>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit 22 live from Caesar's forum in Las Vegas. Lisa Martin, here with Dave Valante. This is day three of our coverage. We've had an amazing, amazing time. Great conversations talking with snowflake executives, partners, customers. We're gonna be digging into data mesh with data.world. Please welcome John loins, the chief product officer. Great to have you on the program, John, >>Thank you so much for, for having me here. I mean, the summit, like you said, has been incredible, so many great people, so such a good time, really, really nice to be back in person with folks. >>It is fabulous to be back in person. The fact that we're on day four for, for them. And this is the, the solution showcase is as packed as it is at 10 11 in the morning. Yeah. Is saying something >>Yeah. Usually >>Chopping at the bit to hear what they're doing and innovate. >>Absolutely. Usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here, especially >>In Vegas. This is impressive. Talk to the audience a little bit about data.world, what you guys do and talk about the snowflake relationship. >>Absolutely data.world is the only true cloud native enterprise data catalog. We've been an incredible snowflake partner and Snowflake's been an incredible partner to us really since 2018. When we became the first data catalog in the snowflake partner connect experience, you know, snowflake and the data cloud make it so possible. And it's changed so much in terms of being able to, you know, very easily transition data into the cloud to break down those silos and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint, data out world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. >>So data mesh basically Jamma, Dani lays out the first of all, the, the fault domains of existing data and big data initiatives. And she boils it down to the fact that it's just this monolithic architecture with hyper specialized teams that you have to go through and it just slows everything down and it doesn't scale. They don't have domain context. So she came up with four principles if I may, yep. Domain ownership. So push it out to the businesses. They have the context they should own the data. The second is data as product. We're certainly hearing a lot about that today this week. The third is that. So that makes it sounds good. Push out the, the data great, but it creates two problems. Self-serve infrastructure. Okay. But her premises infrastructure should be an operational detail. And then the fourth is computational governance. So you talked about data CA where do you fit in those four principles? >>You know, honestly, we are able to help teams realize the data mesh architecture. And we know that data mesh is really, it's, it's both a process in a culture change, but then when you want to enact a process in a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build the process in the architecture that you're trying to build. And the data world data catalog can really help along all four of those axes. When you start thinking first about, let's say like, let's take the first one, you know, data as a product, right? We even like very meta of us from metadata management platform at the end of the day. But very meta of us. When you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and, you know, offices of the CDO with incredible evented analytics, very detailed that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources, to make sure that their data assets are getting adopted and used properly. >>On the, on the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have, you know, incredible joint snowflake customers like Prologis. And we chatted a lot about this in our session here yesterday, where, because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from, from group to group, without having to, to relaunch or run different environments. Like you can do that all within one day catalog platform without having to have separate environments for each of those domains, federated governance. Again, the amount of like data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent. And we provide that in a way that Confederate across those means and make it consistent. >>Okay. So you facilitate across that whole spectrum of, of principles. And so what in the, in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC, who I don't think is who's not using your data catalog, but hello, fresh who may or may not be, but I mean, there, there are numbers and I wanna get to that. But what they've done is they've enabled the domains to spin up their own, whatever data lakes, data, warehouses, data hubs, at least in, in concept, most of 'em are data lakes on AWS, but still in concept, they wanna be inclusive and they've created a master data catalog. And then each domain has its sub catalogue, which feeds into the master and that's how they get consistency and governance and everything else is, is that the right way to think about it? And or do you have a different spin on that? >>Yeah, I, I, you know, I have a slightly different spin on it. I think organizationally it's the right way to think about it. And in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, I, that is really kind of the, the, the only way to do it, right with data.world. You don't have to do that. You can have one platform, one environment, one instance of data.world that spans all of your domains, enable them to operate independently and then federate across. So >>You just answered my question as to why I should use data.world versus Amazon glue. >>Oh, absolutely. >>And that's a, that's awesome that you've done now. How have you done that? What, what's your secret >>Sauce? The, the secret sauce era is really an all credit to our CTO. One of my closest friends who was a true student of knowledge graph practices and principles, and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built a, a, an open and extensible metadata model that we call costs that really takes the best parts of existing open standards in the semantics space. Things like schema.org, DCA, Dublin core brings them together and models out the most typical enterprise data assets providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's, it's really quite something. And it's something all of our customers are, are very impressed with and, and, and, and, you know, are getting a lot of leverage out of, >>And, and we have a lot of time today, so we're not gonna shortchange this topic. So one last question, then I'll shut up and let you jump in. This is an open standard. It's not open source. >>No, it's an open built on open standards, built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically like lock you into our platform. So everything that we have is API driven API available. Your metadata belongs to you. If you need to export your graph, you know, instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We, we worked a lot in with the open data community and we, we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application, but really an entire metadata management platform and extend it even further into their enterprise to, to really catalog all of their assets, but also to build incredible integrations to things like corporate search, you know, having data assets show up in corporate Wiki search, along with all the, the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in. >>So leasing. So it's not exclusive to, to snowflake. It's not exclusive to AWS. You can bring it anywhere. Azure GCP, >>Anytime. Yeah. You know where we are, where we love snowflake, look, we're at the snowflake summit. And we've always had a great relationship with snowflake though, and really leaned in there because we really believe Snowflake's principles, particularly around cloud and being cloud native and the operating advantages that it affords companies that that's really aligned with what we do. And so snowflake was really the first of the cloud data catalogs that we ultimately or say the cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. >>Talk about how data world and snowflake enable companies like per lodges to be data companies. These days, every company has to be a data company, but they, they have to be able to do so quickly to be competitive and to, to really win. How do you help them if we like up level the conversation to really impacting the overall business? >>That's a great question, especially right now, everybody knows. And pro is a great example. They're a logistics and supply chain company at the end of the day. And we know how important logistics and supply chain is nowadays and for them and for a lot of our customers. I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice by adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development, when it became a thing in the early two thousands was how inclusive it was. And that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics, people, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing that could make their company significantly more resilient, especially in the face of economic uncertainty. >>But if you have to sit there and argue with your business stakeholders about the validity of the data, about the, the techniques that were used to do the analysis, and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's, that's really a huge benefit that like Prologis has, has, has realized just tightening that cycle time, building trust, building inclusion, and making sure ultimately humans learn by doing, and if you can be inclusive, it, even, it even increases things like that. We all want to, to, to, to help cuz Lord knows the world needs it. Things like data literacy. Yeah. Right. >>So data.world can inform me as to where on the spectrum of data quality, my data set lives. So I can say, okay, this is usable, shareable, you know, exactly of gold standard versus fix this. Right. Okay. Yep. >>Yep. >>That's yeah. Okay. And you could do that with one data catalog, not a bunch of >>Yeah. And trust trust is really a multifaceted and multi multi-angle idea, right? It's not just necessarily data quality or data observability. And we have incredible partnerships in that space, like our partnership with, with Monte Carlo, where we can ingest all their like amazing observability information and display that in a really like a really consumable way in our data catalog. But it also includes things like the lineage who touch it, who is involved in the process of a, can I get a, a, a question answered quickly about this data? What's it been used for previously? And do I understand that it's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains within a single organization. >>If you're not, that means to suggest you're a data quality. No, no supplier. Absolutely. But your partner with them and then that you become the, the master catalog. >>That's brilliant. I love it. Exactly. And you're >>You, you just raised your series C 15 million. >>We did. Yeah. So, you know, really lucky to have incredible investors like Goldman Sachs, who, who led our series C it really, I think, communicates the trust that they have in our vision and what we're doing and the impact that we can have on organization's ability to be agile and resilient around data analytics, >>Enabling customers to have that single source of truth is so critical. You talked about trust. That is absolutely. It's no joke. >>Absolutely. >>That is critical. And there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're gonna see? >>Oh, you know, I love this. We have such an incredibly innovative team. That's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I, I really truly believe that data analytics needs to be a team sport. It needs to go from, you know, single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture being cloud native, we do, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made snowflake. So great. We are really excited to have announced in March on our own summit. And we're rolling this suite of features out over the course of the year, a new package of features that we call data.world Eureka, which is a suite of automations and, you know, knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in, in the data ops way with significantly less effort, >>Big, big impact there. John, thank you so much for joining David, me unpacking what data world is doing. The data mesh, the opportunities that you're giving to customers and every industry. We appreciate your time and congratulations on the news and the funding. >>Ah, thank you. It's been a, a true pleasure. Thank you for having me on and, and I hope, I hope you guys enjoy the rest of, of the day and, and your other guests that you have. Thank you. >>We will. All right. For our guest and Dave ante, I'm Lisa Martin. You're watching the cubes third day of coverage of snowflake summit, 22 live from Vegas, Dave and I will be right back with our next guest. So stick around.

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit live from Caesar's forum in Las Vegas, Lisa Martin, with Dave Valante. Dave, we have had an action packed two days here, talking with loads of folks. There's been about 10,000 attendees here, the momentum, the excitement for snowflake, what they're building, what they're, what they've announced is huge. >>I'll tell you like this is a getaway day and there's still decent amount of buzz going on in the ecosystem here and the exhibit hall. And I was just saying, when you walk around Las Vegas, you'd never know the economy's about the tank with, you know, inflation is on the rise. I mean, Vegas is packed. >>It is packed it a lot of shows going on here. We are excited to welcome Patrick Barch, the senior director of product management at capital one software to the program. Patrick, it's great to have you. >>Thank you. It's great to be here. >>So we all know capital one. I love the commercials. I'm sure you have a, a large say in how fun and creative they are. Talk to us about capital one software. This is a new business software business. It >>Is. And so, you know, from our founding days in 1994, capital one has always recognized the power of data and technology to create differentiated experiences for our customers. But about 10 years ago, we declared that we were gonna reinvent the way that we build and use technology. One of the key steps in that journey was migrating from our owned and operated data centers to the public cloud. But in order to do that, we needed to build a number of products and platforms to help us operate at scale because the market just wasn't quite there yet. And so capital one software, which we announced last week, Woohoo is our first foray into bringing some of those cloud and data management products to market. >>Talk to us about you. Capital one is one of Snowflake's longest running and largest customers. How does snowflake help facilitate that >>A couple different ways? So first snowflake is a, it's a super powerful platform. They've changed the game when it comes to leveraging data. At scale in the cloud, we were an early investor. We were, we were one of their biggest customers. They've been a great partner along the way, helping us adopt the platform. But for us, when we adopted back in 2018 ish, we realized that with all of this power comes a lot of responsibility. And so we needed to make sure that we were putting good governance and good controls around our usage of snowflake from the start. And so, you know, we, we, we needed to build some, some tools to help us optimize our, our usage of snowflake. >>Okay. So you basically said we're going all in the cloud. You guys have made huge investments in, in AWS and obviously snowflake. And then now you're, you're sort of taking what you did internally and exposing it almost like, like Amazon did Amazon retail and then that's how AWS was born. Okay, awesome. What kind of results did you see internally in terms of the primary benefit? If I understand it is cost savings, but also better data management, right? Is that fair? >>So the, the totality of what we've built internally covers both cost savings, data management, data security, adherence to data privacy legislation. The product that we announced here at summit is really focused on cost optimization for snowflake, right? And so with these tools, we've been able to save about 27% on our projected snowflake costs. We've been able to save our teams about 50,000 hours of manual effort by reducing the number of change orders that they have to execute manually through automated infrastructure management. We've reduced our cost per query by about 43%. And so really what these enabled us to do is just get really efficient with how we use the system. You know, one, one of the challenges you might run into with snowflake is, is unexpected costs. And so by leveraging these tools, we've been able to make sure that our costs are predictable and consistent from month to month, which enables us to budget appropriately. >>And, and that's 50,000 hours person hours over what period of time >>Have to get back to you on the exact amounts? I mean, >>Years, months, several years. Weeks. Yeah. Yeah. Okay. So, but we're talking about tens and tens of millions of dollars, right? If you, I mean, just assume a hundred bucks an hour for, for a person just fully loaded. I mean, I'll just do that math. Okay. And 20% percent on snowflake cost. So here's, here's the question? Well, well, first of all, what's the vision, what's the like gimme a five year vision for, for the software group at capital one, >>We wanna bring capital one's data and cloud management expertise to the masses. Okay. We've spoken to a number of companies that are trying to follow in our footsteps. We've, we've heard again and again, that our challenges are their challenges. Our, the path that we walked is the path that they're trying to walk in. So we are super excited about bringing all of our expertise to the market. >>So start with cost savings, but the vision transcends cost savings, absolutely going into security, privacy, data management, >>Absolutely absolutely workflow. And the, the, you know, the industry's in a super interesting place now where it's very fragmented. There is a galaxy of tools out there. You, you look around here, there's hundreds and hundreds of different solutions, but they're point solutions. They're all going after an individual piece of the management puzzle. And what we found was that we needed to create these integrated experiences that were aligned to our team's jobs to be done, not necessarily in terms of, you know, a capability like cataloging or quality or entitlements, you know, in order to efficiently operate at scale, you need to string those things together in a way that lets your team get their job done. >>So my last question on this flow is, I dunno if you're familiar with you guys, maybe familiar with Sarah Wong and Martin CASAA published a piece that got, you know, pretty wide viewing and discussion. They are out out of Andreesen, a 16 Z that the cost of good sold for SaaS companies who are born in the cloud are gonna become so overwhelming that they're gonna repatriate and start managing themselves. And they use Dropbox as an example. Now Dropbox is storage. So it's very specific niche, you know, and I've talked to many, many companies like snowflake about this, and they're like, eh, that ain't happening anytime soon. How do you feel about that? Because if you look at SAS companies that are born in the cloud, their gross margins are, you know, they don't get to 90%, but they're healthy, you know, 75, you know, sometimes 78% even snowflakes, you know, end of decade forecast Scarelli has it. I think it's 78%. And the reason it's not higher is because of the cloud cost. You gotta pay the cloud bills, my belief and I've argued, this is that's okay. I can negotiate cloud bills. I can work with tools like yours over time to keep those down. And the cloud guys are gonna be competing with each other, but, but what do you make of that Patrick >>Cloud costs? Aren't gonna go down. Data is expanding at an exponential rate. The scale of data today is orders of magnitude versus what it was in on-prem systems. And so, you know, I don't think the cloud providers are too worried because data is exploding at such a, a crazy pace. And so it really becomes about using all of those resources as efficiently as possible. And, and in the cloud where compute is fully elastic, it scales infinitely instantly on demand. You know, it's all about getting it's, it's, it's all about making sure that if you're spending more, you're getting more business value. There's not wastage in the system. >>Same question, but different. Do you feel like strategically organizations generally in capital one specifically will, will, will optimize their time on optimizing or spend their, their effort optimizing the cloud costs? Or do you feel like long term you can actually be cheaper to manage yourself? In other words, our, our cloud benefits of not doing all that heavy lifting offset that potential, you know, cost equation. >>I mean, you saved just so much time and effort and headache, not having to manage physical infrastructure. And so like, you know, snowflake, you can write a sequel command to create a database. You can write a sequel command to create a data warehouse. Like the market will not give up that level of simplicity for managing infrastructure. And so I think at the end of the day, you're gonna, you're gonna see a focus on efficiency because what you really want your teams to be focused on your old, your old DBA and data engineering teams is focused on driving customer value, not in the weeds of infrastructure management. >>And that's why I think you guys, this is a great business that you're starting. And I think you, I, frankly, I think you're gonna get a lot of competition, which is a good thing that says you're in a great business and you guys are first >>Talk about the customer experience. You know, we are also as consumers demanding, we wanna be able to transact ASAP. We wanna make sure that, you know, on the swipe fraud detection happens, how does the Slingshot help facilitate and improve the customer experience if I'm transacting or I'm gonna sign up or I'm getting a mortgage. >>So with Slingshot, we enable your company, regardless of what you do at, at capital one, we're, we're a bank to build more personalized experiences for customers in a more cost effective way. And so Enno is our, our intelligent, personal banking assistant with snowflake. We're enable Enno to do way more than we were previously for less than we would've without some of these tools. >>And that's a huge competitive differentiator because we expect as consumers and of whatever it is. We want a personalized experience, right? That's relevant. That's gonna offer us products and services that might build upon what we've already done. >>It's it's kind of table stakes these days. Yes. And so with these tools and with snowflake, we were able to onboard our business teams were able to onboard over 400 new use cases over, over that same time period. And so really what it's enabled us to do is unlock the innovative power of our company and create more of these customer experiences. >>How does the customer visualize those, those cost savings? And, and, and do, do, do you have some tooling, maybe it's in the works to help them predict what kind of cost savings they have based on some modeling that >>You do. And absolutely. So we enable teams to enforce good governance around infrastructure management, up front by building rules and enabling their teams to create warehouses, create databases. And then once that infrastructure is up and running, we give them a whole bunch of dashboards that show transparency and to spend, we enable chargebacks to lines of business in today's consumption, driven business models. It's hard to reconcile at the end of the month, if you spent what you thought you spent and, and data costs have gone from CapEx to OPEX and, but not everybody is an expert. And so we look at usage data, we look at usage history and we come up with recommendations for how you can save money by, you know, tweaking this or tweaking that or better optimizing your, your compute. >>Should we expect you as you expand your opportunity to take your expertise and aim it at AWS more broadly, maybe Redshift more specifically, Google GCP, big query Azure, what, what should we expect there? >>You know, there's, there's a lot of opportunity to help companies optimize costs across other cloud providers as well. This, this concept of elastic compute, isn't just specific to snowflake. That's certainly one path that we could go down. You know, we have a lot of expertise in, in data management as well, and data privacy, data security. And so that's that, that's another path as well that, that we have expertise in. And so, you know, I think it's, it's an exciting time we're in, we're in an exciting place, but it's early days, >>Did you do a working backwards document? Can you share that with us? >>Fortunately >>Not five, five or 10 years down the road, you may decide to do that, right? >>Yeah. Let me, let me check with my PR person to see if I'm allowed to share here. That's >>I mean, I think this is gonna be a huge success and, and I think it it's, it's, it follows a lot of the things that we've learned from AWS. Yeah. And you guys have been all in there and, and, you know, it's funny, right? We laugh about working backwards, customer obsession, two pizza teams. I mean, it really has changed the sort of way that we think about developing software and, and managing infrastructures. I, I think you're gonna have a, a huge business and I, I wish you the best. >>I, I appreciate that. And the, the thing, a lot of that statement is, you know, internal teams are now starting to demand consumer great experiences for the tools that they use. Yeah, for sure. And so one of the things that we did was treat our internal associates. Like they were external customers, we applied design thinking, we applied product management, we built our experience in terms of what are you trying to accomplish? And what's getting in your way, because that's what people have come to expect with all of these consumer experiences, >>Collaboration. That's right. What last question for you? What would you say to peers in your, whatever, same industry, other industries that are really trying to figure out how to get their hands on data to become a data company, what would you advise them? Why should they choose >>Snowflake gives you so many building blocks out of the box to help you create a, a well-managed data ecosystem? You know, the simplicity with which you can create new infrastructure, define policies for that infrastructure onboard new users. I mean, it, it's one of the platforms in internally capital one that has the highest NPS score. And so, you know, if you're looking to adopt a, a data cloud platform, I mean, snowflake is certainly high up on the list of what you should be looking at. >>That's >>Awesome. How do you, do you consider this a SA, is it a consumption or how do you price for this? >>So we, we don't have published pricing at the moment, but it is, it is a SAS product. You know, what we can share is it'll, it'll be a, you know, small fraction of, of your, of your total credit spend with snowflake and, and >>You're thinking a subscription or, or haven't figured that out yet, >>It it'll likely be a, a consumption model based on, you know. Okay. >>So the, so, so say, you know, it's funny SAS, I get it. Software's a service, but it, but because it's consumption, I think it's like modern SAS. If I can say that, you know, it's cloud >>SAS and it, it, you know, it's more important to make sure right now, because we're so early that we're actually providing the right value to customers. We have a pretty generous trial program going on right now where you can try the, the, the software out for free to make sure it, it fits your needs. So, >>Okay. So you're in trial, right. I should have clarified that you're in trial now. And, and so, yeah, of course you haven't figured out exactly how you're gonna price it yet. But >>The, the, the official posture that we're taking is public preview. We've, we've been in private preview for the last six months. We've onboarded a, a couple of customers who are starting to use the product. And so the, the big announcement this week is we're officially in public preview, come on in. >>So you gotta get product market fit. That's right. Before you figure out your pricing and before you, then you, then you're gonna scale. Great. >>What's been the feedback so far >>Overwhelmingly positive. Somebody stopped by the booth and said, oh my God, that's so cool. We've heard a lot of, wow, we need this right now. You know, it's, I had pretty, pretty high expectations coming in, just based on the value that this is created for capital one, but I've, I've been blown away by, by what I've heard from the people who've stopped by our booth. >>Awesome. Patrick, thank you for joining Dave and me on the program, talking about what you're doing with capital one software seems like you're just in early innings, but so much potential to come. We wish you the best of luck with that. And you have to come back and tell us how it's going. Thanks so much. Thanks for having me, our pleasure for Dave ante. I'm Lisa Martin. You're watching the cube our day three coverage of snowflake summit 22 live from Las Vegas continues after a short break.

