>>Hello, everyone. Welcome to the cubes coverage here. New York city for AWS Amazon web services summit 2022. I'm John furrier, host of the cube with Dave ante. My co-host. We are breaking it down, getting an update on the ecosystem. As the GDP drops, inflations up gas prices up the enterprise continues to grow. We're seeing exceptional growth. We're here on the ground floor. Live at the Summit's packed house, 10,000 people. Eric Bradley's here. Chief STR at ETR, one of the premier enterprise research firms out there, partners with the cube and powers are breaking analysis that Dave does check that out as the hottest podcast in enterprise. Eric. Great to have you on the cube. Thanks for coming on. >>Thank you so much, John. I really appreciate the collaboration always. >>Yeah. Great stuff. Your data's amazing ETR folks watching check out ETR. They have a unique formula, very accurate. We love it. It's been moving the market. Congratulations. Let's talk about the market right now. This market is booming. Enterprise is the hottest thing, consumers kind of in the toilet. Okay. I said that all right, back out devices and, and, and consumer enterprise is still growing. And by the way, this first downturn, the history of the world where hyperscalers are on full pumping on all cylinders, which means they're still powering the revolution. >>Yeah, it's true. The hyperscalers were basically at this two sun system when Microsoft and an AWS first came around and everything was orbiting around it. And we're starting to see that sun cool off a little bit, but we're talking about a gradient here, right? When we say cool off, we're not talking to shutdown, it's still burning hot. That's for sure. And I can get it to some of the macro data in a minute, if that's all right. Or do you want me to go right? No, go go. Right. Yeah. So right now we just closed our most recent survey and that's macro and vendor specific. We had 1200 people talk to us on the macro side. And what we're seeing here is a cool down in spending. We originally had about 8.5% increase in budgets. That's cool down is 6.5 now, but I'll say with the doom and gloom and the headlines that we're seeing every day, 6.5% growth coming off of what we just did the last couple of years is still pretty fantastic as a backdrop. >>Okay. So you, you started to see John mentioned consumer. We saw that in Snowflake's earnings. For example, we, we certainly saw, you know, Walmart, other retailers, the FA Facebooks of the world where consumption was being dialed down, certain snowflake customers. Not necessarily, they didn't have mentioned any customers, but they were able to say, all right, we're gonna dial down, consumption this quarter, hold on until we saw some of that in snowflake results and other results. But at the same time, the rest of the industry is booming. But your data is showing softness within the fortune 500 for AWS, >>Not only AWS, but fortune 500 across the board. Okay. So going back to that larger macro data, the biggest drop in spending that we captured is fortune 500, which is surprising. But at the same time, these companies have a better purview into the economy. In general, they tend to see things further in advance. And we often remember they spend a lot of money, so they don't need to play catch up. They'll easily more easily be able to pump the brakes a little bit in the fortune 500. But to your point, when we get into the AWS data, the fortune 500 decrease seems to be hitting them a little bit more than it is Azure and GCP. I >>Mean, we're still talking about a huge business, right? >>I mean, they're catching up. I mean, Amazon has been transforming from owning the developer cloud startup cloud decade ago to really putting a dent on the enterprise as being number one cloud. And I still contest that they're number one by a long ways, but Azure kicking ass and catching up. Okay. You seeing people move to Azure, you got Charlie bell over there, Sean, by former Amazonians, Theresa Carlson, people are going over there, there there's lift over at Azure. >>There certainly is. >>Is there kinks in the arm or for AWS? There's >>A couple of kinks, but I think your point is really good. We need to take a second there. If you're talking about true pass or infrastructure is a service true cloud compute. I think AWS still is the powerhouse. And a lot of times the, the data gets a little muddied because Azure is really a hosted platform for applications. And you're not really sure where that line is drawn. And I think that's an important caveat to make, but based on the data, yes, we are seeing some kinks in the armor for AWS. Yes. Explain. So right now, a first of all caveat, 40% net score, which is our proprietary spending metric across the board. So we're not like raising any alarms here. It's still strong that said there are declines and there are declines pretty much across the board. The only spot we're not seeing a decline at all is in container, spend everything else is coming down specifically. We're seeing it come down in data analytics, data warehousing, and M I, which is a little bit of a concern because that, that rate of decline is not the same with Azure. >>Okay. So I gotta ask macro, I see the headwinds on the macro side, you pointed that out. Is there any insight into any underlying conditions that might be there on AWS or just a chronic kind of situational thing >>Right now? It seems situational. Other than that correlation between their big fortune 500, you know, audience and that being our biggest decline. The other aspect of the macro survey is we ask people, if you are planning to decline spend, how do you plan on doing it? And the number two answer is taking a look at our cloud spend and auditing it. So they're kind say, all right, you know, for the last 10 years it's been drunken, sail or spend, I >>Was gonna use that same line, you know, >>Cloud spend, just spend and we'll figure it out later, who cares? And then right now it's time to tighten the belts a little bit, >>But this is part of the allure of cloud at some point. Yeah. You, you could say, I'm gonna, I'm gonna dial it down. I'm gonna rein it in. So that's part of the reason why people go to the cloud. I want to, I wanna focus in on the data side of things and specifically the database. Let, just to give some context if, and correct me if I'm, I'm a little off here, but snowflake, which hot company, you know, on the planet, their net score was up around 80% consistently. It it's dropped down the last, you know, quarter, last survey to 60%. Yeah. So still highly, highly elevated, but that's relative to where Amazon is much larger, but you're saying they're coming down to the 40% level. Is that right? >>Yeah, they are. And I remember, you know, when I first started doing this 10 years ago, AWS at a 70%, you know, net score as well. So what's gonna happen over time is those adoptions are gonna get less and you're gonna see more flattening of spend, which ultimately is going to lower the score because we're looking for expansion rates. We wanna see adoption and increase. And when you see flattening a spend, it starts to contract a little bit. And you're right. Snowflake also was in the stratosphere that cooled off a little bit, but still, you know, very strong and AWS is coming down. I think the reason why it's so concerning is because a it's within the fortune 500 and their rate of decline is more than Azure right >>Now. Well, and, and one of the big trends you're seeing in database is this idea of converging function. In other words, bringing transaction and analytics right together at snowflake summit, they added the capability to handle transaction data, Mongo DB, which is largely mostly transactions added the capability in June to bring in analytic data. You see data bricks going from data engineering and data science now getting into snowflake space and analytics. So you're seeing that convergence Oracle is converging with my SQL heat wave and their core databases, couch base couch base is doing the same. Maria do virtually all these database companies are, are converging their platforms with the exception of AWS. AWS is still the right tool for the right job. So they've got Aurora, they've got RDS, they've got, you know, a dynamo DV, they've got red, they've got, you know, going on and on and on. And so the question everybody's asking is will that change? Will they start to sort of cross those swim lanes? We haven't seen it thus far. How is that affecting the data >>Performance? I mean, that's fantastic analysis. I think that's why we're seeing it because you have to be in the AWS ecosystem and they're really not playing nicely with others in the sandbox right now that now I will say, oh, Amazon's not playing nicely. Well, no, no. Simply to your point though, that there, the other ones are actually bringing in others at consolidating other different vendor types. And they're really not. You know, if you're in AWS, you need to stay within AWS. Now I will say their tools are fantastic. So if you do stay within AWS, they have a tool for every job they're advanced. And they're incredible. I think sometimes the complexity of their tools hurts them a little bit. Cause to your point earlier, AWS started as a developer-centric type of cloud. They have moved on to enterprise cloud and it's a little bit more business oriented, but their still roots are still DevOps friendly. And unless you're truly trained, AWS can be a little scary. >>So a common use case is I'm gonna be using Aurora for my transaction system and then I'm gonna ETL it into Redshift. Right. And, and I, now I have two data stores and I have two different sets of APIs and primitives two different teams of skills. And so that is probably causing some friction and complexity in the customer base that again, the question is, will they begin to expand some of those platforms to minimize some of that friction? >>Well, yeah, this is the question I wanted to ask on that point. So I've heard from people inside Amazon don't count out Redshift, we're making, we're catching up. I think that's my word, but they were kind of saying that right. Cuz Redshift is good, good database, but they're adding a lot more. So you got snowflake success. I think it's a little bit of a jealousy factor going on there within Redshift team, but then you got Azure synapse with the Synap product synapse. Yep. And then you got big query from Google big >>Query. Yep. >>What's the differentiation. What are you seeing for the data for the data warehouse or the data clouds that are out there for the customers? What's the data say, say to us? >>Yeah, unfortunately the data's showing that they're dropping a little bit whose day AWS is dropping a little bit now of their data products, Redshift and RDS are still the two highest of them, but they are starting to decline. Now I think one of the great data points that we have, we just closed the survey is we took a comparison of the legacy data. Now please forgive me for the word legacy. We're gonna anger a few people, but we Gotter data Oracle on-prem, we've got IBM. Some of those more legacy data warehouse type of names. When we look at our art survey takers that have them where their spend is going, that spends going to snowflake first, and then it's going to Google and then it's going to Microsoft Azure and, and AWS is actually declining in there. So when you talk about who's taking that legacy market share, it's not AWS right now. >>So legacy goes to legacy. So Microsoft, >>So, so let's work through in a little context because Redshift really was the first to take, you know, take the database to the cloud. And they did that by doing a one time license deal with par XL, which was an on-prem database. And then they re-engineered it, they did a fantastic job, but it was still engineered for on-prem. Then you along comes snowflake a couple years later and true cloud native, same thing with big query. Yep. True cloud native architecture. So they get a lot of props. Now what, what Amazon did, they took a page outta of the snowflake, for example, separating compute from storage. Now of course what's what, what Amazon did is actually not really completely separating like snowflake did they couldn't because of the architecture, they created a tearing system that you could dial down the compute. So little nuances like that. I understand. But at the end of the day, what we're seeing from snowflake is the gathering of an ecosystem in this true data cloud, bringing in different data types, they got to the public markets, data bricks was not able to get to the public markets. Yeah. And think is, is struggling >>And a 25 billion evaluation. >>Right. And so that's, that's gonna be dialed down, struggling somewhat from a go to market standpoint where snowflake has no troubles from a go to market. They are the masters at go to market. And so now they've got momentum. We talked to Frank sluman at the snowflake. He basically said, I'm not taking the foot off the gas, no way. Yeah. We, few of our large, you know, consumer customers dialed things down, but we're