, or the posture typically seems to be, it's a one way relationship, our strategy and what we're pushing, you know, again, not only internally within ourselves, but also with our clients, is it has to be a bidirectional relationship. And so you, you hear of, of the concepts of, you know, the, the, the data clean room where you have two partners coming together and agreeing with certain terms to share data bidirectionally. Like I do believe that is the future in how we need to do, you know, more data collaboration, more data sharing at a scale that we've not quite seen. Yes. Yet >>The security and privacy areas are increasingly critical. We've seen the threat landscape change so dramatically the last couple of years, it's not, will we get hit by a cyber talk? It's when yes. For every industry, right? The privacy legislation that just we've seen it with GDPR, CCPA is gonna become CPR in California, other states doing the same thing. How do you help customers kind of balance that line of being able to share data equitably between organizations between companies do so in a secure way, and in a way that ensures data privacy will be maintained. >>Yeah. Yeah. So first absolutely recognizing, evolving, recognize the evolving regulatory landscape. You mentioned, you know, California, there's actually now 22 states that have a, is it 22 now? Right? Yeah. 22 states that have a privacy act enacted. And our projection is in the next 12 to 18 months, all states will have one. And so absolutely a, a perceived challenge, but one that I think is, is addressable. And, and I think that gets to the spirit of the question for us. There's, there's four dimensions that an organization needs to work through when it comes to data sharing. The first one is back to the, the business goal and objective, like, is there truly a business need? And is there value in sharing data? And it needs to have a very solid business model. Okay. So, so that's the first step. The second step is what are the legal terms? >>What are the legal terms? What can you do? What can't you do? Do you have primary rights, secondary rights? The third dimension is around risk. What is the risk and exposure, not only from a data security perspective, but what is the risk if someone uses a data inappropriately, and then the fourth one is around ethics and the ethical use of data. And we see lots of examples where an organization has consent has rights to the data, but the way they used it might have not necessarily been, you know, among the kind of ethical framing. And so for us, those four dimensions is what guides us and our clients in developing a very robust data, sharing data collaboration framework that ensures it's connected to the overall business strategy, but it provides enough of the guardrails to minimize legal and ethical risk. So >>With that in mind, what do the customer conversations look like? Cause you gotta have a lot of players, the business folks, the data folks, every line of business needs data for its functions. Talk to us about how the customer conversations and projects have evolved as data is increasingly important to every line of business. >>Yes. I would say the biggest channel, or maybe the, the, the denominator at this point that we're seeing bring the, let's say diversity of needs to more common denominator has been AI. So every organization at this point is driving massive AI programs. And in order to really scale AI, you know, the, the algorithm cannot execute without data. Yeah. And so for us, at least in our experience with our customers, AI has almost been the, the, the mechanism to have these conversations across the different business stakeholders and do it in a way that, you know, you're not necessarily boiling the ocean, cuz I think that's the other element that makes this a bit hard is, well, what, what data do you want me to share and for what purpose? And when you start to bring it into sort of more individual swim lanes and, and, and our experience with our customers is AI has sort of been that mechanism to say, am I automating, you know, our factory floor? Am I bringing AI and how we engage and serve our customers? Right? Like it be, it be begins to sort of bring a little bit more of, of that repeatability at a, at an individual level. So that's been a, a really good strategy for us in our customers >>In terms of the customer's strategy and kind of looking forward, what are some of the things that excite you about the, the future of data collaboration, especially given all of the news that snowflake announced just yesterday? >>Yes. Yeah. I think for me, and this is both the little bit of the ambition, as well as the push, it's no longer a question of should it's it's how and for what? And so, so yes, I mean the, the, the snowflake data cloud is a network that allows us to integrate, you know, disparate and unique data assets that have never, you know, been possible before. Right. So we're in this network, it's now a matter of figuring out how to use that and for what purpose. And so I, I go back to, we, each individual organization needs to be figuring out the how, and for what not, when this is the future, we all need it. Yeah. And we just need to figure out how that fits in our individual businesses >>In terms of the, how that's such an interesting, I love how you bring that up. It's not, it's not when it's definitely how, because there's gonna be another competing business or several right there in the rear view mirror, ready to take your place. Yep. If you don't act quickly, how does Deloitte and snowflake help customers achieve the, how quickly enough to be able to really take advantage of data sharing and data collaboration so that they can be very competitive? >>Yeah. So there's two main, maybe even three driving forces in this. What we see is when there's a common purpose across director, indirect competitors and the need to share data. So I think the poster child of this was the pandemic, and we started to see organizations again, either competitively or non-com competitively share data in ways for a greater good, right. When there was a purpose, we believe when that element exists, the ability to share data is going to increase. We believe the next big sort of common purpose out there in the world is around ESG. And so that's gonna be a big driver for sharing data. So that's one element. The other one is the concept of developing integrated value chains. So when you think about any individual business and sort of where they are in that piece of the value chain, developing more integrated value across, let's say a manufacturer of goods with a distributor of those goods that ultimately get to an end customer. >>They're not sharing data in a meaningful way to really maximize their overall, you know, profitability. And so that's another really good, meaningful example that we're seeing is where there's value across, you know, a, what appears to be a siloed set of steps, and really looking at it more as an integrated value chain, the need to share data is the only way to unlock that. And so that's, that's the second one. The, the third one I would say is, is around the need to address the consumer across sort of the multiple personas that we all individually sit. Right? So I go into a bank and I'm, I'm a client. I walk into a retail store and I'm a customer. I walk into my physician's office and I'm a patient at the end of the day. I am still the same person. I am still one. And so that consumer element and the convergence of how we are engaging and serving that consumer is the third, big shift that is really going to bring data collaboration and sharing to the next level. >>Do you think snowflake is, is the right partner of the defacto for delight to do that with? >>Absolutely. I think, you know, the head start of the cloud, the data cloud platform and the network that it's already established with all the sort of data privacy and security constraints around it. Like that's a big, that's a big, you know, check right. That we don't have to worry about. It's there for sure. >>Awesome. Sounds like a great partnership, Juan. Thank you so much for joining me on the program. It's great to have you back on the cube in person sharing what Deloitte and snowflake are doing and how you're really helping to transform organizations across every industry. We appreciate >>Your insights. Yeah. No, thank you for having me here. My pleasure. Always a pleasure. Thank you. >>All right. For Juan. I am Lisa Martin. You're watching the cube live from snowflake summit 22 at Caesar's forum. You write back with our next guest.

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

(upbeat music) >> Hello, welcome back to the Cookie Conundrum, A Recipe For Success an industry conference and summit from Quancast on the demise of third-party cookies. We've got a great industry panel here to break it down. Chris Guenther, senior vice president global head of Programmatic at News Corp. Chris, thanks for coming on. Xiao Lin, managing director solutions at Xaxis, and Somer Simpson, vice president of product at Quancast stellar panel. Looking forward to this conversation. Thanks for coming on and chatting about the cookie conundrum. >> Thank you for having us. >> So, Chris, we'll start with you at News Corp obviously major publisher. Deprecation of third-party cookies affects everyone. You guys have a ton of traffic, ton of audience across multiple formats. Tell us about the impact to you guys and the reliance you guys had on them. And what are you going to do to prepare for this next level change? >> Sure. I mean, I think like everyone in this industry there is a, you know, a significant reliance and I think it's something that a lot talk about audience targeting, but obviously they realize that third party cookies pervasive across the whole ad tech ecosystem, MarTech stack. And so, you know, we have to think about, you know how that impact, you know, our vendor the vendors we work with, what it means in terms of our use cases across marketing, across advertising across site experience. So, you know, without a doubt, it's significant. But you know, we look at it as listen. It's disruptive in disruption and change is always a little scary, but overall it's a it's a long overdue reset. I mean, I think that, you know, our perspective is that the the cookies, as we all know, is it was a crutch, right? It's sort of a technology being used in way it shouldn't. And so, as we look at what's going to happen presumably after Jan 2022, then it's a good way to kind of fix on some bad practices practices that lead to data, leakage, practice sort of devalued for our perspective. Some of the, you know, we offered as, as publishers. And I think that this is a key thing is that we're not just looking to as we look through post gen world, not just kind of recreating the prior world. Because the prior world was flawed or I guess I could say the current world since it hasn't changed yet. But the current world is flawed. Let's not just replicate that. You know, let's make sure that third party cookies goes away other work around like fingerprinting and things like that, you know, also go away. So, you know, philosophically that's where our head's at. And so, you know, as we look at how we are preparing you look at sort of what are the core building blocks of preparing for this world. Obviously one of the key ones is privacy compliance. Like how do we treat our users with consent? You know, obviously are we aligned with the regulatory environments? You know, in some ways we're not looking just to Jan 2022 but Jan 2023, where there's going to be the majority of our audiences, we covered by regulation. And so I think from regulation up to data gathering, to data activation, all built around an internal identifier that we've developed that allows us to have a a consistent look at our user is whether they're logged in or obviously, anonymous. So it's really looking across all those components, across all our sites, and all in a privacy compliant way. So a lot of work to be done, a lot of work in progress but you know, we're excited about what's going on. >> I like how you framed it, you know, old world or next gen kind of the current situation is kind of flawed. And as you think about Programmatic, the concept is mind blowing and what needs to be done. So we'll come back to that because I think that original content view is certainly relevant. It's a huge investment, and you've got great content and audience consuming it. Xiao, from a major media standpoint get your perspective on the impact because you've got clients who want to get their message out in front of the audience at the right time, at the right place and the right context. Right? So yeah, privacy, you got consent and all of these things kind of boiling up how do you help clients prepare? Because now they can go direct to the consumer. You know, everyone, everyone has a megaphone now everyone's you know, everyone's here, everyone's connected. So how are you impacted by this new notion? >> You know, if the cookieless future was a tik tok dance, we'd be dancing right now and at least until the next year. This has been top of mind for us and our clients for quite some time. But I think as each day passes the picture becomes clearer and more in focus. The end of the third party cookie does not mean the end of Programmatic. So clients work with us in transforming their