(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)

(upbeat music) >> Hey everyone, welcome to this CUBE conversation. I'm John Furrier, your host of theCUBE here in Palo Alto, California. We've got Sam Kassoumeh, co-founder and chief operating office at SecurityScorecard here remotely coming in. Thanks for coming on Sam. Security, Sam. Thanks for coming on. >> Thank you, John. Thanks for having me. >> Love the security conversations. I love what you guys are doing. I think this idea of managed services, SaaS. Developers love it. Operation teams love getting into tools easily and having values what you guys got with SecurityScorecard. So let's get into what we were talking before we came on. You guys have a unique solution around ratings, but also it's not your grandfather's pen test want to be security app. Take us through what you guys are doing at SecurityScorecard. >> Yeah. So just like you said, it's not a point in time assessment and it's similar to a traditional credit rating, but also a little bit different. You can really think about it in three steps. In step one, what we're doing is we're doing threat intelligence data collection. We invest really heavily into R&D function. We never stop investing in R&D. We collect all of our own data across the entire IPV force space. All of the different layers. Some of the data we collect is pretty straightforward. We might crawl a website like the example I was giving. We might crawl a website and see that the website says copyright 2005, but we know it's 2022. Now, while that signal isn't enough to go hack and break into the company, it's definitely a signal that someone might not be keeping things up to date. And if a hacker saw that it might encourage them to dig deeper. To more complex signals where we're running one of the largest DNS single infrastructures in the world. We're monitoring command and control malware and its behaviors. We're essentially collecting signals and vulnerabilities from the entire IPV force space, the entire network layer, the entire web app player, leaked credentials. Everything that we think about when we talk about the security onion, we collect data at each one of those layers of the onion. That's step one. And we can do all sorts of interesting insights and information and reports just out of that thread intel. Now, step two is really interesting. What we do is we go identify the attack surface area or what we call the digital footprint of any company in the world. So as a customer, you can simply type in the name of a company and we identify all of the domains, sub domains, subsidiaries, organizations that are identified on the internet that belong to that organization. So every digital asset of every company we go out and we identify that and we update that every 24 hours. And step three is the rating. The rating is probabilistic and it's deterministic. The rating is a benchmark. We're looking at companies compared to their peers of similar size within the same industry and we're looking at how they're performing. And it's probabilistic in the sense that companies that have an F are about seven to eight times more likely to experience a breach. We're an A through F scale, universally understood. Ds and Fs, more likely to experience a breach. A's we see less breaches now. Like I was mentioning before, it doesn't mean that an F is always going to get hacked or an A can never get hacked. If a nation state targets an A, they're going to eventually get in with enough persistence and budget. If the pizza shop on the corner has an F, they may never get hacked because no one cares, but natural correlation, more doors open to the house equals higher likelihood someone unauthorized is going to walk in. So it's really those three steps. The collection, we map it to the surface area of the company and then we produce a rating. Today we're rating about 12 million companies every single day. >> And how many people do you have as customers? >> We have 50,000 organizations using us, both free and paid. We have a freemium tier where just like Yelp or a LinkedIn business profile. Any company in the world has a right to go claim the score. We never extort companies to fix the score. We never charge a company to see the score or fix it. Any company in a world without paying us a cent can go in. They can understand what we're seeing about them, what a hacker could see about their environment. And then we empower them with the tools to fix it and they can fix it and the score will go up. Now companies pay us because they want enterprise capabilities. They want additional modules, insights, which we can talk about. But in total, there's about 50,000 companies that at any given point in time, they're monitoring about a million and a half organizations of the 12 million that we're rating. It sounds like Google. >> If you want to look at it. >> Sounds like Google Search you got going on there. You got a lot of search and then you create relevance, a score, like a ranking. >> That's precisely it. And that's exactly why Google ventures invested in us in our Series B round. And they're on our board. They looked and they said, wow, you guys are building like a Google Search engine over some really impressive threat intelligence. And then you're distilling it into a score which anybody in the world can easily understand. >> Yeah. You obviously have page rank, which changed the organic search business in the late 90s, early 2000s and the rest is history. AdWords. >> Yeah. >> So you got a lot of customer growth there potentially with the opt-in customer view, but you're looking at this from the outside in. You're looking at companies and saying, what's your security posture? Getting a feel for what they got going on and giving them scores. It sounds like it's not like a hacker proof. It's just more of a indicator for management and the team. >> It's an indicator. It's an indicator. Because today, when we go look at our vendors, business partners, third parties were flying blind. We have no idea how they're doing, how they're performing. So the status quo for the last 20 years has been perform a risk assessments, send a questionnaire, ask for a pen test and an audit evidence. We're trying to break that cycle. Nobody enjoys it. They're long tail. It's a trust without verification. We don't really like that. So we think we can evolve beyond this point in time assessment and give a continuous view. Now, today, historically, we've been outside in. Not intrusive, and we'll show you what a hacker can see about an environment, but we have some cool things percolating under the hood that give more of a 360 view outside, inside, and also a regulatory compliance view as well. >> Why is the compliance of the whole third party thing that you're engaging with important? Because I mean, obviously having some sort of way to say, who am I dealing with is important. I mean, we hear all kinds of things in the security landscape, oh, zero trust, and then we hear trust, supply chain, software risk, for example. There's a huge trust factor there. I need to trust this tool or this container. And then you got the zero trust, don't trust anything. And then you've got trust and verify. So you have all these different models and postures, and it just seems hard to keep up with. >> Sam: It's so hard. >> Take us through what that means 'cause pen tests, SOC reports. I mean the clouds help with the SOC report, but if you're doing agile, anything DevOps, you basically would need to do a pen test like every minute. >> It's impossible. The market shifted to the cloud. We watched and it still is. And that created a lot of complexity, not to date myself. But when I was starting off as a security practitioner, the data center used to be in the basement and I would have lunch with the database administrator and we talk about how we were protecting the data. Those days are long gone. We outsource a lot of our key business practices. We might use, for example, ADP for a payroll provider or Dropbox to store our data. But we've shifted and we no longer no who that person is that's protecting our data. They're sitting in another company in another area unknown. And I think about 10, 15 years ago, CISOs had the realization, Hey, wait a second. I'm relying on that third party to function and operate and protect my data, but I don't have any insight, visibility or control of their program. And we were recommended to use questionnaires and audit forms, and those are great. It's good hygiene. It's good practice. Get to know the people that are protecting your data, ask them the questions, get the evidence. The challenge is it's point in time, it's limited. Sometimes the information is inaccurate. Not intentionally, I don't think people intentionally want to go lie, but Hey, if there's a $50 million deal we're trying to close and it's dependent on checking this one box, someone might bend a rule a little bit. >> And I said on theCUBE publicly that I think pen test reports are probably being fudged and dates being replicated because it's just too fast. And again, today's world is about velocity on developers, trust on the code. So you got all kinds of trust issues. So I think verification, the blue check mark on Twitter kind of thing going on, you're going to see a lot more of that and I think this is just the beginning. I think what you guys are doing is scratching the surface. I think this outside in is a good first step, but that's not going to solve the internal problem that still coming and have big surface areas. So you got more surface area expanding. I mean, IOT's coming in, the Edge is coming fast. Never mind hybrid on-premise cloud. What's your organizations do to evaluate the risk and the third party? Hands shaking, verification, scorecards. Is it like a free look here or is it more depth to it? Do you double click on it? Take us through how this evolves. >> John it's become so disparate and so complex, Because in addition to the market moving to the cloud, we're now completely decentralized. People are working from home or working hybrid, which adds more endpoints. Then what we've learned over time is that it's not just a third party problem, because guess what? My third parties behind the scenes are also using third parties. So while I might be relying on them to process my customer's payment information, they're relying on 20 vendors behind the scene that I don't even know about. I might have an A, they might have an A. It's really important that we expand beyond that. So coming out of our innovation hub, we've developed a number of key capabilities that allow us to expand the value for the customer. One, you mentioned, outside in is great, but it's limited. We can see what a hacker sees and that's helpful. It gives us pointers where to maybe go ask double click, get comfort, but there's a whole nother world going on behind the firewall inside of an organization. And there might be a lot of good things going on that CISO security teams need to be rewarded for. So we built an inside module and component that allows teams to start plugging in the tools, the capabilities, keys to their cloud environments. And that can show anybody who's looking at the scorecard. It's less like a credit score and more like a social platform where we can go and look at someone's profile and say, Hey, how are things going on the inside? Do they have two-factor off? Are there cloud instances configured correctly? And it's not a point in time. This is a live connection that's being made. This is any point in time, we can validate that. The other component that we created is called an evidence locker. And an evidence locker, it's like a secure vault in my scorecard and it allows me to upload things that you don't really stand for or check for. Collateral, compliance paperwork, SOC 2 reports. Those things that I always begrudgingly email. I don't want to share with people my trade secrets, my security policies, and have it sit on their exchange server. So instead of having to email the same documents out, 300 times a month, I just upload them to my evidence locker. And what's great is now anybody following my scorecard can proactively see all the great things I'm doing. They see the outside view. They see the inside view. They see the compliance view. And now they have the holy grail view of my environment and can have a more intelligent conversation. >> Access to data and access methods are an interesting innovation area around data lineage. Tracing is becoming a big thing. We're seeing that. I was just talking with the Snowflake co-founder the other day here in theCUBE about data access and they're building a proprietary mesh on top of the clouds to figure out, Hey, I don't want to give just some tool access to data because I don't know