going balls to the >>Wall. Well, if you look at their show before you get in the numbers, you look at the two shows. Snowflake had their summit in person in Vegas. Data bricks has had their show in San Francisco. And if you compare the two shows, it's clear, who's winning snowflake is blew away from a, from a market standpoint. And we were at snowflake, but we weren't at data bricks, but there was really nothing online. I heard from sources that it was like less than 3000 people. So >>Snowflake was 1900 people in 2019, nearly 10,000. Yeah. In 2020, >>It's gonna be fun to sort of track that as a, as an odd caveat to say, okay, let's see what that growth is. Because in fairness, data, bricks, you know, a little bit younger, Snowflake's had a couple more years. So I'd be curious to see where they are. Their, their Lakehouse paradigm is interesting. >>Yeah. And I think it's >>And their product first company, yes. Their go to market might be a little bit weak from our analysis, but that, but they'll figure it out. >>CEO's pretty smart. But I think it's worth pointing out. It's like two different philosophies, right? It is. Snowflake is come into our data cloud. That's their proprietary environment. They're the, they think of the iPhone, right? End to end. We, we guarantee it's all gonna work. And we're in control. Snowflake is like, Hey, open source, no, bring in data bricks. I mean data bricks, open source, bring in this tool that too, now you are seeing snowflake capitulate a little bit. They announce, for instance, Apache iceberg support at their, at the snowflake summit. So they're tipping their cap to open source. But at the end of the day, they're gonna market and sell the fact that it's gonna run better in native snowflake. Whereas data bricks, they're coming at it from much more of an open source, a mantra. So that's gonna, you know, we'll see who look at, you had windows and you had apple, >>You got, they both want, you got Cal and you got Stanford. >>They both >>Consider, I don't think it's actually there yet. I, I find the more interesting dynamic right now is between AWS and snowflake. It's really a fun tit for tat, right? I mean, AWS has the S three and then, you know, snowflake comes right on top of it and announces R two, we're gonna do one letter, one number better than you. They just seem to have this really interesting dynamic. And I, and it is SLT and no one's betting against him. I mean, this guy's fantastic. So, and he hasn't used his war chest yet. He's still sitting on all that money that he raised to your point, that data bricks five, their timing just was a little off >>5 billion in >>Capital when Slootman hasn't used that money yet. So what's he gonna do? What can he do when he turns that on? He finds the right. >>They're making some acquisitions. They did the stream lit acquisitions stream. >>Fantastic >>Problem. With data bricks, their valuation is underwater. Yes. So they're recruiting and their MNAs. Yes. In the toilet, they cannot make the moves because they don't have the currency until they refactor the multiple, let the, this market settle. I I'm, I'm really nervous that they have to over factor the >>Valuation. Having said that to your point, Eric, the lake house architecture is definitely gaining traction. When you talk to practitioners, they're all saying, yeah, we're building data lakes, we're building lake houses. You know, it's a much, much smaller market than the enterprise data warehouse. But nonetheless, when you talk to practitioners that are actually doing things like self serve data, they're building data lakes and you know, snow. I mean, data bricks is right there. And as a clear leader in, in ML and AI and they're ahead of snowflake, right. >>And I was gonna say, that's the thing with data bricks. You know, you're getting that analytics at M I built into it. >>You know, what's ironic is I remember talking to Matt Carroll, who's CEO of auDA like four or five years ago. He came into the office in ma bro. And we were in temporary space and we were talking about how there's this new workload emerging, which combines AWS for cloud infrastructure, snowflake for the simple data warehouse and data bricks for the ML AI, and then all now all of a sudden you see data bricks yeah. And snowflake going at it. I think, you know, to your point about the competition between AWS and snowflake, here's what I think, I think the Redshift team is, you know, doesn't like snowflake, right. But I think the EC two team loves it. Loves it. Exactly. So, so I think snowflake is driving a lot of, >>Yeah. To John's point, there is plenty to go around. And I think I saw just the other day, I saw somebody say less than 40% of true global 2000 organizations believe that they're at real time data analytics right now. They're not really there yet. Yeah. Think about how much runway is left and how many tools you need to get to real time streaming use cases. It's complex. It's not easy. >>It's gonna be a product value market to me, snowflake in data bricks. They're not going away. Right. They're winning architectures. Yeah. In the cloud, what data bricks did would spark and took over the Haddo market. Yeah. To your point. Now that big data, market's got two players, in my opinion, snow flicking data, bricks converging. Well, Redshift is sitting there behind the curtain, their wild card. Yeah. They're wild card, Dave. >>Okay. I'm gonna give one more wild card, which is the edge. Sure. Okay. And that's something that when you talk about real time analytics and AI referencing at the edge, there aren't a lot of database companies in a position to do that. You know, Amazon trying to put outposts out there. I think it runs RDS. I don't think it runs any other database. Right. Snowflake really doesn't have a strong edge strategy when I'm talking the far edge, the tiny edge. >>I think, I think that's gonna be HPE or Dell's gonna own the outpost market. >>I think you're right. I'll come back to that. Couch base is an interesting company to watch with Capella Mongo. DB really doesn't have a far edge strategy at this point, but couch base does. And that's one to watch. They're doing some really interesting things there. And I think >>That, but they have to leapfrog bongo in my >>Opinion. Yeah. But there's a new architecture emerging at the edge and it's gonna take a number of years to develop, but it could eventually from an economic standpoint, seep back into the enterprise arm base, low end, take a look at what couch base is >>Doing. They hired an Amazon guard system. They have to leapfrog though. They need to, they can't incrementally who's they who >>Couch >>Base needs to needs to make a big move in >>Leap frog. Well, think they're trying to, that's what Capella is all about was not only, you know, their version of Atlas bringing to the cloud couch base, but it's also stretching it out to the edge and bringing converged database analytics >>Real quick on the numbers. Any data on CloudFlare, >>I was, I've been sitting here trying to get the word CloudFlare out my mouth the whole time you guys were talking, >>Is this another that's innovated in the ecosystem. So >>Platform, it was really simple for them early on, right? They're gonna get that edge network out there and they're gonna steal share from Akamai. Then they started doing exactly what Akamai did. We're gonna start rolling out some security. Their security is fantastic. Maybe some practitioners are saying a little bit too much, cuz they're not focused on one thing or another, but they are doing extremely well. And now they're out there in the cloud as well. You >>Got S3 compare. They got two, they got an S3 competitor. >>Exactly. So when I'm listening to you guys talk about, you know, a, a couch base I'm like, wow, those two would just be an absolute fantastic, you know, combination between the two of them. You mean >>CloudFlare >>Couch base. Yeah. >>I mean you got S3 alternative, right? You got a Mongo alternative basically in my >>Opinion. And you're going and you got the edge and you got the edge >>Network with security security, interesting dynamic. This brings up the super cloud date. I wanna talk about Supercloud because we're seeing a trend on we're reporting this since last year that basically people don't have to spend the CapEx to be cloud scale. And you're seeing Amazon enable that, but snowflake has become a super cloud. They're on AWS. Now they're on Azure. Why not tan expansion expand the market? Why not get that? And then it'll be on Google next, all these marketplaces. So the emergence of this super cloud, and then the ability to make that across a substrate across multiple clouds is a strategy we're seeing. What do you, what do you think? >>Well, honestly, I'm gonna be really Frank here. The, everything I know about the super cloud I know from this guy. So I've been following his lead on this and I'm looking forward to you guys doing that conference and that summit coming up from a data perspective. I think what you're saying is spot on though, cuz those are the areas we're seeing expansion in without a doubt. >>I think, you know, when you talk about things like super cloud and you talk about things like metaverse, there's, there's a, there, there look every 15 or 20 years or so this industry reinvents itself and a new disruption comes out and you've got the internet, you've got the cloud, you've got an AI and VR layer. You've got, you've got machine intelligence. You've got now gaming. There's a new matrix, emerging, super cloud. Metaverse there's something happening out there here. That's not just your, your father's SAS or is or pass. Well, >>No, it's also the spend too. Right? So if I'm a company like say capital one or Goldman Sachs, my it spend has traditionally been massive every year. Yes. It's basically like tons of CapEx comes the cloud. It's an operating expense. Wait a minute, Amazon has all the CapEx. So I'm not gonna dial down my budget. I want a competitive advantage. So next thing they know they have a super cloud by default because they just pivoted their, it spend into new capabilities that they then can sell to the market in FinTech makes total sense. >>Right? They're building out a digital platform >>That would, that was not possible. Pre-cloud >>No, it wasn't cause you weren't gonna go put all that money into CapEx expenditure to build that out. Not knowing whether or not the market was there, but the scalability, the ability to spend, reduce and be flexible with it really changes that paradigm entire. >>So we're looking at this market now thinking about, okay, it might be Greenfield in every vertical. It might have a power law where you have a head of the long tail. That's a player like a capital one, an insurance. It could be Liberty mutual or mass mutual that has so much it and capital that they're now gonna scale it into a super cloud >>And they have data >>And they have the data tools >>And the tools. And they're gonna bring that to their constituents. Yes, yes. And scale it using >>Cloud. So that means they can then service the entire vertical as a service provider. >>And the industry cloud is becoming bigger and bigger and bigger. I mean, that's really a way that people are delivering to market. So >>Remember in the early days of cloud, all the banks thought they could build their own cloud. Yeah. Yep. Well actually it's come full circle. They're like, we can actually build a cloud on top of the cloud. >>Right. And by the way, they can have a private cloud in their super cloud. Exactly. >>And you know, it's interesting cause we're talking about financial services insurance, all the people we know spend money in our macro survey. Do you know the, the sector that's spending the most right now? It's gonna shock you energy utilities. Oh yeah. I was gonna, the energy utilities industry right now is the one spending the most money I saw largely cuz they're playing ketchup. But also because they don't have these type of things for their consumers, they need the consumer app. They need to be able to do that delivery. They need to be able to do metrics. And they're the they're, they're the one spending right >>Now it's an arms race, but the, the vector shifts to value creation. So >>It's it just goes back to your post when it was a 2012, the trillion dollar baby. Yeah. It's a multi-trillion dollar baby that they, >>The world was going my chassis post on Forbes, headline trillion dollar baby 2012. You know, I should add it's happening. That's >>On the end. Yeah, exactly. >>Trillions of babies, Eric. Great to have you on the key. >>Thank you so much guys. >>Great to bring the data. Thanks for sharing. Check out ETR. If you're into the enterprise, want to know what's going on. They have a unique approach, very accurate in their survey data. They got a great market basket of, of, of, of, of data questions and people and community. Check it out. Thanks for coming on and sharing with. >>Thank you guys. Always enjoy. >>We'll be back with more coverage here in the cube in New York city live at summit 22. I'm John fur with Dave ante. We'll be right back.