>>Hello from the show floor in Las Vegas. This is the snowflake summit 22 at Caesar's forum. We've been live the last day and a half. Lisa Martin here with Dave ante covering a lot of ground. We're so excited to have the chief marketing officer at snowflake. Join us next, Denise Pearson. And welcome back to the cube. >>Thank you so much. So great to be here with you. So great to have you here at SEL meets as well. Thank >>You. That's unreal. Isn't it? Yeah. I mean, everybody's so excited to be face to face and you know, Lisa and I have been doing a few of these shows, but we, we hear the same thing over and over. It's like, oh, so good to be back, right? Yeah. >>Well, even in the keynote yesterday, when we got in, we saw a standing room only there were overflows. People are ready to hear from snowflake in person. And as we were, you were just talking with Frank, I think the 2019 show had less than 2000 people. And now here we are at close to 10,000, this step leap factor in terms of the audience and also the momentum of the company, the capabilities, lot of growth in that timeframe. Yeah, >>No. Yeah. Two, three years ago we were about 1800 people out to Hilton and San Francisco. We had about 40 partners attending this week were close to 10,000 at this year, almost 10,000 people online as well. And over over 200 partners here on the show floor, >>Right? 250 plus sessions, breakouts, keynotes, technical certifications, developer zone, a lot going on here. The buzz has been enormous from yesterday morning. It still is today. Talk about the theme of the event, the world of data collaboration. We've been talking a lot about data collaboration. Yeah. But from Snowflake's perspective, as Dave you've pointed out, that really seems like quite a differentiation of where snowflake is versus the guys in the root view mirror. Yeah. Number >>One of the very unique capabilities with snowflake is that the ability to share data with each other within your ecosystem. So you can both collaborate now on, on with your data, but also collaborate on building in a new business opportunities set together. So I think it's really a message that we, we, we fully fully own. It' really unique differentiator as well. So >>You used to talk about, you still talk about data sharing, but just kind of evolve the messaging to collaboration, explain why and, and how is that a wider scope and more appealing to the ecosystem in your >>Customers? I mean, data sharing is a terminology used for, for years and years, sounds from any data sharing it's about using, you know, FTP or, you know, APIs, those things. And we of course do it in a very, very different way where, where you do it without, you know, APIs so that you can share data with anyone in your ecosystem, without the data actually ever leaving, ever leaving your, your instance. So it's in a very different way. And also the fact that you can, again, you know, build applications together with other companies, you know, in your ecosystem. And it's a, it's a true collaboration around, you know, data in a way we've never seen before >>The other subtle change was data marketplace to marketplace. Why that change explain kind of what's behind that. >>Yeah. One of our big announcements here this week is around building native, you know, data apps and all snowflakes. Now you can both, you know, build the apps and you can distribute them and monetizing them in our marketplace. So in the past, you know, we only really had data sets within our marketplace within the data marketplace at that time. So you could now, you know, we can publish your data, you could monetize your data, but again, now moving forward, you will also be able to again, build apps and distribute them in the marketplace and also monetize them. And for Mon many startups, right? The, the big challenge is just a monetization piece as well. You build your product. You also need to find a way to, to both distribute and, and monetize it, an invoice for that product. And we solve all that for, for our customers. Now, >>A lot of customer growth, I saw Frank's slide yesterday over 5,900. I think you have 500 plus in the Forbes global 2000, a tremendous amount of growth in customers with a million plus ARR. Yes. >>Where >>Are the customers and the ecosystem in terms of that, that what you just described in the, going from the data marketplace to the marketplace are customers and, and the ecosystem influential in saying, Hey, snowflake, we need to go in this direction. >>Yeah. And also one key thing also with larger companies, they have their own marketplaces built, you know, snowflake as well. So you don't have to publish your, your, your data or app on our marketplace. The many of our larger companies, they're building those own marketplaces around themselves, you know, to distribute their data, you know, to their partners. So there are many ways you can, again, distribute and monetize their data. >>What are the marketing challenges? You, you started out kind of better data where simpler data warehouse, cloud data, warehouse, zero to snowflake was kind of the, the messaging and then the rise of the data cloud. And now it's all about applications. You're obviously building on top of that, but how, how have you, how do you think about that sort of messaging architecture going, you know, where you've come from and going forward? >>Yeah. Obviously the capabilities of the data cloud is kind of building and building it every day. And it's also a positioning that we can, you know, grow with as well. The big difference, you know, for us over the past two years is really that we are more and more really talking to the, to the business side, you know, of our, our customers that that's really where the demand is coming from. And we're truly, you know, with the data cloud, we're truly, you know, build bringing the business side and the it side together to solve these, you know, problems. And also, also together with all our partners as well. >>And I was just gonna ask you what, what's the partners role in the data cloud narrative? How do they help accomplish that? >>I would say, I mean, the data cloud is all about the partners it's, and also this event here, this event is not about, you know, snowflake it's about really our partners, you know, and our customers, you know, coming together, the data cloud is really it's. The foundation is of course, you know, the core capabilities, our platform, but then it's also all, all the data that is in there that other companies can access from our customers, but then all the applications and capabilities that are built, you know, by our partners and also our partners like the, you know, or the SI partners that are here, they are the ones, you know, doing the work, you know, with our customers, they are the ones that are, you know, migrating the data to, to snowflake and the data cloud and helping these companies build this new, you know, business model. So snowflake is a very, very partner first company. And the only thing I really care about this week here is that all this, you know, 200 partners here that they're gonna be tremendous successful if they're successful. That means that, you know, all our customers are successful as well. >>So how is your digital strategy evolving and how do you include the partners in that? >>Yeah, I mean, we learned so much over the past, you know, three, three years in regards to that. So, I mean, we all had to just accelerate our, the, the digital growth, you know, of our marketing capabilities and how to do that in a, with our, our partners. So with many of them, you know, we started developing this joint account based digital, you know, marketing program. Some, we just all had to adapt and innovate really fast, and we're gonna continue, of course, a lot of those motions as well. But at the same time, there's nothing like being out and meeting, you know, customers, you know, face to face. And what's also so important is the alignment we have with our local sales organization and our partners as well. So all these marketing programs that we develop in the fields, those are us again, opportunities cannot build those relationships as well. >>Can you talk about the sales marketing alignment at snowflake? I think it seems to be pretty strong, but we've talked a lot in the last day and a half about the retail data cloud healthcare life sciences, media finance. Talk to us about the marketing sales element, how marketing is facilitating, maybe from a campaign perspective, some of those big sales plays in the S yeah, >>Maybe both unique here. Our C Chris Dham, I think has been here early on the show. I mean, we work together for over six years now and we truly work as one, one team. We, we don't really even see the lines between sort of sales and marketings. We truly share exactly, you know, the same objectives every day. We share the same focus on, on putting our customers and our partners, you know, first, every day, his priorities, you know, are my priorities, you know, vice versa. And I think the biggest challenges we see often in some companies between sales and marketing, is that they're just shifting or it's different, you know, priorities. It's so important just to align the priorities and for us to making sure that our teams are all around the world now, or as aligned, you know, as Chris and I are as well, >>Couple other, yeah. Milestones or events come up, you're doing like, you're doing a worldwide tour and you got the dev conference in November, start with the worldwide tour. What's that all about? >>So we get little break near now, here for, for a couple of weeks. And then we're taking all the best of content here for, from, from summits and also all in our partners on a worldwide tour. We're starting in, in Asia, in August, and we're gonna target over 20 cities around the world. So, and again, I think this year, the challenge was many of our European customers and our customers in Asia. They couldn't make it. So we have smaller numbers, you know, coming from those regions. So it's more, more important than ever that we just come out to them, you know, instead, and bring this content in to them. >>Is that all face to face or at Lisa is all face to face. Is there a digital component as well? Yeah, >>It's actually gonna be all face to face and there will be some, some digital components as well. We're ending the tour in San Francisco. And that's also where we go doing all our winter announcements, you know, as well. And also our build our developer conference. That will be all virtual. The big, the global one will be all virtual at the same time, you know, from San Francisco. >>Okay. Am I confusing that with the November developer conference or >>The, that is, that is the conference, but it, that one will be virtual this year. Okay. >>So dev the build is all virtual. >>Yeah. Build be all virtual. And it's just, so we have that opportunity to reach as many people as we possibly can. >>And then is the, is this, is the intent to eventually bring them in to one place? >>Absolutely. I mean, I think the dev conference, the plan is to really take that around the world, you know, as well. We're seeing markets like Israel, for instance, there's a massive developer community that is, that is looking at snowflake right now. Markets like Indonesia, big developer segment as well. So I think it's not about, you know, having people come to us, it's about we, you know, coming out to them. So markets like Israel and Indonesia. And >>Will you also in future summits include a, a development component. You probably have something here. I just haven't seen it yet, but, but like the conference within the conference, or is it more, Hey, we want to cater to the t-shirt crowd, you know, separately, what do you, yeah, >>I think we cater to them separately. And I said again, that we it's really about taking our content, you know, out, out to them. And when we're talking about the developer audience, we're talking about hundreds and thousands of people and they can't physically, you know, come here. So our plan is really to come out and meet them where they are. How >>Did you make the decision to do this summit fourth annual in person? I'm sure the attendance figures are probably blowing your mind, but that's a, that's a big decision and that's a challenging decision to make. How did they go about doing that? >>I think, I think if there was one thing we've learned during the past three years, it's really about that. Adaptability is the new superpower, you know, of bus business. So of course we've had to adapt, you know, you know, every month. And of course, even two months ago, we were not sure, you know, how, how many people that will be able to come here today, but we're incredibly happy. Were they, were they, were they with the number of people that, you know, came here and yeah, we're already storing planning for next year. >>I mean, it definitely must have exceeded your expectations. Is that fair? Or >>We set expectations high. Yeah. Okay. But again, it's that unknown that we all had to deal with, you know, every day. And I think we're gonna continue to have to, to live with that. >>Yeah. Well, this is, yeah, this is one of the largest shows we've done. Yeah. SIM it's a reinvent, obviously different. That was last year, but this year, this is the biggest event I think we've been to, and we've been to some big brand events, so yeah. Yeah. Punching above the weight as usual. >>Yeah. And again, I wanna just give a big shout out to our whole, you know, partner ecosystem, you know, here, because again, this is very much of an ecosystem, you know, partner you in a conference and it's really all our 200 plus partners here making this conference, what it is. I mean, today >>It's remarkable to pace at which you've been able to grow the ecosystem, but why do you think that is? What's the secret there? >>I think we fully understand that we don't solve all the problems ourselves, you know, for, for our customers. It's really an ecosystem of, of products and services that solve those problems and customers. They are looking for vendors that partner well with others. They're looking for vendors that integrate well, you know, with each other. So we always have an outside in view on things and that's something we challenge ourselves every morning. We wake up, how do we put ourselves in the customer's shoes in terms of, of, of their needs and their problems and how to solve those? We don't solve them alone. We, we solve them with these 200 plus in apart. Make >>It sound so simple. >>Speaking of challenges, you have something called the startup challenge. That's in its second annual >>Yes. Tomorrow we're kicking off the, the final of the second annual startup challenge. We have three finalists here, three very different, you know, companies. And we had a couple hundred applications this year and we have everything from a company that makes AI and ML more accessible to a company, focus on, you know, retail, you know, analytics. It's gonna be very exciting tomorrow, big price for the winner. The winner is going to win a million dollar of investment from, from snowflake ventures. So >>Very exciting. It's a nice incentive. It is a nice incentive, >>Very nice incentive. And also all the exposure you will get as well. We will put a lot of our marketing support, you know, behind this companies as well. >>Excellent. >>And now the data driver awards program, we've had a couple of data drivers on the program in the last day >>And a half. Yes. We announced to know those winners as well, you know, early in the week. So a lot of recognition for both our customers, but also we're gonna see, you know, the next interesting companies here to watch tomorrow during the startup challenge, you >>Get a little bit of something for everybody here, right? I mean the, the, the, the partner awards, right? These other little side opportunities for ecosystem to get recognition, sometimes funding it's >>Yeah. Everyone wants to be recognized, you know, for the great work they're doing. So, yeah. Yeah. >>So what's next for marketing, obviously, a break and then you start the, the road show. >>So of course yesterday we made an number of very, very large in announcements. Many of those, you know, we've been working on for years here at snowflake, like Unior, you know, for instance has been probably three years, you know, in the making. So our goal now is to take all those announcements to every customer around the world, both through, you know, local events really starting this week, and then also the world tour this fall. And it's gonna be a big, big focus on the developer segments. Obviously what our most exciting announcements is, the native apps, you know, capabilities. And that finally, you know, we can bring the work, you know, to the data and not again, taking the data to the work. And as you know, our mission has really been around breaking down the data silos. Cause those have been the biggest, you know, challenges companies have faced. That's really, what's been standing in the way for customers to become you a data Rav, and now bringing the work to the data from a developer's standpoint is gonna break down even further, those silos. So, >>Yeah. And it's good physics. >>Yeah. It good physics. Yeah. >>Yeah. Tremendous opportunity. Congratulations on a great successful event. It's not even done yet, but obviously we've seen so much success. Great news coming out. We'll be excited to be hearing some of the outcomes of the road show and the developer conference coming up in the fall. We appreciate your insights, your time and for having the cube here at the summit. >>Thank you for being here. Thank you. Thanks for having >>Me, our pleasure for Denise Pearson and Dave Valante I'm Lisa Martin. You're watching the cubes coverage of snowflake summit 22 live from Las Vegas, Dave and I will be back after a short break.