what's on the other side of those tools. Now they had a robust ecosystem. So I can see this whole vendor risk supply chain challenge around integration as a huge problem space that you guys are attacking. What's your reaction to that? >> Yeah. Integration is tricky because we want to be really particular about who we allow access into our environment or where we're punching holes in the firewall and piping data out out of the environment. And that can quickly become unwieldy just with the control that we have. Now, if we give access to a third party, we then don't have any control over who they're sharing our information with. When I talk to CISOs today about this challenge, a lot of folks are scratching their head, a lot of folks treat this as a pet project. Like how do I control the larger span beyond just the third parties? How do I know that their software partners, their contractors that they're working with building their tools are doing a good job? And even if I know, meaning, John, you might send me a list of all of your vendors. I don't want to be the bad guy. I don't really have the right to go reach out to my vendors' vendors knocking on their door saying, hi, I'm Sam. I'm working with John and he's your customer. And I need to make sure that you're protecting my data. It's an awkward chain of conversation. So we're building some tools that help the security teams hold the entire ecosystem accountable. We actually have a capability called automatic vendor discovery. We can go detect who are the vendors of a company based on the connections that we see, the inbound and outbound connections. And what often ends up happening John is we're bringing to the attention to our customers, awareness about inbound and outbound connections. They had no idea existed. There were the shadow IT and the ghost vendors that were signed without going through an assessment. We detect those connections and then they can go triage and reduce the risk accordingly. >> I think that risk assessment of vendors is key. I was just reading a story about this, about how a percentage, I forget the number. It was pretty large of applications that aren't even being used that are still on in companies. And that becomes a safe haven for bad actors to hang out and penetrate 'cause they get overlooked 'cause no one's using them, but they're still online. And so there's a whole, I called cleaning up the old dead applications that are still connected. >> That happens all the time. Those applications also have applications that are dead and applications that are alive may also have users that are dead as well. So you have that problem at the application level, at the user level. We also see a permutation of what you describe, which is leftover artifacts due to configuration mistakes. So a company just put up a new data center, a satellite office in Singapore and they hired a team to go install all the hardware. Somebody accidentally left an administrative portal exposed to the public internet and nobody knew the internet works, the lights are on, the office is up and running, but there was something that was supposed to be turned off that was left turned on. So sometimes we bring to company's attention and they say, that's not mine. That doesn't belong to me. And we're like, oh, well, we see some reason why. >> It's his fault. >> Yeah and they're like, oh, that was the contractor set up the thing. They forgot to turn off the administrative portal with the default login credentials. So we shut off those doors. >> Yeah. Sam, this is really something that's not talked about a lot in the industry that we've become so reliant on managed services and other people, CISOs, CIOs, and even all departments that have applications, even marketing departments, they become reliant on agencies and other parties to do stuff for them which inherently just increases the risk here of what they have. So there inherently could be as secure as they could be, but yet exposed completely on the other side. >> That's right. We have so many virtual touch points with our partners, our vendors, our managed service providers, suppliers, other third parties, and all the humans that are involved in that mix. It creates just a massive ripple effect. So everybody in a chain can be doing things right. And if there's one bad link, the whole chain breaks. I know it's like the cliche analogy, but it rings true. >> Supply chain trust again. Trust who you trust. Let's see how those all reconcile. So Sam, I have to ask you, okay, you're a former CISO. You've seen many movies in the industry. Co-founded this company. You're in the front lines. You've got some cool things happening. I can almost imagine the vision is a lot more than just providing a rating and score. I'm sure there's more vision around intelligence, automation. You mentioned vault, wallet capabilities, exchanging keys. We heard at re:Inforce automated reasoning, metadata reasoning. You got all kinds of crypto and quantum. I mean, there's a lot going on that you can tap into. What's your vision where you see SecurityScorecard going? >> When we started the company, the rating was the thing that we sold and it was a language that helped technical and non-technical folks alike level the playing field and talk about risk and use it to drive their strategy. Today, the rating just opens the door to that discussion and there's so much additional value. I think in the next one to two years, we're going to see the rating becomes standardized. It's going to be more frequently asked or even required or leveraged by key decision makers. When we're doing business, it's going to be like, Hey, show me your scorecard. So I'm seeing the rating get baked more and more the lexicon of risk. But beyond the rating, the goal is really to make a world a safer place. Help transform and rise the tide. So all ships can lift. In order to do that, we have to help companies, not only identify the risk, but also rectify the risk. So there's tools we build to really understand the full risk. Like we talked about the inside, the outside, the fourth parties, fifth parties, the real ecosystem. Once we identified where are all the Fs and bad things, will then what? So couple things that we're doing. We've launched a pro serve arm to help companies. Now companies don't have to pay to fix the score. Anybody, like I said, can fix the score completely free of charge, but some companies need help. They ask us and they say, Hey, I'm looking for a trusted advisor. A Sherpa, a guide to get me to a better place or they'll say, Hey, I need some pen testing services. So we've augmented a service arm to help accelerate the remediation efforts. We're also partnered with different industries that use the rating as part of a larger picture. The cyber rating isn't the end all be all. When companies are assessing risk, they may be looking at a financial ratings, ESG ratings, KYC AML, cyber security, and they're trying to form a complete risk profile. So we go and we integrate into those decision points. Insurance companies, all the top insurers, re-insurers, brokers are leveraging SecurityScorecard as an ingredient to help underwrite for cyber liability insurance. It's not the only ingredient, but it helps them underwrite and identify the help and price the risk so they can push out a policy faster. First policy is usually the one that's signed. So time to quote is an important metric. We help to accelerate that. We partner with credit rating agencies like Fitch, who are talking to board members, who are asking, Hey, I need a third party, independent verification of what my CISO is saying. So the CISO is presenting the rating, but so are the proxy advisors and the ratings companies to the board. So we're helping to inform the boards and evolve how they're thinking about cyber risk. We're helping with the insurance space. I think that, like you said, we're only scratching the surface. I can see, today we have about 50,000 companies that are engaging a rating and there's no reason why it's not going to be in the millions in just the next couple years here. >> And you got the capability to bring in more telemetry and see the new things, bring that into the index, bring that into the scorecard and then map that to potential any vulnerabilities. >> Bingo. >> But like you said, the old days, when you were dating yourself, you were in a glass room with a door lock and key and you can see who's two folks in there having lunch, talking database. No one's going to get hurt. Now that's gone, right? So now you don't know who's out there and machines. So you got humans that you don't know and you got machines that are turning on and off services, putting containers out there. Who knows what's in those payloads. So a ton of surface area and complexity to weave through. I mean only is going to get done with automation. >> It's the only way. Part of our vision includes not attempting to make a faster questionnaire, but rid ourselves of the process all altogether and get more into the continuous assessment mindset. Now look, as a former CISO myself, I don't want another tool to log into. We already have 50 tools we log into every day. Folks don't need a 51st and that's not the intent. So what we've done is we've created today, an automation suite, I call it, set it and forget it. Like I'm probably dating myself, but like those old infomercials. And look, and you've got what? 50,000 vendors business partners. Then behind there, there's another a hundred thousand that they're using. How are you going to keep track of all those folks? You're not going to log in every day. You're going to set rules and parameters about the things that you care about and you care depending on the nature of the engagement. If we're exchanging sensitive data on the network layer, you might care about exposed database. If we're doing it on the app layer, you're going to look at application security vulnerabilities. So what our customers do is they go create rules that say, Hey, if any of these companies in my tier one critical vendor watch list, if they have any of these parameters, if the score drops, if they drop below a B, if they have these issues, pick these actions and the actions could be, send them a questionnaire. We can send the questionnaire for you. You don't have to send pen and paper, forget about it. You're going to open your email and drag the Excel spreadsheet. Those days are over. We're done with that. We automate that. You don't want to send a questionnaire, send a report. We have integrations, notify Slack, create a Jira ticket, pipe it to ServiceNow. Whatever system of record, system of intelligence, workflow tools companies are using, we write in and allow them to expedite the whole. We're trying to close the window. We want to close the window of the attack. And in order to do that, we have to bring the attention to the people as quickly as possible. That's not going to happen if someone logs in every day. So we've got the platform and then that automation capability on top of it. >> I love the vision. I love the utility of a scorecard, a verification mark, something that could be presented, credential, an image, social proof. To security and an ongoing way to monitor it, observe it, update it, add value. I think this is only going to be the beginning of what I would see as much more of a new way to think about credentialing companies. >> I think we're going to reach a point, John, where and some of our customers are already doing this. They're publishing their scorecard in the public domain, not with the technical details, but an abstracted view. And thought leaders, what they're doing is they're saying, Hey, before you send me anything, look at my scorecard securityscorecard.com/securityrating, and then the name of their company, and it's there. It's in the public domain. If somebody Googles scorecard for certain companies, it's going to show up in the Google Search results. They can mitigate probably 30, 40% of inbound requests by just pointing to that thing. So we want to give more of those tools, turn security from a reactive to a proactive motion. >> Great stuff, Sam. I love it. I'm going to make sure when you hit our site, our company, we've got camouflage sites so we can make sure you get the right ones. I'm sure we got some copyright dates. >> We can navigate the decoys. We can navigate the decoys sites. >> Sam, thanks for coming on. And looking forward to speaking more in depth on showcase that we have upcoming Amazon Startup Showcase where you guys are going to be presenting. But I really appreciate this conversation. Thanks for sharing what you guys are working on. We really appreciate. Thanks for coming on. >> Thank you so much, John. Thank you for having me. >> Okay. This is theCUBE conversation here in Palo Alto, California. Coming in from New York city is the co-founder, chief operating officer of securityscorecard.com. I'm John Furrier. Thanks for watching. (gentle music)