(bright upbeat music) >> Hello, welcome to this CUBE Unstoppable Domain Showcase. I'm John Furrier, host of theCUBE. We've been showcasing all the great content about Web3 and what's going around the corner for Web4. Of course, Unstoppable Domains is one of the big growth stories in the business. Brad Kam, the Co-founder is here with me, of Unstoppable Domains, Brad, great to see you, thanks for coming on this showcase. >> Thanks, pleasure for having me. >> So you have a lot of history in the Web3. They're calling it now, but it's basically crypto and blockchain. You know, the white paper came out and then, you know how it developed was organically. We saw how that happened. Now you're the co-founder of Unstoppable Domains. You're seeing the mainstream, I would say mainstream scene, Superbowl commercials, okay? You're seeing it everywhere. So it is here. Stadiums are named after cryptos, companies. It's here. Hey, it's no longer a fringe, it is reality. You guys are in the middle of it. What's going on with the trend, and where does Unstoppable fit in and where do you guys tie in here? >> I mean, I think that what's been happening in general, this whole revolution around cryptocurrencies and then NFTs and what Unstoppable Domain is doing. It's all around creating this idea that people can own something that's digital. And this hasn't really been possible before Bitcoin. Bitcoin was the first case. You could own money. You don't need a bank, no one else. You know, you can completely control it. No one else can turn you off. Then there was this next phase of the revolution, which is, assets beyond just currencies. So NFTs, digital art. What we're working on is like a decentralized identity, like a username for Web3 and each individual domain name is an NFT. But yeah, it's been a crazy ride over the past 10 years. >> It's fun because, you know, on siliconangle.com, which we founded, we were covering early days of crypto. In fact, our first website, the developer want to be paid in crypto. It's interesting. Price of Bitcoin, I won't say that how low it was. But then you saw the ICO Wave, the token started coming in. You started seeing much more engineering focus, a lot of white papers coming out, a lot of cool ideas. And then now you got this mainstream of this. So I got to ask you, what are the coolest things you guys are working on, because Unstoppable has a solution that solves a problem today, and that people are facing at the same time, it is part of this new architecture. What problem do you guys solve right now that's in market that you're seeing the most traction on? >> Yeah, so it's really about, so whenever you interact with a blockchain, you wind up having to deal with one of these really, really crazy public keys, public addresses. And they're like anywhere from 20 to 40 characters long, they're random, they're impossible to memorize. And going back to even early days in crypto, I think people knew that this tech was not going to go mainstream if you have to copy and paste these things around. If I'm getting ready to send you like a million dollars, I'm going to copy and paste some random string of numbers and letters. I'm going to have no confirmations about who I'm sending it to, and I'm going to hope that it works out. It's just not practical. People have kind of always known there was going to be a solution. And one of the more popular ideas was, doing kind of like what DNS did, which is, instead of having to deal with these crazy IP addresses, this long random string of numbers to find a website, you have a name like a keyword, something that's easy to remember. You know, like a hotels.com or something like that. And so what NFT domains are, is basically the same thing, but for blockchain addresses. And yeah, it's just better and easier. There's this joke that everybody, you know, if you want to send me money, you're going to send me a test transaction of, you know, like a dollar first, just to make sure that I get it. Call me up and make sure that I get it before you go and send the big amount. Just not the way of moving billions of dollars of value is going to work in the future. >> Yeah, and I think one of the things you just point out, make it easier. When you have these new waves, these shifts, we saw it with the web pages. More and more web pages were coming on, more online users. They called it the online populations growing. Here, the same thing's happening. And if the focus is on ease of use, making things simpler to understand, and reducing the step it takes to do things, right? This is kind of what's going on and with the developer community, and what Ethereum has done really well is, brought in the developers. So that's the convergence of all the action. And so, when you (John chuckles) so that's where you're at right now. How do you go forward from here? Obviously, there's business development deals to do, you guys are partnering a lot. What's the strategy? What are some of the things that you can share about some of your business activity that points to how mainstream it is and where it's going? >> So I think the way to think about an NFT domain name is that it's meant to be like your identity on Web3. So, it's going to have a lot of different context. So it's kind of like your Venmo account, where you could send me money to brad.crypto, can be your decentralized website, where you can check out my content at brad.crypto. It can also be my like login kind of like a decentralized Facebook O oth, where I can log into DApps and share information about myself and bring my data along with me. So it's got all of these different things that it can do, but where it's starting is inside of crypto wallets and crypto apps, and they are adopting it for this identity idea. And it's the same form of identity across all your apps. That's the thing that's new here. So, yeah, that's the really big and profound shift that's happening. And the reason why this is going to be maybe even more important than a lot of, you know, your listeners think is that, everyone's going to have a crypto wallet. Every person in the world is going to have a crypto wallet. Every app, every consumer app that you use is going to build one in. Twitter just launched, just built one. Reddit is building one. You're seeing it across all the consumer finance apps. So it's not just the crypto companies that you're thinking of, every app's going to have a wallet. And it's going to really change the way that we use the internet. >> I think there's a couple things you pointed. I want to get your reaction to and thoughts more on this concept of DApps or decentralized applications, DApps or depending on what you call it. This is applications. And that take advantage of the architecture, and then this idea of users owning their own data. And this absolutely reverses the script today. Today, you see Facebook, you see LinkedIn, all these silos, they own the data that you are the product. Here, the users are in control. They have their data, but the apps are being built for it for the paradigm shift here, right? That's what's happening. Is that right? >> Totally, totally. And so, it all starts. I mean, DApp is just this crazy term. It feels like it's this, like really foreign, weird thing. All it means is that you sign in with your wallet instead of signing in with a username and password, where the data is stored inside of that app. Like inside of Facebook. So that's the only real, like, core underneath difference to keep in mind, signing in with the wallet. But that is like a complete sea change in the way the internet works. Because I have this key, this private key, it's on my phone or my device or whatever. And I'm the only one that has it. So, if somebody wanted to hack me, they need to go get access to my device. Two years ago, when Twitter got hacked, Barack Obama and Elon Musk were tweeting the same stuff. That's because Twitter had all the data. And so, you needed to hack Twitter instead of each individual person. It's a completely different security model. It's way better for users to have that. But, if you're thinking from the user perspective, what's going to happen is, is that instead of Facebook storing all of my data, and then me being trapped inside of Facebook, I'm going to store it, and I'm going to move around on the internet, logging in with my Web3 username, my NFT domain name, and I'm going to have all my data with me. And then I could use 100 different Facebooks all in one day. And it would be effortless for me to go and move from one to the other. So, the monopoly situation that we exist in as a society is because of the way data storage works and- >> So that's a huge point. So let's double down on that for one more second. This is a huge point. I want to get your thoughts. So I think people don't understand that in the mainstream having that horizontal traversal or ability to move around with your identity in this case, your Unstoppable Domain and your data allows the user to take it from place to place. It's like going to other apps that could be like Facebook, where the user's in charge. And they're either deciding whether to share their data or not, or they're certainly continuate their data. And this allows for more of a horizontal scalability for the user, not for a company. >> Yeah, and what's going to happen is, as users are building up their reputation. They're building up their identity in Web3. So you have your username and you have your profile and you have certain badges of activities that you've done. And you're building up this reputation. And now apps are looking at that, and they're starting to create social networks and other things to provide me services because it started with the user. And so, the user is starting to collect all this valuable data, and then apps are saying, well, hey, let me give you a special experience based on that. But the real thing, and this is like the core, I mean, this is just like a core capitalist idea, in general. If you have more competition, you get a better experience for users. We have not had competition in Web2 for decades because these companies have become monopolies. And what Web3 is really allowing is, this wide open competition. And that's the core thing. Like, it's not like, you know, it's going to take time for Web3 to get better than Web2. You know, it's very, very early days. But the reason why it's going to work is because of the competitive aspect here. Like it's just so much better for consumers when this happens. >> I would also add to that, first of all, great point, great insight. I would also add that the web presence technology based upon DNS specifically is, first of all, it's asking, so it's not foreign characters, it's not Unicode for the geeks out there. But that's limiting too, it limits you to be on a site. And so, I think the combination of kind of inadequate or antiquated DNS has limitations. So if... And that doesn't help communities, right? So when you're in the communities, you have potentially marketplaces that could be anywhere. So if you have ID, I'm just kind of thinking it forward here. But if you have your own data and your own ID, you can jump into a marketplace, two-sided marketplace anywhere. An app can provide that, if the community's robust, this is kind of where I see the use case going. How do you guys, do you guys agree with that statement and how do you see that ability for the user to take advantage of other competitive or new emerging communities or marketplaces? >> So I think it all comes down. So identity is just this huge problem in Web2. And part of the reason why it's very, very hard for new marketplaces and new communities to emerge is 'cause you need all kinds of trust and reputation. And it's very hard to get real information about the users that you're interacting with. If you're in the Web3 paradigm, then what happens is, is you can go and check certain things on the blockchain to see if they're true. And you can know that they're true 100%. You can know that I have used Uniswap in the past 30 days, and OpenSea in the past 30 days. You can know for sure that this wallet is mine. The same owner of this wallet also owns this other wallet, owns this asset. So having the ability to know certain things about a stranger is really what's going to change behavior. And one of the things that we're really excited about is being able to prove information about yourself without sharing it. So I can tell you, hey, I'm a unique person. I'm an American, I'm not an American, but I don't have to tell you who I am. And you can still know that it's true. And that concept is going to be what enables what you're talking about. I'm going to be able to show up in some new community that was created two hours ago, and we can all trust each other that a certain set of facts are true. And that's possible because- >> And exchange value with smart contracts and other with no middle men involved activities, which is the promise of the new decentralized web. All right, so let me ask you a question on that. Because I think this is key. The anonymous point is huge. If you look at any kind of abstraction layers or any evolution in technology over the years, it's always been about cleaning up the mess or extending capabilities of something that was inadequate. We mentioned DNS, now you got this. There's a lot of problems with Web2, 2.0, social bots. You mentioned bots. Bots are anonymous and they don't have a lot of time in market. So it's easy to start bots, and everyone who does either scraping bots, everyone knows this. What you just pointed out was, in an ops environment that was user choice, but has all the data that could be verified. So it's almost like a blue check mark on Twitter without having your name, kind of- >> It's going to be 100s of check marks, but exactly. 