>>Hey everyone. Welcome back to the Cube's coverage of snowflake summit. 22 live from Las Vegas. We're at Caesar's forum, Lisa Martin, with Dave ante. We've been having some great conversations over the last day and a half. This guy just came from main stage interviewing the CEO, Franks Lubin himself, who joins us after our next guest here, we're gonna be talking customers and successes with snowflake Rosemary Hua joins us the global head of retail at snowflake and Patrick Kelly, the VP of product management at their customer 84 51. Welcome to the program guys. >>Thank you. It's nice to be here. So >>Patrick, 84 51. Talk to us about the business, give the audience an overview of what you guys are doing. And then we'll talk about how you're working with snowflake. >>Yeah, absolutely. Thank you both for, uh, the opportunity to be here. So 84 51 is a retail data science insights and media company. And really what that means is that we, we partner with our, uh, parent company Kroger, as well as consumer packaged goods or brands and brokers and agencies, really to understand shoppers and create relevant, personalized, and valuable experiences for shoppers in source and grocery stores. >>That relevance is key. We all expect that these days, I think the last couple of years as everyone's patience has been wearing. Yeah, very thin. I'm not, I'm not convinced it's gonna come back either, but we expect that brands are gonna interact with us and offer us the next best offer. That's actually relevant and personalized to us. How does AB 4 51 achieve that? >>Yeah, it's a great question. And you're right. That expectation is only growing. Um, and it takes data analytics, data science and all of these capabilities in order to deliver it on that promise, uh, you know, big, a big part of the relationship that retailers and brands have with consumers is about a value exchange. And it's, again, it's about that expectation that brands and retailers need to be able to meet the ever-changing needs of consumers. Uh, whether that be introducing new brands or offering the right price points or promotions or ensuring you meet them where they are, whether it be online, which has obviously been catalyzed by, um, the pandemic over the last two years or in store. So a deep understanding of, of the customer, which is founded in data and the appropriate analytics and science, and then the collaboration back with the retailers and, and the brands so that you can bring that experience to life. Again, that could be a price point on the, on the shelf, um, or it could be a personalized email or, um, website interaction that delivers the right experience for the co for the consumer. So they can see that value and really build loyalty >>In the right time in real time. That's >>One of the most Marrit I'm in real time. That's right. One goes, Mary, I love the concept of the, the actual platform of the retail data cloud. Yes. It's so unique for a technology company. Snowflake's a technology company, you see services companies do it all the time, but yeah, but to actually transform what was considered a data warehouse in the cloud to a platform for data, I call it super cloud. Yeah. Tell us how this came about, um, how you were able to actually develop this and where you are in that journey. >>Yeah, absolutely. It's been a big focus on data sharing. We saw that that's how our customers are interacting with each other is using our data sharing functionality to really bring that ecosystem to life. So that's retailers sharing with their consumer products companies selling through those retailers. And then of course the data service companies that are kind of helping both sides and that data sharing functionality is the kind of under fabric for the data cloud, where we bring in partners. We bring in customers and we bring in tech solutions to the table. Um, and customers can use the data cloud, not only with the powered by partners that we have, but also the data marketplace, getting that data in real time and making some business value out of that data. So that's really the big focus of snowflake is investing in industry to realize the business value >>And talk about ecosystem and how important that is, where, where you leave off and the ecosystem picks up and how that's evolving. >>Absolutely. And I'm sure you can join in on this, but, um, definitely that collaboration between retailers and CPGs, right? I mean, retailers have that rich first party customer data. They see all those transactions, they see when people are shopping and then the brands really need that first party data to figure out what their, how their customers are interacting with their brand. And so that collaborative nature that makes up the ecosystem. And of course, you've got the tech partners in the middle that are kind of providing enrich data assets as well. You guys at 84 51 are a huge part of that ecosystem being, you know, one of the key retailers in, in the United States. Um, have you been seeing that as well with your brands? Yeah, >>Absolutely. I mean data and data science has always been core to the identity of 84 51. Um, and historically a lot of the interaction that we have with brands were through report web based applications, right. And it's a really great seamless way to, to deliver insights to non-technical users. But as the entire market has really started to invest in data and data science and technology and capabilities, you know, we, we launched a collaborative cloud last year and it was really an opportunity for us to reimagine what that experience would look like and to ensure that we are meeting the evolving needs of the industry. And as Rosemary pointed out, you know, data sharing is, is table stakes, right? It's a capability that you don't wanna have to think about. You wanna be thinking about the strategic initiatives, the science that you're gonna create in order to drive action and personalize experiences. So what we've found at 84 51 is really investing in our collaborative cloud, um, and working with leading technology providers like snowflake to make that seamless has been, you know, the, the, the UN unlock to ensure that data and data science can be a competitive advantage for our clients and partners, not just, you know, the retailer in 84 51 >>Is the collaborative cloud built on snowflake. >>Yeah. So the collaborative cloud is really about, um, ensuring that data sharing through snowflake is done seamlessly. So we've really, we've invited our clients and partners to build their own science on 84 51 S first party data asset through Kroger. And our, our data is represents 60 million households, half of the United States, 2 billion transactions annually, the robustness of that data asset. And it's it's it's analysis ready is so impactful to the investment that brands can make in their own data science efforts, because brands wanna invest in data science, not to do data work, not to do cleaning and Muning and, and merging and, and standardizing. They wanna do analysis. That's gonna impact the strategies and ultimately the shopper's lives. So again, we're able to leverage the capabilities of snowflake to ensure data sharing is not part of our day to day conversation. Data sharing is something we can take for granted so that we can talk about the shopper and our strategies. >>So this is why I call it super cloud. So Jerry Chen wrote an article of castles in the cloud. And in there he said, he called it sub clouds. And I'm like, no, it's, uh, by the way, great article. Jerry's brilliant. But so you got AWS, you built on top of AWS. That's right. You got the snowflake data called you're building on top of that. And I was sitting at the table and my kid goes, this is super, I'm like, ah, super clouds. So I didn't really even coin it, but, and then I realized somebody else had use it before, but that is different. It's new, it's around data. It's around vertical industries. Yes. Um, I, I get a lot of heat for that term, but I feel like this look around this industry, everybody's doing that that's that is digital transformation. That's don't you see that with your customers? >>Absolutely. I mean, there's a lot of different industry trends where you can't use your own historical first party data to figure out what customers are doing. I mean, with COVID customers are behaving totally differently than they used to. And you can't use your historical data to predict out of stocks or how the customer's gonna be interacting with your brand anymore. And you need that third party macroeconomic data. You need that third party COVID data or foot traffic data to enrich what your businesses are doing. And so, yes, it, it is a super cloud. And I think the big differentiator is that we are cloud agnostic, meaning that, like you said, you can take the technology for granted. You don't have to worry about where the other person has their tech stack. It's all the same experience on the snowflake super cloud as he put it. So, >>So Patrick, talk about the, the, the impact that you have been able to have during COVID. I mean, everybody had supply chain issues, but, you know, if you took, if you took away the machine learning and the data science that you are initiating, would life have been harder? Do you have data on that? You know, the, the, what if we didn't have this capability during the >>Challenges? No, it's, it's a fantastic question. And I'll actually build on the example that Rosemary, um, offered around COVID and better understanding COVID. So, um, in the past, you know, when we talk about data sharing data collaboration, it's basically wasn't possible, right? What's your tech stack, what's mine. How do we share data? I don't wanna send you my data without go releasing governance. It was a non-starter and, you know, through technology like snowflake, as we launched the collaborative cloud, we actually had a pilot client start right at the beginning of 2020. Um, we, we had, you know, speced out it onto use cases that really impactful for their, for their organization. But of course, what happened is, uh, a pandemic hit us and it became the biggest question, CEO executive team, all the way down is what is happening, what is happening in our stores? >>How are shoppers behaving and what, what that client of ours came to realize is while we, we actually, we have access to the E 4 51 collaborative cloud. We can see half of America's behavior last week down to the basket transaction UPC level. Let's get going. So again, the conversation wasn't about, you know, what data sources, how do we scramble? How do we get it together? What technologies, how do we collaborate? It was immediately focused on building the analysis to better understand that. And, and the outcomes that drove actually were all the way from manufacturing impact to marketing, to merchandising, because that brand was able to figure out, Hey, our top selling products, they're, they're not on the shelves. What are shoppers doing? Are they going to a, another brand? Are they not buying it all together? Are they going to a different size? Are they staying within our product portfolio? Are they going to a competitor? And those insights drove everything again from what do we need to manufacture more to, how do we need to communicate and incent our, our, our shoppers, our, our loyal shoppers also what's happening to our non loyals. Are they looking for an, you know, an alternative that a need that we can serve that level of, of shopper and customer understanding going all the way up to a strategic initiatives is something that is enabled through the Supercloud >><laugh>. How do you facilitate privacy as we're seeing this proliferation of privacy legislation? Yeah. I think there's now 22 states that have individual, and California's changing to CPR a at the beginning of yes, January 23. How do you balance that need that ability to share data? Yeah. Equitably fast, quickly, but also balance consumer privacy requirements. >>I mean, I could take a stab first. I mean, at snowflake, right, there is no better place to share your data that in a governed way than with snowflake data sharing, because then you can see and understand how the other side is using your data. Whereas in traditional methods, using an API or using an FTP server, you wouldn't be able to actually see how the other side is using your data. But in addition to that, we have the clean room where you can actually join on that underlying PII data without exposing it, because you can share functions securely on, on both sides. So I think there is no better place to do it than here at snowflake. Um, and because we deeply understand those policies, I think we are kind of keeping up with the times trying to get in front of things so that our data sharing capabilities stay up to date. When you have to expunge records, identify records with CCPA and, and GDPR and, and all the rest that are coming. Um, and so, so, I mean, I think especially with 84 50 ones, um, you know, collaborative cloud also building on top of the clean room, um, in, in further road in the further roadmap, I think, uh, you're gonna see some of that privacy compliant, data sharing, coming to play as well. You >>Know, what's interesting, Patrick is we were just in that session with the Frank Q and a, and he was very candid about when he was talking about, uh, Apache, uh, I'm sorry. Apache iceberg. Yeah. Yes. And he, he basically flat out said, look, you know, you gotta put it into the snowflake data cloud. It's, it's better there, but people might, you know, want to put it outside, not get locked in, et cetera. But what I'm, I'm listening to you saying it's so much easier for you today that could evolve something open source. And, and how do you think about that in terms of placing your bets? >>Yeah, it, it's a great question and really to go back to privacy, um, as a total topic, I mean, you're right. It's extremely relevant topic. It's, it's, you know, very ever changing right now at 84 51. Privacy is, is first it's the foundation. Um, it it's table stakes and that's from a policy that's from a governance, it's from a technology capability standpoint. And it's part of our, our culture because, um, it, it, because it has to be, uh, and, and so when we, when we think about, you know, the products that we're gonna build, how we want to implement, it's, it's a requirement that we leverage technologies that enable us to secure the governance and ensure that we're privacy compliant. Um, the customer data asset that we have is, is, you know, is extremely valuable as we've talked about in this interview, it's also responsibility. And we take that very, very seriously. And so, you know, Dave, back to your question about, you know, decisions to go, you know, open source or leverage for technologies. So there's always a balance. You know, we, we love to push the, the bounds of innovation and, and we wanna be on the forefront of data, sharing data, science, collaboration for this industry. But at the same time, we balance that with making sure that our technology partners are the right ones, because we are not willing to compromise our governance and our fir and our, our privacy, uh, priorities. >>That's gonna be interesting to see how that evolves. And I, I loved that. Frank was so candid about it. I think the key for any cloud player, including a super cloud is you gotta have an ecosystem without an ecosystem. Forget it. And you see a lot of companies. I mean, we were at Dell tech world. They're kind of, they're at the beginnings of that, but the ecosystems, nothing like this, right. Which is amazing, nothing against, against Dell, they're just kind of getting started and you have to be open. You have to have optionality. Yep. You know, so I, I don't know if we'll see the day where they're including data, bricks, data lakes inside of the snowflake cloud. That will be amazing. <laugh> but you know, you never say never in the world of cloud, >>Do you stranger things, Rosemary and Patrick, thank you so much for joining us talking about what 84 51 is doing powered by snowflake and also the rise of the snowflake retail cloud and what that's doing. We'll have to have you back on to hear what's going on as I'm sure the adoption will continue to increase. Absolutely. Thank you so much to both for having us, our pleasure. You appreciate this for our guests. I'm Lisa Martin. He's Dave ante stick around Dave will be back with Frankman CEO of snowflake. Next. You won't wanna miss it.

>>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.

(upbeat music) >> Welcome back to Vegas, baby. Lisa Martin here with theCUBE. We are live at Caesar's Forum covering Snowflake Summit 22. This is day two of our wall to wall coverage on theCUBE you won't want to miss. We've got an exciting customer story to talk to you about next with Novartis and Snowflake. Please welcome two guests to theCUBE. Loïc Giraud, Global head digital delivery, Novartis. I hope I got the name right. >> Yes. Hi, thank you. >> I did my best. >> Absolutely. >> Lisa: (laughs) Jesse Cugliotta also joins us. Global Industry Lead, Healthcare and Life Sciences at Snowflake. Welcome with theCUBE, gentlemen. >> Thank you for having us. Good morning. >> So it was great to hear Novartis is a household word now, especially with what's gone on in the last two years. I had a chance to see the Keynote yesterday, heard Novartis mention in terms of a massive outcome that Snowflake is delivering that we're going to get to. But Loic talk to us about Novartis global 500 organization. You rank among the world's top companies investing in R&D, the massive portfolio and you're reaching nearly 800 million patients worldwide. That's huge, but there's been a lot of change in the healthcare and life sciences industry, especially recently. Talk to us about the industry landscape. What are you seeing? >> As you described, Novartis is one of the top life science company in the world. We are number three. We operate in 150 countries, and we have almost 120,000 employees. Our purpose is actually to reimagine medicine for the use of data science and technology and to extend people's life. And we really mean it. I think, as you mentioned, we treat eight or 9 million patient per year with our drugs. We expect to treat more than a billion patients in near time soon. Over the last few years, especially during COVID, our digital transformation help us to accelerate the drug discovery and then the commiseration of our drug to markets. As it was mentioned in the Keynote yesterday, we have actually been able to reduce our time to market. It used to take us up to 12 years and cost around 1.2 billion to discover and commercialize drug. And now we've actually use of technology like Snowflake, we have been able to reduce by two to three years, which ultimately is a benefit for our patients. >> Absolutely. Well, we're talking about life and death situations. Talk about... You mentioned Novartis wants to reimagine medicine. What does that look like? Where is data in that and how is Snowflake an enabler of reimagining medicine? >> So data is core for our asset, is a core of enterprise process. So if you look at our enterprise, we are using data from the research, for drug development, in manufacturing process, and how do we market and sell our product through HCPs and distribute it to reach our patients. If you build through our digital transformation we have created this integrated data ecosystem, where Snowflake is a core component. And through that ecosystem, we are able to identify compounds and cohorts, perform clinical trials, and engage HCPs and HGOs so that can prescribe drugs to serve our patient needs. >> Jesse, let's bring you into the conversation. Snowflake recently launched its healthcare and life sciences data cloud. I believe that was back in March. >> It was. >> Just a couple of months ago. Talk to us about the vertical focus. Talk to us about what this healthcare and life sciences data cloud is aiming to help customers like Novartis achieve. >> Well, as you mentioned there, Snowflake has made a real pivot to kind of focus on the various different industries that we serve in a new way. I think historically, we've been engaged in really, all of the industries across the major sectors where we participate today. But historically we've been often engaging with the office of IT. And there was a recognition as a company that we really need to be able to better speak the language of our customers in with our respective industries. So the entire organization has really made a pivot to start to build that capability internally. That's part of the team that I support here at Snowflake. And with respect to healthcare and life sciences, that means being able to solve some of the challenges that Loic was just speaking about. In particular, we're seeing the industry evolve in a number of ways. You bring up clinical research in the time that it takes to actually bring a drug to market. This is a big one that's really changed a lot over the last couple of years. Some of the reasons are obvious and other ones are somewhat opportunistic. When we looked at what it takes to get a drug to market, there's several stages of clinical research that have to be participated in, and this can often take years. What we saw in the last couple of years, is that all of a sudden, patients didn't want to physically participate in those anymore, because there was fear of potential infection and being in a healthcare facility. So the entire industry realized that it needed to change in terms of way that it would engage with patients in that context. And we're now seeing this concept of decentralized clinical research. And with that, becomes the need to potentially involve many different types of organizations beyond the traditional pharma, their research partners, but we're starting to see organizations like retail pharmacies, like big box retailers, who have either healthcare delivery or pharmaceutical arms actually get involved in the process. And of course, one of the core things that happens here is that everyone needs a better way to collaborate and share data amongst one another. So bringing this back to your original question, this concept of being able to do exactly that is core to the healthcare and the life sciences data cloud. To be able to collaborate and share data amongst those different types of organizations. >> Collaboration and data sharing. It seems to me to be a differentiator for Snowflake, in terms of being able to deliver secure, governed powerful analytics and data sharing to customers, partners to the ecosystem. You mentioned an example of the ecosystem there and how impactful to patients' lives, that collaboration and data sharing can be. >> That's absolutely right. It's something that if you think about all of the major challenges that the industry has had historically, whether it is high costs, whether it are health inequities, whether it is physicians practicing defensive medicine or repeat testing, what's core to each one of these things is kind of the inability to adequate collaborate and share data amongst all of the different players. So the industry has been waiting for the capability or some sort of solution to be able to do this, I think for a long, long time. And this is probably one of the most exciting parts of the conversations that we have with our customers, is when they realize that this is possible. And not only that it's possible within our platform, but that most of the organizations that they work with today are also Snowflake customers. So they realize that everyone's already here. It's just a matter of who else can we work with and how do we get started? >> Join the party. >> Exactly. >> Loic talk to us about Novartis's data journey. I know you guys have been, I believe using Snowflake since 2017 pre pandemic. But you had a largely on-premises infrastructure. Talk to us about the decision of Novartis to go to the cloud, do it securely and why you chose to partner with Snowflake. >> So when we started our journey in 2018, I think the ambition that our CEO, was to transform all enterprise processes for the use of digital tech. And at the core of this digital tech is data foundation. So we started with a large program called Formula One, which aim to integrate all our internal and external data asset into an integrated platform. And for that, I think we've built this multicloud and best upgrade platform, where Snowflake is a core component. And we've been able to integrate almost 1,000 data asset, internal and external for the platform to be able to accelerate the use of data to create insight for our users. In that transformation, we've realized that Snowflake could be a core component because of the scalability and the performance with large dataset. And moreover, when Snowflake started to actually open collaboration for their marketplace, we've been able to integrate new data set that are publicly available at the place that we could not do on ourself, on our own. So that is a core component of what we are trying to do. >> Yeah, and I think that's a great example of really what we're talking about here is that, he's mentioning that they're going out to our marketplace to be able to integrate data more easily with some of the vendors there. And that is kind of this concept of the healthcare and life sciences data cloud realized, where all of a sudden, acquiring and bringing data in and making it ready for analysis becomes much faster, much easier. We continually see more and more vendors coming to us saying, I get it now, I want in. Who else can I work with in this space? So