investments into real business outcomes based on our expertise and based on our tech. So we continue to be in a great position to lead, to educate, to partner, and to grow with them along this cookieless future. The impact will be all encompassing in changing the ways we do things now and also accelerating the things that we've already been building on. So we take it from the top. Planning will have a huge impact because it's going to start becoming more strategic around real business outcomes. We're omni-channel. So clients wants to drive outcomes through multiple touch points of a consumer's journey. Whether that's programmatic, whether that's as a cookie free environment like connected TV, out of home, audio, gaming, and so forth. So we're going to see more of these strategic holistic plans. Creative will have a lot of impact. It will start becoming more important with Creative testing, Creative insights, you know, Creative in itself is cookieless. So there will be more focus on how to drive a brand dialogue, to connect to consumers with less targeting, with less cookies. With the cohesiveness of holistic planning, Creative can align through multiple channels. And lastly, the role of AI will become increasingly important. You know, we've always looked to build our tech, our products, to compliment new and existing technology as well as the client's own data and tech stack to deliver these outcomes for them. And AI in its core is just taking inputted data and having an output of your desired outcomes. So input data could be DSP data beyond cookies such as browser, such as location, such as contextual, a publisher taking client's first party data, first party CRM data like store visitation sales site activity. And using that to optimize in real time regardless of what vendor or what channel we're on. So as we're learning more about this cookieless dance, we're helping our clients on the steps of it, and also introducing our own moves. >> That's awesome. Data is going to be a key value proposition, you know connecting in with content real time. Great stuff. Somer, with your background in journalism and you're the tech VP of product at Quancast. You have the keys to the kingdom over there. It's interesting, journalism is about truth you know, good content, original content. But now you have a data challenge, problem, opportunity on both sides, brands and publishers coming together. This is a data problem in a way. It's a tech stack, not so much just, you know getting the right ads to show up at the right place, the right time. It's really bigger than that now. What's your take on this? >> You know, I, so first I think that consumers already sort of accept that there is a reasonable value exchange, you know, for their data, in order to access free content. Right? And that's a critical piece for us to all kind of understand. Over the past. Yeah, probably two years, since even before the GDPR, we've been doing a ton of discovery with customers, both publishers and marketers. And so, you know, we kind of known this this cookie going away thing is, has been coming and you know, Google's announcement just kind of confirmed it. And it's been really really interesting since Google's announcement how the conversations have changed with our customers and other folks that we talk to. And I've almost gone from being like a product manager to a therapist because there's such an emotional response. From the marketer perspective, there's real fear there. There's like, Oh my God, how you know, it's not just about delivering ads. It's about how do I control frequency? How do I measure, you know, success? You know, because the technology has grown so much over the years to really give marketers the ability to deliver personalized, you know, advertising good content to consumers and be able to monitor it and control it so that it's not too, too intrusive. On the publisher perspective side, we see a slightly different response. It's more of a yes. Right? You know, we're taking back control and we're going to stop the data leakage. We're going to get the value back for our inventory. Both things are a good thing. But if it's not managed, it's going to be like ships passing in the night. Right? In terms of, you know, them coming together. Right? And that's the critical pieces that they have to come together. They have to get closer. You got to cut out a lot of like that LUMAscape in the middle so that they can talk to each other and understand what's the value exchange happening between marketers and publishers and how do we do that without cookies? >> Yeah. It's a fascinating, I love your insight there. I think it's so relevant. And it's got broader implications because, you know, if you look at how data is impacting some of these big structural changes and refactoring of industries look at cybersecurity, you know no one wants to share their data but now if they share, they get more insight more machine learning, benefit, more AI benefit. So now we have the sharing notion but that goes against counter the big guys that want a walled garden. They want to hoard all the data and control that to provide their own personalization. So you have this confluence of, hey I want to hoard the data and then now I want to share the data. So Chris and Homer, in the wheelhouse you've got original content and there's other providers out there. So is there the sharing model coming? with privacy and these kinds of services is the open come back again? How do you guys see this? The confluence of open versus walled gardens. Because you need the data to make machine learning good. >> I'll start off. I mean, listen, I think you have to give credit to the walled gardens I've created. And I think as we look as publishers, what are we offering to our clients? What are we offering to the buy-side? We need to be compelling. We shouldn't just be, obviously, as journalists I think that there is a case of, you know the importance of funding journalism. But ultimately we need to make sure we're meeting the the KPIs and the business needs of the buy-side. And I think around that, it is, you know there's sort of three core pillars to that. It's ease of access, it's scope of activation and targeting, and finally, measurable results. So as I think, as us, as an individual publisher of so we have multiple publications so we do have scale, but then in partnership with other publishers perhaps organizations like Prebid, you know I think we can, we're trying to address that. And I think we can offer something that's compelling and transparent in terms of what these results are. But obviously, you know, I want to make sure it's clear that transparent terms of results, but obviously where there's privacy in terms of the data. And I I think we've all heard about like data clean rooms, a lot of them out there flogging those wares. And I think there's something valuable, but you know I think it's who is sort of the right partner or partners, and ultimately who allows us to get as close as possible to the buy side. And so that we can share that data for targeting shared for perhaps for measurement, but obviously all in a privacy compliant way. >> Somer, what's your take on this? Because you talk about the future of the open internet democratization. The network effect that we're seeing in virality and across multiple omni-channels as Xiao pointed out, it's happening. That's the distribution now. So that's almost an open garden model. So it's like >> Yeah. And yeah, it's, it's, you know back in the day, you know, Nightrider who was the first group that I, that I worked for, you know each of those individual properties were not hugely valuable on their own from a digital perspective. but together as a unit, they became valuable. Right. And got a scale for advertisers. Now we're in a place where, you know, I kind of think that each of those big networks are going to have to come together and work together to compare in size to the, to the walled gardens. And yeah, this is something that we've talked about before, an open garden. I think that's the definitely the right route to take. And I agree with Chris. It's about publishers getting as close to the marketers as possible, working with the tech companies that enable them to do that, and doing so in a very privacy centric way. >> Xiao how do we bring the brands and agencies together to get ready for third-party cookies? Because there is a therapist moment here of it's going to be okay, the parachute will open. The future is not going to be as grim. It's a real opportunity, but if managed properly. What's your take on this? Is it just more first party data strategy? And what's your assessment of this? >> So we're collaborating right now with ball grants on how to distill very complex cookieless future you know, what's going to happen in the future. To six steps that we can take right now and marketers should take. The first step is gather Intel on what's working on your current campaign analyzing the data sets across cookie free environment. So you can translate those tactics eventually when the cookies do go away. So we have to look at things like temporal or time analysis. We can look at log level data. We can look at site analytics data. We can look at brand measurement tools and how Creative really impacts the campaign success. The second thing we can look at is geo-targeting strategies. The geo-targeting strategy has been underrated because the granularity and DL data could go down all the way to the local level, even beyond zip code. So for example, the census block data. And this is especially important for CPG brands. So we're working closely with the client teams to understand not only the online data, but the offline data and how we can utilize that in the future. We want to optimize investments around markets that are working, so strong markets, and then test in underperforming markets. The third thing we can look at is contextual. So contextual by itself is cookie free. We could build on small-scale usage to test and learn various keywords and content categories based sets, working closely with partners to find ways to leverage their data, to mimic audiences that you are trying to target right now with cookies. The fourth one is publisher data or publisher targeting. So working with your publishers that you have strong relationships with who can curate similar audiences using their own first party data and conducting RFIs to understand the scale and reach against your audience and your future roadmap. So work with your top publishers based on historical data to try to recreate your best strategies. The fifth thing, and I think this is very important, is first party data. That's going to matter more than ever in the cookieless future. Brands will need to think about how to access and develop the first party data starting with the consumer, seeing of value in exchange for the information it's a goldmine and understanding your consumer their intent, their journey. And you need a really great data sciences team to extract insights out of that data, which will be crucial. So partner with strategic onboarding vendors and vet their ability to accept first party data into a clean room environment for targeting, for modeling, for insights. And lastly, the sixth thing that we can do is begin inform prospecting by dedicating test budget to start gaining learnings about cookieless. One, one place that we can start, and it is under invested right now, is Safari and Firefox. They have been cookieless for quite some time. So you can start here and begin testing here. Work with your data scientist team to understand the right mixes to target and start exploring other channels outside of just programmatic cookies. Like CTV, digit auto home, radio, gaming, and so forth. So those are the six steps that we're taking right now with our clients to prepare and plan for the cookieless future. >> So, Chris, let's go back to you. What's the solution here? Is there one, is there multiple solutions? What's the future look like for a cookieless future? >> I think the one certain answer is there definitely is not just one solution. As we all know right now, there seems to be endless solutions, a lot of ideas out there, proposals when the W3C, work happening within other industry bodies, you know, private company solutions being offered. And you know, it's a little bit, it's enough to make everyone's head spin and to try to track it to understand it and understand the impact. And as a publisher, we're obviously, you know, a lot of people are knocking on our door, you know they're saying, hey, our solution is one that it's going to bring in lots of money. You know, all the buy-side is going to use it. This is the one like unlock all the spend. And it's our experience so far is that none of these solutions are, cause I think everyone's still testing and learning. No one on the buy side from our, from our knowledge is really committed to one or a few. It's all about a testing stage. I think that, you know, putting aside all that noise I think what matters the most to us as publishers, actually something Somer mentioned before. It's about control. You know, if we're going to work with a, you know, again outside of our sort of independent internal identifier work that we're doing, if we're going to work with an outside party or an outside approach, does it give us control >> As a publisher to ensure that it