'Cause there's so many different things that you're going to want to communicate to strangers, but that's exactly the right mental model. It's going to be these check marks for all kinds of different contexts. And that's what's going to enable people to trust that they're, you know, you're talking to a real person or you're talking to the type of person you thought you were talking to, et cetera. But yeah, it's, you know, I think that the issues that we have with bots today are because Web2 has failed at solving identity. I think Facebook at one point was deleting half a billion fake accounts per quarter. Something like the entire number of user profiles they were deleting per year. So it's just a total- >> And they spring up like mushrooms. They just pop up, to think that's the problem. I mean, the data that you acquire in these siloed platforms is used by them, the company. So you don't own the data, so you become the product as the cliche goes. But what you guys are saying is, if you have an identity and you pop around to multiple sites, you also have your digital footprints and your exhaust that you own. Okay, that's time, that's reputation data. I mean, you can cut it any way you want, but the point is, it's your stuff over time, that's yours. And that's immutables on the blockchain, you can store it and then make that permanent and add to it. >> Exactly. >> That's a time based thing versus today, bots that are spreading misinformation can get popped up when they get killed. They just start another one. So time actually is a metric for quality here. >> Absolutely. And people already use it in the crypto world to say like, hey, this wallet was created greater than two years ago. This wallet has had transactions for at least three or four years. Like this is probably a real, you know, this is probably a legitimate user. And anybody can look that up. I mean, we can we go look it up together right now on Etherscan, it would take a minute. >> Yeah, (indistinct). Yeah, I'm a big fan, I can tell, I love this product. I think you guys are going to do really well. Congratulations, I'm a big fan. I think this is needed. What are some of the deals you've done? blockchain.com is one and Opera. Can you take us through those deals and why they're working with you? Let's start with blockchain.com. >> Yeah, so the whole thing here is that, this identity standard for Web3 apps need to choose to support it. So, you know, we spent several years as a company working to get as many crypto wallets and browsers and crypto exchanges to support this identity standard. Some of the largest and probably most popular companies to have done this are, blockchain.com, for example, blockchain.com, one of the largest crypto wallets in the world. And you can use your domain names instead of crypto addresses. And this is super cool because blockchain.com in particular focuses on onboarding new users. So they're very focused on how we're going to get the next 4 billion internet users to use this tech. And they said, usernames are going to be essential. Like, how can we onboard this next several billion people if we have to explain to them about all these crazy addresses. And it's not just one, like we want to give you 10, 40 character addresses for all these different contexts. Like, it's just no way people are going to be able to do that without having a user name. So, that's why we're really excited about what blockchain.com's doing. They want to train users that this is the way you should use the tech. >> Yeah, and certainly no one wants to remember. I remember how writing down all my... You know, I was never a big wallet fan 'cause of all the hacks I used to write it down and store it in my safe. But if the house burns down or I kick the can who's going to find it, right? So again, these are all important things. Your key storing it, securing it, super important. Talk about Opera. That's an interesting partnership because it's got a browser that people know what it is. What are they doing different? Almost imagine they're innovating around the identity and what people's experiences with what they touch. >> Yeah, so this is one of those things that's a little bit easier and I strongly encourage everybody to go and try DApps after this. 'Cause this is going to be one of those concepts, it can be a little easier if you try it than if you hear about it. But the concept of a wallet and a browser are kind of merging. So it makes sense to have a wallet inside of your browser. Because when you go to a website, the website's going to want you to sign in with your wallet. So having that be in one app is quite convenient for users. And so Opera was one of the trailblazers, a traditional browser that added a crypto wallet so that you can store money in there. And then also added support for domain names for payments and for websites. So, you can type in brad.crypto and you can send me money, or you can type in brad.crypto into the browser and you can check out my website. I've got a little NFT gallery. You can see my collection up there right now. So that's the idea is that, browsers have this kind of superpower in Web3. And what I think is going to happen, Opera and Brave have been kind of the trailblazers here. What I think is going to happen is that, these traditional browsers are going to wake up and they're going to see that integrating a wallet is critical for them to be able to provide services to consumers. >> I mean, it is an app. I mean, why not make it a DApps as well? Because why wouldn't I want to just send you crypto, like Venmo, you mentioned earlier, which people can understand that concept. Venmo, let me make my cash. Same concept here. But built in to the browser, which is not a browser anymore it's a reader, a DApp reader, basically with a wallet. All right, so what does this mean for you guys and the marketplace? You got Opera pushing the envelope on browsing, changing the experience, enabling the applications to be discovered and navigated and consumed. You got blockchain.com with the wallets and being embedded there. Good distribution. Who are you looking for for partners? How do people partner? Let's just say theCUBE wants to do NFTs, and we want to have a login for our communities, which are all open. How do we partner with you? Or do we? We have to wait or is there a... I mean, take us through the partnership strategy. How do people engage with Unstoppable Domains? >> Yeah, so, I mean, I think that if you're a wallet or a crypto exchange, it's super easy, we would love to have you support being able to send money using domains. We also have all sorts of different kind of marketing activities we can do together. We can give out free stuff to your communities. We have a bunch of education that we do. We're really trying to be this onboarding point to Web3. So there's, I think a lot of cool stuff we can do together on the commercial side and on the marketing side. And then the other category that we didn't talk about was DApps. And we now have this login with ensemble domains, which you kind of alluded to there. And so you can log in with your domain name and then you can give the app permission to get certain information about you or proof of information about you, not the actual information, if you don't want to share it, because it's your choice and you're in control. And so, that would be another thing. Like, if you all launch a DApps, we should absolutely have login with Unstoppable there. >> Yeah, there's so much headroom here. You got a short term solution with exchange. Get that distribution, I get that, that's early days of the foundation, push the distribution, get you guys everywhere. But the real success comes in for the login. I mean, the sign in single sign in concept. I think that's going to be powerful, great stuff. Okay, future, tell us something we don't know about Unstoppable Domains that people might be interested in. >> I think the thing that you're going to hear about a lot from us in the future is going to be around this idea of identity, of being able to prove that you're a human and be able to tell apps that. And apps are going to give you all kinds of special access and rewards and all kinds of other things, because you gave 'em that information. So that's probably, that's the hint I'm going to drop. >> You know, it's interesting, Brad. You bring trust, you bring quality verified data, choose intelligence software and machine learning, AI and access to distributed communities and distributed applications. Interesting to see what the software does with that. Cause it traditionally didn't have that before. I mean, just in mind blowing. I mean, it's pretty crazy. Great stuff. Brad, thanks for coming on. Thanks for sharing the insight. The Co-founder of Unstoppable Domains, Brad Kam. Thanks for stopping by theCUBE's Showcase with Unstoppable Domains. >> Thanks for having me. (bright upbeat music)

>>Oh, welcome to this cube unstoppable domain showcase. I'm John for your host of the cube and showcasing all the great content about web three. And what's around the corner for web. For of course, stoppable domains is one of the big growth stories in the business bread. Can the co-founders here with me have ensembles mains break. Great to see you. Thanks for coming on the showcase. >>So you have a lot of history in the, in the web three, they're calling it net, but it's basically crypto and blockchain. You know, the white paper came out and then, you know how it developed was organically. We saw how that happened. Now, the co-founder was titled domains. You seeing the mainstream, I would say main street scene, super bowl commercials. Okay. You're seeing it everywhere. So it is, it is here. Stadiums are named after cryptos companies. It's here. Hey, it's no longer a fringe. It is reality. You guys are in the middle of it. What's what's going on with the trend. And where does unstoppable fit in? Where do you guys tie in here? >>I mean, I think that what's been happening in general, this whole revolution around cryptocurrencies and then in FTEs and what unstoppable domains is doing, it's all around creating this idea that people can own something that's digital. And this hasn't really been possible before Bitcoin Bitcoin was the first case. You could own money. You don't need a bank. No one else. You can completely control it. No one else can turn you off. Then there was this next phase of the revolution, which is assets beyond just currencies. So, and if T is digital art, what we're working on is like a decentralized identity, like a username for web three and each individual domain name is a is an NFT. But yeah, it's a, it's been a, it's been a, it's been a crazy ride over the past. >>It's fun because you on siliconangle.com, which we founded, we were covering early days of crypto. In fact, our first website, the developer want to be paid in crypto is interesting price of Bitcoin. I won't say that how low it was, but then you saw, you saw the, you know, the ICO way, the token started coming in, you started seeing much more engineering, focused, a lot of white papers coming out, a lot of cool ideas. And then now you got this mainstream of it. So I had to ask you, what are the coolest things you guys are working on because ensemble has a solution that solves a problem today, and that people are facing at the same time. It is part of this new architecture. What problem do you guys solve right now? That's in market that you're seeing the most traction on. >>Yeah. So it's really about, so whenever you inter interact with a blockchain, you wind up having to deal with one of these really, really crazy public keys, public addresses. And they're like anywhere from 20 to 40 characters, long they're random, they're impossible to memorize. And going back to even early days in crypto, I think if people knew that this tech was not going to go mainstream, if you have to copy and paste these things around, if I'm getting to send you like a million dollars, I'm going to copy and paste some random string of numbers and letters. I'm going to have no confirmations about who I'm sending it to. And I'm going to hope that it works out. It's just not practical people. Who've kind of always known there was going to be a solution. And one of the more popular ideas was doing kind of like what DNS did, which is instead of having to deal with these crazy IP addresses this long, random string of numbers to find a website, you have a name, like a keyword, something that's easy to remember, you know, like a hotels.com or something like that. And so what NFT domains are, is basically the same thing, but for blockchain addresses and yeah, it's just, it's just better and easier. There's this joke that everybody, if you want to send me money, you're going to send me a test transaction of, you know, like a dollar first, just to make sure that I get it, call me up and make sure that I get it before you go and send the big amount. I'm just not the way moving, you know, billions of dollars of value is going to work in the future. >>Yeah. And I think one of the things you just pointed out, make it easier. One of these, when you have these new waves, these shifts we saw with the web web pages, more and more web pages were coming on more online users, they call the online population is growing here, the same thing's happening. And the focus is on ease of use, making things simple, to understand and reducing the step it takes to do things, right. This is kind of, kind of what is going on and with the developer community and what a theory has done really well is brought in the developers. So that's the, that's the convergence of all the action. And so when you, so that's where you're at right now, how do you go forward from here? Obviously see this business development deals to do. You guys are partnering a lot. What's the strategy? What are some of the things that you can share about some of your business activity that points to how mainstream it is and where it's going? Okay. >>So I think the, the, the, the way to, the way to think about, and, and T domain name is that it's meant to be like your identity on web three. So it's gonna have a lot of different contexts. It's kind of like your, your Venmo account, where you could send me money to Brad dot crypto can be your decentralized website, where you can check out my content at Brad dot crypto. It can also be my like login kind of like a decentralized Facebook OAuth, where I can log into ADAPs and share information about myself and bring my data along with me. So it's got all of these, all of these different, all these different things that it can do, but where it's starting is inside of crypto wallets and crypto apps, and they are adopting it for this identity, this identity idea. And it's the same identity across all your apps. >>That's the thing that's kinda, that's new here. So, so yeah, that's the, that's the really, that's the really big and profound shift that's happening. And the reason why this is going to be maybe even more important, a lot of, you know, your, your listeners thing is that everyone's going to have a crypto wallet. Every person in the world is going to have a crypto wallet. Every app, every consumer app that you use is going to build one in Twitter, just launched, just built one. Reddit is building one. You're seeing it across all the consumer finance apps. So it's not just the crypto companies that you're thinking of. Every app is going to have a wallet, and it's going to really, it's going to really change the way that we use the internet. >>I think there's a couple of things you pointed. I want to get your reaction to and thoughts more on this constant adapts or decentralized applications or dimension when you call it, this is applications and that take advantage of, of the architecture and then this idea of users owning their own data. And this absolutely reverses the script today. Today, you see Facebook, you see LinkedIn, all these silos, they own the data. The, you are the product here. The users are in control. They have their data, but the apps are being built for it for the paradigm shift here. Right. That's what's happening. Is that right now? >>Totally, totally. And, and so it all starts, I mean, DAP is just this crazy term. It feels like it's this like really foreign, weird thing. All it means is that you sign in with your wallet instead of signing in with a username and password where the data is stored inside of that app, like inside of Facebook. So that's, that's the only real, like core underneath difference to keep in mind signing in with a wallet. But that is like a complete sea change in the way the internet works, because I have this, this key, this private key it's on my phone or my device or whatever. And I'm the only one that has it. So if somebody wanted to hack me, they need to go get access to my device. Two years ago, when Twitter got hacked, Barack Obama and Elon Musk were tweeting the same stuff. >>That's because Twitter had all the