(soft music begins) >> Welcome to this CUBE conversation. I'm Lisa Martin, I'm joined by Jeremy Swift, the CEO and co-founder of Cordial. Jeremy, welcome to theCUBE, it's great to have you on the program. >> Hey, thanks so much Lisa, it's great to be here with you. >> Making this conversation's work very socially distanced, but I'd love to understand a little bit about Cordial. What do you offer and how do you help customers? >> Yeah, yeah, I appreciate the question. I guess for starters Cordial is a cross-channel messaging and data platform. Our clients, let me tell you a little bit about what that actually means. But I would say our clients can collect all of their unstructured, kind of disparate customer and business data from wherever it lives within their tech stack. And then ultimately use that data to build audience segments, gather insights about that data and about their customers. Kind of discover some trends on that too and then ultimately automate and orchestrate hyper-personalized customer experiences at enterprise scale. And when I say experiences too, to define that a little bit. I really am talking about, frankly, kind of a wealth of interactions that a customer might have with a brand. So, that could be things like transforming your promotional, your triggered and your transactional email, communications to your SMS and your MMS messages, to your push in your in-app messages, targeted direct mail, all the way, actually, frankly, to things like in-store devices like clienteling experiences and things like that when you're physically going into the store, whenever we can get back into that, a little bit more consistently, I would say. And then even things like sending targeted audiences to third-party social platforms like a Google Adwords or a Facebook or whatnot. So, in short, I would say we're the underlying data platform and the activation layer that helps brands better communicate with their customers because they ultimately understand their customers better. Um, yeah, go ahead. >> You mentioned hyper-personalized and we've been talking about personalization for a long time. And especially as the more demanding we consumers get, we expect brands to know who we are, offer us the right things that are in sequence and offer me something I've already purchased. But define hyper-personalized customer experiences. >> Yeah, yeah, it's a really good question. You know, I think this is a significant piece that when we think about kind of the marketing language or lingo that gets used out there, this is probably one that gets used a little bit flippantly. It really is this idea of taking the individualized behaviors of you, Lisa, of me, Jeremy, and looking at those in a full view, not just what I did in this moment but what is my history with your brand tell me? And how do also some of those behaviors now also, maybe predict future behaviors as well. And using that data to ultimately drive and derive the content that is being put into the message. So, hyper-personalized meaning truly, one-to-one, like very, very discreet or descriptive pieces of data that ultimately tie to unique pieces of content that are going to drive a great experience or a particular behavior. So, some examples of maybe how we deploy that with folks, we work with brands like Backcountry or Revolve Clothing, Eddie Bauer, 1-800 contacts, we work with brands like that to help them drive revenue growth through things like, again, hyper-personalized messages drives higher revenue per message. It helps them significantly increase their customer lifetime value, again because the experience that they're creating for them is very tailored, very unique to that individual. So, some things that we measure ourselves on with respect to that and things where we're really proud of are things like our clients are generating a 250 X ROI. And typically they're achieving triple digit revenue growth within their first 30 days using Cordial, our platform, because of the data layer that we have there, we built a transformations product to that just last year alone for our clients transformed and activated over 110 billion customer data records, again for our clients there. And probably the thing that I think excites me the most and frankly kind of gets back to some of my roots in my history of why we started this business too, but, it really is our implementation process as well. So, brands want to hyper-personalize, they want to do all these things that we talk about. But often they think, man, the process to get there is going to take me a really long time. Again, one of the things we really pride ourselves on is that implementation process. It's 90% faster than the legacy marketing clouds out there within the market. To give you an idea of how incredibly fast that is. Our enterprise clients are up in sending typically in less than seven days. That really is unheard of to nearly all enterprise brands but we really pride ourselves on the flexibility of cordless technology coupled with our incredibly talented services team that really helps unlock that for many of our brands and customers. >> So, big numbers that you mentioned, customers achieving impressive metrics-based business outcomes. You talked about the 250 X ROI, triple digit revenue growth very quickly. You also talked about your implementation process being 90% faster than legacy marketing clouds. Talk to me about the actual data platform. And I'd like to kind of unpack that into some of the things that differentiate it. >> Yeah, yeah, absolutely. I guess first and foremost, on the data platform side of things, that really is a significant differentiator. And I guess even before I jump into that though, too. I would be remissed actually if... I think that's natural to probably jump right into product as being the key differentiator at the end of the day. But when I really do honestly think about what differentiates Cordial in the market and what are the things that we really hang our hat on, I can honestly say Lisa, like first and foremost it really is our people. Again, I know it's really natural to go straight to product and talk about the features and the functions or how you thought about building a particular thing. And again, those things are highly important in this kind of digital transformation era that we're in. But I would say in a market that is incredibly saturated with a lot of players across it, within marketing technology and brands, trying to differentiate who does what and who they should work with, at the end of the day we really do believe that creating and enabling a culture of world-class human beings that live out for succinct values. Those for us are things like communicate better than the rest, being tenacious about our clients and the problems we solve for them, acting like owners and then really being kind of on-mission, if you will, to be Cordial, we think that those things are differentiated and frankly really necessary, especially in today's society and culture that we're in. I'm happy to talk about some of the product side of things there though, too. But I'll pause in there for a second-- >> I love that you've said that about one of the key differentiators is the corporate culture. That's one of the things that a lot of companies, legacy companies struggle with. Especially in dynamic times like this, but I would always thought for all the tech shows I've been to, over the many, many years that the customer experience is dependent and inextricably linked to the employee experience. It sounds like you've kind of built the company with that in mind. >> I think you have to. Again, robots have not taken over the world yet, right? And so, this really is still about people combined with technology and how those two things married together. Not just on our side, in terms of what we're bringing to bear for our clients but the experiences our clients are having too, you know. Our clients are working with their IT department or with their engineers and their marketing teams, and they have to figure out how do you make all those things very harmonious together. I just think that at the end of the day, the experience that your people are bringing, the empathy that you're bringing to people, especially in this environment where we've been virtual and you don't get that face-to-face contact, you don't get to maybe delve deep into understanding people, relationally. I just think it's really important. And we, as a business, again, I have this said to me often, and in turn, I said often too, is you can't name your company Cordial and be a jerk at the end of the day. So, there really is a level of empathy that I think needs to be brought through in everything that we do. We're not just out to be, you know, a world-class technology company for our clients. We know our clients expect that from us but we really want to be great human beings at the end of the day, which I think that's really the kind of the link that creates really great partnerships at the end of the day. >> I completely agree and I think especially now more than ever, that infusion of empathy is so critical for businesses in any industry. I do want to unpack the data architecture. You talked about customers being able to get to unstructured customer business data from wherever it lives in the stack. How do you enable that? >> Yeah, yeah, absolutely. Again, that product side is from a differentiators perspective, it's significant. I would say we purposely built Cordial with a data architecture to accommodate just that, to accommodate any number of channels but also an infinite amount of data sources. And then in turn I alluded to this earlier but the ability to manipulate and restructure or transform that data coming in, or going out of Cordial to maybe other systems within a client's tech stack. This differentiation really is significant compared to the legacy clouds, but it is also significant, I would say relative to other kind of next gen options within the market. Investing in Cordial for a brand is... it's a huge step forward in terms of digital kind of future-proofing themselves and how they're setting themselves up to meet the needs of a really rapid evolving consumer and the experience the consumer expects to have with the brands. So, brands collect daily more and more information about users' behaviors and patterns, and we've frankly just see an incredible opportunity for our clients to learn from their customers and the massive amount of data that that client is kind of showing or exhibiting to them. And then putting that to action, putting that to work in terms of the experience that they're creating for their customer, you know, this kind of ties that word of empathy back to it as well. Even though we're talking about digital communications for a brand, it's still a human interaction, it's still a relationship. And so, if we can help brands really understand their data, again through a data architecture that's really purpose-built to really ingest all of that in and then activate that in terms, of their messaging that they're sending out to folks. That can create a level of empathy, that might sound altruistic, I don't believe it is. I think we as human beings, as a professional, if my job is to communicate my brand or my products to my end customer, I would want to do that with a level of empathy, with a level of sincerity, with a level of understanding and knowledge that tells that end customer I know who you are, I'm paying attention. I'm not being creepy and big brother-ish about it, but I'm paying attention. I want to show that I understand you, no differently, frankly than the relationships that we all have as human beings. I mean, if I walked into a conversation with you, Lisa, and I've known you for two years but I started asking you all the same boilerplate questions of like, hey, can you tell me your name again? And who are you? And where did you come from? And what school did you go to? You would kind of think that's so odd. You don't... I thought you knew me but you're not acting like you know me. I think we're all about creating an opportunity for brands to be able to do that with their customers and do it with a level that the customer goes, you know me, you get me, you understand what my desires, wishes, or patterns are with your business. >> Right? No, I think that's so interesting. And I agree with you. The opportunity is just getting bigger and bigger and bigger and bigger. It's not just more data is born and created, more data sources are born and created. The consumer demand is only increasing. So you mentioned, I want to talk about customer tech. You talked about, you mentioned Eddie Bauer being a customer. Eddie Bauer is a legacy organization which has been around for a long time. But I also know you guys work with, with younger, fresher, maybe more cloud native companies. I wonder, though, how an Eddie Bauer goes about fast implementation, you said 90% faster than legacy data platforms. I wonder how an Eddie Bauer goes through that. 