I think that's a perfect example of how this starts to become real for folks. >> Well, it sounds like the marketplace has been an enabler, Loic, of the expansion of use cases. You've grown this beyond drug development. I read that you're developing new products and services for healthcare providers to personalize treatments for patients, which we all are demanding patients. We want that personalized care. But talk about the marketplace as a facilitator of those expanding use cases that Snowflake is powering. >> Yes. That's right. I mean we have currently almost 65 use cases in production and we are in advanced progress for over 200 use cases and they go across all our business sector. So if you look at drug development, we are monitoring our clinical trials using Snowflake. If you look at our omnichannel marketing, we are looking at personalization of information with our HCPs and HGOs using snowflake. If you look at our manufacturing process, we are looking at yet management, freight optimization, inventory, insight. So almost across all the industry sectors that we have, I think we are using the platforms to be able to deliver faster information to our users. >> And that's what we all want. Faster information. I think in the pandemic we learned that access to real time data in every industry wasn't a nice to have. That was a- >> Necessity. >> Absolute necessity. >> Yeah. >> And made the difference for companies that survived and thrived and those that didn't. That's something that we learned. But we also learned that the volume of data just continues to proliferate. Loic, you've been in the industry a couple of decades. What do you see? And you've got, obviously this great foundation now with Snowflake. You've got 65 use cases you said in production. What's the future of the data culture in healthcare and life sciences from your perspective? >> So my perspective. It is time now we give the access to our business technologies to be able to be self-sufficient using digital product. We need to consumerize digital technology so they can be self-sufficient. The amount of problems that we have to solve, and we can now solve with new technology has never been there. And I think where in the past, where in the next few years that you will see an accelerated generation of insight and an accelerated process of medicine by empowering the business technologies to use a technology that like Snowflake and over progress. >> What are your thoughts Loic, of some of the, obviously a lot of news coming out yesterday from Snowflake, we mentioned standing room only in the Keynote. This I believe is north of 10,000 attendees. People are ready to engage in person with Snowflake, but some of the news coming out, what is your perspective? You've been a partner of theirs for a while. What do you see from Snowflake in terms of the news, the volume of customers it's adding, all that good stuff? >> I must say I was blown away yesterday when Frank was talking about the ramp up of customers using Snowflake. But also, and I think in Benoit and Christian, and they talk about the innovation. When you look at native application or you look at hybrid tables, we saw a thing there. And the expansion of the marketplace by monetization application, that is something that is going to accelerate the expansion, not only on the company, but the integration and the utilization of customers. And to Jesse's point, I think that it is key that people collaborate using the platform. I think we want to collaborate with suppliers and providers and they want to collaborate with us. But we want to have a neutral environment where we can do that. And Snowflake can be that environment. >> And do it securely, right? Security is absolutely- >> Of course. I mean that's really table stake for this industry. And I think the point that you just made Loic, is very important, is that, the biggest question that we're often asked by our customers is who else is a customer within this industry that I can collaborate with? I think as Loic here will attest to, one of the challenges within life sciences in particular is that it is a highly regulated industry. It is a highly competitive industry, and folks are very sensitive about referenceability. So about things like logo usage. So to give some ideas here, people often have no idea that we're working with 28 of the top 50 global pharma today, working with seven of the top 12 global medical device companies today. The largest CROs, the largest distributors. So when I say that the party is here, they really are. And that's why we're so excited to have events like these, 'cause people can physically introduce themselves to one another and meet, and actually start to engage in some of these more collaborative discussions that they've been waiting for. >> Jesse, what's been some of the feedback that you've heard the last couple of days on the healthcare and life sciences data cloud? You've obviously finally gotten back to engaging with customers in person. But what are some of the things, feed on this street have said that you've thought, we made the absolute right decision on this pivot? >> Yeah, well I think some of it speaks to the the point I was just speaking about, is that they had no idea that so many of their peers were actually working with Snowflake already and that how mature their implementations have actually been. The other thing that folks are realizing is that, a lot of the technologies that serve this ecosystem, whether they're in the health tech space, whether they're clinical management or commercial engagement or supply chain planning technologies, those companies are also now pivoting to Snowflake, where they're either building a part or the entirety of their platform on top of ours. So it offers this great way to start to collaborate with the ecosystem through some of those capabilities that we spoke about. And that's driving new use cases in commercial, in supply chain, in pharmacovigilance, in clinical operations. >> Well, I think you just sum up beautifully why the theme of this conference is the world of data collaboration. >> Yes, absolutely. >> The potential there, that Snowflake is unleashing to the world is I think is what's captivating to me. That you just scratch on the surface about connecting and facilitating this collaboration and this data sharing in a secure way across industries. Loic, last question for you. Take us home with what is next for Novartis. You've done a tremendous amount of digitalization. 65 use cases in production with Snowflake. What's next for the company? >> See, I think that in next year's to come, open collaboration with the ecosystem, but also personalization. If you look at digital medicine and access to patient's informations, I think this is probably the next revolution that we are entering into. >> Excellent. And of course those demanding patients aren't going to want anything slower or less information. Guys, thank you for joining me on the program talking about the Novartis-Snowflake collaboration. The partnership, the outcomes that you're achieving and how this is really dramatically impacting the lives of hundreds of millions of people. We appreciate your time and your insights. >> Thank you for having us. This was fun. >> My pleasure. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching theCUBE. This is live from Las Vegas, day two of our coverage of Snowflake Summit 22. I'll be right back with my next guest, so stick around. (upbeat music)

(Upbeat music) >> Hey everyone. Welcome back to theCUBE's continuing coverage day two Snowflake Summit '22 live from Caesar's forum in Las Vegas. Lisa Martin here with Dave Vellante, bringing you wall to wall coverage yesterday, today, and tomorrow. We're excited to welcome Matthew Carroll to the program. The CEO of Immuta, we're going to be talking about removing barriers to secure data access security. Matthew, welcome. >> Thank you for having me, appreciate it. >> Talk to the audience a little bit about Immuta you're a Snowflake premier technology partner, but give him an overview of Immuta what you guys do, your vision, all that good stuff. >> Yeah, absolutely, thanks. Yeah, if you think about what Immunta at it's core is, we're a data security platform for the modern data stack, right? So what does that mean? It means that we embed natively into a Snowflake and we enforce policies on data, right? So, the rules to be able to use it, to accelerate data access, right? So, that means connecting to the data very easily controlling it with any regulatory or security policy on it as well as contractual policies, and then being able to audit it. So, that way, any corporation of any size can leverage their data and share that data without risking leaking it or potentially violating a regulation. >> What are some of the key as we look at industry by industry challenges that Immuta is helping those customers address and obviously quickly since everything is accelerating. >> Yeah. And it's, you're seeing it 'cause the big guys like Snowflake are verticalizing, right? You're seeing a lot of industry specific, you know, concepts. With us, if you think of, like, where we live obviously policies on data regulated, right? So healthcare, how do we automate HIPAA compliance? How do we redesign clinical trial management post COVID, right? If you're going to have billions of users and you're collecting that data, pharmaceutical companies can't wait to collect that data. They need to remove those barriers. So, they need to be able to collect it, secure it, and be able to share it. Right? So, double and triple blinded studies being redesigned in the cloud. Government organizations, how do we share security information globally with different countries instantaneously? Right? So these are some of the examples where we're helping organizations transform and be able to kind of accelerate their adoption of data. >> Matt, I don't know if you remember, I mean, I know you remember coming to our office. But we had an interesting conversation and I was telling Lisa. Years ago I wrote a piece of you know, how to build on top of, AWS. You know, there's so much opportunity. And we had a conversation, at our office, theCUBE studios in Marlborough, Massachusetts. And we both, sort of, agreed that there was this new workload emerging. We said, okay, there's AWS, there's Snowflake at the time, we were thinking, and you bring machine learning, at time where we were using data bricks, >> Yeah. >> As the example, of course now it's been a little bit- >> Yeah. Careful. >> More of a battle, right, with those guys. But, and so, you see them going in their different directions, but the premise stands is that there's an ecosystem developing, new workloads developing, on top of the hyper scale infrastructure. And you guys play a part in that. So, describe what you're seeing there 'cause you were right on in that conversation. >> Yeah. Yeah. >> It's nice to be, right. >> Yeah. So when you think of this design pattern, right, is you have a data lake, you have a warehouse, and you have an exchange, right? And this architecture is what you're seeing around you now, is this is every single organization in the world is adopting this design pattern. The challenge that where we fit into kind of a sliver of this is, the way we used to do before is application design, right? And we would build lots of applications, and we would build all of our business logic to enforce security controls and policies inside each app. And you'd go through security and get it approved. In this paradigm, any user could potentially access any data. There's just too many data sources, too many users, and too many things that can go wrong. And to scale that is really hard. So, like, with Immuta, what we've done, versus what everyone else has done is we natively embedded into every single one of those compute partners. So ,Snowflake, data breaks, big query, Redshift, synapse on and on. Natively underneath the covers, so that was BI tools, those data science tools hit Snowflake. They don't have to rewrite any of their code, but we automatically enforce policy without them having to do anything. And then we consistently audit that. I call that the separation of policy from platform. So, just like in the world in big data, when we had to separate compute from storage, in this world, because we're global, right? So we're, we have a distributed workforce and our data needs to abide by all these new security rules and regulations. We provide a flexible framework for them to be able to operate at that scale. And we're the only ones in the world doing it. >> Dave Vellante: See the key there is, I mean, Snowflake is obviously building out its data cloud and the functions that it's building in are quite impressive. >> Yeah. >> Dave Vellante: But you know at some point a customer's going to say, look I have other stuff, whether it's in an Oracle database, or data lake or wherever, and that should just be a node on this global, whatever you want to call it, mesh or fabric. And then if I'm hearing you right, you participate in all of that. >> Correct? Yeah We kind of, we were able to just natively inject into each, and then be able to enforce that policy consistently, right? So, hey, can you access HIPAA data? Who are you? Are you authorized to use this? What's the purpose you want to query this data? Is it for fraud? Is it for marketing? So, what we're trying to do as part of this new design paradigm is ensure that we can automate nearly the entire data access process, but with the confidence and de-risk it, that's kind of the key thing. But the one thing I will mention is I think we talk a lot about the core compute, but I think, especially at this summit, data sharing is everything. Right? And this concept of no copy data sharing, because the data is too big and there's too many sets to share, that's the keys to the kingdom. You got to get your lake and your warehouse set with good policy, so you can effectively share it. >> Yeah, so, I wanted to just to follow up, if I may. So, you'd mentioned separating compute from storage and a lot of VC money poured into that. A lot of VC money poured into cloud database. How do you see, do you see Snowflake differentiating substantially from all the other cloud databases? And how so? >> I think it's the ease of use, right? Apple produces a phone that isn't much different than other competitors. Right? But what they do is, end to end, they provide an experience that's very simple. Right? And so yes. Are there other warehouses? Are there other ways to, you know you heard about their analytic workloads now, you know through unistore, where they're going to be able to process analytical workloads as well as their ad hoc queries. I think other vendors are obviously going to have the same capabilities, but I think the user experience of Snowflake right now is top tier. Right? Is I can, whether I'm a small business, I can load my debt in there and build an app really quickly. Or if I'm a JP Morgan or, you know, a West Farmer's I can move legacy, you know monolithic architectures in there in months. I mean, these are six months transitions. When think about 20 years of work is now being transitioned to the cloud in six months. That's the difference. >> So measuring ease of views and time to value, time to market. >> Yeah. That's it's everything is time to value. No one wants to manage the infrastructure. In the Hudup world, no one wants to have expensive customized engineers that are, you know, keeping up your Hudup infrastructure any longer. Those days are completely over. >> Can you share an example of a joint customer, where really the joint value proposition that Immuta and Snowflake bring, are delivering some pretty substantial outcomes? >> Yeah. I, what we're seeing is and we're obviously highly incentivized to get them in there because it's easier on us, right? Because we can leverage their row and com level security. We can leverage their features that they've built in to provide a better experience to our customers. And so when we talk about large banks, they're trying to move Terra data workloads into Snowflake. When we talk about clinical trial management, they're trying to get away from physical copies of data, and leverage the exchanges of mechanism, so you can manage data contracts, right? So like, you know, when we think of even like a company like Latch, right? Like Latch uses us to be able to oversee all of the consumer data they have. Without like a Snowflake, what ends up happening is they end up having to double down and invest on their own people building out all their own infrastructure. And they don't have the capital to invest in third party tools like us that keep them safe, prevent data leaks, allow them to do more and get more value out of their data, which is what they're good at. >> So TCO reduction I'm hearing. >> Matthew Carroll: Yes, exactly. >> Matt, where are you as a company, you've obviously made a lot of progress since we last talked. Maybe give us the update on you know, the headcount, and fundraising, and- >> Yeah, we're just at about 250 people, which scares me every day, but it's awesome. But yeah, we've just raised 100 million dollars- >> Lisa Martin: Saw that, congratulations. >> Series E, thank you, with night dragon leading it. And night dragon was very tactical as well. We are moving, we found that data governance, I think what you're seeing in the market now is the catalog players are really maturing, and they're starting to add a suite of features around governance, right? So quality control, observability, and just traditional asset management around their data. What we are finding is is that there's a new gap in this space, right? So if you think about legacy it's we had infrastructure security we had the four walls and we protect our four walls. Then we moved to network security. We said, oh, the adversary is inside zero trust. So, let's protect all of our endpoints, right? But now we're seeing is data is the security flaw data could be, anyone could potentially access it in this organization. So how do we protect data? And so what we have matured into is a data security company. What we have found is, there's this next generation of data security products that are missing. And it's this blend between authentication like an, an Okta or an AuthO and auth- I'm sorry, authorization. Like Immuta, where we're authorizing certain access. And we have to pair together, with the modern observability, like a data dog, to provide an a layer above this modern data stack, to protect the data to analyze the users, to look for threats. And so Immuta has transformed with this capital. And we brought Dave DeWalt onto our board because he's a cybersecurity expert, he gives us that understanding of what is it like to sell into this modern cyber environment. So now, we have this platform where we can discover data, analyze it, tag it, understand its risk, secure it to author and enforce policies. And then monitor, the key thing is monitoring. Who is using the data? Why are they using the data? What are the risks to that? In order to enforce the security. So, we are a data security platform now with this raise. >> Okay. That, well, that's a new, you know, vector for you guys. I always saw you as an adjacency, but you're saying smack dab in the heart >> Matthew Carroll: Yes. Yeah. We're jumping right in. What we've seen is there is a massive global gap. Data is no longer just in one country. So it is, how do we automate policy enforcement of regulatory oversight, like GDPR or CCPA, which I think got this whole category going. But then we quickly realized is, well we have data jurisdiction. So, where does that data have to live? Where can I send it to? Because from Europe to us, what's the export treaty? We don't have defined laws anymore. So we needed a flexible framework to handle that. And now what we're seeing is data leaks, upon data leaks, and you know, the Snowflakes and the other cloud compute vendors, the last thing they ever want is a data leak out of their ecosystem. So, the security aspects are now becoming more and more important. It's going to be an insider threat. It's someone that already has access to that and has the rights to it. That's going to be the risk. And there is no pattern for a data scientist. There's no zero trust model for data. So we have to create that. >> How are you, last question, how are you going to be using a 100 million raised in series E funding, which you mentioned, how are you going to be leveraging that investment to turn the volume up on data security? >> Well, and we still have also another 80 million still in the bank from our last raise, so 180 million now, and potentially more soon, we'll kind of throw that out there. But, the first thing is M and A I believe in a recessing market, we're going to see these platforms consolidate. Larger customer of ours are driving us to say, Hey, we need less tools. We need to make this easier. So we can go faster. They're, even in a recessing market, these customers are not going to go slower. They're moving in the cloud as fast as possible, but it needs to be easier, right? It's going back to the mid nineties kind of Lego blocks, right? Like the IBM, the SAP, the Informatica, right? So that's number one. Number two is investing globally. Customer success, engineering, support, 24 by seven support globally. Global infrastructure on cloud, moving to true SaaS everywhere in the world. That's where we're going. So sales, engineering, and customer success globally. And the third is, is doubling down on R and D. That monitor capability, we're going to be building software around. How do we monitor and understand risk of users, third parties. So how do you handle data contracts? How do you handle data use agreements? So those are three areas we're focused on. >> Dave Vellante: How are you scaling go to market at this point? I mean, I presume you are. >> Yeah, well, I think as we're leveraging these types of engagements, so like our partners are the big cloud compute vendors, right? Those data clouds. We're injecting as much as we can into them and helping them get more workloads onto their infrastructure because it benefits us. And then obviously we're working with GSIs and then RSIs to kind of help with this transformation, but we're all in, we're actually deprecating support of legacy connectors. And we're all in on cloud compute. >> How did the pivot to all in on security, how did it affect your product portfolio? I mean, is that more positioning or was there other product extensions that where you had to test product market fit? >> Yeah. This comes out of customer drive. So we've been holding customer advisory boards across Europe, Asia and U.S. And what we just saw was a pattern of some of these largest banks and pharmaceutical companies and insurance companies in the world was, hey we need to understand who is actually on our data. We have a better understanding of our data now, but we don't actually understand why they're using our data. Why are they running these types of queries? Is this machine, you know logic, that we're running on this now, we invested all this money in AI. What's the risk? They just don't know. And so, yeah, it's going to change our product portfolio. We modularized our platform to the street components over the past year, specifically now, so we can start building custom applications on top of it, for specific users like the CSO, like, you know, the legal department, and like third party regulators to come in, as well as as going back to data sharing, to build data use agreements between one or many entities, right? So an SMP global can expose their data to third parties and have one consistent digital contract, no more long memo that you have to read the contract, like, Immuta can automate those data contracts between one or many entities. >> Dave Vellante: And make it a checkbox item. >> It's just a checkbox, but then you can audit it all, right? >> The key thing is this, I always tell people, there's negligence and gross negligence. Negligence, you can go back and fix something, gross negligence you don't have anything to put into controls. Regulators want you to be at least negligent, grossly negligent. They get upset. (laughs) >> Matthew, it sounds like great stuff is going on at Immuta, lots of money in the bank. And it sounds like a very clear and strategic vision and direction. We thank you so much for joining us on theCUBE this morning. >> Thank you so much >> For our guest and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's coverage of day two, Snowflake Summit '22, coming at ya live, from the show floor in Las Vegas. Be right back with our next guest. (Soft music)