data. And so you needed to hack Twitter instead of each individual person, it's a completely different security model. It's, it's way better for users to have that. But if you're thinking from the user perspective what's going to happen is, is that instead of Facebook storing all of my data, and then me being trapped inside of Facebook, I'm going to store it. And I'm gonna move around on the internet, logging in with my web three username, my, my, my NFT domain name. And I'm going to have all my data with me. And then I could use a hundred different Facebooks all in one day. And it would be effortless for me to go and move from one to the other. So the monopoly situation that we exist in as a society is because of the way data storage works. >>So that's the huge point. So let's just, let's double down on that for one more. Second, this is huge point. I want to get your thoughts. I think people don't understand that in the mainstream having that horizontal traversal or, or, or the ability to move around with your identity in this case, your unstoppable domain and your data allows the user to take it from place to place. It's like going to other apps that could be Facebook where the user's in charge. And they're either deciding whether to share their data or not, or are certainly continually their data. And this allows for more of a horizontal scalability for the user, not for a company. >>Yeah. And what's going to happen is, is users are building up their reputation. They're building up their identity in web three. So you have your username and you have your, your profile and you have certain badges of, you know, activities that you've done. And you're building up this reputation. And now apps are looking at that and they're starting to create social networks and other things to provide me services because I, it started with the user as, or the user is starting to collect all this valuable data. And then apps are saying, well, Hey, let me give you a special experience based on that, but the real thing, and this is like, this is like the core mean, this is just like a core capitalist idea. In general, you have more competition, you get a better experience for users. We have not had competition on, on, in web two for decades because these companies have become monopolies. And what web three is really allowing is this wide open competition. And, and that is what, that's the core thing. Like, it's not like, you know, it's going to take time for, for, for web three to get better than web two. You know, it's very, very early days, but the reason why it's going to work is because of the competitive aspect here. Like you can just, it's just so much better for consumers when this happened. >>I would also add to that, first of all, great point, great insight. I would also add that the web presence technology based upon DNS specifically is first of all, it's asking, so it's not foreign characters. It's not union code for, for the geeks out there, but that's limiting to its limits you to be on a site. And so I think the combination of kind of inadequate or antiquated DNS has limitations. So if, and that doesn't help communities, right? So when you're in the communities, you have potentially marketplaces, that could be anywhere. So if you have a ID and just kind of thinking it forward here, but if you have your own data and your own ID, you can jump into a marketplace two-sided marketplace anywhere. And app can provide that if the community is robust, this is kind of where I see the use case going. How do you guys, do you guys agree with that statement and how do you see that ability for the user to take advantage of other competitive or new emerging communities or marketplace? >>So I think it all comes down. So I identity is just this huge problem in web two. And part of the reason why it's very, very hard for new marketplaces and new communities to emerge is because you need all kinds of trust and reputation. And it's very hard to get, to get real information about the users that you're interacting with. If you're, if you're in the web three paradigm, then what happens is, is you can go and check certain things on the blockchain to see if they're true. And you can know that they're true. A hundred percent. You can know that I have used unit swab in the past 30 days and open, see in the past 30 days, you can know for sure that this wallet is mine. The same owner of this wallet also owns this other wallet, owns this certain asset. So all of having the ability to know certain things about a stranger is really what's going to change behavior. >>And one of the things that we're really excited about is being able to prove information about yourself without sharing it. So I can tell you, Hey, I'm a unique person. I'm an American, I'm not an American, but I don't have to tell you who I am. And, and you can still know that it's true. And, and that is that concept is going to be what enables, what you're talking about. I'm going to be able to show up in some new community that was created two hours ago, and we can all trust each other that a certain set of facts are true. And that's possible because of >>Exchange and exchange value with smart contracts and other no middlemen involved activities, which is the promise of the new decentralized web. All right. So let me ask you a question on that, because I think this is key. The anonymous point is huge. If you look at any kind of abstraction layers or any evolution in technology over the years, it's always been about cleaning up the mess or the, or extending capabilities of something that was inadequate. We mentioned DNS. Now you got this, there's a lot of problems with web two, 2.0, social bots. You mentioned bots, bots are anonymous and they don't have a lot of time in market. So it's easy to start bots and everyone who does either scraping bots, everyone knows this. What you just pointed out was an ops environment that was user choice, but has all the data that could be verified. So it's almost like a blue check mark on Twitter without your name, >>Kind of, it's good. It's going to be hundreds of check marks, but exactly, because there's so many different things that you're going to want to be, you're going to want to communicate to strangers, but that's exactly the right. That's exactly the right mental model. It's going to be these check marks for all kinds of different contexts. And that's, what's going to enable people to trust that they're, you know, you're talking to a real person or you're talking to the type of person you thought you were talking to, et cetera. But yeah, it's, it's, you know, I, I think that the issues that we have with bots today are because a web tool has failed at solving identity. I think Facebook at one point was deleting half a billion fake accounts per quarter. Something like the entire number of user profiles. They were deleting per you know, per year. So it's just a total. >>They spring up like mushrooms. They just pop up the thing. This is the problem. I mean, the data that you acquire in new siloed platforms is used by them, the company. So you don't own the data. So you become the product as the cliche goes. But what you're saying is if you have an identity and you pop around to multiple sites, you also have your digital footprints and your exhaust that you own. Okay. That's time. That's reputation data. I mean, you can cut it any way you want, but the point is, it's your stuff over time, that's yours and that's immutable. It's on the blockchain. You can store it and make that permanent and add to it. Exactly. That's, that's a time-based thing versus today, bots that are spreading misinformation can, can get popped up when they get killed. They just start another one. So time actually is a metric for quality here. >>Absolutely. And people already use it in the crypto world to say like, Hey, this wallet was created greater than two years ago. This wallet has had, you know, head has had transactions for at least three or four years. Like this is probably a real, you know, this is probably a legit legitimate user and anybody can look that up. I mean, we could go look it up together right now on, on ether scan. It would take, you know, a minute. >>Yeah. It's awesome. Yeah. I'm a big fan. I can tell, I love this product. I think you guys are gonna do really well. Congratulations. I'm a big fan. I think this is needed. What are some of the deals you've done? blockchain.com has won an opera. Can you take us through those deals and why they're working with you? We'll start with blockchain.com. >>Yeah. So the whole thing here is that this identity standard for web three apps need to choose to support it. So we spent several years as a company working to get as many crypto wallets and browsers and crypto exchanges to support this, to support this identity standard. Some of the, some of the, the, the largest, and probably, you know, most, most popular companies to have done. This are blockchain.com. For example, blockchain.com, one of the largest crypto wallets in the world. And you can use your domain names instead of crypto addresses. And, and, and this is, this is, this is super cool because blockchain.com in particular focuses on onboarding new users. So they're very focused on how we're going to get the next 4 billion internet users to use this tech. And they said, you know, usernames are going to be essential. Like, how can we onboard this next several billion people? If we have to explain to them about all these crazy addresses, and it's not just one, like we want to give you 10 40 character addresses for all these different contexts. Like, it's just, it's just, it's just no way people are gonna be able to do that without, without having a username. So that's why we're really excited about, about what blockchain that comes through. And they, they, they want to train users that this is the way you should use it. >>Yeah. And certainly no one wants to remember. I remember writing down all my writing. I, I'm not, I was never a big wallet fan cause all the hacks, I used to write it down and store it in my safe. But if the house burns down or I, I kick the can I'm, who's going to find it. Right? So again, these are all important things, your key storing it, securing it, super important. Talk about opera. And that's an interesting partnership because it's got a browser and people know what it is, what are they doing? Different almost imagine they're innovating around the identity and what people's experiences with, what they touch. >>So this is, this is one of those things. That's a little bit easier. And I strongly encourage everybody to go and try dApps after this. Cause this is going to be one of those concepts to be a little easier. If you, if you try it, then if you hear about it, but the concept of a wallet and a browser are kind of merging. So it makes sense to have a wallet inside of your browser. Because when you go to a website, the website is going to want you to sign in with your wallet. So having that be in one app is quite convenient for users. And so opera was one of the trailblazers, a traditional browser that added a crypto wallet so that you can store money in there. And then also added support for domain names, for payments and for websites. So you can type in Brad dot crypto and you can send me money or you can type in Brad dot crypto into the browser and you can check out my website. I've got a little NFT gallery. You can see my collection up there right now. So that's the, that's the idea is that browsers have this kind of super power in a web three. And what I think is going to happen opera and brave have been kind of the trailblazers here. But I think is going to happen is that these traditional browsers are going to wake up and they're going to see that integrating a wallet is critical for them to be able to provide services to consumers. >>I mean, it is an app. I mean, why not make it a D app as well? Because why wouldn't I want to just send you crypto, like Venmo, you mentioned earlier, which people can understand that concept. Ben, let me make my cash. Same concept here, but built in to the browser, which is not a browser anymore. It's a, a reader, a D app reader, basically with a wallet. All right. So, so what does this mean for you guys in the marketplace? You've got opera pushing the envelope on browsing, changing the experience, enabling the applications to be discovered and navigated and consumed. You got blockchain.com, blockchain.com with the wallets and being embedded there. Good distribution. How, what, who are you looking for for partners? How do people partner? Let's just say the cube wants to do NFTs and we want to have a login for our communities, which are all open. How do we partner with you? Or do we have to wait? Or is there a, I mean, take us through the partnership strategy. How do we, how do people engage with unstoppable Dwayne's >>Yeah, so, I mean, I think that if you're, you know, if you're a wallet or a crypto exchange, it's super easy, we would love to have you support being able to send money using domains. We also have all sorts of different kind of marketing activities we can do together. We can give out free stuff to, to your communities. We have a bunch of education that we do. We're really trying to be this onboarding point to web three. So there's, I think a lot of, a lot of cool stuff we can do together on the commercial side and on the, the, the marketing side. And then the other category that we didn't talk about was dabs. And we now have this login with unstoppable domains, which you kind of alluded to there. And so you can log in with your domain name and then you can give the app permission to get certain information about you or proof of information about you, not the actual information, if you don't want to share it because it's your choice and you're in control. And so that would be, that would be another thing. Like if you all launch a DAP, we should absolutely have log-in with unstoppable. >>Yeah. There's so much headroom here. You've got a short-term solution with exchange. Get that distribution. I get that that's early days of the foundation, push the distribution, get you guys everywhere. But the real success comes in for the login. I mean, the sign-on single sign-on concept. I think that's going to be powerful, great stuff. Okay. Future, tell us something we don't know about ensemble domains that people might be interested in. >>I think it's really, I think the thing that you're going to hear about a lot from us in the future is going to be around this idea of identity, of being able to prove that you're a human and be able to tell apps that and apps are going to give you all kinds of special access and rewards and all kinds of other things, because, because you gave them that information. So that's the that's, that's probably, that's the hint I'm going to drop. >>Yeah. It's interesting. Brad, you bring trust, you bring quality verified data to intelligence, software, and machine learning, AI and access to distributed communities and distributed applications. Interesting to see what the software does, what that, cause it traditionally didn't have that before. I mean just in mindblowing, I mean, it's pretty crazy great stuff, Brad. Thanks for coming on. Thanks for sharing the insight. Co-founder unstoppable domains, Brad camp. Thanks for stopping by the cubes. Showcase with unstoppable domains.