'Cause I imagine they replaced a legacy marketing platform with Cordial? >> They did, they did. They actually replaced a handful of kind of legacy platforms and systems that they had in place. And Eddie Bauer is just like, I would say, many other kind of mainstay brands that you and I grew up with to where if they want to compete, if they want to really be on the cutting edge, they need to innovate quickly. They need to evolve from maybe legacy systems to newer systems. Like you said, maybe what more digital native or digital first brands are starting out with, when they launched their business. Eddie Bauer is a really cool story though. Again, it's kind of an iconic brand at the end of the day but they came to us with a really clear set of challenges. And the first and foremost, again, kind of goes back to the point we were talking about, which was Cordial help us consolidate our data from multiple sources that we have. Online, offline order data, loyalty information that they had, a disparate unique customer IDs that they had across all the different databases they had. They had geolocation data, they had product data, customer behavior data, a lot of data all sitting in different places. So first and foremost, like help us get that organized in one central place, being within Cordial's data platform. And then from there they wanted to use those data sources, right? It's not just about bringing it together. It's about now, what do you do with it? How do you activate that? And in the case with Eddie Bauer, they used Cordial to dynamically render... This is a cool example actually, dynamically render a message to each individual customer containing their rewards balance, the expiration date of that reward, a unique bar code specific to that individual to eliminate fraud there... what was it? Nearest store address that they had as well as a map of the store location. All within the message that they were receiving. And by clicking on that message it immediately activated kind of an API sequence behind the scenes, transparent to the user, but something that Eddie Bauer had never been able to do before. And that API sequence that initiated, generated a personalized pass for that particular customer that loaded directly into their wallet, on their device for them to be able to redeem in store. By doing that, it actually then enabled the ability for, if I'm near an Eddie Bauer store, let's say within a mile of it, Eddie Bauer can immediately push a notification to my phone without even having a branded app on the phone, saying, Hey you have a $20 rewards certificate in your wallet, it expires in seven days. You're a mile away from your closest store. Click here and we'll navigate you to that store. Some really cool use cases that really helped them kind of take some big steps forward in that digital transformation for them as a brand. And I would just say kind of going back to even the AWS piece of this too. All of that might sound easy at the end of the day. It's incredibly difficult to do that across millions of customers in minutes, it's very difficult. And I can genuinely say that our experiences and the work that we've done with AWS cloud services is a huge piece of making all of that a reality. And oddly enough, Eddie Bauer actually is an AWS customer as well. And some of those synergies in terms of how we're able to sync up data via Kinesis Streams and S3 buckets and things like that, and be able to make that data very operable was a huge advantage, I think for us, especially in terms of speed to market and the ability to get these kind of programs up and live for a brand like them. >> So, when you were looking at building Cordial and co-founding it back in 2014. Were you drawn to AWS right away? Because you just had this sense that we have to go this direction to enable this complexity to be achieved at scale? >> I mean, yes. I mean, unequivocally 2014 feels like eons ago. It's not really, I guess at the end of the day, but there was... I mean there was no other even remotely viable competitive option at the time to even consider. There are obviously are plenty of cloud services out there now, but I mean that was probably the shortest negotiation we had as co-founders about what we should do with respect to that. It was immediately, I mean that was part of our thesis, was all of the legacy clouds were in co-lo centers and trying to figure out migration plans to even get some of their infrastructure into the cloud. And we said, let's just start straight in the cloud right from day one, it's a huge competitive advantage. It gives us speed, it gives us scale. It gives us all sorts of things that we can immediately start unlocking value with. And so, yeah, when we started Cordial, AWS was, I mean that was day one. We initiated that and they've been an incredibly strategic partner for us ever since then. >> One of the things that are wrapping up here that I always find interesting when you're looking at new technologies like yours, you're right. 2014 does seem like eons ago, but it really wasn't. But you working with legacy, iconic brands like an Eddie Bauer that was probably at one point all paper-based transactions, having to digitize and digitally transform to meet their customers where they are now that need to marry online and offline behaviors to deliver that hyper-personalized experience. I know you guys also work with companies like Revolve as well. So, this is the technology that any type of business, historic, new, can use and implement, sounds like fairly quickly to make big impacts. And I think nowadays being able to deliver information in real-time that's hyper-personalized, it's going to be a make or break for companies that survive this new era that we're living in. >> Yeah, it, it really is. And again, for us, fundamentally goes back to again, why we set out to build Cordial? My co-founders and I actually had history in building one of the first gen, what I characterize as kind of a legacy platform now in this space. And frankly, after seeing or 15 years of seeing marketers struggle to get those platforms to scale to the level of data and sophistication out there, we knew there was a better way for marketers and technologists to work together. And we intimately then knew the way that this should be architected. So, as you said, in 2014, I mean, we set out to build Cordial to really transform the way marketers and technologists, you know, how they collaborated to fundamentally change the experiences their customers were having with their brands. Again, it was very, I guess, heart-centered on some level as the way that I would put that, this was never monetarily driven for us. It was all around, I think our own personal frustrations of not being able to meet those needs of our customers. And it wasn't a fault of the previous kind of legacy platforms. It was just technology had evolved and frankly consumers and digital devices had evolved enough to where there needed to be somebody and some brand or some companies who said, let's rethink this, let's rebuild this from the ground up. You mentioned Revolve, which Revolve Clothing is just a really cool example. They've been a Cordial client now for four years, they're going on their fifth year with Cordial. And they really are an incredible success story. One of the stories that I just, it's kind of like a crown jewel that we take a lot of pride in. I know our teams do with respect to Revolve is, when they came to us, they had, gosh! Two automations in place that it took them roughly about two years working with the legacy cloud, even to get those in place. And in a matter of their first eight months with Cordial they had nearly 30 new automations live using Cordial. And those 30 automations had generated eight times more revenue than they had previously generated with automations. It was an incremental at the time roughly almost $12 million in net new revenue that was unrealized for the business prior to that. And, you know, that's something we take a lot of pride in. We take a lot of pride in again, the speed of how quickly we can help a brand be able to do this, but it's not just a matter of getting something up and running. It's about the results that we can drive for them. We hold ourselves accountable to that and we expect our clients to hold us accountable to that as well. Next gen technology or new technology or modern technology for the sake of just new is uninteresting, unless it is actually... not just incrementally moving things forward for your business but I would say it needs to be an outsized set of results that you're driving for them to frankly make that even just the mental hurdle to get over that, make that worth it at the end of the day. >> Yeah. I was looking at some of the customer stories on your website and was very impressed seeing those metrics-based business outcomes. 'Cause that's what it's about. It's about that and delivering that at speed and with agility. Jeremy, I wish we had more time 'cause I know we could keep talking, but I really enjoyed understanding more about Cordial and what you guys do. And I look forward to seeing what's to come. >> So good, thanks so much, Lisa. It was wonderful. >> My pleasure, for Jeremy Swift, I'm Lisa Martin, you're watching theCUBE. (soft music ends)

>> live from London, England. It's the queue covering a ws summat. London twenty nineteen, Brought to you by Amazon Web services, >> come to the A. W s summit in London's Excel Center. I'm Susanna Street, and David Aunty is my co host today on the Cube. This means so much to talk about here at the summit today to do with machine learning and a I. And I'm really pleased to say that we have to really key people here to discuss this. We've got time. Tom Summerfield, who is head off commerce, a foot asylum on also Richard Potter, who is the CEO of Peak. Now you guys have really formed a partnership. Haven't you put asylum? Is a leisure wear really? Retailer started in bricks and mortar stores. Really moved online on Peak is a pioneer for artificial intelligence. System's really well to get together. What What sparked? Really your demands. Ready for their services, Tom. >> Yeah, well, so way knew that we needed to be doing something with data on A and we didn't really know exactly what it would be Way were interested in personalization, but then also in a bigger picture, like a wider digital transformation piece for the business where well established bricks, a martyr business, but then a fast growing online business. And we're interested to know how way could harness the momentum of the stores to help the digital side of the business and also vice versa. On we thought data would be the key, and we ended up having a conversation with the guys at Peak, and that's exactly what we've been able to do. Actually, on the back of that deliver, we're delivering a hyper personal experience for our consumers Now. >> I was one of the statue that I notice when looking into what you be doing, a twenty percent increase in email revenue. So that's quite remarkable, Really. So Richard, tell us, you know how you're able to do this? What kind of services that you lean on? T make those kind of result. >> It's a combination of a lot of things, really. You know, you obviously need people who know what they're doing from a returning a business perspective. Married with technical experts, data science algorithms, data. Um, I think specifically how we've done it is a pig's built, a fairly unique A I system that becomes almost like the central brain within our customers. Businesses on off that algorithms help automate certain business processes and deliver tangible uplifts in business performance like the twenty eight percent uplifting sales here, Um, in order to do it. So it's quite a long journey, I suppose. The outlook we took when we started collaborating was was that if we could deliver that hyper personalized shopping experience, we were always going to be ableto show customers the right product at the right time. And if we were doing that that we would lead Toa High brand engagement, higher loyalty higher on higher lifetime values of customers. And that's and that's what's shown to be the case in silent example. >> Yeah, definitely that echo that. You know that the high profits hypothesis wass If you can show the right custom of the right product at the right time, then their purchase frequency