>>Welcome back everyone to the Cube's live coverage of snowflake summit 22, we are live in Las Vegas. Caesar's forum, Lisa Martin, Dave Valante, Dave. This is day one of a lot of wall action on the, >>Yeah. A lot of content on day one. It, it feels like, you know, the, the reinvent fire hose yes. Of announcements feels like a little mini version of that. >>It does. That's a good, that's a good way of putting it. We've been unpacking a lot of the news. That's come out, stick around, lots more coming. We've got two guests joining us from tech systems global services. Please welcome Devon. Pania managing director and Shai Sheva of us senior and Shire. Shrivastava senior manager, guys. Great to have you on the cube. >>Thank you so much. Good to see you. And it's great to be in person. Finally, it's been a long UE, so excited to be here. >>Agree. The keynote this morning was not only standing room only, but there was an overflow area. >>Oh my goodness. We have a hard time getting in and it is unbelievable announcement that we have heard looking forward for an exciting time. Next two days here >>Absolutely exciting. The, the cannon shotgun of announcements this morning was amazing. The innovation that has been happening at snowflake and you know, this clearly as partner has been, it just seems like it's the innovation flywheel is getting faster and faster and faster. Talk to us a little bit, Devon about tech systems. Give us the audience a little bit of an overview of the company, and then talk to us about the partnership with snowflake. >>Sure. Thank you. Lisa tech system global services is a full stack global system integrator working with 8% of fortune 500 customers helping in accelerating their business as well as technology modernization journey. We have been a snowflake partner since 2019, and we are one of the highest accredited sales and technical certification with snowflake. And that's what we have earned as a elite partner or sorry, emerging partner with snowflake last year. And we are one of the top elite partner as well. >>Yeah. So since 2019, I mean, in the keynote this morning, Frank showed it. I think Christian showed it as well in terms of the amount of, of change innovation that's happened since 2019 Ellen, we were talking before we went live to share about the, the last two years, the acceleration of innovation cloud adoption digital transformation. The last two years is kind of knock your head back. You need a yeah. A whiplash collar to deal with that. Talk about what you've seen in the last three years, particularly with the partnership and how quickly they are moving and listening to their customers. >>Yeah. Yeah. I think last two years really has given pretty much every organization, including us and our customers a complete different perspective. And that's, that's the exact thing which Christian was talking about, you know, disruption, that's the that's that has been the core message, which we have seen and we've got it from the customers. And we have worked on that right from the get go. We have, you know, all our tools and technology. We are working hand in hand with snowflake in terms of our offerings, working with customers, we have tools. We talk about, you know, accelerators quote unquote that's that helps our customers, you know, to take it from on-prem systems to all the way to the snowflake data cloud and that too, you know, fraction of seconds. You talk about data, you talk about, you know, code conversion, you talk about data validation. So, you know, there are ample amount of things, you know, in terms of, you know, innovation, all workload, I've heard, you know, those are the buzzwords today, and those are like such an exciting time out here. >>So before the pandemic, you know, digital transformation, it was, it was sort of a thing, but it was, it was also a lot of complacency around it. And then of course, if you weren't in a digital business, you were out of the business and boom. So you talked to bang about the stack. You guys obviously do a lot in cloud migration. What's changed in cloud migration. And how is the stack evolving to accommodate that? >>That's a great question there when last two years, it's absolutely a game changer in terms of the digital transformation. Can we believe that 90% of world's data that we have produced and captured is in last two years? It's, isn't that amazing? Right. And what IDC is predicting by 20 25, 200 terabytes of data is going to be generated. And most of them is going to be unstructured. And what we are fascinated about is only 0.5% of unstructured data is currently analyzed by the organization to look at the immense opportunity in front of us and with Snowflake's data cloud, as well as some of the retail data cloud finance and healthcare data cloud launching, it's going to immensely help in processing that unstructured data and really bring life to the data in making organization and market leader. >>Quick, quick fall, if I could, why is, is such a small, why is so much data dark and not accessible to organizations? What's >>The, that's a, that's a great question. I think it's a legacy that we have been trained such a way that data has to be structured. It needs to be modeled, but last decade or so we have seen note it hasn't required that way. And all the social media data being generated, how we communicate in a world is all arm structure, right? We don't create structured data and put it into the CSV and things like that. It's just a natural human behavior. And I think that's where we see a lot of potential in mining that dataset and bringing, you know, AI ML capabilities from descriptive to diagnostic analysis, moving forward with prescriptive and predictive analytics. And that's what we heard from snowflake in Christian announce, Hey, machine learning workload is going to be the key lot of investment happening last 10 years. Now it's going to, you know, capitalize on those ROI in making quick decisions. >>Should you talk to me about those customer conversations? Obviously they have they've transformed and evolved considerably. Yeah. But for customers that have this tremendous amount of unstructured data, a lot of potential as you talked about dung, but there's gotta be, it's gotta be a daunting task. Oh yeah. But these days, every company has to be a data company to be successful, to be competitive and to deliver the experience that the demanding consumers expect. Yeah. How do you start with customers? Where do they start? What's that conversation like and how can tech systems help them get rid of that kind of that daunting iceberg, if you will and get around >>It. Yeah, yeah, yeah, exactly. And I think you got the right point there. Unstructured data is just the tip of the iceberg we are talking about and we have just scratched little surface of it, you know, it's it's and as the one was mentioning earlier, it's, it's gone out those days, you know, where we are talking about, you know, gigabytes of data or, you know, terabytes. Now we are talking about petabytes and Zab bytes of data, and there are so many, and that's, that's the data insight we are looking for and what else, you know, what best platform you can get better than, you know, snowflake data cloud. You have everything in there. You talk about programmability today. You know, Christian was talking about snow park, you know, that, that gives you all the cutting edge languages. You talk about Java, you talk about scale, you talk about Python, you know, all those languages. >>I mean, there were days when these languages, you need to bring that data to a separate platform, process it and then connect it. Now it is right there. You can connect it and just process it. So I think that's, that's the beginning. And to start the conversation, we always, you know, go ahead and talk to the customers and, you know, understand their perspective, know where they want to start, you know, what are their pain points and where they, they want to go, you know, what's their end goal, you know, how they want to pro proceed, you know, how they want to mature in terms of, you know, data agility and flexibility and you know, how do they want to offer their customers? So that's, that's the basically, you know, that's our, the path forward and that's how we see it. >>And just, >>Just to add on top of that, Dave, sorry about that. What we have seen with our customers, the legacy mindset of creating the data silos, primarily because it's not that they wanted it that way, but there were limitations in terms of either the infrastructure or the unlimited scalability and flexibility and accent extensibility, right? That's why those kind of, you know, work around has been built. But with snowflake unified data cloud platform, you have everything in unified platform and what we are telling our customers, we need to eliminate the Datalog. Yes, data is a new oil, but we need to make sure that you eliminate the Datalog within the enterprise, as well as outside the enterprise to really combine then and get a, you know, valuable insight to be the market leader. >>You know, when the cube started, it was 2010. And I remember we went to Hadoop world and it was a lot of excitement around big data and yes, and it turned out, it didn't quite live up to the expectations. That's an understatement, but we, we learned a lot and we made some strides and, and now we're sort of entering this, this new era, but you know, the, the, the last era was largely this big batch job right now, today. You're seeing real time, you know, we've, we've projected out real time in, is gonna become more and more of a thing. How do you guys see the, the sort of data patterns changing and again, where do you see snowflake fitting in? >>Yeah. Great question. And they, what I would have to say, just in a one word is removing the complexity and moving towards the simplicity. Why the legacy solutions such as big data didn't really work out well, it had all the capabilities, but it was a complex environment. You need to really be, you know, knowing a lot of technical aspect of it. And your data analyst were struggling with that kind of a tool set. So with snowflake simplicity, you can bring citizen data scientists, you can bring your data scientists, you can bring your data analysts, all of them under one platform, and they can all mine the data because it's all sitting in the one environment, are >>You seeing organizations change the way they architect their data teams? And specifically, are you seeing a decentralization of data teams or you see, you mentioned citizen data scientists, are you seeing lines of business take more ownership of the data or is it still cuz again, that big data era created this data science role, the data engineering role, the data pipeline, and it was sort of an extension of the sort of EDW. We had a, a few people, maybe one or two experts who knew how to use the system and you build cubes. And it was sort of a, you know, in order of magnitude more complex than that could maybe do more, but are you seeing it being pushed out to the lines of business? >>That's a great question. And I think what we are seeing in the organization today is this time is absolutely both it and business coming together, hand in hand. It's not that, Hey, it, you do this data pipeline work. And then I will analyze this data. And then we'll, you know, share the dashboards to the CEO. We are seeing more and more cohesiveness within the organization in making a path forward in making the decision intelligence very, very rapid. So I think that's a great change. We don't need to operate in silos. I think it's coming together. And I think it's going to create a win-win combination for our >>Customers. Just to add one more point, what the one has mentioned. I think it's the world of data democratization we are talking about, you know, data is available there, insights. We need to pull it out and you know, just give it to every consumer of the organization and they're ready to consume it. They are, they are hungry. They are ready to take it. You know, that's, that's, that's something, you know, we need to look forward for. >>Well, absolutely look forward to it. And as you talked about, there's so much potential it's we see the tip of the iceberg, right? There's so much underneath that guys. I wish we had more time to continue unpacking this, but thank you so much for joining Dave and me on the program, talking about tech systems and snowflake, what you guys are doing together and what you're enabling those end customers to achieve. We appreciate your insights. >>Yeah. Thank you so much. It's an exciting time for us. And we have been, you know, partnering with snowflake on retail data cloud launch, as well as some upcoming opportunity with manufacturing and also the financial competency that we have earned. So I think it's a great time for us ahead in future. So >>Excellent. Lots to come from Texas systems guys. Thank you. We appreciate your time. Thank you. >>Appreciate it. Thank you. Let it snow. I would say let >>It snow, snow. Let it snow. I like that. You're heard of your life from hot Las Vegas for our guests and Dave ante. I'm Lisa Martin. We are live in Las Vegas. It's not snowing. It's very hot here. We're at the snowflake summit, 22 covering that stick around Dave and I will be joined where next guests in just a moment.

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22. We are live at Caesar's forum in Vegas, Lisa Martin, with Dave ante, excited to welcome a VIP fresh from the keynote stage, the SAP, a product at snowflake Christian C Claman Christian. Thank you so much for joining us on the queue today. >>Thank you for having me very exciting. >>And thanks for bringing your energy, loved your keynote. I thought, wow. He is really excited about all of the announcements jam packed. We, and we didn't even get to see the entire keynote talk to us about, and, and for the audience, some of the things going on the product revenue in Q1 fiscal 23, 390 4 million, 85% growth, lot of momentum at snowflake. No doubt. >>So I think that the, the punch line is our innovation is if anything, gaining speed. Uh, we were over the moon excited to share many of these projects with customers and partners, cuz some of these efforts have been going on for multiple years. So, um, lots of interesting announcements across the board from making the existing workloads faster, but also we announced some new workloads getting into cyber security, getting into more transactional workloads with uni store. Um, so we're very excited. >>Well first time being back, this is the fourth summit, but the first time being back since 2019 a tremendous amount has changed for snowflake in that time, the IPO, the massive growth in customers, the massive growth in growth in customers with over 1 million in ARR, you talked about one of the things that clearly did not slow down during the last two years is innovation at snowflake. >>Yeah, that, that, that for, for sure, like, um, even though we, we had a, um, highly in the office culture, we did not miss a beat the moment that we said, Hey, let's all start doing zoom based calls. We, we did. So, uh, I dunno if you saw the, the first five minute minutes of my section in the keynote. Yeah. We, we originally talked about summarizing it and no we're gonna spend 40 minutes here. So we did a one minute clip and whatever gets flashed there. So no, the, the pace of innovation, I think it's second to none and maybe I'll highlight the something that we're very proud of. Snowflake is a single product, a single engine. So if we're making a query performance enhancement, it will help the cyber security workload and the low high concurrency, low latency workload. And eventually we're starting to see some of those enhancements all the way to uni store. So, so we get a lot of leverage out of our investments. What's >>Your favorite announcement? >>That's like picking children. Of course. Um, I think the native applications is the one that looks like, eh, I don't know about it on the surface, but it has the biggest potential to change everything like create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >>Well, I I've been saying for a while now that you have this application development stack over here, the database is kind of here and then you have the analytics and data pipeline stack. Those are those separate worlds. We, we talk about bringing data and AI and machine intelligence into applications. The only way that that is actually gonna move forward is if you bring those worlds together is a good example of that happening, um, within a proprietary framework, uh, it's probably gonna happen open source organically and you can sort of roll your own. Is that by design or is it just sort of happening? Well, >>The, the, they bring it all into a single platform obviously by design, cuz there is so much friction today on making all the pieces work together, which database do I use for transactions and how do I move data to my analytics system? And how do I keep system, uh, reference data in sync between the two? So, so it's complicated and our mission was remove all of this friction from, from, from the equation. Uh, the open source versus not the way we think about it is opensourcing open formats or even open APIs it's does it help us deliver the solution that we want for our customer? Does it help us solve their problems? In certain instances, it has done in the past and we've opened source frameworks in, in others. We mentioned at the keynote today, the, the integration of iceberg tables, that's an strong embrace of open technologies, but that does not mean that we want to continue to innovate in our formats. A lot of what you see in the open formats is because snowflake proprietary, uh, innovation. So, uh, we have a very clear philosophy around this. Well >>Like any cloud player, you have to bring open source tools in and make them available for your application developers. But take us through an example of, of uni store and specifically how you're embracing transaction data. What's a customer gonna actually do take us paint a picture >>For us. I I'm gonna give you a very simple use case, but I love it because it, it shows the power of the scenario today. When people are ingesting data into snowflake, you wanna do some book capping associating with those loads. So imagine I have, I dunno, a million files. How many of those files have I loaded? Imagine that one of those loads fail, how do you keep in sync? Whether the data made or not with your bookkeeping today, if you had to do it with a separate transactional database for the bookkeeping and the loading in, in snowflake, it is a lot of complexity for you to know what's where with uni store, you can just say, I'm gonna do the bookkeeping with these new table. It's called hybrid tables. The lows are transactional and all of this is a single transaction. So for, for anyone that has dealt with inconsistencies in database world, this is like a godsend. >>Okay. So my interpretation of that's all about what happens when something goes wrong >><laugh> which is a lot of the, everything about transactions. Yeah. It's what happens when goes wrong and goes wrong. Doesn't mean failures like goes wrong is when you're debiting money from your bank account, not having enough balance that counts as go wrong and the transactions should be aborted. So yes, transactions are all about conflict management and we're simplifying that in a broader set of use cases >>And, and in recovery. So you're, you're in fast recovery. So you're, you're the, the business impact of what you're doing is to sort of simplify that process. Is that the easy way to >>Boil down? Pretty much everything we do is about simplification. Like we, we we've seen organizations are large focusing on wrestling infrastructure as opposed to what are the business problems for a Frank who reference something that, that, that I believe very much in like, which is mission alignment. We are working on helping our customers achieve what they're set out to achieve, not giving them more technology for them to their goal to become, to wrestle the infrastructure. So it's all about ease of use all about simplification removal, friction, >>Just so if I may, so mission alignment, you know, you always hear about technology companies that, you know, provide infrastructure or a service, and then the customer takes that and, and, you know, monetizes it pretty much on their own. What the big change that I'm discerning from these announcements is you're talking about directly monetizing and participating in that monetization as a technology partner, but also the marketplace as well. >>Correct. And I would say in some ways this is not new. This has been happening for the last couple of years with data. Like if you just saw our industry data cloud launches, the financial services cloud, it comes with data providers that help you achieve specific outcomes on a specific industry. Mm-hmm <affirmative> what we're doing now is saying, it's not just data. Maybe it's some business logic, maybe it's some machine learning, maybe it's some user interface. So I think we're just turning the knob on collaboration and it's a continuation of what we've been doing. >>Talk a little bit more about mission alignment. When I heard Frank, Sweetman talk about that this morning. I always love that when I hear cultural alignment with organizations, but as you just said, it's really about enabling our customers to deliver outcomes to their customers as the SVP product. Can you, uh, talk a little bit about how the customers are influencing the product roadmap, the innovations and the speed with which things are coming out at snowflake? >>Yeah, so great question. We have several organizations at snowflake that are organized by vertical by industry. So the, the major sales organization is part of ed that the marketplace business development team is organized like that. We have a separate team that provides top leadership by industry vertical, um, globally. And then even within our solution engineering, there is verticals. So we have a longitudinal view of all the different functions and what do we need to do to achieve a set of use cases in a vertical? And all of those functions are in con constant communication with us on this is where the product is, um, seeing an opportunity or could do better for that vertical. So yeah, I can tell you, and obviously we love when, when there's alignment between those, but that's not always the case. You heard us talk about clean rooms now for some time, clean rooms are applicable to almost any industry, but it's red hot for media and advertising, third party, cookie deprecation, and all of that. So we, we get to, to see that lens, that our innovation is informed by industries. >>So we, we're seeing, obviously the evolution of snowflake we talked about in the keynotes today, you guys talked about 2019 and, you know, pre 2019, even it was to me anyway, your first phase was, Hey, we got a simpler EDW. You know, we're gonna pick that off and put it in the cloud and make it elastic and separate compute from storage, all that kind of cool stuff. And then during the pandemic, it was really IPO, but also the data cloud concept, you sort of laid that vision out. And now you're talking about application development, monetization, what I call the super cloud that layer. Right. Okay. So I, are >>You determin it best? >>Yes. You talk about this, uh, these announcements, how they fit into that larger vision where you're >>Going. Great question. The, the, the notion of the data cloud has not changed one bit. The data cloud thesis is that we want to provide amazing technology for our customers, but also facilitate collaboration and content exchange VR platform. And all that we did today is expand what that content can be. It's not just data or little helper function, it's entire applications, entire experiences. That is the, the summing up the, the, the impact of our announcements today. That, that that's the end of it. So it's still about the data cloud. >>So what is impressive to me is that you guys wouldn't couldn't have a company without the hyperscalers, right? It would be a lot different, right? So you built on top of that and, and now you have your customers building their own super clouds. I call it, I get a lot of grief for that term it's but the, the, the big area of criticism I get is, ah, that's just SAS. And I'm like, no, it's not, no, uh, I, I is everybody public who's announcing stuff. I, I better be careful, but you have customers that are actually building services, taking their data, their tooling, their proprietary information, and putting it on the snowflake data cloud and building their own clouds. Yeah. That's different. Then that's not multi-cloud, which is I can run on a different cloud and it's not, is it sass? If it feels like it's something new from a, from your perspective, is, is it different? >>I, I, I love that you called out that running on all clouds is not what we do right. This days, everyone is multi-cloud, you, you run on a VM or a container, and I multi-cloud check, no, we have a single platform that does multi-region multi-cloud but also cross region cross cloud globally, that that is the essence of what we're doing. So it, it is enabling new capabilities. >>I've I've also said, you know, in many respects, the super cloud hides, the underlying complexity, you think about things like exploiting graviton and a developer. Doesn't need to worry about that. You're gonna worry about that. Uh, but at the same time, they, the, as you get into the develop, the world of application development, some of your developers may want access to some of those cloud primitives. Are you providing both? What's the strategy there? >>Generally not in some areas, we, we, we, I would say bleed through some details that are material, but think of the reality of someone that wants to build a solution, it's really difficult to build an awesome solution in one cloud, Hey, you need to do this. What's the latest instance, and is gravity tank gonna help you or not all of that. Now do it for another one and then do it for another one. And I can tell you it's really difficult because we go through that exercise. Snowflake pouring to a new cloud is somewhere between one and two years of effort and not, not a small number of people because you're looking at security models and storage models. So that's the value that we give to anyone