>>Hello, and welcome to the cubes. Special showcase with unstoppable domains. I'm John furrier, your host of the cube here in Palo Alto, California and Matt Gould, who is the founder and CEO of unstoppable domains. Matt, great to come on. Congratulations on the success of your company on stumbled domains. Thanks for kicking off this showcase. >>Thank you. Happy to be here. So >>Love, first of all, love the story you got going on here. Love the approach, very innovative, but you're also on the big web three wave, which we know where that leads into. Metaverse unlimited new ways. People are consuming information, content applications are being built differently. This is a major wave and it's happening. Some people are trying to squint through the hype versus reality, but you don't have to be a rocket science to realize that it's a cultural shift and a technical shift going on with web three. So this is kind of the what's happening in the market. So give us your take. What's your reaction? You're in the middle of it. You're on this wave. >>Yeah. Well, I would say it's a torrent of change and the get unleashed just over a decade ago with Bitcoin coming out and giving people the ability to have a digital items that they could actually own themselves online. And this is a new thing. And people coming, especially from my generation of millennials, they spend their time online in these digital spaces and they've wanted to be able to own these items. Do you see it from, you know, gaming and Fortnite and skins and Warcraft and all these other places, but this is really being enabled by this new crypto technology to just extend a whole lot more, uh, applications for money, which everyone's familiar with, uh, to, uh, NFT projects, uh, like boarding school. >>You know, I was listening to your podcast. You guys got a great pot. I think you're on a 117 episodes now and growing, you guys do a deep dive. So people watching check out the unstoppable podcast, but in the last podcast, man, you mentioned, you know, some of the older generations like me, I grew up with IP addresses and before the web, they called it information super highway. It wasn't even called the web yet. Um, but IP was, was generated by the United States department of commerce and R and D that became the internet. The internet became the web back then it was just get some webpages up and find what you're looking for. Right. Very analog compared to what's. Now, today, now you mentioned gaming, you mentioned, uh, how people are changing. Can you talk about your view of this cultural shift? And we've been talking about in the queue for many, many years now, but it's actually happening now where the expectation of the audience and the users and the people consuming and communicating and bonding and groups, whether it's gaming or communities are expecting new behaviors, new applications, and it's a forcing function. >>This shift is having now, what's your reaction to that? What's your explanation? >>Yeah, well, I think, uh, it just goes back to the shift of peoples, where are they spending their time? And if you look today, most people spend 50% plus of their time in front of a screen. And that's just a tremendous amount of effort. But if you look at how much, how much of assets are digital, it's like less than 1% of their portfolio would be some sort of digital asset, uh, compared to, you know, literally 50% of every day sitting in front of a screen and simultaneously what's happening is these new technologies are emerging around, uh, cryptocurrencies, blockchain systems, uh, ways for you to track the digital ownership of things, and then kind of bring that into, uh, your different applications. So one of the big things that's happening with web three is this concept of data portability, meaning that I can own something on one application. >>And I could potentially take that with me to several other applications across the internet. And so this is like the emerging digital property rights that are happening right now. As we transitioned from a model in web to where you're on a hosted service, like Facebook, it's a walled garden, they own and control everything. You are the product, you know, they're mining you for data and they're just selling ads, right? So to assist them where it's much more open, you can go into these worlds and experiences. You can take things with you, uh, and you can, you can leave with them. And most people are doing this with cryptocurrency. Maybe you earn an in-game currency, you can leave and take that to a different game and you can spend it somewhere else. Uh, so the user is now enabled to bring their data to the party. Whereas before now you couldn't really do that. And that data includes their money or that includes their digital items. And so I think that's the big shift that we're seeing and that changes a lot and how applications, uh, serve up to user. So it's going to change their user experiences. For instance, >>The flip, the script has flipped and you're right on. I agree with you. I think you guys are smart to see it. And I think everyone who's on this wave will see it. Let's get into that because this is happening. People are saying I'm done with being mined and being manipulated by the big Facebooks and the LinkedIns of the world who were using the user. Now, the contract was a free product and you gave it your data, but then it got too far. Now people want to be in charge of their data. They want to broker their data. They want to collect their digital exhaust, maybe collect some things in a game, or maybe do some commerce in an application or a marketplace. So these are the new use cases. How does the digital identity architecture work with unstoppable? How are you guys enabling that? Could you take us through the vision of where you guys came on this because it's unique in an NFT and kind of the domain name concept coming together? Can you explain? >>Yeah. So, uh, we think we approach the problem for if we're going to rebuild the way that people interact online, uh, what are kind of the first primitives that they're going to need in order to make that possible? And we thought that one of the things that you have on every network, like when you log on Twitter, you have a Twitter handle. When you log on, uh, you know, Instagram, you have an Instagram handle, it's your name, right? You have that name that's that's on those applications. And right now what happens is if users get kicked off the platform, they lose a hundred percent of their followers, right? And theirs. And they also, in some cases, they can't even directly contact their followers on some of these platforms. There's no way for them to retain this social network. So you have all these influencers who are, today's small businesses who build up these large, you know, profitable, small businesses online, uh, you know, being key opinion leaders to their demographic. >>Uh, and then they could be D platform, or they're unable to take this data and move to another platform. If that platform raised their fees, you've seen several platforms, increase their take rates. You have 10, 20, 30, 40%, and they're getting locked in and they're getting squeezed. Right. Uh, so we just said, you know what, the first thing you're going to want to own that this is going to be your piece of digital property. It's going to be your name across these applications. And if you look at every computer network in the history of computing networks, the end up with a naming system, and when we've looked back at DDA desk, which came out in the nineties, uh, it was just a way for people to find these webpages much easier, you know, instead of mapping these IP addresses. Uh, and then we said to ourselves, you know, uh, what's going to happen in the future is just like everyone has an email address that they use in their web two world in order to, uh, identify themselves as they log into all these applications. >>They're going to have an NFT domain in the web three world in order to authenticate and, and, uh, bring their data with them across these applications. So we saw a direct correlation there between DNS and what we're doing with NFT domain name systems. Um, and the bigger breakthrough here is at NMT domain systems or these NFT assets that live on a blockchain. They are owned by users to build on these open systems so that multiple applications could read data off of them. And that makes them portable. So we were looking for an infrastructure play like a picks and shovels play for the emerging web three metaverse. Uh, and we thought that names were just something that if we wanted a future to happen, where all 3.5 billion people, you know, with cell phones are sending crypto and digital assets back and forth, they're gonna need to have a name to make this a lot easier instead of, you know, these long IP addresses or a hex addresses in the case of Porto. >>So people have multiple wallets too. It's not like there's all kinds of wallet, variations, name, verification, you see link trees everywhere. You know, that's essentially just an app and it doesn't really do anything. I mean, so you're seeing people kind of trying to figure it out. I mean, you've got to get up, Angela got a LinkedIn handle. I mean, what do you do with it? >>Yeah. And, and then specific to crypto, there was a very hair on fire use case for people who buy their first Bitcoin. And for those in the audience who haven't done this yet, when you go in and you go into an app, you buy your first Bitcoin or Ethereum or whatever cryptocurrency. And then the first time you try to send it, there's this, there's this field where you want to send it. And it's this very long text address. And it looks like an IP address from the 1980s, right? And it's, it's like a bank number and no one's going to use that to send money back and forth to each other. And so just like domain names and the DNS system replace IP addresses in Ft domains, uh, on blockchain systems, replace hex addresses for sending and receiving, you know, cryptocurrency, Bitcoin, Ethereum, whatever. And that's its first use case is it really plugs in there. So when you want to send money to someone, you can just, instead of sending money to a large hex address that you have to copy and paste, you can have an error or you can send it to the wrong place. It's pretty scary. You could send it to John furrier dot, uh, NFT. And uh, so we thought that you're just not going to get global adoption without better UX, same thing. It worked with the.com domains. And this is the same thing for the coin and other >>Crypto. It's interesting to look at the web two or trend one to two web one went to two. It was all about user ease of use, right? And making things simpler. Clutter, you have more pages. You can't find things that was search that was Google since then. Has there actually been an advancement? Facebook certainly is not an advancement. They're hoarding all the data. So I think we're broken between that step of, you know, a free search to all the resources in the world, to which, by the way, they're mining a lot of data too, with the toolbar and Chrome. But now where's that web three crossover. So take us through your vision on digital identity on web to Google searching, Facebook's broken democracy is broken users. Aren't in charge to web three. >>Got it. Well, we can start at web one. So the way that I think about it is if you go to web one, it was very simple, just text web pages. So it was just a way for someone to like put up a billboard and here's a piece of information and here's some things that you could read about it. Right. Uh, and then what happened with web two was you started having applications being built that had backend infrastructure to provide services. So if you think about web two, these are all, you know, these are websites or web portals that have services attached to them, whether that's a social network service or search engine or whatever. And then as we moved to web three, the new thing that's happening here is the user is coming on to that experience. And they're able to connect in their wallet or their web three identity, uh, to that app and they can bring their data to the party. >>So it's kind of like web one, you just have a static web page whip, two, you have a static web page with a service, like a server back here. And then with three, the user can come in and bring their database with them, uh, in order to have much better app experiences. So how does that change things? Well, for one, that means that the, you want data to be portable across apps. So we've touched on gaming earlier and maybe if I have an end game item for one, a game that I'm playing for a certain company, I can take it across two or three different games. Uh, it also impacts money. Money is just digital information. So now I can connect to a bunch of different apps and I can just use cryptocurrency to make those payments across those things instead of having to use a credit card. >>Uh, but then another thing that happens is I can bring in from, you know, an unlimited amount of additional information about myself. When I plug in my wallet, uh, as an example, when I plug in to Google search, for instance, they could take a look at my wallet that I've connected and they could pull information about me that I enabled that I share with them. And this means that I'm going to get a much more personalized experience on these websites. And I'm also going to have much more control over my data. There's a lot of people out there right now who are worried about data privacy, especially in places like Europe. And one of the ways to solve that is simply to not store the data and instead have the user bring it with them. >>I always thought about this and I always debated it with David laundry. My cohost does top down governance, privacy laws outweigh the organic bottoms up innovation. So what you're getting at here is, Hey, if you can actually have that solved before it even starts, it was almost as if those services were built for the problem of web two. Yes, not three. Write your reaction to that. >>I think that is, uh, right on the money. And, uh, if you look at it as a security, like if I put my security researcher hat on, I think the biggest problem we have with security and privacy on the web today is that we have these large organizations that are collecting so much data on us and they just become these honeypots. And there have been huge, uh, breaches like Equifax, you know, a few years back is a big one and just all your credit card data got leaked, right? And all your, uh, credit information got leaked. And we just have this model where these big companies silo your data. They create a giant database, which is worth hundreds of millions of dollars, if not, billions, to be attacked. And then someone eventually is going to hack that in order to pull that information. Well, if instead, and you can look at this at web three. >>So for those of the audience who have used the web three application, one of these depths, um, you know, trade cryptocurrencies or something, you'll know that when you go there, you actually connect to your wall. So when you're working with these web, you connect, you, you know, you bring your information with you and you connect it. That means that the app has none of that storage, right? So these apps that people are using for crypto trading cryptocurrency on depths or whatever, they have no stored information. So if someone hacks one of these DFI exchanges, for instance, uh, there's nothing to steal. And that's because the only time the information is being accessed is when the users actively using the site. And so as someone who cares about security and privacy, I go, wow, that's a much better data model. And that give so much more control of user because the user just permissions access to the data only during the time period in which they're interacting with the application. Um, and so I think you're right. And like, we are very excited to be building these tools, right? Because I see, like, if you look at Europe, they basically pass GDPR. And then all the companies are going, we can't comply with that and they keep postponing it or like changing a little bit and trying to make it easier to comply with. But honestly we just need to switch the data models. So the companies aren't even taking the data and then they're gonna be in a much better spot. >>The GDPR is again, a nightmare. I think it's the wrong approach. Oh, I said it was screwed up because most companies don't even know where stuff is stored. Nevermind how they delete someone's entering a database. They don't even know what they're collecting. Some at some level it becomes so complicated. So right on the money are good. Good call out there. Question for you. Is this then? Okay. So do you decouple the wallet from the ID or are they together? Uh, and is it going to be a universal wallet? Do you guys see yourselves as universal domains? Take me through the thinking around how you're looking at the wallet and the actual identity of the user, which obviously is super important on the identity side while it, is that just universal or is that going to be coming together? >>Well, I think so. The way that we kind of think about it is that wallets are where