average order Volumetrics all start to move positively and ultimately than affecting their long term engagement with our brand, which increases revenue on also delivers a more, you know, a frictionless consumer experience, hopefully for the customer, >> because I suppose your experience is the same. So many companies out there they're sitting on this huge pile of data, yet they don't know how to best optimize that data. When did you first realize, Richard that there was this kind of gap in the market for Pete to grow? >> Yeah, I think data and analytics have come on a bit of a journey away from common sense reporting tio more advanced analytics. But when you get a I and machine learning what you're talking about, his algorithms being our self learning make predictions about things that actually fundamentally changes the way businesses can operate on DH. And in this case, a great example is you know, we're sending hyper personalized marketing communications, Teo, every single for silent customer. They don't realize necessarily that they are tailored to them, but they just become more relevant. But it doesn't require a digital marketing to create every single one of those campaigns or emails and even trigger the sending of those materials. Brain takes care of that. It can automate it. And what the marketer needs to do is it's faded, engaging content and set up digital campaigns. And then and then and then you're left with this capability where eyes saying you might be a market for this product. Let's let's send you something that might appeal to you on DH that just gives that gives a marketing team scale. And then, as we move into other use cases like in the supply chain for film and delivery of product the same thing the team's just get huge scale out of letting algorithms do those things for them. Andi, I suppose the realization for us that there was that gap in the market was just that you can see the out performance of certain cos you can see that Amazon attributes five percent of their sales to their machine learning recommendation systems. I think Netflix says eighty five percent of all content is consumed >> because it's Al Burns. Andi. Companies >> like that can harness machine learning to such a great degree. How does how did howto other businesses do it? Who can't access that talent pool of Silicon Valley or along the global? You know, the global talent leaders in tech and that's that's where we had the insight that his peak way could create a company that gave our custom is that that technology and that capability Teo deliver that same kind results that the Amazon and Netflix >> so before the Internet brand's had all the power you could price however you wanted if you overprice, nobody even even knew. And the Internet was sort of like the revenge of the consumer. Aye, aye, And data now gives the brands the ability to learn more about its customers. But you have to be somewhat careful, don't you? Because their privacy concerns obviously DPR etcetera. So you have to have a value proposition for the customer, as you were saying, which they made are you know that machine is providing these offers, but they get value out of it. So how do you guys think about that in terms of experience for the customer? And how do you draw that balance? >> I think from my angle, that Richard touch on a couple of bits there to do it scale first and foremost across the entire alarm on Thai network of consumers is killer element to it. But to deliver that personal experience, I think consumers nowadays are so they're more expectant of this. Really. We would have considered it innovation a couple of years ago, but now actually it's expected, I think, from the consumer. So it's actually in the name ofthe You have to move forward to stand still. So but way think where we're right at the front of this at the moment. And we're really looking now how we optimize the journey for the consumer so that actually we know if we're from some transactional data that we have in a little bit of over behavioral data that, you know, we're really conscious of the whole GDP, our peace and stuff, and that's really, really relevant and super important. Andi, I'm pleased to say that you know, we have that. We know that by a peek, it's completely on lock down from that perspective as >> well. Where did the data's where the data source of comfort. You mentioned some transaction data. Where is the other day to come from using show social data and behavioral data? Where does that come? >> So those elements of social data, some of it is a little bit black box. You can't always access it, and that's a GDP, our peace there, and rightly so. Actually, in some cases we have a loyalty scheme which allows us to understand our Kashima's better in our bricks and mortar retail, which is really cool that we've got some of that transactional data on a customer level from the stars. We know that some people in our sector maybe don't have that, so that so that allows us to complete sort of single customer view, which then we can aggregate in peaks brain, then transaction data on the website in the app and bits off browsing, you know, just within our own network. You know where customs potentially being and reacted with somethin. A piece of content. Janet within the website, that's that's how we build that view. >> Do you think this is the way that more bricks and more two stores Khun survive? Because so many are closing in high streets up down the UK and in other countries because simply they're not really delivering what the customer wants? >> Yeah, I think so. We rich now. Both feel quite strongly now that wear so onto this now a little bit. It's a really As as our relationship for the two businesses has evolved, it's become clearer and clearer that actually we've armed with this. You know this data, our fingertips, we can actually breathe fresh life into the stores, and it's in the eye of proper true Omnichannel retailing way. Don't mind where the cost consumer spends the money. We just need to be always on in a connected environment so that A Z said before pushing the right product at the right time. And when they're when they're in market, we turn up the mark the message a little bit. But then understanding when they're not in market and maybe to back off him and maybe we warn them what with a little bit of a different type of message then and actually we're trapped with one challenge ourselves to send but less better marketing communications to our consumers. But absolutely that store piece is now, so we tail back. Our store opening strategy is a business to focus more on the digital side of things, but now it's possible that way might open some more stores now, but it will be with a more reform strategy of wet, wet where, why we need to do that? >> Isn't this ironic? The brick and mortar marketplaces getting disrupted by online retailers, obviously Amazons, that big whale in the marketplace, and your answer to that is to use Amazons, cloud services and artificial intelligence to pave the way for your future. Yeah, I mean, that's astounding when you think about >> me. Yeah, this sort of unified commerce approach, Tio, you know, there's a place in the world for shops. It's like it's not Romance isn't completely dead and going shopping. It turns out, you know so on. Actually, yeah, we're using honesty in the eight of us, but we'LL hire our friends at Peak. Yeah, it's it's some irony there. I think it's really cool. >> And that decision that you made obviously wasn't made made lightly. But you saw the advantages of working with the clouds outweighing the potential trade offs of competition. >> Yeah, I mean, that's not that was never really, really no, I'm certainly not know. I think this is something that is happening, that data, and on harnessing it in a safe, responsible, effective way, I believe, is the future of all commerce. So >> that as far as security is concerned because, of course, we have had data breaches your customers, credit card details, access. How do you ensure that it's as secure as possible in the way that you you you choose the services I think >> that come that just comes down to best practice infrastructure on the way we look at it, a peak is there's no bear tools in the world to do that, then the same technologies that Amazon themselves use. It's to do with how you configure those services until ls to make it secure, you know, And if you have an unsecure open database on a public network, of course that's not secure. But you could have the same thing in your own infrastructure, and it wouldn't be secure. So I think the way we look at it is exactly the same thing on actually, being in the Amazon prime for us gives us a greater comfort, particularly in terms of co location of date centers and like making sure that our application fails over into different locations. It gives us infrastructure we couldn't afford otherwise, and then on top of that, we get all these extra pieces of technology that can make us even more secure than we could do. Otherwise we'd have to wait, have to employ an army of infrastructure engineers, and we don't have to do that because we run on Yes. >> Okay, so we were able to eliminate all that heavy lifting. That same goes. You've got this corpus of data. I'm interested in how long it took to get through. A POC trained the models how much data science was involved. How much of a heavy lift was that? Yeah, well, I think for >> us we better be pretty rapid. Actually, we started working together in January last year, so we're only just sort of year into that. >> And in that faith in that entire >> sofa length of of our relationship, we've gone from high for personalizing digital campaigns to recommendation systems on a website to now optimizing customer acquisition on social media and then finally into the supply chain and optimizing demand and so on it. And I think there's >> a lot of reasons >> why we've been able to do it quickly. But that's fundamental to the technologies that the peak is built. There's two. There's two sides to it. Our technologies cut out a lot of the friction so way didn't run a proof of concept. We were able to just pick it up, run with it and deliver value. And that's to do with I think, the product that peak is built. But then you obviously need a a customer who's who's going on a transformation journey and is hungry to make that make that stick in London on. Then when the two come together, >> I think that it's an interesting point that, though, because while suite for asylum, we always I always say it's that we're not. We're not massive, but we're not tiny, but it's the sort place you Khun turn upon a Monday and say, I've had an idea about something and we're not doing it by Friday. That's That's a nice, agile culture. It can create some drama as well. Possibly. I think it's really straightforward to get straight into it. And I think this is where some of the bigger, um, sleepier high street retailers that Amar, fixed in a in a brick from our world, needs to not be too afraid to come out and start embracing it, because I think some of them are trying now. I think it might be a little bit late for some now, but it's just it's just it just wasn't that hard really to get going >> and you've seen the business results, can you share any measurements? or quantification. We've >> got a really a really good one that we're just talking about at the moment. Actually, Way were able to use segmentation tools within within the peak brain Teo to use them on Social than Teo. Create lookalike audiences. So Facebook Custom tools, Right? We'LL help you create audiences that it thinks you're the right buyer. It's complex algorithms itself, but we almost took a leap ahead of their algorithms by fire, our algorithms uploading our own segments to create a more sophisticated lookalike audience. We produced a row US results or return on that spend. People are not familiar with that of eight thousand four hundred percent, which Wei would normally be happy as a business, we've sort of seven, eight hundred percent. If you're running that that we've say on AdWords campaign or something like that, that's quite efficient campaign. So it's at zero. We were a bit like it felt like it's a mistake that, you >> know that is >> not the right, >> Yeah, but not so that's super cool. And that's really that's really opened our eyes to the potential of punishing that the, you know, our sort of piquet I brain to then bring it onto Social on. Do more outward. Advertise on there. >> So moving the goal post meant that your teeth are really high school. Thank you. Thank you very much for telling us all about that time someone feels on which floor. Sir. Thank you for joining me and David Auntie here at the eight of US Summit in London. Merchant to come on the King.