know, wants to build a solution and target customers in all three clouds. I >>Mean, people are still gonna do it themselves, but they're gonna spend a lot more and they're gonna lose their focus on what their real business is. And there'll still be that. I think that D DIY market is enormous for you guys, huge >>Opportunity. And there's also the question on what is the cost of that analysis and that effort. And can we amortize it on behalf of all of our customers? Like we talk about graviton, we have not talked about the many things that we evaluated that were not better price performance for our customers. That evaluation happened. That value was delivered by not moving there. >>And when you do it yourself, the curve looks like, okay, Hey, we can do it ourselves. We can make it pretty Inex. And then, and then the costs are gonna decline, but what really happens, like developing a mobile app, you gotta maintain it. And then if you don't have the scale and you don't have the engineering resources, you're just, the, the costs are gonna continue to go through the roof. I, >>I, I love that you compare it to mobile apps. Like, yeah. I still don't understand why every company that wants to build an app has to build two <laugh>. They got it. Yeah. There is no super cloud for the phone. >>Right. >>That's sort of our, our, our broad vision. Not yet. Not, not the phone, but the super cloud. Yeah, >>Yeah, absolutely. >>You >>Get it. This is, and you look out the ecosystem here. I mean, what a difference that you've been pointing this out, Lisa from, from, from 2019, a lot of buzz, it's all about innovation. You see this at, at thing at the reinvent is like the super bowl obviously. And you see that and it used to be, oh, how is, how is AWS gonna compete with snowflake and separate compute with stores? That's I, I feel like in a large way, that's all gone. It's like, okay, how do we like rise the whole, the whole industry? And that's really where the innovation is. >>We have an amazing partnership with AWS and they benefit from what we do. Yes. There's some competitive elements, but we're changing so many things creating so much opportunity that we're more aligned than not. Yeah. >>Last question for you is continuing on the part AWS partnership front, how does a partner like AWS and other partners, how do they fit into the data cloud narrative that you're talking about to customers? >>I would say that other than the one or two teams that are directly competitive, the rest of their teams are part of in data cloud. Like, uh, our relationship with SageMaker as an example is amazing. And a lot of what we wanna deliver to our customers is choice around machine learning, frameworks and tools. And they're part of the data cloud. We're working with them on how do you push down computation to avoid getting data out, to reinforce governance? So I, I would say that and, and go look at it that they have a hundred and something teams. So if two teams out of hundreds, uh, are, are the competitive element, we are largely aligned. And they're part of data cloud. >>Yeah. I mean, you, your customers consume a lot of compute and storage for, >>For a lot. Yes. >>AWS and, and also, you know, increasingly Azure and, and Google. I mean, it's, um, pretty amazing times, uh, Christian, I want to ask you about, um, couple of terms. Uh, one term that came up a couple of times today in Frank's keynote, he said, I'm not gonna call it a data mesh out kind of out of respect for the purists, which is cool, I thought, but then you had a customer stand up Geico and said, we're building a data. Mesh JPMC is, is speaking at this event, building a data mesh. And I look at things through that prism and say, okay, data mesh is about, you know, decentralization. Some, I I'd be curious as to whether or not you tick that box, but it's about building data products. It's about, uh, uh, self-service infrastructure. And it's about automated computational governance. You are actually tipping a lot of the ticking, a lot of those boxes and, and Mike, I guess the big one is, are, are you building a bigger walled garden? But I, I think you would say, no, it's a, it's a giant distributed network, but, but what, what, what do you say to that? We, >>The latter, the latter, yeah, giant distributed, open cloud and open in the sense that we want anyone to plug in and, and someone can say, well, but I cannot read your file formats. Sure. You can with what we announced today, but it's not about that. Our APIs are open. We have rest APIs. We have JDC ODC, probably most popular interfaces ever. Um, and we want everyone to be part of it. If anything, there's lots of areas that we would not want to go into ourselves cause we want partners and customers to go in there. So, no, we we're looking at a very broad ecosystem. We win based on the value created on top of the platform. Yeah. >>And I makes total sense to me. I mean, I think the imaculate conception of data mesh might be a purely open source version of snowflake. I just don't see that happening anytime soon. And so I, I think you're gonna, you are, I wrote about this creating a defacto standard and >>Exactly, and, and I don't like to get into the terminology that, oh, is the data measure? Not, no go look at the concepts like people used to say, but snowflake is not a data lake. Okay. What is the data lake? It's just a pattern. And if you follow the pattern and you can do it, that's fine. Then there's the, uh, emotional quasi-religious overlay open versus not, I think that's a choice. Not necessarily the concept, >>It's a moving target. I mean, I Unix used to be open. You know, that was the, I agree. Now, the reason why I do think the data mesh conversation is important is because Shaak Dani, when she defined data mesh, she pointed out in my view. Anyway, the problems of getting value outta data is that you go through these hyper specialized teams and they're they're blockers in the organization. And I think you in many respects are attacking that. And it's an organizational issue. >>The, the insights in the pattern are a hundred percent value and aligned with what we do, which is they, you want some amount of centralization, some amount of decentralization living in harmony. Uh, yeah. I have no problem with, with terminology. >>And the governance piece is, is, is massive. Especially it's the, the picture's becoming much more clear. Um, whatever's in the data cloud is a first class citizen, right? And you give all these wonderful benefits. I mean, the interesting thing, what you're doing with Dell and, and pure, I, I asked you that on the analyst call, it's a start. You know, I, I, I mean, >>And I said it briefly in, in, in the keynote this morning, we're publishing a set of standard conformance tests. So any storage system can plug into data cloud. >>Yeah. >>And by the way, it's based on S three APIs, another defect of standard. Like it's not a standard, but everyone is emulating that. And we're plugging >>Into that. Yeah. Nobody's complaining against, against S3 API >>About it is a, oh, it's not a Apache project. We shouldn't, who cares. Everyone has standard horizon net. That's it? >>Well, we've seen the mistakes of the past with this. I mean, look at, look at Hadoop, right? There was this huge battle between, you know, Cloudera and Horton works and map, oh, map bar is proprietary. Oh, Horton works is purely open. Cloudera is open. They're, they're all gone now. I mean, not gone, but they're just, they didn't have it. Right. You know, they, they got unfocused. I go back to Frank's book. They were trying to do too much to, to too many of those, the, the, the zoo animals and you can't fund it all >>To be effective for us. It's very important. I can give you, I don't know, 20 announcements or 50 announcements from the conference, but they're all going a singular goal. And it's, this do not trade off governance of data with the ability to get value out of data. That's everything we do. >>And that's critical for every company in every industry these days that has to be a data company to be, to survive, to be competitive, to be able to extract value from data. If data's currency, how do I leverage a tool like snowflake to be able to extract insights from it that I can act on and create value for my organization, Geico was on stage this morning. Everyone knows Geico and their beloved, um, gecko. Yeah. Is there another customer that you had that you think really articulates the value of the data cloud and to Dave's point how snowflake is becoming that defacto standard data platform? >>Well, we had Goldman Goldman Sachs on stage as well today. And he, he, he, he mentioned it that people think of Goldman as investment banking and all of that, but no, at the heart of what they do, there's a lot of data. And how do they make better decisions? So I think we could run through 20 different examples cuz your premise is the most important. Everything is a data problem. If it is not a data problem, you're not collecting the right data and getting the sense that you could be getting. >>These guys are public, right. >>Adobe. >>Yeah. Right. Adobe's doing it. Yeah. I dunno if the other one is, I don't wanna say, I'll have to ask you off camera, but the other financial firm building a super cloud, right. <laugh> yeah. I call it super cloud. So let be taking advantage of uni store. Yeah. To bring different data types in and monetize it. That's to me, that's the future of data. That's that's been the holy grail, right. >>We, we tried to emphasize that this is, is not a, Hey six, six months ago. We decided to do this. No, this is years in the making mm-hmm <affirmative>, which is why we were so excited to finally share it. Cuz you don't wanna say three years from now, we're gonna have something. No, it was the, now we have it. We have it in preview and it's working at it is as close to the holy grail as it gets. >>Yeah. I mean, look, pressure's on Kristin. Let's face it. Enterprise data warehouse failed to live up to the promises. Uh, certainly the data lakes fail to deliver master data management, all that's a Hadoop, all that stuff. There was a lot of hype around that. And a lot of us got really excited. Me included and then customers spent and they were underwhelmed. Yeah. So you know, you, you, you gotta deliver, you say it, you gotta do it. >>And correct. And then the, the other thing is I would say all of those waves of technology, there was no real better choice. >>Right. They added value. I wouldn't >>Debate that. You have to give it a shot. Like when you've bought 20 different appliances and you have all these silos and someone sells you, Hey, Hadoop will unify it. It sounds good. Just didn't do it. >>Yeah. And no debate that it brought some value for those that were agree. Sophisticated enough to deploy it. And I agree. Yeah. But, but this is a whole different ball game. >>Oh, everything we want to do is democratize and simplify mm-hmm <affirmative> yeah. We could go build something that I don't know. 10 companies in the world could use. That's not the sweet spot. Like how do we advance like the, the state of value generation in the world? That's the scale that we're talking about is go make it easy, accessible for everyone. >>Governed >>Governance and imperative this these days it's law. Yes. So >>Yeah, you have to, but it's not, it's, that's a, that's a ch really difficult challenge to create what I'll call automated or computational governance in a federated manner. That's not trivial. >>And that's our thesis. Everything we're doing is snow park, big announcement today. Python. I I've had people tell me well, but Python should be easy to host the Python run time. Like you can do it. Like I think in a week it took us years. Why? Oh, secure. Oh, details a lot. And <inaudible> mentioned it like securing. That is no easy, uh, feed >>Christian. Thank you so much for joining Dave and me bringing your energy from the keynote stage to the cube, set, breaking down some of the major announcements that have come out today. There's no doubt that the flywheel of innovation at snowflake is alive well and moving quickly, >>Innovation is, uh, at an all time hat snowflake. Thank you for having me. All >>Right. Our pleasure Christian from our guest, Dave ante, Lisa Martin here live in Las Vegas at Caesar's forum covering snowflake summit 22. We right back with our next guest.

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22 live from Las Vegas. Caesar's forum. Lisa Martin here with Dave Valante. We've got a couple of guests here. We're gonna be talking about every day. AI. You wanna know what that means? You're in the right spot. Kurt UL joins us, the chief customer officer at data ICU and the mod Conn, the head of AI and ML strategy at snowflake guys. Great to have you on the program. >>It's wonderful to be here. Thank you so much. >>So we wanna understand Kurt what everyday AI means, but before we do that for the audience who might not be familiar with data, I could give them a little bit of an overview. What about what you guys do your mission and maybe a little bit about the partnership? >>Yeah, great. Uh, very happy to do so. And thanks so much for this opportunity. Um, well, data IKU, we are a collaborative platform, uh, for enterprise AI. And what that means is it's a software, you know, that sits on top of incredible infrastructure, notably snowflake that allows people from different backgrounds of data, analysts, data, scientists, data, engineers, all to come together, to work together, to build out machine learning models and ultimately the AI that's gonna be the future, uh, of their business. Um, and so we're very excited to, uh, to be here, uh, and you know, very proud to be a, a, a very close partner of snowflake. >>So Amad, what is Snowflake's AI strategy? Is it to, is it to partner? Where do, where do you pick up? And Frank said today, we, we're not doing it all. Yeah. The ecosystem by design. >>Yeah. Yeah, absolutely. So we believe in the best of breed look. Um, I think, um, we, we think that we're the best data platform and for data science and machine learning, we want our customers to really use the best tool for their use cases. Right. And, you know, data ICU is, is our leading partner in that space. And so, you know, when, when you talk about, uh, machine learning and data science, people talk about training a model, but it's really the difficult part and challenges are really, before you train the model, how do you get access to the right data? And then after you train the model, how do you then run the model? And then how do you manage the model? Uh, that's very, very important. And that's where our partnership with, with data, uh, IKU comes in place. Snowflake provides the platform that can process data at scale for the pre-processing bit and, and data IKU comes in and really, uh, simplifies the process for deploying the models and managing the model. >>Got it. Thank >>You. You talk about KD data. Aico talks about everyday AI. I wanna break that down. What do you mean by that? And how is this partnership with snowflake empowering you to deliver that to companies? >>Yeah, absolutely. So everyday AI for us is, uh, you know, kind of a future state that we are building towards where we believe that AI will become so pervasive in all of the business processes, all the decision making that organizations have to go through that it's no longer this special thing that we talk about. It's just the, the day to day life of, uh, of our businesses. And we can't do that without partners like snowflake and, uh, because they're bringing together all of that data and ensuring that there is the, uh, the computational horsepower behind that to drive that we heard that this morning in some of the keynote talking about that broad democratization and the, um, let's call it the, uh, you know, the pressure that that's going to put on the underlying infrastructure. Um, and so ultimately everyday AI for us is where companies own that AI capability. They're building it themselves very broad, uh, participation in the development of that. And all that work then is being pushed down into best of breed, uh, infrastructure, notably of course, snowflake. Well, >>You said push down, you, you guys, you there's a term in the industry push down optimization. What does that mean? How is it evolving? Why is it so important? >>So Amma, do you want to take a first step at that? >>Yeah, absolutely. So, I mean, when, when you're, you know, processing data, so saying data, um, before you train a, uh, a model, you have to do it at scale, that that, that data is, is coming from all different sources. It's human generated machine generated data, we're talking millions and billions of rows of data. Uh, and you have to make sense of it. You have to transform that data into the right kind of features into the right kind of signals that inform the machine learning model that you're trying to, uh, train. Uh, and so that's where, you know, any kind of large scale data processing is automatically pushed down by data IQ, into snowflakes, scalable infrastructure. Um, so you don't get into like memory issues. You don't get into, um, uh, situations where you're where your pipeline is running overnight, and it doesn't finish in time. Right? And so, uh, you can really take advantage of the scalable nature of cloud computing, uh, using Snowflake's infrastructure. So a lot of that processing is actually getting pushed down from data I could down into the scalable snowflake compute engine. How >>Does this affect the life of a data scientist? You always hear a data scientist spend 80% of the time wrangling data. Uh, I presume there's an infrastructure component around that you trying, we heard this morning, you're making infrastructure, my words, infrastructure, self serve, uh, does this directly address that problem and, and talk about that. And what else are you doing to address that 80% problem? >>It, it certainly does, right? Uh, that's how you solve for, uh, data scientists needing to have on demand access to computing resources, or of course, to the, uh, to the underlying data, um, is by ensuring that that work doesn't have to run on their laptop, doesn't have to run on some, you know, constrained, uh, physical machines, uh, in, in a data center somewhere. Instead it gets pushed down into snowflake and can be executed at scale with incredible parallelization. Now what's really, uh, I important is the ongoing development, uh, between the two products, uh, and within that technology. And so today snowflake, uh, announced the introduction of Python within snow park, um, which is really, really exciting, uh, because that really opens up this capability to a much wider audience. Now DataCo provides that both through a visual interface, um, in historically, uh, since last year through Java UDFs, but that's kind of the, the two extremes, right? You have people who don't code on one side, you know, very no code or a low code, uh, population, and then a very high code population. On the other side, this Python, uh, integration really allows us to, to touch really kind the, the fat center of the data science population, who, uh, who, for whom, you know, Python really is the lingua franca that they've been learning for, uh, for decades now. Sure. So >>Talking about the data scientist, I wanna elevate that a little bit because you both are enterprise customers, data ICO, and snowflake Kurt as the chief customer officer, obviously you're with customers all the time. If we look at the macro environment of all the challenges, companies have to be a data company these days, if you're not, you're not gonna be successful. It's how do we do that? Extract insights, value, action, take it. But I'm just curious if your customer conversations are elevating up to the C-suite or, or the board in terms of being able to get democratize access to data, to be competitive, new products, new services, we've seen tremendous momentum, um, on, on the, the part of customer's growth on the snowflake side. But what are you hearing from customers as they're dealing with some of these current macro pains? >>Yeah, no, I, I think it is the conversation today, uh, at that sea level is not only how do we, you know, leverage, uh, new infrastructure, right. You know, they they're, you know, most of them now are starting to have snowflake. I think Frank said, uh, you know, 50% of the, uh, fortune 500, so we can say most, um, have that in place. Um, but now the question is, how do we, how do we ensure that we're getting access to that data, to that, to that computational horsepower, to a broader group of people so that it becomes truly a transformational initiative and not just an it initiative, not just a technology initiative, but really a core business initiative. And that, that really has been a pivot. You know, I've been, you know, with my company now for almost eight years, right. Uh, and we've really seen a change in that discussion going from, you know, much more niche discussions at the team or departmental level now to truly corporate strategic level. How do we build AI into our corporate strategy? How do we really do that in practice? And >>We hear a lot about, Hey, I want to inject data into apps, AI, and machine intelligence into applications. And we've talked about, those are separate stacks. You got the data stack and analytics stack over here. You got the application development, stack the databases off in the corner. And so we see you guys bringing those worlds together. And my question is, what does that stack look like? I took a snapshot. I think it was Frank's presentation today. He had infrastructure at the lowest level live data. So infrastructure's cloud live data. That's multiple data sources coming in workload execution. You made some announcements there. Mm-hmm, <affirmative>, uh, to expend expand that application development. That's the tooling that is needed. Uh, and then marketplace, that's how you bring together this ecosystem. Yes. Monetization is how you turn data into data products and make money. Is that the stack, is that the new stack that's emerging here? Are you guys defining that? >>Absolutely. Absolutely. You talked about like the 80% of the time being spent by data scientists and part of that is actually discovering the right data. Right. Um, being able to give the right access to the right people and being able to go and discover that data. And so you, you, you go from that angle all the way to processing, training a model. And then all those predictions that are insights that are coming out of the model are being consumed downstream by data applications. And so the two major announcements I'm super excited about today is, is the ability to run Python, which is snow park, uh, in, in snowflake. Um, that will do, you know, you can now as a Python developer come and bring the processing to where the data lives rather than move the data out to where the processing lives. Right. Um, so both SQL developers, Python developers, fully enabled. Um, and then the predictions that are coming out of models that are being trained by data ICU are then being used downstream by these data applications for most of our customers. And so that's where number, the second announcement with streamlet is super exciting. I can write a complete data application without writing a single line of JavaScript CSS or HTML. I can write it completely in Python. It's it makes me super excited as, as a Python developer, myself >>And you guys have joint customers that are headed in this direction, doing this today. Where, where can you talk about >>That? Yeah, we do. Uh, you know, there's a few that we're very proud of. Um, you know, company, well known companies like, uh, like REI or emeritus. Um, but one that was mentioned today, uh, this morning by Frank again, uh, Novartis, uh, pharmaceutical company, you know, they have been extremely successful, uh, in accelerating their AI and ML development by expanding access to their data. And that's a combination of, uh, both the data ICU, uh, layer, you know, allowing for that work to be developed in that, uh, in that workspace. Um, but of course, without, you know, the, the underlying, uh, uh, platform of snowflake, right, they, they would not have been able to, to have re realized those, uh, those gains. And they were talking about, you know, very, very significant increases in inefficiency everything from data access to the actual model development to the deployment. Um, it's just really, really honestly inspiring to see. >>And it was great to see Novartis mentioned on the main stage, massive time to value there. We've actually got them on the program later this week. So that was great. Another joint customer, you mentioned re I we'll let you go, cuz you're off to do a, a session with re I, is that right? >>Yes, that's exactly right. So, uh, so we're going to be doing a fireside chat, uh, talking about, in fact, you know, much of the same, all of the success that they've had in accelerating their, uh, analytics, workflow development, uh, the actual development of AI capabilities within, uh, of course that, uh, that beloved brand. >>Excellent guys, thank you so much for joining Dave and me talking about everyday AI, what you're doing together, data ICO, and snowflake to empower organizations to actually achieve that and live it. We appreciate your insights. Thank you both. You guys. Thank you for having us for our guests and Dave ante. I'm Lisa Martin. You're watching the Cube's live coverage of snowflake summit 22 from Las Vegas. Stick around our next guest joins us momentarily.