is, you know we control the, the data from our users, you know there isn't that data leakage, it's privacy compliant. You know, what information gets shared out there? What is it what's released within, you know within the bitstream? If it is something that's attached to a, someone, a declared user, a registered user that if that then is not somehow amplified or leverage off in another site in a way that is leveraging bit stream data or fingerprinting and going again. And so I think that the spirit of what we're trying to do in a post third party cookie world. And so those controls are critical. And I think to have those controls as publisher we have to be collectively be disciplined. And you know, what solutions that we sort of we test out and what we eventually adopt. But even when that adoption point arrives it definitely will not be one. There will be multiple because there's just too many cases to address. >> Great, great insight there from you guys at News Corp. Somer, let's get back to you. I want to get your thoughts. You've been in many waves of innovation, ups and downs. We're on a new one now. We talked about the open internet and democratization. Journalism is under a lot of pressure now but there's now a wave of quality people, really leaning in towards fighting misinformation, understanding truth and community and data is at the heart of it. What do you see as the new future for journalists to reward journalism? Is there a way, is there a path forward? >> So there's what I hope is going to happen. And then I'm just going to ignore what could, right. You know, there's a trend in market right now at a number of fronts, right? So there are marketers who are leaning in to wanting to spend their marketing dollars with quality journalists, focusing on BiPAC owned and operated, really leaning into supporting those businesses that have been and those publishers that have been ignored for years. I really hope that this trend continues. We are leaning into helping marketers curate that supply, right. And, and really, you know, speak with their dollars about the things that they support and value in market. So I'm hoping that that trend continues. And it's not just sort of like a marketing blip but we will do everything possible to kind of encourage that behavior and give people the information that they need to find. You know, truly high quality journalism. >> That's awesome. Chris, Xiao, Somer, thanks for coming on and sharing your insight on this panel on the cookieless future. Before we go, just quick summary, each of you if you don't mind just giving a quick sound bite or bumper sticker of what we can expect. If you had to throw a prediction for what's going to happen in the next 24 months. Chris, we'll start with you. >> It's going to be quite a ride. I think that's an understatement. I think that there, I wouldn't be surprised if if Google delays the change to the Chrome by a couple months. And may give the industry some much needed time. But no one knows, I guess, I guess I'm not except for someone somewhere, we are deep within Chrome. So I think we all have to operate in a way that changes that happen, changes that happen quickly. And it's going to cover across all facets of the industry, all facets of, you know, from advertising and marketing. So just be prepared. >> Okay. Xiao. Along those same lines, be prepared. Nobody knows what's going to happen in the future. You know, we're all dancing in this together. I think for us, it's planning and preparing and also building on what we've already been working on. So omni-channel, AI, Creative, and I think clients will lean more into those different channels. >> Awesome. Somer, take us home. Last words. >> I think we're in the throwing spaghetti against the wall stage, right? So this is a time of discovery of leaning and trying everything out learning and iterating as fast as we possibly can. >> Awesome. And I love the cat in the background over your shoulder. I can't stop staring at your wonderful cat. Somer, thanks for coming on. Xiao, Chris, thanks for coming on this awesome panel industry breakdown of the Cookie Conundrum, a Recipe for Success data AI open the future is here. It's coming. It's coming fast. I'm John Furrier with theCUBE. Thanks for watching.

>>what? Hello, I'm john free with the cube. I want to welcome Conrad Feldman, the founder and Ceo of Kwan cast here to kick off the quan cast industry summit on the demise of third party cookies. The events called the cookie conundrum, a recipe for success. The changing advertising landscape, super relevant conversation just now. More than ever. Conrad welcome to your own program kicking this off. Thanks for holding this event. It's a pleasure. Great to chat with you today. So a big fan been following your company since the founding of it. Been analytics is always the prize of any data driven company. Media. Anything's all data driven now. Um, talk about the open internet because now more than ever it's under siege. As I, as I mentioned in my open, um, we've been seeing the democratization, a new trend of decentralization. We're starting to see um, you know, everyone's present online now, Clay Shirky wrote a book called, here comes everyone in 2005. Well everyone's here. Right? So you know, we're here, it's gonna be more open. But yet people are looking at as close right now. You're seeing the big players, um, or in the data. What's your vision of this open internet? >>Well, an open internet exists for everyone. And if you think about the evolution of the internet, when the internet was created for the first time really in history, anyone that had access to the internet could publish the content, whatever they were interested in and could find an audience. And of course that's grown to where we are today, where five billion people around the world are able to engage in all sorts of content, whether that's entertainment or education, news, movies. What's perhaps not so widely understood is that most of that content is paid for by advertising and there's a lot of systems that support advertising on the open Internet and some of those are under siege today certainly. >>And what's the big pressure point? Is it just more control the data? Is it just that these walled gardens are wanting to, you know, suck the audience in there? Is that monetization driving it? What's where's the friction? >>Well, the challenges is sort of the accumulation of power into a really small number of now giant corporations who have actually reduced a lot of the friction that marketers have in spending their money effectively. And it means that those companies are capturing a disproportionate spend of the ad budgets that fund digital content. So the problem is if more of the money goes to them, less of its going to independent content creators. It's actually getting harder for independent voices to emerge and be heard. And so that's the real challenges. That has more power consolidates into just a limited number of tech giants. The funding path for the open Internet becomes constrained and there'll be less choice for consumers without having to pay for subscriptions. >>Everyone knows the more data you have the better and certainly, but the centralized power when the trend is going the other way, the consensus is everyone wants to be decentralized more truth, more trust all this is being talked about on the heels of the google's news around, you know, getting rid of third party cookies and others have followed suit. Um, what does this mean? I mean, this cookies have been the major vehicle for tracking and getting that kind of data. What is gonna be replaced with what is this all about? And can you share with us what the future will look like? >>Sure, Well, just as advertising funds the open Internet is advertising technology that supports that advertising spend. It supports sort of the business of advertising that funds the open Internet. And within all of that technology is the need for different systems to be able to align around um the identification of for example, a consumer, Have they been to this site before? Have they seen an ad before? So there's all of these different systems that might be used for advertising for measurement, for attribution, for creating personalization. And historically they've relied upon the third party cookie as the mechanism for synchronization. Well, the third party cookie has been in decline for some time. It's already mostly gone from actually apple safari browser, but google's chrome has so much control over how people access the internet. And so it was when Google announced that chrome was going to deprecate the third party cookie, that it really sort of focus the minds of the industry in terms of finding alternative ways to tailor content and ultimately to just simply measure the effectiveness of advertising. And so there's an enormous amount of um innovation taking place right now to find alternative solutions. >>You know, some are saying that the free open internet was pretty much killed when, you know, the big comes like facebook and google started bringing all this data and kind of pulls all sucks all the auction in the room, so to speak. What's this mean with cookies now getting, getting rid of um, by google has an impact publishers because is it helpful? I mean hurtful. I mean, where's the where is that, what the publisher impact? >>Well, I don't think anyone really knows right now. So first of all, cookies weren't necessarily a very good solution to the sort of the challenge of maintaining state and understanding those sorts of the delivery of advertising and so on. It's just the one that's commonly used, I think for different publishers it may mean different things. But many publishers need to be able to demonstrate the value and the effectiveness of the advertising solutions that they deliver. So they'll be innovating in terms of how they use their first party data. They'll be continuing to use contextual solutions that have long been used to create advertising relevant, relevant. I think the big question of course is how we're going to measure it that any of this is effective at all because everyone relies upon measuring advertising effectiveness to justify capturing those budgets in the first place. >>You know, you mentioned contextual come up a lot also in the other interviews we've done with the folks in the around the internet around this topic of machine learning is a big 12 What is the impact of this with the modernization of the solution? You mentioned cookies? Okay cookies, old technology. But the mechanisms in this ecosystem around it or not, it funds the open internet. What is that modern solution that goes that next level? Is it contextual metadata? Is that shared systems? What's the it's the modernization of that. >>It's all of those and and more. There's no there's no single solution to replace the third party cookie. There'll be a combination of solutions. Part of that will be alternative identity mechanisms. So you know, you will start to see more registration wars to access content so that you have what's called a deterministic identify there will be statistical models so called probabilistic models, contextual has always been important. It will become more important and it will be combined with we use contextual combining natural language processing with machine learning models to really understand the detailed context of different pages across the internet. You'll also see the use of first party data and there are discussions about shared data services as well. I think there's gonna be a whole set of different innovations that will need to inter operate and it's going to be an evolutionary process as people get used to using these different systems to satisfy the different stages of the media fulfillment cycle from research and planning to activation to measurement. >>You know, you put up walled gardens. I want to just touch on the on on this kind of concept of walled gardens and and and and compare and contrast that with the demand for community, open internet has always fostered a community vibe. You see network effects mostly in distinct user communities or subnets of sub networks. If you will kind of walled gardens became that kind of group get together but then became more of a media solution to make the user is the product, as they say, facebook's a great example, right? People talk about facebook and from that misinformation abuse walled garden is not the best thing happening right now in the world, but yet is there any other other choice? That's how they're going to make money? But yet everyone wants trust, truth community. Are they usually exclusive? How do you see this evolving, what's your take? >>Well, I think the open internet is a, is a forum where anyone can have their voice, uh, put their voice out there and have it discovered and it's in that regard, it's a it's a force for good look. I think there are there are challenges, obviously in terms of some of the some of the optimization that takes place with inside the walled gardens, which is, is sort of optimized to drive engagement can have some unintended consequences. Um obviously that's something that's, that's broadly being discussed today and the impact on society, but sort of more at a more pointed level, it's just the absorption of advertising dollars. There's a finite amount of money from advertisers. It's estimated to be $400 billion