>> Live. From San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. (techno music) >> Hello, everyone, welcome back to our day two of live coverage here in San Francisco, California for Google Next's conference called Next 2018, Google Next 2018 is the hashtag. I'm John Furrier with Dave Vellante. We're kickin' off day two. We just heard the keynotes, they're finishing up. Most of the meat of the keynote is out there, so we're going to just dive in and start the analysis. We got a tight schedule again, great guests, we have all the cloud-native folks comin' up from Google. We're going to hear from customers, and from partners. We're going to hear all the action. We're going to break it down for you. But first we want to do kind of a breakdown on the keynote, do analyze it and give some critical analysis, and also, things we think Google's doing great. Dave, day two, we've got three days of wall-to-wall coverage, go to the siliconangle.com for special journalism cloud series, a lot of articles hitting, a lot of CUBE videos, go to theCube.net, just check out those videos. That's our site, where all the videos are. Dave, day one, we had a great close yesterday; I thought it was phenomenal. But I thought we nailed it, today, too. And one of the things we were talkin' about in the first day close, editorially, was saying, hey, you know, this AI is super important. Today, in the keynote, more AI, more under the covers, more speed of announcements. Google kind of taking a playbook out of Amazon, let's get some announcements out there, I wouldn't say that the pace of announcements meets AWS, in terms of the announcements, but the focus is on a very few core things: AI, RollaData, Cloud-Native, Cloud Functions, Cloud Services Platform. This is the Google, that they're lifting the curtain. We're startin' to see some action. Your thoughts on the keynote... >> Well, I think you're absolutely right, I think Google realizes that it's got to compete with Amazon, from the keynote standpoint, demonstrating innovations, putting out a lot of function. I will say this, maybe it doesn't match Amazon's pace of innovation and announcements, but when you compare what these cloud-guys do with the traditional enterprise shows that we go to, there's no comparison. Even this morning, keynote day two, was drinking from a fire hose, there are dozens of announcements that Google made today. I would say just a couple of things, critical analysis, Google, everything is very scripted, as is all these shows, Amazon is very scripted as well, but they're reading everything, which I don't like, I would rather see them have a little bit more teleprompter, friendly, sort of presentation. So that's just sort of a little side comment. But the content is very good. The big themes I took away today, even though they didn't use this term, is really they're treating infrastructure as code. They're deploying infrastructure and microservices from code, as developers. So that was a theme that cut through the entire morning. Big announcement was the GA of Cloud Functions. It's been in beta, now it's Serverless, it's been in beta for a long time. And then a number of other announcements that we're going to go through and talk about, but those were some of the big highlights. But AutoML, I want to talk about that a little bit, talk a lot about developer agility. Threw out a couple of examples of customers, we heard from Chevron, we heard from Twitter, so they're starting to give examples, again, not as many Amazon, but real customers in the enterprise, customers like Mastercard, so, they're dropping some names... You're starting to see their belief manifest into actual adoption. But I'd like to ask you, John, what's your sense of the adoption bell curve, and the maturity curve, of the Google customer? >> Great question, I think for me, just kind of squinting through all of the noise, and looking at the announcements specifically, and how the portfolio of the show's going, it's very clear that Google is saying, we are here to play, we are here to win, we're going to take the long game on this cloud business. We have a ton to bring to the table, I call it the "bring out the Howitzers, the big guns." And they're doing that, they're bringing major technology, BigQuery, BigTable, Spanner, and a variety of other things, from the core Google business, bringing that out there and making it consumable; said that yesterday. Today, we looked at what's goin' on. You're seeing AI within G Suite. Leading by example, by demonstrating, look at it, this is how we use AI, you could use it, too, but not jamming AI and G Suite down the throats of the customer. AI and BigTable, I thought was pretty significant, because you can now bring machine learning and artificial intelligence, so to speak, into a data warehouse-like environment, where there's not a lot of data movement, data prep, it just happens. And then the Cloud Services Platform, the CSP, that Eyal Menor, the Vice President of Engineering, rolled out, I found interesting. The key move there was Cloud Functions. They now need to have Serverless up and running, and obviously Lambda's AWS. The uptake on the enterprise with Lambda has been significant, more than they thought. We heard that from Amazon, so I expect that Cloud Functions, and having this foundational layer with Kubernetes doubling down. The Kubernetes, Istio, and these Cloud Functions, represent that foundation. Knative open source projects, again, another arrow in their quiver around their open source contribution. This is Google, they're bringing the goods to the party, the open source party. This is an under-appreciated value proposition, in my opinion; I think a lot of people don't understand the implications of what's going to go on with this. This upstream contribution, and the downstream benefits that's going to come from their contra open source, is highly strategic. We used to call it, in the old days, "Kool-Aid injection." That's the way you ingratiate into the community with your software, ultimately the best software should win. There's not a lot of politics in open source, as there was once was, so I think that's fine. Now, to the question of migration, Google Cloud is showin' some customers up there, but I don't think they're going to, they're a long ways away from winning enterprises. What you see Google winning now is the AlphaTechies. The guys who were, and gals, who know tech, they know scale, and they can come in and appreciate the goodness of Google, they can appreciate the 10x advantages we heard from Danielle, with Spanner. These are what I call people with massive tech chops. They understand the tech, they've had problems, they need an aspirin, they need a steroid, and they need a growth hormone, right? They don't just need a pain-killer, they need solutions. These guys can make it happen. They jump in, take the machinery, and make that scale. The second level on the trajectory of their growth, on the adoption curve, is what I call, "Smart SMB, Smart enterprises." These are enterprises that have really strong technical people, where the internal conversations is not "if we should go to cloud," it's "how should we go to cloud?" And the DNA of the makeup of the technical people will decide the cloud they go with. And if it's engineering-led, meaning they have strong network operations, strong dev-team, then they have people who know what they're doing, they gravitate to Google Cloud. The third phase, which I think is not yet attainable, although aspirational, for Google, is the classic enterprise. "Man, I've been buying IT for years, oh my god, I'm like a straight-jacket of innovation, nothing's happening!" They're like, "we got to go to the cloud, how do we do it?" It's a groping for a strategy, right? So, Amazon gets those guys, because there's some things that shadow IT that Amazon can deliver, in more options, than what Google has. So I think I don't see Google knockin' that down in the short term, anytime soon. They can do plenty of business. Again, this is a trajectory that has an economy of scale to it, as an advantage, as a competitive advantage, by doing that. If Google tries to become Amazon, and meet their trajectory, the diseconomies of scale plays against Google. This is critical, Google does not want to do that, and they're not doing that, so I think the strategy of Google is right on the money. Nail the early adopters, the alpha geeks. Hit the engineering teams within the smartest companies, or small businesses, and then wait to hit that mainstream market, two, three years from now. So I think there's a multi-year journey for Google. Again, this diseconomies of scale is not what they want, they have tons of leverage in the tech, and the data, and the AI. So to me, they're right on track. They're now getting into the phase two. Smart. I give them credit for that. >> Let me pick up on a couple of things you said, and tie it into the keynotes from this morning. But I want to start with some of the conversations that you and I had last night, and around the show, with some of the GCP users. So, we've been asking them, okay, well how do you like GCP? Whaddya like? What don't you like? How does it compare with Azure? How does it compare with Amazon? And the feedback has been consistent. Tech is great, a lot of confidence in the tech. Obviously what Google's doing is they're using the tech internally, and then they're pointing it to the external world. It comes out in beta, and then they harden it, like they did today with Serverless and GOGA. The tech's great. Documentation has a little bit to be desired; we heard that as a consistence theme. Functionality not as rich in the infrastructure side as AWS, and not as enterprise app friendly as Azure, but very, very solid capabilities. This comes from people in financial services, people in healthcare, people from oil and gas. So, it's been consistent feedback that we've heard across the user base. You mentioned Knative; Knative is a new open source project, that brings Serverless to Kubernetes, and it was brought forth by Pivotal, IBM, RedHat, SAP, obviously Google, and others. Again, a big theme of the keynotes this morning was developer agility, bringing microservices, and services, and things like Kubernetes, to the developer community. Now, I want to talk about another example of a customer, Chevron. Is Google crushing it in traditional enterprise IT in the cloud? Well, no, you're bringing up the point that they're not. But, what they are doing, is doing well in places where people are solving data-oriented business problems with technology. Is that IT? It's not a traditional IT, but it's technology. Let me give you an example, Chevron was up on stage today, and they gave an example of they have thousands and thousands of docs, of topographical data points, and they use this thing called AutoML to ingest all the data into a model that they built, and visualize that data, to identify high-probability drilling zones and sites in the Gulf of Mexico. Dramatically compressed the time that it would have taken. In fact, they wouldn't have been able to do this. So they ingested the data, auto-categorized all the data to simplify it, put it into buckets, and then mapped it into their model, which was tuned over time, and identified the higher probability of sites for drilling. That's using tech to solve a business problem, drive productivity; Google crushes it with those type of data applications, really good example. >> And AutoML drives that, and this is where, again, a machine learning, AutoML, AI operation, we mentioned that yesterday, the IT operations sector is going to be decimated. But I think the big tell sign for me is when I look at the cloud shows, Amazon definitely has competition with Google, so that anyone who says Google's way far back in the market share, which you know I think is bastardized, I think those market share numbers don't mean anything because there's so much sandbagging going on; I could look at any one and say Microsoft's just sandbagging the numbers, and Amazon not really, if Amazon could probably sandbag the numbers even more by putting revenue from their partner ecosystem. Google throws G Suite in there, but they could throw AdWords in there and say technically that's running on their cloud, and be the number one cloud. What is a good cloud? When you have a cloud, if you can make a situation where you can take a customer and get them on the cloud easily, in a simplified, accelerated way, that is a success formula. What you heard on stage today was kind of, naw, I won't say underplayed, they certainly played it up and got some applause, is Velostrata and these services. They bought a company called Velostrata in May of this past year, and what they do is essentially the