>>Hello. And welcome back to the cubes coverage here. Live in Las Vegas for 80 bucks. Re-invent 2021. We're in person been two years since the cube has been on the ground here at a live event, it's a hybrid event. Check them out online. AWS has got to reinvent site as well as cube online. I'm Jennifer, your host got a great guest here from Latin America. Honeywell is VP of Latin America for AWS, a lot of global change, but the regions, a lot of great stuff, cultural integration. If you will, a skills people all around the world using cloud compute. Jaime's great, but coming on the cube. Thanks for coming on. >>Thank you, John. Thank you. It's a pleasure to be >>Here with you. Um, I wish I could speak in the native tongue, but I can't. I ended it, but I know online there's some special rooms that people have on the cube sites. So a lot of tech, a lot of cloud native in the world, I'm seeing Latin America and all the events we've done had great participation in the cloud ecosystem in Latin America, a lot of young talent, a lot of things happening. What's what's going on. >>Well, as you can see around us today, a lot of things are happening in the cloud. We are in this inflection point in the industry of technology that is accelerating innovation, accelerating transformation all over the world. And obviously Latin America is not an exception. We're seeing this momentum. We're seeing large enterprise companies leveraging the cloud to transform their customer experiences, to drive innovation. We're seeing startups to drive competitiveness and try to compete with the world. And that's also enabling a lot of younger generations to move faster, to innovate, to dream big and drive new ideas. So you're seeing that same momentum in Latin America, all across the region. But this is the one John and we, and we are seeing this happening for many years ahead. >>You know, I love inflection points and I've been saying this and just wrote a blog post about it on siliconangle.com that we are now at another inflection point where cloud is going next gen, where in any kind of revolution, every inflection point, this cultural revolution starts with the young people. And I've never seen an impact with Kubernetes and microservices and the modern approach of the younger generation. It's like if I was 20, that'd be a kid in the candy store. What I don't have to build land is there for me. I got to don't have to provision any servers. So the I'm seeing an impact for the younger generation around cloud and it's global phenomenon. What's what's going on in the younger talent in Latin America. Well, >>Let's just say, I mean, generations see inflection as opportunity, opportunity to make new things happen to, as I said to dream big and actually enabled their ideas to become a reality. And that's where you're seeing all across the region. You see this in Brazil, you see these in Argentina, you see this Columbia, Mexico, largest startup communities that are competing with the world. And you have, you know, we have an example like Newman that was here this morning, like started seven years ago, 2014 with a view transforming the financial services experience. That's where we're seeing all across Latin America, because >>The young kids slinging APIs around with containers. Now you've got the container movement. We had a great showing from Brazil and our DockerCon event. Um, net, very notable, um, intelligence coming out of that area. Amazing young talent. I'm just blown away by the, by the work, but in the region itself is still transformation. So I know you're, you're well known for doing really big deals at AWS. Uh, I can say that big banks, multimillion dollar deals. So there's growth there there's existing business transforming while new entrepreneurs are coming in. It's kind of a best of both worlds. What's the, what's the growth look like. >>Uh, as you mentioned, very large enterprises understand that the cloud and a transformation of culture is going to allow them to innovate them, to have loyal customers, every large enterprise customer. We're thinking about different ways to contact their customers, transforming the experience you're seeing customers like like Bancolombia that are migrating their legacy systems into the cloud in order to make faster decisions, to increase agility, to increase innovation and lead their people. Because at the end journey is all about the people that their people build on behalf of their customers and transform their experiences. >>You know, one of the things I noticed during the pandemic, and I'd love to get your reaction to this because I know you're living that as well every day, even before the pandemic, but since everything went virtual now hybrid, you're seeing a very low friction point to get in and collaborate. There's almost a new social construct, connective tissue between no boundaries. So you can have an event like here at reinvent, we're in person, but yet there's an online community digitally engaging. So we're starting to see cell formation where people around the world are getting together. How has it impacted how you manage and how you engage with your customers in your region? >>Well, as I said, it's a combination of many things. Our customers are still like people in person. That's why we have our business in Brazil. We have obviously in Argentina, Colombia, Mexico, Costa Rica, Peru, we still have presses. There we are where we work very close with our customers. We understand they need and what they want to do. But now, for example, during the last two years, I've had the opportunity to leverage in technology, be present in what we call virtual trips in most of our countries full day experiences. And I have to tell you at the beginning, I was concerned. I didn't have the opportunity to meet some of these people before today. When I see them in person here in re-invent this like, as if we had met from four. So as you say is the hybrid experience that allows us to be in-person with our customers, with our partners across the region, but also in a remote base, having the opportunity to build the same relations. And that's what technology is enabling better experience, faster innovation and moral agility and growth all across Latin America. >>So it's one of the things I talked to Adams Leschi about before reinvent a week ago, um, on a bank exclusive interview with him was he was very adamant about the clouds expanding everywhere. Honestly, you've got the edge in manufacturing, ADP percent everywhere, but he mentioned the regions, the continued growth of regions. It's been 10 years since Latin America. How's that impacted what you got going on there. And what's next from a region perspective. And how has that changed the landscape >>While you're touching? John is probably the most important thing we're seeing. You're absolutely right. We started 10 years ago, December 14 in Brazil with an office and a region there Caesar will launch offices in most of our countries. Now the important thing here is how our technology is enabling our companies, Latin American companies. We have 17 million companies in Latin America be more competitive. You know, some examples, I just mentioned Nubank, but we have that is competing with very large companies. You have Bancolombia you have GBM in Mexico. So what we're seeing is our companies be able by leveraging the latest and best technology to compete with the world and to drive that competitiveness that we need. The other thing about talent. If we enable and empower our Latin American talent or builders to build these new experiences, that's, what's going to allow the region to accelerate their growth, their competitiveness, and their social benefits. >>It was interesting too, is that you can see from the trends to do that. You can do it really fast now instantly. So it's, it's an amazing opportunity. Um, I gotta ask you while you're here, cause I'm really curious. I'm sure the viewers will be as well. What's what's going on in Latin America from a trend stamp. What's the vibe like? What's the, what's the environment like what's the, what's the mindset like there in those regions, from an entrepreneurship perspective, from a cloud enablement perspective, a cultural perspective, what's your report? How would you report on that? >>First of all, we're seeing the cloud accelerate all across Latin America. And I, and as I said, it's really day one for all of us. The other thing is that our customers are understanding that digital transformation is not a technology transformation. It's a cultural transformation leverage by leveraging technology for that to happen. It's about people. It's about mechanisms in the company. It's about the way companies make decisions. And that's why, why the power of the cloud is so important in impact to empower these people, to make things happen. In fact, what we're seeing in Latin America is CEOs of some of these companies like Bancolombia CEO is very engaged in this transformation where he's reviewing technology, he's understanding the cloud because that's how they realize or how they understand the importance of, you know, changing their companies, focused on their customers. The other thing is Latin American companies understand that they need to understand their customer needs work backwards from that and leverage that their technology, the cloud in order to improve the experiences of the >>Costumes. So I had to put you on the spot on a question. I gotta ask you, you know, if this is 10 years of re-invent, we've been here for nine. And I remember the first one we went with the second one. Wasn't many people here were like getting guests from the hallway. Hey, come on up on the cube. And now we can't, there's no open spots. Um, 15 years is how old Amazon is Amazon web services. So, so as Adam takes over and you have Amazon going in the next 15 years, what's your vision on how that evolves? Because you know, you're looking at the pandemic ending and pandemic has proven to a lot of people that digital works here, but as exposed what doesn't work, you can't hide the ball anymore if your business, but you're exposed. If you're in the cloud or you've got modern software, if no one's using it, it's not working change it. You can do it fast. So the whole hiding behind, you know, I bought this project, what this software, old guard, new guard, I mean, you can't hide the volume where, so that changes things, but also the creativity of refactoring business is also there. So you got, you got fear. I don't can't hide the ball and you're exposed to opportunity. >>What's your reaction to that? In fact, what I was going to say is where we see some opportunity. I mean, if you see 15 years side where you see, first of all, is all customers in Latin America or everywhere else leveraging the cloud. That's the most important thing. Number two, people leveraging technology to make things happen. It's about building. It's about me. And we talk about this before is when you realize that people are looking for better ways to improve their experience, launching the startups. And this is in finance, in the financial services. This is in manufacturing. This is in all the different industries across Latin America. We see opportunity. The other one, John is a region like Latin America understands that with people you need to enable them. It's about talent. And in order to enable talent, you need to educate them. So in AWS, we're actually investing a lot of time and effort to what to give them the best training content in their local language to launch programs that allow them to innovate like activate that enables to start off to launch. So what we're doing is giving Vilders younger generation tools to be more successful and again, dream big and make things happen now. So the next 15 years, Saba opportunity transforming faster decision-making agility in the way companies move and also driving competitiveness in Latin America to be able to compete in a globalized environment because everything is interconnected and it's about global reach today. And that's why we need our talent to invest, educate, to drive the transformation of the region. >>The global connectedness is a real point there. Great insight. I think the cultural revolutions here, the younger generations engaged existing businesses transforming, which means if they don't do it right, they're going to lose it to the other guy, other people. So I have, okay. Final question for you. Thanks for coming on. Appreciate your time. I know you're busy looking at the pandemic ending. What's the major patterns that you're seeing in Latin America, around companies strategies to transform out of the pandemic, a growth strategy, because everyone I talked to was like, we're going to come out with a tailwind and we're going to be on the upward slope. Obviously they're using cloud of course, but is there a pattern of that coming out of the pandemic with an upward growth? So >>We're seeing all across Latin America companies looking for better ways to reach our customers. That is the fact traditional touch points are not enough. Now they are building on top of that. So we are seeing Latin American companies invested, transform their legacy systems in order to look for different ways to approach the customers. Number two, we're seeing Latin American companies to leverage data in order to make better, more informed and faster decisions and to scale their business and accelerate and empower their teams. We're seeing companies in Latin America, investing in tools to let their people make things happen. As I said before, cultural transformation, digital transformation is about people. It's about fast decision making and it's about leveraging the technology to make it happen. We're seeing a lot of startup communities across our countries, new ideas, taking place. And as you know, AWS has always been focused on let known supporting startups and those new ideas. So we're seeing a lot of things happen in the region. A lot of momentum, a lot of growth. And what we're seeing is the cloud enabling that growth, that opportunity that you were talking about with our view that 15 years out, a lot of new business models are going to be late making hat. They can have >>Great point. I think just to highlight that one key thing, talent, you just add talent to the cloud capabilities. You can get there faster, you do it with a team, even better. Um, collaborations changing. Just the ability to capture opportunities are now faster than when we were growing up. They have a better don't think literally that you wish you were 20. Again, I do with all this code out there. >>And that's where we say it's about the people. And I can tell you from one of our biggest investments, my biggest investments is given the talent that opportunity, given our best training content in local language so they can learn new and better ways of making things happen. So again, as I said, leveraging supporting startups to grow. So all the problems around talent for Latin American cities, for our customers and our partners, because at the end, we understand that our partners expand our solutions to the market. And these are partners that allow us to be present in the many countries that are part of Latin America. >>Well, we'd love your vision, love your, love, your, your insight. And we will have a cube region in your area, and we're going to contact you. The cube will open their doors for the Latin America community. So look for that this year. Thanks for coming on. Now, >>joining you and hosting you in our countries. You're going to see a lot of enthusiasm, passion, and growth and opportunity Latina, >>A lot of great action. The younger generations engaged the older generations transforming the business models. The cloud is going next, gen. This is the cube bringing all the live action. You're watching the queue, the leader in global tech coverage. I'm John Farrow, your host. Thanks for watching.

>>Fly from San Francisco. It's the cube covering RSA conference, 2020 San Francisco brought to you by Silicon angle media >>and welcome back. You're ready Jeffrey here with the cube. We are a day four here at the RSA conference in Moscone Thursday. We've been going all day Monday, Tuesday, Wednesday, Thursday. It's a huge conference over 40,000 people, you know, kind of the first big us conference after the mobile world Congress thing with a coronavirus. So we were all kind of curious to see how it would work out. There was some companies that pulled out but you know Rohit and the team stayed the course, they got the support they needed from the city and it's turned out to be quite a show. So I'm sure there's a lot of people all over the industry kind of watching this as an indicator of how do you execute a conference and these kinds of crazy times. So we're excited for our next guest. He's Andy Smith, the senior vice president of marketing for Centrify. >>Andy, great to see you. Good to be here, Jeff. Doing great. So you said you've been coming to this show for a while, you're a seasoned veteran of the industry. First off kind of general impressions of this show versus versus other kinds of RSAs you've been doing in the past. It's super interesting to watch. It ebbs and flows of the security industry, right? I mean I've been 15 years over the past 25 I've been at this show and you've seen it be big and then shrink down, you know, to one hall and then the two halls again. I mean what's interesting the last couple of years is it's, it's big again, like security is hot. We know budgets are going up, a breach, cultures out there. And so, you know, the IC, the RSA show is a reflection of what's happening with the industry when you look at the size and number of attendees. >>Right. The other kind of theme this year was the human centric, uh, boat. And we had row head guy on just a little bit earlier in his keynote. I thought it was really interesting. It was not about security per se. It was not about threats and detection. It was really about stories and narratives and peoples and kind of taking that back as an industry. I wonder, you know, kinda your impression as this kind of human centric theme as we're surrounded by tech tech and more tech. It is, if you think about human centric, it's a, it's a big piece of your, your security strategy, right? I mean, uh, what, there was just this morning, uh, one of the sharks got fished, right? Lost $400,000. One of the, yeah. And so, uh, you know, educating people about looking out for fishing attacks, right? Uh, uh, looking at insiders who are one of our biggest threats and you know, they're, they're a huge piece of this is not technology at all. >>Right? I thought Wendy's keynote was great too from Cisco. Talking about everything we do on computers is about clicking. And yet we tell people, you know, click the download the patch, but don't click on anything else. And really, you know, kind of taken an approach that people need to be part of the solution. They're not these horrible people that keep clicking on the wrong things, but you really need to integrate them into your strategy. Yeah, absolutely. I mean, it's about educating your workforce. It's about educating consumers, right? Whether you're talking B to C security or whether you're talking to me to be that human element and educating to be diligent right to you, you got to know a little bit about how to look for something that might be suspicious and know what is, what you should be clicking on, what you shouldn't. There's, there's not a lot of technology that can solve that for you. >>It's getting out and, and, and making sure people are educated. And unfortunately, the bad guys have been working hard on their grammar and, uh, and doing all the AI on the background. So, you know, it's not, a lot of things today are not easily identifiable like they used to. They've gotten, that's no longer really kind of a baseline, a hope not to click that thing. They've gotten way better. Right? So rather than these attacks that are spray and pray, they're going after, you know, just going after anybody. They can, they're targeted now. Right? So spear fishing, right. And uh, and so specific individuals. And that's why one of the things that, that is a little bit coming up at this show and something that we talk about is identity centric security. So that you've got a tie, that kind of human element to your security. >>You know, there's network centric, but getting identity centric and tying that human element to your security aspect, making sure the security, the identity technologies and the security technologies are working together. That is brings that human element into your own security strategy. And when you, when you talk about identity, how should people be thinking about identity? Because clearly we see the kind of the rise in multi-factor now, right? We have to do, we have to go to the, our phones all the time with the code. Now we're hearing people, you know, can spoof identity, they can Smoove faces. I guess identity is not a face, but you know, some of these indicators of identity. So when you help people think about identity, what are some of the factors they should think about? What are the things they don't but they should be thinking about? Yeah, yeah. >>I mean some of the things that we talked a lot about is multifactor authentication. So although yes, right, real sophisticated people can have ways of getting around that, but most attackers and hackers are lazy, right? They're going to go for somebody who's got no multi-factor in place, like even doing the basics is way better than doing nothing. I mean, the statistics bear out that you do a little something right? And then you can always step it up and get more sophisticated where you've got tokens that you have to put your finger on, right? And you know, you can get smart cards and all those kinds of things. You can get much more sophisticated, but multi-factor in general works. I mean, you're just going to take it a far bit above. But what's interesting about identity, because we always think of humans, right? But when we talk identity, where this market is going is identity is machines. >>You have to give a machine an identity, you have to give a service account, an identity, you have to give a microservice identity. And these more and more, this is just completely automated world. This isn't humans logging into things anymore. This is microservices talking to each other. Each of those needs an identity needs an authorization cause they have accounts that can be hacked also. Right? So the you need protect those just as much as needed to protect those human accounts. It's funny cause we, we cover a lot of RPA shows, right? And the whole talk of, of of people that do RPA, right, is that they're, they're, they treat them as people, right? They treat them as kind of like your little assistance, your own little bot to do little tasks that you assigned them to do. So treating them with kind of an identity protocol. >>Then that gives all the authorizations and you kind of leverage all that back end is the way to integrate them into the workforce. Absolutely. It's all about access controls, authentication, authorization. Those are the controls that have been there forever. You're supplying these two new types of identities and you know, the, we're in the privileged access management space, so it used to always be a windows admin or a Unix Linux admin logging into a physical box, right? And so it was about protecting those accounts. But more and more it's about giving a machine and identity and a microservice and identity and how are those things talking to each other? We're protecting, that's all completely automated with dev ops. You think about if I have a, as I moved to the cloud, I want to be able to scale out dynamically, right? Uh, horizontally, vertically. So all of a sudden new servers, virtual servers or containers just popping up automatically. >>You have to be able to control the access to all those automatically, dynamically on the spot, and then they shrink back down. You need to get rid of all that, right? So the automation that's come into our space, although the same, I'm still trying to do authentication, authorization, same type of privilege access controls we've been doing for 30 years, but how they're applied in this new world is much different right now. What about then you layer you layer on top of that zero trust, so I definitely want to identify, but I have zero trust and I'm presuming at some point in time you might end up either being a bad guy or some bad guy's going to come in via your credential. How does the zero trust piece fit on top of the identity kind of management? It's really why we're talking about identity centric security now is because you can't, you, you have to assume somebody on your network. >>You can't trust all those perimeter controls that are there. The reality is they're going to get in and so that identity centric security starts at that access layer and not not trusting just because you got onto the network that, Oh, sure, here you go. You can, you can do whatever you want. That's where zero trust comes in. I don't, every time I want to get access to a piece of data or a system, et cetera, I need to do that F indication that authorization apply, that multi-factor. Those are all identity centric controls that result in this, this journey towards the zero trust world. It's, it's funny, uh, I've sat down with Mike and Caesar, uh, for scout and you know, he talks about when they do the little sniff on all the little devices that are plugged into the networks and it's usually multiples back of what people think are on the network, especially remote location. >>People are plugging stuff in. But then too, you know, like you said in the machine, identify, you know, what should a logic cam do and how should it act. And as soon as it starts acting and asking for things in accounts payable, maybe that's not necessarily what a lot to take camera wants do or should be doing. Yeah. Yeah. And so first there's