this year in digital advertising. So it's a huge amount of money in terms of funding the open Internet, which sounds great except for its increasingly concentrated in a tiny number of companies. And so, you know, our job at Quan cast as champions of the free and open Internet is to help direct money effectively to publishers across the open internet and give advertisers a reliable, repeatable way of accessing the audiences that they care about in the environment they care about and delivering advertising results. >>It's a publisher, we care a lot about what our audience wants and try to serve them and listen to them. If we could get the data, we want that data and then also broker in the monetization with advertisers, who might want to reach that audience in whatever way. So this brings up the question of, you know, automation and role of data. You know, this is a huge thing to having that data closed loop, if you will for for publishers. But yet most publishers are small, some niche. And even as they can become super large, they don't have all the data and more, the more data, the better the machine learning. So what's the answer to this as it goes forward? How do we get there? What's the dots that that we need to connect to get that future state? >>So I think it takes it takes companies working together effectively. I think a really important part of it is, is a more direct conversation with consumers. We've seen that change beginning to happen over the past few years with the introduction of regulations that require clear communication to consumers about the data that's captured. And y and I think that creates an opportunity to explain to your audience is the way in which content is funded. So I think that consumer that consumer conversation will be part of the collective solution. >>You know, I want to as we wind down this kickoff segment, get your thoughts and vision around um, the evolution of the internet and you guys have done some great work at quan Cast is well documented, but everyone used to talk about traffic by traffic, then it became cost of acquisitions. PPC search. This is either mechanisms that people have been using for a long, long time, then you know, your connections but audience is about traffic, audience traffic. If this if my family is online, doesn't it become about networks and the people. So I want to get your thoughts and your vision because if community is going to be more important than people agree that it is and things are gonna be decentralized, more openness, more voices to be heard. You need to dress ability. The formation of networks and groups become super important. What's your vision on that? >>So my vision is to create relevance and utility for consumers. I think that's one of the things that's often forgotten is that when we make advertising more relevant and useful for consumers, it automatically fulfils the objectives that publishers and marketers have, everyone wins when advertising is more relevant. And our vision is to make advertising relevant across the entire open internet so that that ad supported model can continue to flourish and that five billion and hopefully many more billions in the future, people around the world have access to high quality, diverse content. >>If someone asked you Conrad, what is quant cast doing to make the open internet viable now that cookies are going away? What's the answer? >>So well, the cookie pieces is a central piece of it in terms of finding solutions that will enable sort of planning activation and measurement post cookies and we have a lot of innovation going on. There were also working with a range of industry bodies and our and our partners to build solutions for this. What we're really trying to do is to make buying the open internet as straightforward for marketers as it is today and buying the walled gardens. The reason the walled gardens capture so much money is they made it really easy for marketers to get results, marketers would like to be able to spend their money across all of the diverse publishes the open internet. You know, our job at Comcast is to make it just as easy to effectively spend money in funding the content that they really care about in reaching the audiences that they want. >>Great stuff. Great Mission. Conrad, thanks for coming on. Conrad Feldmann founder and Ceo here at the cookie conundrum recipe for success event, Quant Cast Industry summit on the demise of third party cookies. Thank you. Conrad appreciate it. Thank you. Yeah, I'm john ferrier, stay with us for more on the industry event around the middle cookies. Mhm Yeah, yeah, thank you. Mhm. Welcome back to the Qantas industry summit on the demise of third party cookies, the cookie conundrum, a recipe for success. I'm john furrier host of the cube, the changing landscape of advertising is here and shit Gupta, founder of you of digital is joining us chief. Thanks for coming on this segment. Really appreciate, I know you're busy, you've got two young kids as well as providing education to the digital industry, you got some kids to take care of and train them to. So welcome to the cube conversation here as part of the program. >>Yeah, thanks for having me excited to be here. >>So the office of the changing landscape of advertising really centers around the open to walled garden mindset of the web and the big power players. We know the big 34 tech players dominate the marketplace so clearly in a major inflection point and we've seen this movie before Web mobile revolution which was basically a reply platform NG of capabilities. But now we're in an error of re factoring the industry, not re platt forming a complete changing over of the value proposition. So a lot at stake here as this open web, open internet, global internet evolves. What are your, what's your take on this, this industry proposals out there that are talking to this specific cookie issue? What does it mean? And what proposals are out there? >>Yeah, so, you know, I I really view the identity proposals and kind of to to kind of groups, two separate groups. So on one side you have what the walled gardens are doing and really that's being led by google. Right, so google um you know, introduce something called the privacy sandbox when they announced that they would be deprecating third party cookies uh as part of the privacy sandbox, they've had a number of proposals unfortunately, or you know, however you want to say they're all bird themed for some reason, I don't know why. Um but the one, the bird theme proposal that they've chosen to move forward with is called flock, which stands for Federated learning of cohorts. And essentially what it all boils down to is google is moving forward with cohort level learning and understanding of users in the future after third party cookies, unlike what we've been accustomed to in this space, which is a user level understanding of people and what they're doing online for targeting tracking purposes. And so that's on one side of the equation, it's what google is doing with flock and privacy sandbox now on the other side is, you know, things like unified I. D. Two point or the work that I. D five is doing around building new identity frameworks for the entire space that actually can still get down to the user level. Right? And so again, unified I. D. Two point oh comes to mind because it's the one that's probably got the most adoption in the space. It's an open source framework. So the idea is that it's free and pretty much publicly available to anybody that wants to use it and unified, I need to point out again is user level. So it's it's basically taking data that's authenticated data from users across various websites you know that are logging in and taking those authenticated users to create some kind of identity map. And so if you think about those two work streams right, you've got the walled gardens and or you know, google with flock on one side and then you've got unified I. D. Two point oh and other I. D. Frameworks for the open internet. On the other side, you've got these two very differing type of approaches to identity in the future. Again on the google side it's cohort level, it's going to be built into chrome. Um The idea is that you can pretty much do a lot of the things that we do with advertising today, but now you're just doing it at a group level so that you're protecting privacy, whereas on the other side of the open internet you're still getting down to the user level. Um And that's pretty powerful. But the the issue there is scale, right? We know that a lot of people are not logged in on lots of websites. I think the stat that I saw is under five of all website traffic is authenticated. So really if you if you simplify things you boil it all down, you have kind of these two very differing approaches. >>I guess the question it really comes down to what alternatives are out there for cookies and which ones do you think will be more successful? Because I think, you know, the consensus is at least from my reporting, in my view, is that the world agrees. Let's make it open, Which one is going to be better. >>Yeah, that's a great question, john So as I mentioned, right, we have we have to kind of work streams here, we've got the walled garden work streams, work stream being led by google and their work around flock, and then we've got the open internet, right? Let's say unified I. D to kind of represents that. I personally don't believe that there is a right answer or an endgame here. I don't think that one of them wins over the other, frankly, I think that, you know, first of all, you have those two frameworks, neither of them are perfect, they're both flawed in their own ways. There are pros and cons to both of them. And so what we're starting to see now is you have other companies kind of coming in and building on top of both of them as kind of a hybrid solution. Right? So they're saying, hey, we use, you know, an open I. D. Framework in this way to get down to the user level and use that authenticated data and that's important. But we don't have all the scale. So now we go to google and we go to flock to kind of fill the scale. Oh and hey, by the way, we have some of our own special sauce, right? We have some of our own data, we have some of our own partnerships, we're gonna bring that in and layer it on top. Right? And so really where I think things are headed is the right answer, frankly, is not one or the other. It's a little mishmash of both. With a little extra something on top. I think that's that's what we're starting to see out of a lot of companies in the space. And I think that's frankly where we're headed. >>What do you think the industry will evolve to, in your opinion? Because I think this is gonna, you can't ignore the big guys on this because these programmatic you mentioned also the data is there. But what do you think the market will evolve to with this, with this conundrum? >>So, so I think john where we're headed? You know, I think we're right now we're having this existential existential crisis, right? About identity in this industry, because our world is being turned upside down, all the mechanisms that we've used for years and years are being thrown out the window and we're being told they were gonna have new mechanisms, Right? So cookies are going away device ids are going away and now we got to come up with new things and so the world is being turned upside down and everything that you read about in the trades and you know, we're here talking about it, right? Like everyone's always talking about identity right now, where do I think this is going if I was to look into my crystal ball, you know, this is how I would kind of play this out. If you think about identity today. Right? Forget about all the changes. Just think about it now and maybe a few years before today, Identity for marketers in my opinion has been a little bit of a checkbox activity. Right? It's been hey, um, okay, uh, you know ad tech company or a media company, do you have an identity solution? Okay. Tell me a little bit more about it. Okay, Sounds good. That sounds good. Now can we move on and talk about my business and how are you going to drive meaningful outcomes or whatever for my business? And I believe the reason that is, is because identity is a little abstract, right? It's not something that you can actually get meaningful validation against. It's just something that, you know. Yes, You have it. Okay, great. Let's move on, type of thing. Right. And so that, that's, that's kind of where we've been now, all of a sudden The cookies are going away, the device ids are going away. And so the world is turning upside down in this crisis of how are we going to keep doing what we were doing for the last 10 years in the future. So everyone's talking about it and we're trying to re engineer right? The mechanisms now if I was to look into the crystal ball right 2 3 years from now where I think we're headed is not much is going to change. And what I mean by that john is um uh I think that marketers will still go to companies and say do you have an ID solution? Okay tell me more about it. Okay uh Let me understand a little bit better. Okay you do it this way. Sounds good. Now the ways in which companies are going to do it will be different right now. It's flock and unified I. D. And this and that right. The ways the mechanisms will be a little bit different but the end state right? Like the actual