migration. We had a guest on, a user yesterday, migrating from Oracle to Spanner, 10x value, major reduction in price. They didn't say 10x, but significant; we'll try to get those numbers, she wouldn't say. But what Velostrata does is allows you to migrate to existing apps in a very easy, non-disruptive way, from on-prem to the cloud. This is the killer app for the leading clouds. They need tools to move workloads and databases to their cloud, because as clients and enterprises start to do taste tests, kick the tires in cloud, they're going to want to know what's the better cloud. So, the sales motto is simply go try it before you buy it. It's cloud. You can rent it. This is the value of the cloud. So, Amazon's done an extremely awesome job at this, Google has to step up, and I think Velostrata's one of many. I think the Kubernetes piece is critical, around managing legacy workloads, and adding new cloud natives. Between Velostrata, and the Knative, and the Cloud Functions, I think Google is shoring up their offerings, and it makes them a formidable competitor for certain workloads, and those early adopters, and that Stage Two, small, medium, or Smart enterprise, as a foundational element. I think that is a tell sign, and I got to give them props for that, and again, you can get an Oracle database into cloud, you're going to win a lot of business. If you can get an app workload running on Google Cloud seamlessly, in a very easy, meaningful way, it's just going to rain money. >> So let's talk about something we just talked about, how Google's not crushing it in traditional enterprise apps, but let's talk about some-- >> For now. >> of things we heard today, where they're trying to get into that space. So they announced today support on GCP for Oracle RAC, real application clusters, and exit data, and then SAP, via a partnership with Accenture. So Accenture does crush it with Oracle and SAP. Now, here's the problem: Oracle will play its licensing games, we've seen this with Amazon, where essentially, Oracle's license costs are double in AWS, they'll do the same thing for Google, I guarantee it, than they are in Oracle's cloud. So, 2x. It's already incredibly expensive. So, Oracle's going to use its pricing strategy to lock out competitors. So, that's a big deal, but we also saw some stuff on security: Cloud Armor, automatically defending against DDoS attacks, that's a big deal. We heard about shielded VMs, so secure VMs within GCP. These are things that traditional enterprises, it's going to resonate with traditional enterprises. >> Yeah, but here's the thing, then, we have one final point. I know we're going to run over a little bit of time, here, but I wanted to get it out there. You mentioned Oracle and the licenses. It's not just about Oracle, and their costs, and that disadvantage that could happen for a lot of people, and what cloud clearly has some benefits on a lot of cost. Here's the problem, like any Mafia business, Dave, we always talk about the cloud Mafias, and the on-premise Mafias. Oracle has an ecosystem of people who make a boatload of money around these licenses. So, you have a lot of perverse incentives around keeping the old stuff around, okay? So, as the global SIs, you mentioned Accenture, Deloitte, and others, those guys may salute the Google Cloud flag and the ecosystem, but at the end of the day, it's going to come down to money for them. So, if the perverse incentive is to stay in the old ways, saying "hey, okay, if we keep the license in there I get more better billing hours and I can roll out more deployments." Because what clouds do, and what Google's actually enabling, is enabling for the automation of those systems and those services, so you're going to see a future, very quickly, where half of the work that Accenture and Deloitte get paid on is going to be gone. From weeks to minutes; months, to weeks, to minutes. This is not a good monetization playbook for Accenture, and those guys. >> Well. >> So Google has to shift a ecosystem strategy that's smart and makes people money. At the end of the day-- >> No doubt. >> That's going to be a healthy ecosystem for every dollar of Google spend, it has to be at least 5 to 15x ecosystem dollars. I just don't see it right now. >> The big consultancies love to eat at the trough, as we like to say. But let's talk about the ecosystem, because you and I, we've walked the floor a couple times now. We mentioned Accenture, Cognizant is here, RedHead is here, KPMG, Salesforce, Marketo, Tata, everybody's here. UiPath, a startup in RPA; Cohesity's here. Rubrik's here, Intel's here, everybody's here, except AWS isn't here. >> Obviously. >> (chuckles softly) And Microsoft's not here. The other point that I think is worth mentioning, is again, big theme here is internally tested and then we point it at the market. Chevron, Autotrader, Mastercard, you're starting to see these names trickle out, other traditional enterprise. They announced today a partnership with NetApp for file sharing, for NFS workloads. So you're seeing NetApp lean in to the cloud in a big way. NetApps, back! You know you were seein' that. You saw Twitter on the Google Cloud. So you're seeing more and more examples of real companies, real businesses. >> I'll just end this segment by saying one thing quickly, the high IQ people in the industry, whether it's customers, partners, or vendors, are going to have to increase their 3D chess game, because as the money shifts around, the zero-sum game in my mind, it's going to shift to the value. Things are going to get automated either way, and that could be core businesses. So, the innovative dilemma is in play for many, many people. You got to be smart, and you got to land in a position, you got to know where the puck is going to be, skate to where the puck is going to be. It's going to require the highest IQ: tech IQ, and also business IQ, to make sure that you are making money as the world turns, because those dollars are up for grabs. The dollars are shifting as the new ecosystem rolls out. If you're relying on old ways to make money, you are in for a world of hurt if you don't have a plan. So, to me, that's the big story, I think, in the cloud that Google's driving. Google's driving massive acceleration, massive value creation, massive ecosystem opportunities, but it's not your grandfather's ecosystem, it's different. So we're going to see, we're going to test people, we're going to challenge it, we're going to have conversations here in TheCube. The day two of three days of live coverage. I'm John Furrier with Dave Vellante. Stay with us as we kick off day two. We'll be right back. (techno music)

>> Live from San Francisco, it's The Cube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello, everyone. Welcome back to our live coverage. It's The Cube here, exclusive coverage of Google Cloud, Google Next 2018. I'm John Furrier, co-host with Dave Vellante, both co-founders of The Cube and SiliconANGLE, here with our special guest Diane Greene, who's the CEO of Google Cloud, legend in the industry, former CEO of VMware, among other great things. Diane, great to see you, great to have you on The Cube for the first time. >> Really fun to be here, I'm really happy to be here. >> One of the things about Google Cloud that's interesting that we've been observing is, you mentioned on stage, two years now in, you're starting to see some visibility into what Google Cloud is looking to do. They're looking to make things really easy, fast, and very developer-centric, an open source culture of inclusion, culture of openness, but hardcore performance. Talk about that vision and how that's translating as you're at the helms driving the big boat here. >> Yeah, sure. Obviously we had this amazing foundation with our modern enterprise company, Google Cloud. But what we've done with Google Cloud is we've realized that Google values engineering so much, and so do our customers. So one is, we're taking a very engineering-centric approach. People really love our open source philosophy. And then we're so double down on both security and artificial intelligence. So if you have this underlying, incredibly advanced, scaled infrastructure, high performance, security, availability, and all the goodness, and then you start taking people somewhere where they can really take advantage of AI, where they can be more secure than anywhere else and you have the engineering to help them really exploit it and to listen to the customer, it's about where they want to go, we're just getting incredible results. >> I've been following Google since the founders, Sergey and Larry, started it, it's been fun to watch. They really are the biggest Cloud ever to be built and Facebook certainly has built-- >> We have seven applications that have over a billion active users. >> Massive scale-- >> And actually, we're just this week on track to have the next one drive. >> 25 years of expertise. I've seen them move from buying servers to making their own, better airflow, just years and years of trajectory, of economies of scale, and then when Google started The Cloud a couple years ago, it's like, oh, great, everyone wants to be like Google so we'll just offer our Googleness to everyone and they're like wait, that didn't really work. People want to consume what Google has, not necessarily be Google, because not everyone can be Google. So there's a transition where Google's massive benefits are now being presented and sold, or offered as a service. This is a core strategy. What should people know about? Because people are squinting through all this market share, this company's got more revenue than that one, and if I bundle in AdWords and G Suite, you'd be the number one Cloud provider on the planet by far. So buyers are trying to figure out who's better for what. How do you talk to customers if someone says, are you behind, are you winning, how do I know if Google Cloud is better than the other Cloud? >> Well, the only way you're going to know is to kind of do a proof of concept and see what happens, you know, pull back the covers. But what we can explain to people is that we're so... One is that it's all about information. That's why I say Google's a modern enterprise company because we're about it. I said that in my keynote. We take information, we organize it, and we supercharge it. We give a lot of intelligence to it and that's what every business needs to do, and we're the best in the world at it. And then AI is this revolutionary thing going on where you can just apply it to anything. Someone made a joke about Cloud, they said it's like butter, it's better with everything. Well, The Cloud is better with everything. I think it's AI, actually. So when you combine our ability to manage data, our ability to do artificial intelligence, with our open source and then our security, not to mention the fact that the underlying infrastructure is, everybody pretty much acknowledges the most advanced technology in the world, it's a pretty unbeatable competition, I mean combination. But the thing is, we needed to bring it to market in a way that everybody could trust it and use it. One of the first things we did, which we hadn't had to do, is serving our internal customers. Have roadmaps, so customers can know what's going on, and what's coming when, that we won't ever turn something off, and all those things that an enterprise company expects and needs, for good reason. I have to say, our engineering team is loving working with external customers. Everybody said, you'll never get that engineering team caring about customers. And I knew we would because we had the same quality engineers at VMware and they loved it. And I knew it was just a matter of getting everybody to see how many interesting things that we-- >> And it's problems to solve, by the way, too. >> There's so many problems to solve and we're having even broader impact now, going to the enterprise, going to every company. >> You said in your keynote, IT is no longer a cost center, it's a key driver of business. Tech is now at the core of every product. You go back 15 years, I remember somebody said to me, have you seen what VMware can do and how fast it can spin up a server? That was cost, right? >> Yeah. >> Talk about the enterprise today. When you talk to customers, what are those problems they're solving, what are those opportunities? >> There is a class of customers, typically the internet companies, they are looking for the best infrastructure, they are looking to save cost, but they're also looking, you know, are people realizing, why should I do it all? Why don't I concentrate on my core competence? It's well known we've had Snap from day one and we were in their prospectus when they filed to go public. Then we have Twitter, we recently announced