like knowing what that device is giving you an identity so he know what it is, know what it should be doing. It has a role, it has specific access and authorization rights that are granted to it. So the logic camera, if I know what that camera is, you have an identity. I know what it's supposed to be doing. I should be able to restrict the access it has to just what it needs to do. Right. Rather than it's got root account to do whatever or some God account to create, you know, like those are the kinds of controls we have in place. >>And it's just logical identity management controls that have been there forever. But you're a, once you can identify those devices connected, you can, you can give them those, you know, limited. There's talk about least privilege, right? That's again, a 30 year old control, but giving at least privilege on just what it should do and nothing more. And do you see in the future just more and more kind of multifactor, uh, validation points that we'll have to get added to the, to the process as we move from single factor to factor, however many factors is going to take? For sure. Yeah. I mean, so the multi-factor, cause there's one thing are you authenticate yourself at the front door, right? So that's what most authentication is, but there's this concept of continuous authentication. You're the trust in that, uh, that initial authentication degrades as your session goes on. >>Right? So the longer I've had a session open, you know, is that still that same person or that same service that is clicking away at the keyboard there? There's cool stuff, wrong continuous authentication where there they can tell it's still the same person based on the cadence. They click on the keyboard, other biometric methods, the swiping I do on my phone and stuff like that. So there's ways to have continuous concepts now called continuous authentication. Right? And so I absolutely see that those behavior based, uh, types of, uh, of authentication. You're going out through a user's entire session. So I want to shift gears a little bit. One of the things that amazes me about this show, and I don't know when it was small, but it's been big ever since I've been coming. It's right, there's so many vendors here, there's so many companies in this and there's so many kinds of stories that a lot of really enthusiastic people work in booths that are screaming at you to come over and tell you all the great things they do. >>From a marketing point of view, you're, you're the SVP marketing. How do you, you know, kind of package your messaging, how do you kind of break through the clutter? What advice do you give to, to buyers, um, to help them kind of navigate what is a, a very large, loud and complex system? Yeah, it's a, it's a complex battle, right? So you have to be able to, because there are so many different technologies here, uh, in, in the security arena, uh, we're all fighting for the same share of wallet in a sense. Right? And so first you have to identify yourself with something people recognize a market that people recognize like identity, privilege, access management, endpoint security, you know, et cetera. But then you have to differentiate yourself within that market, right? So you've got to add something to the market space I'm in to that gives a little twist. >>So for us, it's identity centric, privilege access management and that, you know, we suppose that against Balt centric or you know, something else that we've tried to put the other bets. So you try to, in your message, you got to categorize what's the space I'm in and how do I differentiate? And in something as short and brand-able as possible. And then you got to have this kind of ongoing solutions, partnership relationship with, with your clients, right? Because this is not something you're going to be switching things out that frequently and, and, and, and the landscape and the threats evolve and change so rapidly. I think we've had a number of people come on to publish this report or that report, his report, he's come out every six months and there's actually the online version so he can keep up with what happened today or what happens tomorrow. >>So not an easy, uh, not an easy kind of marketing challenge to stay relevant, stay connected and state stay really in people's mind. Well, and you know, there's, there's awareness aspects to it and it is really just what really helps is you just create as many happy customers as you can. Right? I mean, you're amazed at the how connected this industry actually is. I mean, the attendees that are coming to this conference, they know each other. They've been coming here from here. It's just like we have. Right, right. And a word of mouth between people who have used your technology, they share that with something else. I mean the security industry as big as it is, it's, it's super interconnected. One person goes from one company to the other and so tons of business just comes from word of mouth, referral, etc. So the happier you can keep your customers, the more uh, you know, mind share. >>You can get up there. Okay. Last question before I let you go. We just like to say we just had row hit on one of the topics was they just got bought by a symphony. I think it's symphony, a private equity firm. Um, we met the other night at a, at a cocktail party put on by Tom Thoma Bravo and you were at Centrify before they came in. And after, you know, I think some people are kind of confused, you know, what is private equity, how does it impact the company? So wonder if you can kind of share, you know, how that transition has come along and you know, kind of give us an update on what's going on at Centrify and where you guys are going next. Yeah, so we were acquired about a year and a half ago now, uh, by private equity and you know, they basically, they take later stage companies and uh, help them get, uh, profitable, uh, they increased value and then they look for going, taking that company IPO or selling it off, et cetera. >>Right? But it's really about looking for opportunities, uh, in existing market with larger companies, the venture capitalists will go after smaller, much larger risks. These are bigger dollar amounts, right? Larger companies. But then they, they look about how to optimize. They're very sophisticated on how to run a B to B business. Tama Bravo happens to have a huge investment in security and it comes like eight or 10 companies there the other night. Yeah. So they, they realize that this is a hot space right now. So they've, if they can take a company and create value that they realize that there's more stuff popping up. There's probably money being invested in. And one of the things that, but not all private equities created equal. Yes, they are about all about kind of optimizing, increasing value. But what we really found with Tom or Bravo is they're interested in investing in that company, looking at other folds and acquisitions, et cetera. >>And that's a part of a strategy for me as a, as a manager and an I'm part of the executive team. When you're backed, they don't have the money to go after acquisitions. Uh, like that they, you know, they make these smaller investments. We're talking about Bravo actually does have the capital to look at other things that can be immediately accretive and add to your value. And that's a, a real part of our strategy now that didn't exist before we were owned by PE. I think they spun out a whole nother, another company out of what your technology say. Correct. Exactly. So one of the unique things about our particular acquisition is Centrify was both a privileged access management. And a identity as a service. And I Daz a company and they looked at what we were doing and they said, geez, you're really selling to two different markets and it's two different sales cycles and two different business models. >>We could actually create more value if we split these up and each of you focused on your individual markets. And so that there's a, there's an MQ and a market segment and a wave for IDASS and there's an MQ and a wave, you know, et cetera for Pam. But there's not anything that does both. And that's what Centrify was. So they actually, we, we completely divested of our IDASS capabilities spun off in an entirely separate company called adaptive. And so over the last year, that's was a lot of the work that was going on. It was, was splitting this company, uh, uh, into two. But it really provided us a much more focused to go after the market that we were going after. Well, they wouldn't come in if they didn't see some opportunity to, uh, to pull some more value out that wasn't really being unlocked. Absolutely. Right. Andy, we'll thank for taking a few minutes and uh, and great to catch up and best you for the rest of the show. Awesome. Thanks a lot, Jay. He's Andy. I'm Jeff. You're watching the cube where? At the RSA show in San Francisco. Thanks for watching. We'll see you next time.

>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019. Brought to you by UI path. Hello everyone and welcome >>do the cubes live coverage of UI path forward. I'm your host, Rebecca Knight, co-hosting alongside of Dave Volante. We are joined by Layla Delage. She is the chief information and digital officer at Coca-Cola. ECEK thanks so much for coming on the show. Thank you. Great to be here. Very exciting. And also Christina Perala, she is the group RPA lead at Generali. Thank you so much for coming into, for inviting me. Thank you. So I want to hear from you both about what, what your industry is and what your role is. Level. Let's start with you. Okay, great. Um, so we are, um, one of the Rogers bottlers within the Coca-Cola system. Uh, we produce, distribute and sell Coca Cola company products. The operating around 10 countries are middle East and central Asia and parts of middle East, Pakistan, Syria and Turkey. They are actually born out of Turkey and that's where our central offices, um, we've operate with 26 plants, around 8,500 employees. >>Uh, we serve a consumer base of 400 million and we have around close to 1 billion, uh, customers. Uh, and we continue to invest in the countries where we operate. And my role is to film and my role is all things digital within this community. So leading technologists, leading technology, all things digital. Yes. So Christina, tell us about Generali. Generalia. Sikora Zuni is a leading insurance company as the presidency. Enough 50 countries worldwide and more than a 70,000 employees that were wider. So it's a bigger company, not only for insurance. And my role with the internet rally group is to leader the LPA program. So I'm inside of the group that I in digital. So am I inside this group, I'm very focused on smart process automation. So RPA plus AI, because a has a, we already know all I loudly, LPA without a AI is announcer nowadays. So we have to keep on talking about AI, machine learning algorithms to enrich, uh, uh, the capabilities of basic robotic sell, hand reach, also the Antwerp and automation of processes. You're the CIO and the CDO. Yes. Yes. That's unique. First of all, there's one that's unique too. It's even more unique than a woman has both roles. So what's the reason behind it? So, um, there's definitely a reason behind it. I joined the Coca Cola >>system about a year ago, so I'm just a over a year in the company. The reason actually I wanted to make sure that we highlight the CIO and CTO CDO role together is, um, I want to advocate for all the it organizations to transform and really get into the digital world and get into the world of advanced technologies, become strategic business partners. Get out of the kitchen, I call it kitchen kitchen, it, you know, get out of the managing of data centers or cloud and um, just the core foundational systems and applications. Get into the advanced technology, understand the business, gain business acumen and deliver solutions based on business needs. So to highlight that, I want to make sure that I hold the role of both and I'm able to be advocate of both worlds. Cause digital without it support is not able to accomplish what they need to accomplish and it needs to get into more of the digital space. And Christina, as the RPA, you write bots, you evangelize the organization. >>Um, mostly the second. So in generally we have a, a very, uh, so, uh, sort of ivory the organization. So for something we are very decentralized, for example, for the developing of robots or the deploying for the action, the operational stuff and so on. Uh, but uh, for some stuff like a guidelines, uh, uh, risk framework to ensure that robots can do their work in the right way with notice to all for the business processes, uh, for this stuff before guidelines, framework, best practice sharing. We are a central centralized, we, we try to be centralized. So, uh, my role is to try to collect is to collect and not try and super lat, uh, best practices and share with you in the companies chair, uh, um, the best use cases. And, uh, also tried to gather what are the main concerns, what are the difficulties in order to a facilitator and to boost smarter process automation of the option. So >>Laila, you are up on the main stage this morning. You, I Pat highlighted Coca Cola itchy as a, as a customer that is embraced automation, embrace the UI pass solution. So tell us a little bit about the challenges you are facing and then why you chose I a UI path. So as I joined the company, uh, I introduced a very strong digital strategy that required a lot of change and it's within a company that has been very successfully operating all these years and doing pretty much know what to do very well. And all of a sudden with digital we are starting to disrupt the, are trying to say, Hey, we've got to change the way, do some of the things. Um, so belief in digital and belief that it can really bring efficiency and outcomes was very important. And I needed a quick win. I needed to have a technology or a solution or an outcome that I would generate very quickly and show to the whole organization that this can be done and we can do this as Coca-Cola. TJ. >>So that was, that was RPA, that was our PA for this fascinates me because you're an incumbent business, been around for a long time. you're a bottler and distributor, right? So yeah, processes are around the bottling plants and the distribution system. Yes. And now you're transforming into a digital business. Yes. I'll put data at your core. Totally not start his daytime customer. Okay. So describe the difference between the traditional business and what it looks like when you've transformed, particularly from a data perspective. And then I want to understand what role RPA plays. So we are definitely a very data rich company, however, to call ourselves data rich and to call it a strategic asset, I first need to capture and control my data and I have to treat it like a strategic asset. So that is a huge transformation. The second, once you treat it as an asset, how do you generate more insights? >>And I call this augmenting the gut feeling. I have an amazing gut feeling in the company. How do I augment that with data and provide our, this is partners and then our customers and our suppliers and some of the information. And then obviously future maturity level is, you know, shared economy and data monetization, et cetera. So that's how I describe within the company. And then assets, other assets like our plants and coolers cooler, we call it cooler, you know, where do you actually see all our products? They are called, they are visible and they are available, but they are also in that set where I can turn them into a digital cooler and I can do so much more with the cooler that standing. And I recently, in one of our leadership meetings I said we have as many coolers as the um, population on the fishy Island, which is close to 1 million. >>So just imagine in this new world, in this digital era, everything that you can do by just having a cooler, 1 million coolers present out there on the street, I can serve the consumers, I can serve customers with very different information. So that's kind of what I mean by turning the business into a digital business. So that's an awesome story. By the way, how does RPA fit into that vision? RPA is everywhere in division. So I said when I started the journey, uh, any digital journey has some Muslim battles for me. There are four must win battles. I need to get certain things right in it, in the, and that was one, one of the Mustin battles was alteration. So we have to create efficiency, we have to optimize, we have to streamline. And we said automation first. Um, and we started with, I call it robotics and automation. >>And I agree with what you said, Christina. It's more than just robots. It's actually a strategic application. It could be a good old ERP. It's the RPA, it's AI, it's all the other technologies that are out there that they bring the two of them brings. So how do you create this end to end solution using all the trends, technologies to create optimization? Uh, our goal was how do we get back to our customer much faster. We had so many customer facing processes and they're going to be there forever. They are a very customer centric customer into company obviously. So how do I get back to my customer faster? How do I make my employees just happy? They were working on so many things would be until midnight over time during weekends. How do I take that away from them? So we called it lifting the weight of the shoulders and giving you a new capabilities. So again, augmentation and then giving them that space. So we had uh, three of my employees upskilled and reskilled themselves. They became a developers in the robotics space, a couple of fire functional, um, colleagues are now reskilling themselves because now they have the time to reskill. More importantly, they have the time to actually leverage their expertise and they are so much more motivated. The engagement, the employee engagement is increasing. So that's how we are positioning RPA. Pristina ICU >>nodding a lot, your head too. A lot of what Layla is saying. I'm wondering if you can talk to about any best practices that have emerged as you've implemented RPA at Generali to what you've learned. Yes, for sure. Um, we have a lot of processes automated, uh, all around the group. Uh, but we are not, we have not reached our maximum or, uh, benefits, uh, gaining. So what we need to do right now is to try to boost the smart process automation, uh, via analyzing the issue around value, Cena. So each business area of the value chain because currently we have countries that has, that have a different level of maturity. So, so some countries are at the very beginning and we have to help them with best practice sharings with a huge case, successful use cases. And we are, uh, we have a lot of help from parts into, in this because locally and who I Potter as a, a very strong presence and is very powerful in doing that. >>And, uh, now, uh, our next mouth are very focused on try to, um, uh, deep dive, the vertical, our area of the issue around value chain and identify which are the processes inside them are best to automated. Uh, uh, Basinger. Uh, these activities are not so you, I part, we'd, his experience has created a heat mapper, value chain Heath mapper. And so it's given up as some advice where to focus our strengths, our hand energy in automating. And I think that this is a very huge, uh, uh, support that you are UI parties given us. So it's not just a matter of, okay, let's start, uh, uh, do some, uh, process assessment in order to identify which processes are the best candidates to be automated. But, uh, we have, uh, how our back, uh, us. So we, we are, uh, we have the backing of UI pass saying it's better to do that and automate in depth, uh, processes of that, but Oh, the value chain. So we are starting a program to do that with all the countries or the vertical area of the country. So, and I think that this could really bring a, uh, high benefits and can, uh, uh, drive us to, uh, really having a scaling up in using a smart process, automation and UI. But you a bot ecosystem not only are, so >>one of the nice things about RPA is you can take the software robots and apply them to an existing process. A lot of times changing processes and a lot of times almost always changing processes is painful. However, we've talked to some customers that have said by applying RPA to our business, it's exposed some really bad processes. Have you experienced that and can you maybe share that experience with it? Absolutely. So for us, one of the initial, um, robots, we applied to a customer facing process. It was our field team trying to get back to our customer with a, with some information. And we realize that the, um, the cycle time was very long. And the reason is there are four functions involved in answering the question and seven different applications are being touched all the way from XL to ERP to CRM. So what we did obviously bringing a strategic solution to fix the cycle time and reduce that to streamline the process was going to take us long. So RPA was great help. We reduced the cycle time by putting a robot and we were able to get back to ours, priests, sales team in the field in matter of minutes. What used to take hours was now being responded to in minutes. Now that doesn't mean that process is perfect, but that's our next step. So we created value for our customer and our sales team within the field, um, before, you know, streamlining and going into a bigger initiatives. So then you could share Christina. >>Yes. Uh, so, um, it is necessary to automate something that could be automated. So, uh, it is necessarily to out optimize the process before automating it, but sometimes it's better to automate it as Caesar because, uh, also the not optimize the process can bring value if ultimated. So let me share an example. If you, for example, have to migrate some data obviously is a one shot, uh, uh, activity. But with the robot you can do it in a very short, well sharp timer. Maybe it's not the best, uh, process to be automated, but that could be useful as well. So it's always a matter of understanding the costs and the benefits. Uh, and sometimes, uh, FBA is very quickly, is very quick to be implemented and can be, can have a, also a lot of savings instead of integrating instead of doing more complex things. >>And then other things, uh, that it's important to take into account is that, uh, uh, after having a automating goal, all the low hanging fruits and so the processes with a low cost, uh, uh, low complexity and high benefits, uh, then it starts to facer when it's necessary to understand how to the end to end processes. Because, uh, it happens, uh, in, uh, some of our countries that, uh, the second phase is very difficult because, uh, the situation is that you have very, um, a lot of very fermented processes. And so before automating it is necessary to apply operational efficiency methodology, lean six Sigma, rare business process for engineering and then automate it. So it's a longer trip. And our Amer as group head office in general is to give these kinds of methodologies and best practices for all kinds of level of maturity in our countries. So finally, w what is the customer is the employee response then in terms of how you're talking a lot about streamlining, getting rid of these tedious tasks that took forever, how, how our employees reacting to the implementation. >>So we, um, we actually launched the, uh, announce announced RPA robotics and automation with a Hekaton in our company. And we invited 40 colleagues from various functions and two and everybody from the business was there and they participated actually in gathering ideas and prioritizing what matters most to the company. And we looked at customer, we looked at compliance, we look to the employee and we actually with during the hackathon you iPad team helped us to go live with one of the robots. They were mesmerized. They couldn't believe that this could happen. I think that's where we kind of engaged them and now going forward everyone who generate the idea was part of the building of the robots so they continue to be engaged to me allowed them to name the robots so they start naming and once the robots were alive yet literally had some of our teams who are dancing from happiness and I think that said it all. That was the strongest voice of our business partner and we published that video. So our business partners became our advocates and that's really our how we born the robotic and automation within CCI. We have so many advocates right now they are coming to us. Our business partners are coming to us with more use cases and they are actually, they are sharing with rest of the system within Coca-Cola and with the group that we are part of locally in Turkey, they are sharing their stories. So now we have a hype going on in the system. >>Yes. And in generally, um, at the beginning, uh, we face some fears in our employees fears of losing their job, but fear is not be able to use this kind of technology. Uh, but, uh, also with the help of HR because I, Charlie is, uh, driving a huge program of upskilling and reskilling of people. Uh, nowadays, uh, also hand user are very happy to use robotics, uh, because, uh, uh, when they realize that they can really help in their activities, in their very boring and not useful activities, they are very happy to enjoy this, this program. But it is so, uh, it, it was a trip, a journey with the employees to make them understand that it's not something that, uh, is affecting their job. So, at least in generally group, we are, we are programming, uh, these, uh, uh, or employees, uh, journey in order to make them, uh, uh, to have more, uh, uh, awareness about robotics and not be scared about it. Layla and Christina, thank you both so much for coming on the cube. It was wonderful. Thank you very much for you. I'm Rebecca Knight for Dave Volante. Please stay tuned for more of the cubes live coverage of UI path forward.