way in which we operate as an industry and kind of like the view of the landscape in my opinion will be very simple or very similar, right? Because marketers will still view it as a tell me you have an ID solution. Make me feel good about it. Help me check the box and let's move on and talk about my business and how you're going to solve for my needs. So I think that's where we're going. That is not by any means to discount this existential moment that we're in. This is a really important moment where we do have to talk about and figure out what we're going to do in the future. My just my viewpoint is that the future will actually not look all that different than the present. >>And I'll say the user base is the audience. Their their data behind it helps create new experiences, machine learning and Ai are going to create those and we have the data you have the sharing it or using it as we're finding shit Gupta great insight dropping some nice gems here. Founder of you of Digital and also the Adjunct professor of Programmatic advertising at Levi School of Business and santa Clara University professor. Thank you for coming dropping the gems here and insight. Thank you. >>Thanks a lot for having me john really appreciate >>it. Thanks for watching. The cooking 100 is the cube host Jon ferrier me. Thanks for watching. Mhm. Yeah. Mhm. Hello welcome back to the cookie conundrum recipe for success and industry conference and summit from Guanacaste on the demise of third party cookies. Got a great industry panel here to break it down chris Gunther Senior Vice president Global Head of programmatic at news corp chris thanks for coming on Zal in Managing Director Solutions at Z axis and Summer Simpson. Vice president Product at quan cast stellar panel. Looking forward to this conversation. Uh thanks for coming on and chatting about the cookie conundrum. Thank you for having us. So chris we'll start with you at news corp obviously a major publisher deprecation of third party cookies affects everyone. You guys have a ton of traffic, ton of audience across multiple formats. Um, tell us about the impact to you guys and the reliance he has had on them. And what are you gonna do to prepare for this next level change? >>Sure. I mean, I think like everyone in this industry there's uh a significant reliance and I think it's something that a lot of talk about audience targeting but obviously that reliance on third party cookies pervasive across the whole at tech ecosystem Martek stack. And so you know, we have to think about how that impact vendor vendors, we work with what it means in terms of use cases across marketing, across advertising, across site experience. So, you know, without a doubt, it it's it's significant, but you know, we look at it as listen, it's disruptive, uh, disruption and change is always a little scary. Um, but overall it's a, it's a long overdue reset. I mean, I think that, you know, our perspective is that the cookies, as we all know was it was a crutch, right sort of a technology being used in way it shouldn't. Um, and so as we look at what's going to happen presumably after Jan 2022 then it's, it's a good way to kind of fix on some bad practices practices that lead to data leakage, um, practice or devalue for our perspective, some of the, you know, we offered as as publishers and I think that this is a key thing is that we're not just looking to as we look at the post gender world, not just kind of recreating the prior world because the prior world was flawed or I guess you could say the current world since it hasn't changed yet. But the current world is flawed. Let's not just not, you know, let's not just replicate that. Let's make sure that, you know, third party cookie goes away. Other work around like fingerprinting and things like that. You know, also go away so philosophically, that's where our heads at. And so as we look at how we are preparing, you know, you look at what are the core building blocks of preparing for this world. Obviously one of the key ones is privacy compliance. Like how do we treat our users with consent? Yeah, obviously. Are we um aligned with the regulatory environments? Yeah. In some ways we're not looking just a Jan 2022, but Jan 23 where there's gonna be the majority of our audiences we covered by regulation. And so I think from regulation up to data gathering to data activation, all built around an internal identifier that we've developed that allows us to have a consistent look at our users whether they're logged in or obviously anonymous. So it's really looking across all those components across all our sites and in all in a privacy compliant way. So a lot of work to be done, a lot of work in progress. But we're >>excited about what's going on. I like how you framed at Old world or next gen kind of the current situation kind of flawed. And as you think about programmatic, the concept is mind blowing and what needs to be done. So we'll come back to that because I think that original content view is certainly relevant, a huge investment and you've got great content and audience consuming it from a major media standpoint. Get your perspective on the impact because you've got clients who want to get their their message out in front of the audience at the right time, at the right place and the right context. Right, So your privacy, you got consent, all these things kind of boiling up. How do you help clients prepare? Because now they can go direct to the consumer. Everyone, everyone has a megaphone, now, everyone's, everyone's here, everyone's connected. So how are you impacted by this new notion? >>You know, if if the cookie list future was a tic tac, dance will be dancing right now, and at least into the next year, um this has been top of mind for us and our clients for quite some time, but I think as each day passes, the picture becomes clearer and more in focus. Uh the end of the third party cookie does not mean the end of programmatic. Um so clients work with us in transforming their investments into real business outcomes based on our expertise and based on our tech. So we continue to be in a great position to lead to educate, to partner and to grow with them. Um, along this uh cookie list future, the impact will be all encompassing in changing the ways we do things now and also accelerating the things that we've already been building on. So we take it from the top planning will have a huge impact because it's gonna start becoming more strategic around real business outcomes. Uh where Omni channel, So clients want to drive outcomes, drew multiple touch points of a consumer's journey, whether it has programmatic, whether it has uh cookie free environment, like connected tv, digital home audio, gaming and so forth. So we're going to see more of these strategic holistic plans. Creative will have a lot of impact. It will start becoming more important with creative testing. Creative insights. You know, creative in itself is cookie list. So there will be more focused on how to drive uh brand dialogue to connect to consumers with less targeting. With less cookies, with the cohesiveness of holistic planning. Creative can align through multiple channels and lastly, the role of a. I will become increasingly important. You know, we've always looked to build our tech our products to complement new and existing technology as well as the client's own data and text back to deliver these outcomes for them. And ai in its core it's just taking input data uh and having an output of your desired outcome. So input data could be dSP data beyond cookies such as browser such as location, such as contextual or publisher taking clients first party data, first party crm data like store visitation, sales, site activity. Um and using that to optimize in real time regardless of what vendor or what channel we're on. Um So as we're learning more about this cookie list dance, we're helping our clients on the steps of it and also introducing our own moves. >>That's awesome. Data is going to be a key value proposition, connecting in with content real time. Great stuff. Somewhere with your background in journalism and you're the tech VP of product at quan cast. You have the keys to the kingdom over there. It's interesting Journalism is about truth and good content original content. But now you have a data challenge problem opportunity on both sides, brands and publishers coming together. It's a data problem in a way it's a it's a tech stack, not so much just getting the right as to show up at the right place the right time. It's really bigger than that now. What's your take on this? >>Um you know, >>so first >>I think that consumers already sort of like except that there is a reasonable value exchange for their data in order to access free content. Right? And that's that's a critical piece for us to all kind of like understand over the past. Hi guys, probably two years since even even before the G. D. P. R. We've been doing a ton of discovery with customers, both publishers and marketers. Um and so you know, we've kind of known this, this cookie going away thing has been coming. Um And you know, Google's announcement just kind of confirmed it and it's been, it's been really, really interesting since Google's announcement, how the conversations have changed with with our customers and other folks that we talked to. And I've almost gone from being like a product manager to a therapist because there's such an emotional response. Um you know, from the marketing perspective, there's real fear there. There's like, oh my God, how you know, it's not just about, you know, delivering ads, it's about how do I control frequency? How do I, how do I measure, you know, success? Because the technology has has grown so much over the years to really give marketers the ability to deliver personalized advertising, good content, right. The consumers um and be able to monitor it and control it so that it's not too too intrusive on the publisher perspective side, we see slightly different response. It's more of a yes, right. You know, we're taking back control and we're going to stop the data leakage, we're going to get the value back for our inventory. Um and that both things are a good thing, but if it's, if it's not managed, it's going to be like ships passing in the night, right? In terms of um of, you know, they're there, them coming together, right, and that's the critical pieces that they have to come together. They have to get closer, you got to cut out a lot of that loom escape in the middle so that they can talk to each other and understand what's the value exchange happening between marketers and publishers and how do we do that without cookies? >>It's a fascinating, I love love your insight there. I think it's so relevant and it's got broader implications because, you know, if you look at how data's impact, some of these big structural changes and re factoring of industries, look at cyber security, you know, no one wants to share their data, but now if they share they get more insight, more machine learning, benefit more ai benefit. So now we have the sharing notion, but that goes against counter the big guys that want to wall garden, they want to hoard all the data and and control that to provide their own personalization. So you have this confluence of, hey, I want to hoard the data and then now I want to share the data. So so christmas summer you're in the, in the wheelhouse, you got original content and there's other providers out there. So is there the sharing model coming with privacy and these kinds of services? Is the open, come back again? How do you guys see this uh confluence of open versus walled gardens, because you need the data to make machine learning good. >>So I'll start uh start off, I mean, listen, I think you have to give credit to the walled gardens have created, I think as we look as publishers, what are we offering to our clients, what are we offering to the buy side? We need to be compelling. We shouldn't just be uh yeah, actually as journalists, I think that there is a case of the importance of funding journalism. Um but ultimately we need to make sure we're meeting the KPI is and the business needs of the buy side. And I think around that it is the sort of three core pillars that its ease of access, its scope of of activation and targeting and finally measurable results. So as I think is us as an individual publishers, so we have, we have multiple publications. So we do have scale. But then in partnership with other publishers perhaps to organizations like pre bid, you know, I think we can, you know, we're trying to address that and I think we can offer something that's compelling um, and transparent in terms of what these results are. But obviously, you know, I want to make sure it's clear transparent terms of results, but obviously where there's privacy in terms of the data and I think the form, you know, I think we've all heard a lot like data clean rooms, a lot of them out there flogging those wears. I think there's something valuable but you know, I think it's the right who is sort of the right partner or partners um and ultimately who allows us to get as close as possible to the buy side. And so that we can share that data for targeting, share it for perhaps for measurement, but obviously all in a privacy compliant >>way summer, what's your take on this? Because you talk about the future of the open internet democratization, the network effect that we're seeing in Vire al Itty and across multiple on the on the channels. Is that pointed out what's happening? That's the distribution now. So um that's almost an open garden model. So it's like um yeah, >>yeah, it's it's um you know, back in the day, you know, um knight ridder who was who was the first group that I that I worked for, um you know, each of those individual properties, um we're not hugely valuable on their own from a digital perspective, but together as a unit, they became valuable, right, and got scale for advertisers. Now we're in a place where, you know, I kind of think that each of those big networks are going to have to come together and work together to compare in size to the, to the world gardens. Um, and yeah, this is something that we've talked about before and an open garden. Um, I think that's the, that's the definitely the right route to take. And I and I agree with chris it's, it's about publishers getting as close to the market. Is it possible working with the tech companies that enable them to do that and doing so in a very privacy centric >>way. So how do we bring the brands and agencies together to get ready for third party cookies? Because there is a therapist moment here of it's gonna be okay. The parachute will open. The future is not gonna be as as grim. Um, it's a real opportunity. But if managed properly, what's your take on this is just more first party data strategy and what's your assessment of this? >>So we collaborated right now with ball grants on how did this still very complex cookie list future. Um, you know what's going to happen in the future? 