people have their financial interactions online. Right. And then identity is much more about, it's kind of like being your passport. So it's like your driver's license for the internet. So these are two kind of separate products we see longer term, uh, and they actually work together. So, you know, like if you have a domain name, it actually is easier to make deposits into your wallet because it's easier to remember to send money to, you know, method, rules dot crypto. And that way it's easier for me to receive payments or whatever. And then inside my wallet, I'm going to be doing defy trades or whatever. And doesn't really have an interaction with names necessarily in order to do those transactions. But then if I want to, uh, you know, sign into a website or something, I could connect that with my NFT domain. >>And I do think that these two things are kind of separate. I think there's, we're gonna still early. So figuring out exactly how the industry is gonna shake out over like a five to 10 year time horizon. And it may be a little bit more difficult and we could see some other emerging, uh, what you would consider like cornerstones of the crypto ecosystem. But I do think identity and reputation is one of those. Uh, and I also think that your financial applications of defy are going to be another. So those are the two areas where I see it. Um, and just to, you know, a note on this, when you have a wallet, it usually has multiple cryptocurrency address. So you're going to have like 50 cryptocurrency addresses in a wallet. Uh, you're going to want to have one domain name that links back to all those, because you're just not going to remember those 50 different addresses. So that's how I think that they collaborate. And we collaborate with several large wallets as well, uh, like blockchain.com, uh, and you know, another 30 plus of these, uh, to make it easier for sending out and receiving cryptocurrency. >>So the wallet, basically as a D app, the way you look at it, you integrate whatever you want, just integrate in. How do I log into decentralized applications with my NFT domain name? Because this becomes okay, I got to love the idea, love my identity. I'm in my own NFT. I mean, hell, this video is going to be an NFT. Soon. We get on board with the program here. Uh, but I do, I log into my app, I'm going to have a D app and I got my domain name. Do I have to submit, is there benchmarking, is there approval process? Is there API APIs and a SDK kind of thinking around it? How do you thinking about dealing with the apps? >>Yeah, so all of the above and what we're trying to, what we're trying to do here is build like an SSO solution. Uh, but that it's consumer based. So, uh, what we've done is adapted some SSL protocols that other people have used the standard ones, uh, in order to connect that back to an NFT domain in this case. And that way you keep the best of both worlds. So you can use these authorization protocols for data permissioning that are standard web to API APIs. Uh, but then the permissioning system is actually based on the user controlled in FTE. So they're assigning that with their private public key pair order to make those updates. Um, so that, that allows you to connect into both of these systems. Uh, we think that that's how technology typically impacts the world is it's not like you have something that just replaces something overnight. >>You have an integration of these technologies over time. Uh, and we really see these three components in MTU domains integrating nicely into regular apps. So as an example in the future, when you log in right now, you see Google or Facebook, or you can type in an email address, you can see not ensemble domains or NFT, uh, authorization, and you can SSO in with that, to that website. When you go to a website like an e-commerce website, you could share information about yourself because you've connected your wallet now. So you could say, yes, I am a unique individual. I do live in New York, uh, and I just bought a new house. Right. And then when you permission all that information about yourself to that application, you can serve up a new user experience for you. Um, and we think it's going to be very interesting for doing rewards and discounts, um, online for e-commerce specifically, uh, in the future, because that opens up a whole new market because they can ask you questions about yourself and you can deliver that information. >>Yeah. I really think that the gaming market has totally nailed the future use case, which is in game currency in game to engagement in game data. And now bringing that, so kind of a horizontally scalable, like surface areas is huge, right? So, you know, I think you're, that's huge success on the concept. The question I have to ask you is, um, you getting any pushback from ICANN, the international corporates have name and numbers. They got dot everything now.club, cause the clubhouse, they got dot, you know, party.live. I mean, so the real domain name people are over here, web too. You guys are coming out with the web three where's that connect for people who are not following along the web three trend. How do they, how do you rationalize the, the domain angle here? >>Yeah, well, uh, so I would say that NFTE domains or what domains on DNS were always meant to be 30 plus years ago and they just didn't have blockchain systems back in the nineties when they were building these things. So there's no way to make them for individuals. So what happened was for DNS, it actually ended up being the business. So if you look at DNS names, there's about 350 million registrations. They're basically all small business. And it's like, you know, 20 to 50 million small businesses, uh, who, uh, own the majority of these, uh, these.com or these regular DNS domain names. And that's their focus NFTE domains because all of a sudden you have the, uh, the Walton, if you have them in your wallet and your crypto wallet, they're actually for individuals. So that market, instead of being for small businesses is actually end-users. So, and instead of being for, you know, 20 to 50 million small businesses, we're talking about being useful for three to 4 billion people who have an internet connection. >>Uh, and so we actually think that the market size we're in a few domains and somewhere 50 to 100 X, the market size for traditional domain names. And then the use cases are going to be much more for, uh, individuals on a day-to-day basis. So it's like people are gonna want you on to use them for receiving cryptocurrency versus receiving dollars or payments or USCC point where they're going to want to use them as identifiers on social networks, where they're going to want to use them for SSO. Uh, and they're not gonna want to use them as much for things like websites, which is what web is. And if I'm being perfectly honest, if I'm looking out 10 years from now, I think that these traditional domain name systems are gonna want to work with and adopt this new NFC technology. Cause they're going to want to have these features for the domain next. So like in short, I think NMT domain names or domain names with superpowers, this is the next generation of, uh, naming systems and naming systems were always meant to be identity networks. >>Yeah. They hit a car, they hit a glass ceiling. I mean, they just can't, they're not built for that. Right. So I mean, and, and having people, having their own names is essentially what decentralization is all about. Cause what does a company, it's a collection of humans that aren't working in one place they're decentralized. So, and then you decentralize the identity and everything's can been changed so completely love it. I think you guys are onto something really huge here. Um, you pretty much laid out what's next for web three, but you guys are in this state of, of growth. You've seen people signing up for names. That's great. What are the, what are the, um, best practices? What are the steps are people taking? What's the common, uh, use case for folks we're putting this to work right now for you guys? Why do you see what's the progression? >>Yeah. So the, the thing that we want to solve for people most immediately is, uh, we want to make it easier for sending and receiving crypto payments. And I, and I know that sounds like a niche market, but there's over 200 million people right now who have some form of cryptocurrency, right? And 99.9% of them are still sending crypto using these really long hex addresses. And that market is growing at 60 to a hundred percent year over year. So, uh, first we need to get crypto into everybody's pocket and that's going to happen over the next three to five years. Let's call it if it doubles every year for the next five years, we'll be there. Uh, and then we want to make it easier for all those people to sit encrypted back and forth. And I, and I will admit I'm a big fan of these stable coins and these like, you know, I would say utility focused, uh, tokens that are coming out just to make it easier for, you know, transferring money from here to Turkey and back or whatever. >>Uh, and that's the really the first step freight FTE domain names. But what happens is when you have an NFTE domain and that's what you're using to receive payments, um, and then you realize, oh, I can also use this to log into my favorite apps. It starts building that identity piece. And so we're also building products and services to make it more like your identity. And we think that it's going to build up over time. So instead of like doing an identity network, top-down where you're like a government or a corporation say, oh, you have to have ID. Here's your password. You have to have it. We're going to do a bottoms up. We're going to give everyone on the planet, NFTE domain name, it's going to give them to the utility to make it easier to send, receive cryptocurrency. They're going to say, Hey, do you want to verify your Twitter profile? Yes. Okay, great. You test that back. Hey, you want to verify your Reddit? Yes. Instagram. Yes. Tik TOK. Yes. You want to verify your driver's license? Okay. Yeah, we can attach that back. Uh, and then what happens is you end up building up organically, uh, digital identifiers for people using these blockchain, uh, naming systems. And once they have that, they're gonna just, they're going to be able to share that information. Uh, and that's gonna lead to better experiences online for, uh, both commerce, but also just better user experiences. >>You know, every company when they web came along, first of all, everyone, poo-pooed the web ones. That was terrible, bad idea. Oh. And so unreliable. So slow, hard to find things. Web two, everyone bought a domain name for their company, but then as they added webpages, these permalinks became so long. The web page address fully qualified, you know, permalink string, they bought keywords. And then that's another layer on top. So you started to see that evolution in the web. Now it's kind of hit a ceiling here. Everyone gets their NFT. They, they started doing more things. Then it becomes much more of a use case where it's more usable, not just for one thing. Um, so we saw that movie before, so it's like a permalink permanent. Yeah. >>Yes. I mean, if we're lucky, it will be a decentralized bottoms up global identity, uh, that appreciates user privacy and allows people to opt in. And that's what we want to build. >>And the gas prices thing that's always coming. That's always an objection here that, I mean, blockchain is perfect for this because it's immutable, it's written on the chain. All good, totally secure. What about the efficiency? How do you see that evolving real quick? >>Well, so a couple of comments on efficiency. Uh, first of all, we picked domains as a first product to market because, you know, as you need to take a look and see if the technology is capable of handling what you're trying to do, uh, and for domain names, you're not updating that every day. Right? So like, if you look at traditional domain names, you only update it a couple of times per year. So, so the usage for that to set this up and configure it, you know, most people set up and configure it and then it'll have a few changes for years. First of all, the overall it's not like a game problem. Right, right, right. So, so that, that part's good. We picked a good place to start for going to market. And then the second piece is like, you're really just asking our computer, system's going to get more efficient over time. >>And if you know, the history of that has always been yes. Uh, and you know, I remember the nineties, I had a modem and it was, you know, whatever, 14 kilobits and then it was 28 and then 56, then 100. And now I have a hundred megabits up and down. Uh, and I look at blockchain systems and I don't know if anyone has a law for this yet, but throughput of blockchains is going up over time. And you know, there's, there's going to be continued improvements over this over the next decade. We need them. We're going to use all of it. Uh, and you just need to make sure you're planning a business makes sense for the current environment. Just as an example, if you had tried to launch Netflix for online streaming in 1990, you would have had a bad time because no one had bandwidth. So yeah. Some applications are going to wait to be a little bit later on in the cycle, but I actually think identity is perfectly fine to go ahead and get off the ground now. >>Yeah. The motivated parties for innovations here, I mean, a point cast failed miserably that was like the, they try to stream video over T1 lines, but back in the days, nothing. So again, we've seen those speeds double, triple on homes right now, Matt. Congratulations. Great stuff. Final tick, tock moment here. How would you summarize short in a short clip? The difference between digital identity in web two and web three, >>Uh, in, in web too, you don't get to own your own online presidents and in web three, you do get to own it. So I think if you were gonna simplify it really web three is about ownership and we're excited to give everyone on the planet a chance to own their name and choose when and where and how they want to share information about themselves. >>So now users are in charge. >>Exactly. >>They're not the product anymore. Going to be the product might as well monetize the product. And that's the data. Um, real quick thoughts just to close out the role of data in all this, your view. >>We haven't enabled users to own their data online since the beginning of the internet. And we're now starting to do that. It's going to have profound changes for how every application on the planet interacts with >>Awesome stuff, man, I take a minute to give a plug for the company. How many employees you got? What do you guys looking for for hiring, um, fundraising, give a quick, a quick commercial for what's going on, on unstoppable domains. Yeah. >>So if you haven't already check us out@ensembledomains.com, we're also on Twitter at unstoppable web, and we have a wonderful podcast as well that you should check out if you haven't already. And, uh, we are just crossed a hundred people. We've, we're growing, you know, three to five, a hundred percent year over year. Uh, we're basically hiring every position across the company right now. So if you're interested in getting into web three, even if you're coming from a traditional web two background, please reach out. Uh, we love teaching people about this new world and how you can be a part of it. >>And you're a virtual company. Do you have a little headquarters or is it all virtual? What's the situation there? >>Yeah, I actually just assumed we were a hundred percent remote and asynchronous and we're currently in five countries across the planet. Uh, mostly concentrated in the U S and EU areas, >>Rumor to maybe you can confirm or admit or deny this rumor. I heard a rumor that you have mandatory vacation policy. >>Uh, this is true. Uh, and that's because we are a team of people who like to get things done. And, but we also know that recovery is an important part of any organizations. So if you push too hard, uh, we want to remind people we're on a marathon, right? This is not a sprint. Uh, and so we want people to be with us term. Uh, we do think that this is a ten-year move. And so yeah. Do force people. We'll unplug you at the end of the year, if you have >>To ask me, so what's the consequence of, I don't think vacation. >>Yeah. We literally unplug it. You won't be able to get it. You won't be able to get into slack. Right. And that's a, that's how we regulate. >>Well, when people start having their avatars be their bot and you don't even know what you're unplugging at some point, that's where you guys come in with the NFD saying that that's not the real person. It's not the real human And FTS. Great innovation, great use case, Matt. Congratulations. Thanks for coming on and sharing the story to kick off this showcase with the cube. Thanks for sharing all that great insight. Appreciate it. >>John had a wonderful time. All right. Just the >>Cube unstoppable domains showcasing. We got great 10 great pieces of content we're dropping all today. Check them out. Stay with us for more coverage on John furrier with cube. Thanks for watching.