Spotify, and so forth. So those are very technically sophisticated. People, they come, they use BigQuery, they use our data analytics and our infrastructure. But then you get into the businesses, and we've taken this completely verticals approach. So they're coming to solve whatever problems it is they have. And because we have these exceptional tools and we're building platform tools, a lot of them with applied AI in every vertical, they can come to us and we can talk to them in their language and solve their problems. I talked about it in my keynote, with IT driving revenue, everybody's re-engineering how they do business. It's the most exciting time I've ever seen in the enterprise. I mean, I've always though tech was interesting, but now, it's the whole world. >> It's everywhere. You have an engineeering background, you went to MIT, studied there. If you were the lead engineer of most of these companies that are re-architecting and re-engineering, they're almost re-platforming their companies. They're allowed to think differently, it's not just an IT purchase, because they're not buying IT anymore, they're deploying platforms. >> And they're digitizing their whole business. They're using their information, they're using their data. That changes so many business processes. It changes what they can do with their customers, how they can talk to them, it changes how they can deliver anything. So it's just a radical rethink of... It's so amazing when we work deeply with the customer because they might start out talking about infrastructure and how they're going to move to The Cloud and how we can help them, and then we start talking about all the things our technologies can do for them and what's possible. And they'll kind of pause and then they'll come back and they'll go, holy cow, we are rethinking our whole company, we are redefining our mission, we're much more, you know, it's very exciting. >> I had a chance to interview some of your employees and the phrase comes up, kid in the candy store a lot. So I've got to ask you, with respect to customers, is there more of an engineering focus? As you see some of the adoption, you mentioned Twitter, Spotify, these are internet companies, these are nerds, they love to geek out, they know large scale, so not a hard sell to get them over the transom into the scale of The Cloud. As you get to the enterprise, is there a makeup, is their an orientation that attracts Google to them, and why are you winning these deals? Is it the tech, the people, the process, obviously the tech's solid, but-- >> It's a combination of all of the above. What'll happen is we'll all come in and start pitching these companies, and what we do, we really understand what they're trying to do. And then we send in the appropriate engineers for what it is they're trying to do. You get this engineer-to-engineer collaboration going that lets us know exactly how to help that company. >> They give you a list and you go, check, I've done that. Okay, next, check, check, you go down the checkbox, or is it-- >> Well, we brainstorm with them, and companies like that, because they don't necessarily understand all the technology. I always like to think what an engineering orgs does is one, it gets requirements from the customers about what they need, and we call that all the table stakes, and we get it done, and some of it's pretty hard to do. But then, the engineers, after they get to know customers, they can invent things that the customer had no idea was possible, but that solves their problem in a much more powerful way. And so, that's the magic. And that's how we're going into the market. Wherever we can, we'll take things and make it available to everybody. We're very, you know, that open source philosophy of all technology is for everybody, and it's a very nice environment to work in. >> The number one sound John and I have been talking all day about in your keynote was, security's the number one worry, AI is the number one opportunity. >> I was writing my keynote and it hit me. I'm like, oh, this is how it is. >> So please, when you talk to customers, how are you addressing that worry, and how are you addressing the opportunity? >> We're pretty proud of our security because it really is, at every layer, very deeply integrated, thought through. We don't think in terms of a firewall because if you get inside that firewall, all bets are off. So it's really everything you do needs to be looked at and you've got to make sure, and that's why the Chromebook with the hardware based two-factor authentication, and G Suite. Google, which went to that, and since we did, not a single one of our 85,000 employees have been phished. Kind of amazing. >> Yeah. >> Because it's the biggest source of attack. >> Ear phishing is the easiest way to get in. >> Yeah, but you cannot do it once you have that combination. It's all the way up there, all the way down to proprietary chips that check that the boot hasn't been tampered with every time you boot. Our new servers all have it, our Chromebooks all have it, and then everything in between. We think we have an incredibly powerful, we had to add in enterprise features like fine-grain security controls, ways to let our users manage their own encryption keys. But anyhow, we have just at a really phenomenal, and our data centers are so bulletproof. We have those catchers that'll pick up a car. We even have one of those. We had a UPS truck try and tailgate someone and got picked up in it. >> The magic of the engineering at Google. This is the value that we hear from customers, is that, we get that the technology and the engineers are there, we see the technology. But you've been involved in transformative businesses, beyond where Dave was mentioning, certainly changed IT. And it was new and transformed. Cloud's transformational as well. We were just talking earlier about the metaphor of the horse and buggy versus the car, things get automated away, which means those jobs now are gone, but new functionality. You're seeing a lot of automation machine learning, AutoML is probably one of the hottest trends going on right now. AI operations seems to be replacing what was categorically an industry, IT operations. You're starting to see IT again being disrupted. And the shifting into the value up the stack. And this is developers. >> That's the point. Because I don't feel like, yes, all those really painful jobs are going away. >> That no one wanted to do. >> That no one wanted to do anyhow. VMware was the same way. We eliminated tons of drudgery. And AI is doing it systematically across every industry but then you repurpose people. Because we still need so many people to do things. I gave the example in my keynote about the dolphin fins and using AutoML to find them and identify them. Well, that was PhD researchers and professors were looking at that. Is that what they should be doing? I don't think so. You free them up and think of the discoveries they're going to make. I mean, humans are really smart. I think all humans are, we just have to do a better job at helping them realize their potential. >> I want to talk about that, that's a great point. Culture's everything. I also interviewed some of your folks. I just wrote an article on my Forbes column about the four most powerful women in Google that aren't Diane Greene. It was some of your key lieutenants. >> That was a great piece. >> The human story came up, where you have machines and humans working together. One of the conversations was, artistry is coming back to software development. We were on this thread of modern software developers is not just your software engineer anymore. You don't need three PhDs to write code. The aperture of software development engineering and artistry and craft is coming back. What's your reaction to that? Because you're starting to see now a new level of range of software opportunities for everybody. >> Yeah, my daughter is a computer science major and she just taught at coding camp this summer, and they started from kindergarten and went up. It was amazing to hear what those kids were doing. I think a lot of applications are almost going to be like assembling lego. You have all these APIs you can put in, you have all these open source libraries, you have Serverless, so you just plop it in these little containers, and everything is taken care of for you. You're right, it's like a new age in building applications. You will still need, Google needs systems engineers but-- >> Under the hood, you've got to fix engines, mechanics. >> You guys talked about this in your article, the shifts toward creativity becomes a much more important ingredient. >> And also the human computer interface and the UX. You heard from Target, I was talking to him, they do an agile workshop for six weeks for all their developers. Their productivity, he said, an order of magnitude higher. I think the productivity of developers, in The Cloud, with all these technologies, is across the board, an order of magnitude better, at a minimum. >> Mike McNamara, the CIO of Target, was up on stage with you today. >> Yeah, he's a really impressive person. >> So I want to ask you about differentiation. You talked about open source, and specifically your contribution to open source, that's different from most Cloud players. The other thing you talked about, and I want to understand it better, is that you provide consistency with a common core set of primitives. What do you mean, and why is that important? >> Right. So when we build out all our services, we want to have one uniform way of thinking about things. So, how do you do queueing? It's common across every service. How do you do security? It's common across every service. Which means it's very intuitive and it's easy to use this system. Now, it slows you down. Software development at that layer, when you have to do that, goes more slowly. And if you have to make a change, you know, in a core primitive, everybody's got to change, right? However, you take the other side, where everybody just builds a service vertically and with disregard for how things are done, and now you've got this potpourri of ways to do things and everybody has to have specialized expertise in every service. So it really slows down the operators and the developers. You get a lot of inconsistency. So it's super high value and I have to believe people are going to start appreciating that and it's really going to be-- >> I think that's a huge problem that people don't really understand. Just as an example, if you're building out a data pipeline and tapping all these different services, you've got then different APIs for every single service that you have to become an expert at. >> That's exactly right. >> That's a real challenge. Like you said, from a software development-- >> And it's annoying. >> Yes, users who really understand this stuff are getting annoyed with it. But it's an interesting trade-off and a philosophy that you've taken that's quite a bit different from-- >> Well, Google has such a high bar for how they do things. >> That sounds foundational though. It's slower, but it's more foundational. But doesn't that accelerate the value? So the value's accelerated significantly-- >> Oh yes. >> So you go a little slower down. >> Our going a little slower makes everybody else go way faster, at a higher quality. The trade-off, it wins. >> Diane, thank you for taking the time to join us in The Cube today. >> I want to ask one final question. Culture in Google Cloud, how would you describe the DNA within Google Cloud? A lot of energy, a lot of enterprise expertise coming in big time, a lot of great stuff happening. How would you describe the DNA of Google Cloud? >> I would say just tremendous excitement because we're just moving so fast, we're scaling so fast, we're sort of barely in control, it's moving so fast. But such good things happening and the customers are loving us. It's so rewarding and everybody's increasingly taking more and more ownership and really making sure that we do super high quality work for our customers. Everybody's proud, we're all really proud. >> What's the one thing that you want people to know about that they may not know about Google Cloud, that they should definitely know about? >> Geez, you know, it's worth coming to and giving it a try. The biggest thing is how early we are, and it's the right place to be because you want the highest quality, you want the most advanced technology. And AI and security are pretty important. >> Diane Greene, the CEO of Google Cloud here inside The Cube, live in San Francisco. We're at the Moscone Center. I'm John Furrier with Dave Vellante. We'll be back with more live coverage. Stay with us for more from day one of three days of live coverage. We'll be right back.