2, 6 steps that we can take right now and market should take. Um, The first step is to gather intel on what's working on your current campaign, analyzing the data sets across cookie free environment. So you can translate those tactics eventually when the cookies do go away. So we have to look at things like temperature or time analysis. We could look at log level data. We could look at site analytics data. We can look at brand measurement tools and how creative really impacts the campaign success. The second thing we can look at is geo targeting strategies. The geo target strategy has been uh underrated because the granularity and geo data could go down all the way to the local level, even beyond zip code. So for example the census black data and this is especially important for CPG brands. So we're working closely with the client teams to understand not only the online data but the offline data and how we can utilize that in the future. Uh We want to optimize investments around uh markets that are working so strong markets and then test and underperforming markets. The third thing we can look at is contextual. So contextual by itself is cookie free. Uh We could build on small scale usage to test and learn various keywords and content categories based sets. Working closely with partners to find ways to leverage their data to mimic audiences that you are trying to target right now with cookies. Um the 4th 1 is publisher data or publisher targeting. So working with your publishers that you have strong relationships with who can curate similar audiences using their own first party data and conducting RFs to understand the scale and reach against your audience and their future role maps. So work with your top publishers based on historical data to try to recreate your best strategies. The 15 and I think this is very important is first party data, you know, that's going to matter more than ever. In the calculus future brands will need to think about how to access and developed the first party data starting with the consumers seeing a value in exchange for the information. It's a gold mine and understanding of consumer, their intent, the journey um and you need a really great data science team to extract insights out of that data, which will be crucial. So partner with strategic onboarding vendors and vet their ability to accept first party data into a cleaner environment for targeting for modeling for insight. And lastly, the six thing that we can do is begin to inform prospect prospecting by dedicating test budget to start gaining learnings about cookie list 11 place that we can start and it is under invested right now is Safari and Firefox. They have been calculus for quite some time so you can start here and begin testing here. Uh work with your data scientist team to understand the right mix is to to target and start exploring other channels outside of um just programmatic cookies like CTV digital, out of home radio gaming and so forth. So those are the six steps that we're taking right now with our clients to uh prepare and plan for the cookie list future. >>So chris let's go back to you. What's the solution here? Is there one, is there multiple solutions? What's the future look like for a cookie was future? >>Uh I think the one certain answers, they're definitely not just one solution. Um as we all know right now there there seems to be endless solutions, a lot of ideas out there, proposals with the W three C uh work happening within other industry bodies uh you know private companies solutions being offered and you know, it's a little bit of it's enough to make everyone's head spin and to try to track it to understand and understand the impact. And as a publisher were obviously a lot of people are knocking on our door. Uh they're saying, hey our solution is one that is going to bring in lots of money, you know, the all the buy side is going to use it. This is the one like I ma call to spend um, and so expect here and so far is that none of these solutions are I think everyone is still testing and learning no one on the buy side from our, from our knowledge is really committed to one or a few. It's all about a testing stage. I think that, you know, putting aside all that noise, I think what matters the most to us as publisher is actually something summer mentioned before. It's about control. You know, if we're going to work with a again, outside of our sort of, you know, internal identifier work that we're doing is we're going to work with an outside party or outside approach doesn't give us control as a publisher to ensure that it is, we control the data from our users. There isn't that data leakage, it's probably compliant. What information gets shared out there. What is it, what's released within within the bid stream? Uh If it is something that's attached to a somewhat declared user registered user that if that then is not somehow amplified or leverage off on another site in a way that is leveraging bit stream data or fingerprinting and going against. I think that the spirit of what we're trying to do in a post third party cookie world so that those controls are critical and I think they have those controls, his publisher, we have collectively be disciplined in what solutions that we we test out and what we eventually adopt. But even when the adoption point arrives, uh definitely it will not be one. There will be multiple because it's just too many use cases to address >>great, great insight there from, from you guys, news corp summer. Let's get back to you. I want to get your thoughts. You've been in many waves of innovation ups and downs were on a new one. Now we talked about the open internet democratization. Journalism is under a lot of pressure now, but there's now a wave of quality people really leaning in towards fighting misinformation, understanding truth and community and date is at the heart of it. What do you see as the new future for journalists, reward journalism is our ways their path forward. >>So there's uh, there's what I hope is going to happen. Um, and then I'm just gonna ignore what could write. Um, you know, there's there's a trend in market right now, a number of fronts, right? So there are marketers who are leaning into wanting to spend their marketing dollars with quality journalists, focusing on bipac owned and operated, really leaning into into supporting those businesses that have been uh, those publishers that have been ignored for years. I really hope that this trend continues. Um We are leaning into into helping um, marketers curate that supply right? And really, uh, you know, speak with their dollars about the things that that they support. Um, and uh, and and value right in market. So I'm hoping that that trend continues and it's not just sort of like a marketing blip. Um, but we will do everything possible to kind of like encourage that behavior and and give people the information they need to find, you know, truly high quality journalism. >>That's awesome chris Summer. Thanks for coming on and sharing your insight on this panel on the cookie list future. Before we go, just quick summary each of you. If you don't mind just giving a quick sound bite or bumper sticker of what we can expect. If you had to throw a prediction For what's going to happen in the next 24 months Chris We'll start with you. >>Uh it's gonna be quite a ride. I think that's an understatement. Um I think that there, I wouldn't be surprised if if google delays the change to the chrome by a couple of months and and may give the industry some much needed time, but no one knows. I guess. I guess I'm not except for someone somewhere deep within chrome. So I think we all have to operate in a way that changes to happen, changes to happen quickly and it's gonna cover across all facets of the industry, all facets of from advertising, marketing. So just be >>prepared. >>Yeah, along the same lines, be prepared, nobody knows what's going to happen in the future. Uh You know, while dancing in this together. Uh I think um for us it's um planning and preparing and also building on what we've already been working on. Um So omni channel ai um creative and I think clients will uh lean more into those different channels, >>awesome. So we'll pick us home, last word. >>I think we're in the throwing spaghetti against the wall stage. Right, so this is a time of discovery of leaning in trying everything out, Learning and iterating as fast as we possibly >>can. Awesome. And I love the cat in the background over your shoulder. Can't stop staring at your wonderful cat. Thanks for coming on chris, Thanks for coming on. This awesome panel industry breakdown of the cookie conundrum. The recipe for success data ai open. Uh The future is here, it's coming, it's coming fast. I'm john fryer with the cube. Thanks for watching. Mhm. Yeah. Mhm. Mhm. Welcome back to the Quant Cast industry summit on the demise of third party cookies. The cookie conundrum, a recipe for success. We're here peter day. The cto of quad cast and crew T cop car, head of product marketing quad cast. Thanks for coming on talking about the changing advertising landscape. >>Thanks for having us. Thank you for having >>us. So we've been hearing this story out to the big players. Want to keep the data, make that centralized control, all the leverage and then you've got the other end. You got the open internet that still wants to be free and valuable for everyone. Uh what's what are you guys doing to solve this problem? Because cookies go away? What's going to happen there? How do people track things you guys are in this business first question? What is quan cast strategies to adapt to third party cookies going away? What's gonna be, what's gonna be the answer? >>Yeah. So uh very rightly said, john the mission, the Qantas mission is the champion of free and open internet. Uh And with that in mind, our approach to this world without third party cookies is really grounded in three fundamental things. Uh First as industry standards, we think it's really important to participate and to work with organizations who are defining the standards that will guide the future of advertising. So with that in mind, we've been participating >>with I. A. B. >>Tech lab, we've been part of their project Triarc. Uh same thing with pre bid, who's kind of trying to figure out the pipes of identity. Di di di di di pipes of uh of the future. Um And then also is W three C, which is the World Wide Web Consortium. Um And our engineers and our engineering team are participating in their weekly meetings trying to figure out what's happening with the browsers and keeping up with the progress they're on things such as google's block. Um The second uh sort of thing is interoperability, as you've mentioned, there are lots of different uh I. D. Solutions that are emerging. You have you I. D. Two point oh, you have live RAM, you have google's flock. Uh And there will be more, there are more and they will continue to be more. Uh We really think it is important to build a platform that can ingest all of these signals. And so that's what we've done. Uh The reason really is to meet our customers where they are at today. Our customers use multiple different data management platforms, the mps. Um and that's why we support multiple of those. Um This is not going to be much different than that. We have to meet our customers where we are, where they are at. And then finally, of course, which is at the very heart of who contrast is innovation. Uh As you can imagine being able to take all of these multiple signals in including the I. D. S. And the cohorts, but also others like contextual first party um consent is becoming more and more important. Um And then there are many other signals, like time, language geo location. So all of these signals can help us understand user behavior intent and interests um in absence of 3rd party cookies. However, uh there's there's something to note about this. They're very raw, their complex, they're messy all of these different signals. Um They are changing all the time, they're real time. Um And there's incomplete information isolation. Just one of these signals cannot help you build a true and complete picture. So what you really need is a technology like AI and machine learning to really bring all of these signals together, combine them statistically and get an understanding of user behavior intent and interests and then act on it, be it in terms of providing audience insights um or responding to bid requests and and so on and so forth. So those are sort of the three um fundamentals that our approach is grounded in which is industry standards, interoperability and and innovation. Uh and you know, you have peter here, who is who is the expert So you can dive much deeper into >>it. Is T. T. O. You've got to tell us how is this going to actually work? What are you guys doing from a technology standpoint to help with data driven advertising in a third party cookie list world? >>Well, we've been um This is not a shock, you know, I think anyone who's been close to his space has known that the 3rd Party Cookie has been um uh reducing inequality in terms of its pervasiveness and its longevity for many years now. And the kind of death knell is really google chrome making a, making the changes that they're gonna be making. So we've been investing in the space for many years. Um and we've had to make a number of hugely diverse investment. So one of them is in how as a marketer, how do I tell if my marketing still working in the world without >>computers? The >>majority of marketers completely reliant on third party cookies today to tell them if they're if they're marketing is working or not. And so we've had to invest heavily and statistical techniques which are closer to kind of economic trick models that markets are used to things like out of home advertising, It's going to establishing whether they're advertising is working or not in a digital environment actually, >>just as >>often, you know, as is often the case in these kind of times of massive disruption, there's always opportunity to make things better. And we really think that's true. And you know, digital measurement has often mistaken precision for accuracy. And there's a real opportunity to kind of see the wood for the trees if you like. And start to come with better methods of measuring the affections of advertising without third party cookies. And in fact to make countless other investments in areas like contextual modeling and and targeting that third party cookies and and uh, connecting directly to publishers rather than going through this kind of bloom escape that's gonna tied together third party cookies. So if I was to enumerate all the investments we've made, I think we'll be here till midnight but we have to make a number of vestments over a number of years and that level investments only increasing at the moment. >>Peter on that contextual. Can you just double click on that and tell us more? >>Yeah, I mean contextual is unfortunately these things, this is really poorly defined. It can mean everything from a publisher saying, hey, trust us, this dissipated about CVS to what's possible now and has only really been possible the last couple of years, which is to build >>statistical >>models of the entire internet based on the content that people are actually consumed. And this type of technology requires massive data processing capabilities. It's able to take advantage of the latest innovations in there is like natural language processing and really gives um computers are kind of much deeper and richer understanding of the internet, which ultimately makes it possible to kind of organize, organized the Internet in terms of the types of content of pages. So this type of technology has only been possible the last two years and we've been using contextual signals since our inception, it's always been massively predictive in terms of audience behaviours, in terms of where advertising is likely to work. And so we've been very fortunate to keep the investment going um and take advantage of many of these innovations that have happened in academia and in kind of uh in adjacent areas >>on the ai machine learning aspect, that seems to be a great differentiator in this day and age for getting the most out of the data. How is machine learning and ai factoring into your platform? >>I think it's, it's how we've always operated right from our interception when we started as a measurement company, the way that we were giving our customers at the time, we were just publishers, just the publisher side of our business insights into who their audience was, were, was using machine learning techniques. And that's never really changed. The foundation of our platform has always been, has always been machine learning from from before. It was cool. A lot of our kind of, a lot of our core teams have backgrounds in machine learning phds in statistics and machine learning and and that really drives our our decision making. I mean, data is only useful if you can make sense of it and if you can organize it and if you can take action on it and to do that at this kind of scout scale, it's absolutely necessary to use machine learning technology. >>So you mentioned contextual also, you know, in advertising, everyone knows in that world that you've got the contextual behavioural dynamics, the behavior that's kind of generally everyone's believing is happening. The consensus is undeniable is that people are wanting to expect an environment where there's trust, there's truth, but also they want to be locked in. They don't wanna get walled into a walled garden, nobody wants to be in the world, are they want to be free to pop around and visit sites is more horizontal scalability than ever before. Yet, the bigger players are becoming walled garden, vertical platforms. So with future of ai the experience is going to come from this data. So the behavior is out there. How do you get that contextual relevance and provide the horizontal scale that users expect? >>Yeah, I think it's I think it's a really good point and we're definitely this kind of tipping point. We think, in the broader industry, I think, you know, every published right, we're really blessed to work with the biggest publishers in the world, all the way through to my mom's vlog, right? So we get to hear the perspectives of publishers at every scale. I think they consistently tell us the same thing, which is they want to more directly connected consumers, they don't wanna be tied into these walled gardens, which dictate how they must present their content and in some cases what content they're allowed to >>present. >>Um and so our job as a company is to really provide level >>the playing field a little bit, >>provide them the same capabilities they're only used to in the walled gardens, but let's give them more choice in terms of how they structure their content, how they organize their content, how they organize their audiences, but make sure that they can fund that effectively by making their audiences in their environments discoverable by marketers measurable by marketers and connect them as directly as possible to make that kind of ad funded economic model as effective in the open Internet as it is in social. And so a lot of the investments we've made over recent years have been really to kind of realize that vision, which is, it should be as easy for a marketer to be able to understand people on the open internet as it is in social media. It should be as effective for them to reach people in the environment is really high quality content as it is on facebook. And so we invest a lot of a lot of our R and D dollars in making that true. We're now live with the Comcast platform, which does exactly that. And as third party cookies go away, it only um only kind of exaggerated or kind of further emphasizes the need for direct connections between brands and publishers. And so we just wanna build the technology that helps make that true and gives the kind of technology to these marketers and publishers to connect and to deliver great experiences without relying on these kind of walled >>gardens. Yeah, the Director Director, Consumer Director audience is a new trend. You're seeing it everywhere. How do you guys support this new kind of signaling from for for that's happening in this new world? How do you ingest the content and just this consent uh signaling? >>Uh we were really fortunate to have an amazing, amazing R and D. Team and, you know, we've had to do all sorts to make this, you need to realize our vision. This has meant things like, you know, we have crawlers which scan the entire internet at this point, extract the content of the pages and kind of make sense of it and organize it uh, and organize it for publishers so they can understand how their audiences overlap with potential competitors or collaborators. But more importantly, organize it for marketers. So you can understand what kind of high impact opportunities are there for them there. So, you know, we've had to we've had to build a lot of technology. We've had to build analytics engines, which can get answers back in seconds so that marketers and publishers can kind of interact with their own data and make sense of it and present it in a way that's compelling and help them drive their strategy as well as their execution. We've had to invest in areas like consent management because we believe that a free and open internet is absolutely reliant on trust and therefore we spend a lot of our time thinking about how do we make it easy for end users to understand who has access to their data and easy for end users to be able to opt out. And uh and as a result of that, we've now got the world's most widely adopted adopted consent management platform. So it's hard to tackle one of these problems without tackling all of them. Were fortunate enough to have had a large enough R and D budget over the last four or five years, make a number investments, everything from consent and identity through context, your signals through the measurement technologies, which really bring advertisers >>and Publishers places together great insight. Last word for you is what's the what's the customer view here as you bring these new capabilities of the platform, uh what's what are you guys seeing as the highlight uh from a platform perspective? >>So the initial response that we've seen from our customers has been very encouraging, both on the publisher side as well as the marketer side. Um I think, you know, one of the things we hear quite a lot is uh you guys are at least putting forth a solution, an actual solution for us to test Peter mentioned measurement, that really is where we started because you cannot optimize what you cannot measure. Um so that that is where his team has started and we have some measurement very, very uh initial capabilities still in alpha, but they are available in the platform for marketers to test out today. Um so the initial response has been very encouraging. People want to engage with us um of course our, you know, our fundamental value proposition, which is that the Qantas platform was never built to be reliant on on third party data. These stale segments like we operate, we've always operated on real time live data. Um The second thing is, is our premium publisher relationships. We have had the privilege of working like Peter said with some of the um biggest publishers, but we also have a very wide footprint. We have first party tags across um over 100 million plus web and mobile destinations. Um and you know, as you must have heard like that sort of first party footprint is going to come in really handy in a world without third party cookies, we are encouraging all of our customers, publishers and marketers to grow their first party data. Um and so that that's something that's a strong point that customers love about us and and lean into it quite a bit. Um So yeah, the initial response has been great. Of course it doesn't hurt that we've made all these are in the investments. We can talk about consent. Um, and you know, I often say that consent, it sounds simple, but it isn't, there's a lot of technology involved, but there's lots of uh legal work involved as it as well. We have a very strong legal team who has expertise built in. So yeah, very good response. Initially >>democratization. Everyone's a publisher. Everyone's a media company. They have to think about being a platform. You guys provide that. So I congratulate Peter. Thanks for dropping the gems there. Shruti, thanks for sharing the product highlights. Thanks for, for your time. Thank you. Okay, this is the quan cast industry summit on the demise of third party cookies. And what's next? The cookie conundrum. The recipe for success with Kwan Cast. I'm john free with the cube. Thanks for watching. Mm

>>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 The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of think 21. And right now we're gonna talk about banking in the post isolation economy. I'm very pleased to welcome our next guest. Look at wag lee is the general manager, Global banking financial markets at IBM and john Degnan is the global ceo and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. >>Thank you. Yeah >>that's my pleasure. Look at this current economic upheaval. It's quite a bit different from the last one, isn't it? I mean liquidity doesn't seem to be a problem for most pecs these days. I mean if anything they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. >>So so dave, I think, like you say, it's, you know, it's a it's a state and a picture that in a significantly different from what people were expecting. And I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into into place after the, you know, the financial crisis last time around, right? And therefore this time, you know, a health crisis did not become a financial crisis, because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. I think if you look at it though, maybe two or three things ready to call out firstly, there's a there's a massive regional variation. So if you look at the U. S. Banking industry, it's extremely buoyant and I'll come back to that in a minute in the way in which is performing, you know, the banks that are starting to report their first quarter results are going to show profitability. That's you know significantly ahead of where they were last year and probably some of the some of their best performance for quite a long time. If you go into europe, it's a completely different picture. I think the banks are extremely challenged out there and I think you're going to see a much bleaker outlook in terms of what those banks report and as far as Asia pacific is concerned again, you know because they they have come out of the pandemic much faster than consumer businesses back into growth. Again, I think they're showing some pretty buoyant performance as far as as far as banking performance is concerned. I think the piece that's particularly interesting and I think him as a bit of a surprise to most is what we've seen in the U. S. Right. And in the US what's actually happened is uh the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with this facts that's driving deal raised, you know, deal based fee income for the banks. The volatility in the marketplace is meaning that trading income is much much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were. They were expecting consumer businesses definitely down. If you look at the credit card business, it's down. If you look at, you know, lending activity that's going down going out is substantially less than where it was before. There's hardly any lending growth because the economy clearly is flat at this moment in time. But again, the good news that, and I think this is a worldwide which are not just in us, the good news here is that because of the liquidity and and some of the special measures the government put out there, there has not been the level of bankruptcies that people were expecting, right. And therefore most of the provisioning that the banks did um in expectation of non performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to see provisions being released as well, which are kind of flattering, flattering the income, flattering the engine. I think going forward that you're going to see a different picture >>is the re thank you for the clarification on the regional divergence, is that and you're right on, I mean, european central banks are not the same, the same position uh to to affect liquidity. But is that nuances that variation across the globe? Is that a is that a blind spot? Is that a is that a concern or the other other greater concerns? You know, inflation and and and the the pace of the return to the economy? What are your thoughts on that? >>So, I think, I think the concern, um, you know, as far as the european marketplace is concerned is um you know, whether whether the performance that and particularly, I don't think the level of provisions in there was quite a generous, as we saw in other parts of the world, and therefore, you know, is the issue around non performing loans in in europe, going to hold the european uh european banks back? And are they going to, you know, therefore, constrain the amount of lending that they put into the economy and that then, um, you know, reduces the level of economic growth that we see in europe. Right? I think, I think that is certainly that is certainly a concern. Um I would be surprised and I've been looking at, you know, forecasts that have been put forward by various people around the world around inflation. I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term, I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one which is becoming top of mind for chief executives, is this whole area around operational resiliency. So, you know, regulators universally are making very very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are and you know, the U. K. Has taken the lead on this and they are going so far as even requiring non executive directors to be liable if banks are found to not have the right policies in place. This is now being followed by other regulators around the world. Right. So so that is very much drop in mind at this moment in time. So I think discretionary investment is going to be put you know, towards solving that particular problem. I think that's that's one issue. I think the other issue is what the pandemic has shown is that and and and this was very evident to me and I mean I spent the last three years out in Singapore where you know, banks have become very digital businesses. Right? When I came into the U. S. In my current role, it was somewhat surprising to me as to where the U. S. Market place was in terms of digitization of banking. But if you look in the last 12 months, you know, I think more has been achieved in terms of banks becoming digital businesses and they've probably done in the last two or three years. Right. And that the real acceleration of that digitization which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintech and big tex has exactly, it's really accelerated. Right, Right. Just to give you an example, Babel is the second largest financial services institutions in the US. Right. So that's become a real problem I think with the banking industry is going to have to deal with >>and I want to come back to that. But now let's bring john into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital, We certainly saw over the pandemic, remote work, huge, huge volumes of things like TPP and and and and and mortgages and with dropping rates, etcetera. So john, how is the tech stack Been altered in the past 14 months? >>Great question. Dave. And it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or we're not competitive in serving their clients. And as a result they've had to do very deep uh architectural transformation and digital transformation around their core platforms. Their systems of analytics and their systems different end systems of engagement In terms of the core processing systems that many of these institutions, some in many cases there are 50 years old And with any 50 year old application platform there are inherent limitations. There's an in flex itty inflexibility. There's an inability to innovate for the future. There's a speed of delivery issue. In other words, it can be very hard to accelerate the delivery of new capabilities onto an aging platform. And so in every single case um institutions are looking to hybrid cloud and public cloud technology and pre packaged a ai and prepackaged solutions from an I. S. V. Ecosystem of software vendor ecosystem to say. As long as we can crack open many of these old monolithic cause and surround them with new digitalization, new user experience that spans every channel and automation from the front to back of every interaction. That's where most institutions are prioritizing. >>Banks aren't going to migrate, they're gonna they're gonna build an abstraction layer. I want to come back to the disruption is so interesting. The coin base I. P. O. Last month see Tesla and microstrategy. They're putting Bitcoin on their balance sheets. Jamie diamonds. Traditional banks are playing a smaller role in the financial system because of the new fin text. Look at, you mentioned Paypal, the striped as Robin Hood, you get the Silicon Valley giants have this dual disrupt disruption agenda. Apple amazon even walmart facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah. I mean I think to a large extent that is that has already happened, right? Because I think if you look at, you know, if you look at the experience in ASia, right? And you look at particularly organizations like and financial, you know, in India, you look at organizations like A T. M. You know, very substantial chance, particularly on the consumer payments side has actually moved away from the banks. And I think you're starting to see that in the west as well, right? With organizations like, you know, cloud, No, that's coming out with this, you know, you know, buying out a later type of schemes. You've got great. Um, and then so you've got paper and as you said, strike, uh and and others as well, but it's not just, you know, in the payment side. Right. I think, I think what's starting to happen is that there are very core part of the banking business. You know, especially things like lending for instance, where again, you are getting a number of these Frontex and big, big tech companies entering the marketplace. And and I think the threat for the banks is this is not going to be small chunks of market share that you're going to actually lose. Right? It's it's actually, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment site. Right? So it actually went to all of these moment pop shops and then offered q are based um, 12 code based payment capabilities to these very small retailers, they were charging about half or a third or world Mastercard or Visa were charging to run those payment rails. They took market share overnight. You look at the Remittance business, right? They went into the Remittance business. They set up these wallets in 28 countries around the Asean region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western Union and all of these, all of these others. So, so I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always right, which is the reason we're losing is because the playing field is not even, this is not about playing fields. Been even write, all of these businesses have been subject to exactly the same regulation that the banks are subject to. Regulations in Singapore and India are more onerous than maybe in other parts of the world. This is about the banking business, recognizing that this is a threat and exactly as john was saying, you've got to get to delivering the customer experience that consumers are wanting at the level of cost that they're prepared to pay. And you're not going to do that by purely sorting out the channels and having a cool app on somebody's smartphone, Right? If that's not funny reported by arcade processes and legacy systems when I, you know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore, I make a payment. The payment is instantaneously clear, right? That's where the banking system is going to have to get to. In order to get to that. You need to water the whole stack. And the really good news is that many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the site to do it. And incumbent bank can do it and it can do it in a sensible period of time at a sensible level of investment. A lot of IBM s business across our consulting as well as our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>yeah, taking friction out of the system, sometimes with a case of crypto taking the middle person out of the system. But I think you guys are savvy, you understand that, you know, you yeah, Jamie Diamond a couple years ago said he'd fire anybody doing crypto Janet Yellen and says, I don't really get a Warren Buffett, but I think it's technology people we look at and say, okay, wait a minute. This is an interesting Petri dish. There's, there's a fundamental technology here that has massive funding that is going to inform, you know, the future. And I think, you know, big bags are gonna lean in some of them and others, others won't john give you the last word here >>for sure, they're leaning in. Uh so to just to to think about uh something that lick it said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy, They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the app ecosystem going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms And we're not just thinking about uh retail banking here. Your question around the industry that disruption from Bitcoin Blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization right now. I'm working with several organizations right now exactly on how to build custody systems to take advantage of these non fungible digital assets. It's a hard, hard topic around which there's an incredible appetite to invest. An incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready. Ai infused data infused technologies >>Guys, I want to have you back. I wish I had more time. I want to talk about SPAC. So I want to talk about N. F. T. S. I want to talk about technology behind all this. You really great conversation. I really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks very much indeed for having us. It was a real pleasure. >>Really. Pleasure was mine. Thank you for watching everybody's day. Volonte for IBM think 2021. You're watching the Cube. Mhm.