okay welcome back to thecube's coverage at splunk.com 2021 virtual i'm john furrier with thecube we're here live in the studios of splunk's event here we're all together broadcasting out all over the world here with chris dieglemeyer chief social impact officer for splunk great to see you thanks for coming on great thanks for having me today i love the title chief social impact officer because we're bringing in data unlocks value well you know that and yes it's the theme of the show society has really been impacted by misinformation what context we've seen examples of how data has been good and been bad yes so there's a divide there so you're this is a big part of your talk yes it it's a big part of me and it's going to be even a bigger part of splunk going forward so as many people know they've heard of the digital divide right and that was about access to information communication technologies and it was coined 20 years ago 2001 and we've made progress on that digital divide but now we have all that infrastructure or a lot of it and so on top of that we have the data divide and that's the increasing and expanding use of data and the gap between using that to solve commercial and provide commercial value in contrast to solving our social and environmental challenges and so the the important thing about it is we're early enough that with urgent action we can try to close that gap um and really make a difference in the world so let's get started let's define the data divide and give some specific examples where you see it in action on the pro side and where there's some work needed yeah so all so the definition is again that that gap between using we we have all this data being used for commercial value and a relatively weak use of data being used to solve our social and environmental challenges and we've got four kind of key barriers that we've identified that need to be addressed which will get to you know the questions and how we solve it one is access so think about it think of the data that google has and where that is in access compared to probably the department of education in any country around the world so access is big second is capacity we need both financial resources investing in solving our social and environmental problems and we need data scientists data stewards great data people working to solve our social and environmental problems just as we are in the corporate sector and then the third one is investment choices and this one is a little bit of a be in my bonnet and this happens mostly in the private sector so we all know you know every year it's like what what hits the return on investment criteria and solving social and environmental challenges often does not uh doesn't have that quite time frame return on investment and think about if we'd identified this data divide 20 years ago for climate because companies are doing phenomenal work now about climate what if we had been doing that work 20 years ago around sustainability around efficiency and then the last piece is actionable solutions that we can replicate so those are kind of the four barriers um and again i think we've got a lot of potential and examples there isn't one issue i can think of where more data isn't going to help us you know this is so important i feel very strongly about this because i've seen examples where i've seen really strong people start ngos or non-profits or just building an app and they abandon it because they can't get there fast enough so the idea that cloud and data accessibility can be there you get to see some success and you can double down on that's the cloud way yes so i think this is something that people want to know the playbook so you know where where are people being successful what can people do yeah to take advantage of it yeah so i think that's a really good important point um is transitioning to the cloud so think of the nonprofit sector it's barely there yet so all of us who are investors philanthropists we need to be supporting the nonprofit sector be cloud enabled and cloud forward similarly with government i i you know there's example after example where you know whether it's health whether it's child and human services their data is in file cabinets think about that think of prime so we need to digitize those then we need to data enable that so that we can see those insights that are coming out around those solutions you know it's always the you know it's always a discussion in the industry inside the ropes and now on mainstream but getting data to the right place at the right time yeah is a really important thing it's a technical latency all these things but practically it has societal impact where would you rank the progress bar in terms of where we are on the digital divide because i can see healthcare for instance having access to the right information or it could be something on the government side where it could be related to climate change or hey get this involved where are we on this so i i would say on the digital divide which is the infrastructure piece um for most definitely high-income countries mid-income countries we've actually made progress and so they have that they're all you know network they're cloud but now they have all this data they don't know what to do with right and so what we need to kind of now build on that infrastructure to solve for that data and i'll just you know a splunk example one of our customers the netherlands um in their court system right with using splunk they were able to enable real-time data to inform court decisions so historically the judge would ask you know this happened in covid where are we on bankruptcy cases right and historically somebody would call somebody they'd call somebody they go dig the files and they get the information three months real time this is what's happening with bankruptcy in real time with covid is going to change those decisions that impact people's lives so you add that on top i mean we have environmental examples working with net zero schools we have it and we worked with the healthcare coalition with mitre to enable real-time data with a number of other companies so um where so i would say we're further along on the digital divide we're at step one on the data divide yeah doug merritt was talking earlier today about how you know this data plan that splunk has evolved into this catch basin for all the data and then it becomes useful and really taking us through the journal now security and it's this control plane that's enabling yeah i think to me that's a real key thing here so i have to ask do you see envision a future where we have a data commons where um citizens and could tap into the data and in the gov 2.0 is kind of on that vision yeah what do you where do you see this what do you say well i i think and i i know doug has talked about this before too from a values standpoint of especially with government moving to open data and then what we have to do is we have to protect privacy which actually splunk is really good at doing uh so you've got to take that individual data out of there but then once you get these big data pools into these big data lakes you'll be able to see insights that you couldn't see before you know it's interesting that i remember when the internet came around and how the u.s government's very active it seems now that that tech policy has always been kind of like oh yeah we're kind of involved in dc but now tech is so important and with all the backlash on the facebooks of the world of you know how democracy was broken there's an opportunity yeah and the lawmakers and the people who make the laws are kind of lawyers they're not really techies so so like policy's got to change how do we do that yeah oh gosh if i could solve that one on policy change but but i want to make a comment because i think it's really important because you reference and the situation facebook is in is common knowledge i give a lot of credit to splunk as you know a data platform company saying we see this data divide coming and we're going to step to the table now and do something about it because there's a lot of other companies that knew these challenges if they looked out three five years and they made personal or company choices not to do something about it so transparency is super important getting that out there and and being again in data and just saying it's not all roses right and and so take being a purpose-driven company is about making those decisions as a company to have an impact so then to answer your question on policy um i would say i think it's really complicated and tricky because data moves at the speed of sound and policy moves kind of like a turtle and so i think what we need to have happen is companies going to sometimes have to lead the way and hold themselves accountable and then work in partnership with policy to make you know policy changes that impact everybody so again we're strong advocates of open data you know we we can't make the government do it but we can be a voice for it in service of bridging the state this data divide is a great conversation i wish we had more time for the last minute just give a quick plug for what splunk's doing specifically and how people could get involved and participate yeah so i'll kind of i'd say three things one is at this early stage we're kind of raising the flag to governments out there to philanthropy to nonprofits like we all need to be paying attention to this we're going to be investing in more research on it because it is at such an early stage we've identified these barriers but we've got to go much deeper and build collaborations around the solution so we're going to be mobilizing our partners and our customers we have a 100 million dollar pledge where we donate our product nonprofits we and the equally important thing as i talked about it's our talent right it's getting the talent to help these organizations it's our strategic giving so we're mobilizing you know all of our assets around this pledge we have a 50 million dollar impact fund which is around four purpose data enabled companies so we're trying to do it across a multitude of platforms is that investment fund deploying now or has it been making investments in companies already yeah we've made um three investments refrain ai is one about using machine learning and ai around the jobs of the future and retraining so it's still or it was launched just a couple years ago so we're still early in the 50 million dollar fund so we'll be doing more of that sounds like a great opportunity for people out there watching enable enable the people to change the world yeah that's what splunk's all about right now exactly chris thanks for coming on appreciate great thank you okay the data divide we're bringing you all the data here from the cube live here in the splunk studios i'm john furrier with thecube thanks for watching thank you

>> From around the globe, it's theCUBE, with coverage of KubeCon and CloudNativeCon Europe 2021 Virtual. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Hello, welcome back to theCUBE's coverage of KubeCon 2021 CloudNativeCon Europe Virtual, I'm John Furrier your host, preview with Brian Gracely from Red Hat Senior Director Product Strategy Cloud Business Unit Brian Gracely great to see you. Former CUBE host CUBE alumni, big time strategist at Red Hat, great to see you, always great. And also the founder of Cloudcast which is an amazing podcast on cloud, part of the cloud (indistinct), great to see you Brian. Hope's all well. >> Great to see you too, you know for years, theCUBE was always sort of the ESPN of tech, I feel like, you know ESPN has become nothing but highlights. This is where all the good conversation is. It's theCUBE has become sort of the the clubhouse of tech, if you will. I know that's that's an area you're focused on, so yeah I'm excited to be back on and good to talk to you. >> It's funny you know, with all the events going away loved going out extracting the signal from the noise, you know, game day kind of vibe. CUBE Virtual has really expanded, so it's been so much more fun because we can get more people easy to dial in. So we're going to keep that feature post COVID. You're going to hear more about theCUBE Virtual hybrid events are going to be a big part of it, which is great because as you know and we've talked about communities and ecosystems are huge advantage right now it's been a big part of the Red Hat story. Now part of IBM bringing that mojo to the table the role of ecosystems with hybrid cloud is so critical. Can you share your thoughts on this? Because I know you study it, you have podcasts you've had one for many years, you understand that democratization and this new direct to audience kind of concept. Share your thoughts on this new ecosystem. >> Yeah, I think so, you know, we're sort of putting this in the context of what we all sort of familiarly call KubeCon but you know, if we think about it, it started as KubeCon it was sort of about this one technology but it's always been CloudNativeCon and we've sort of downplayed the cloud native part of it. But even if we think about it now, you know Kubernetes to a certain extent has kind of, you know there's this feeling around the community that, that piece of the puzzle is kind of boring. You know, it's 21 releases in, and there's lots of different offerings that you can get access to. There's still, you know, a lot of innovation but the rest of the ecosystem has just exploded. So it's, you know, there are ecosystem partners and companies that are working on edge and miniaturization. You know, we're seeing things like Kubernetes now getting into outer space and it's in the space station. We're seeing, you know, Linux get on Mars. But we're also seeing, you know, stuff on the other side of the spectrum. We're sort of seeing, you know awesome people doing database work and streaming and AI and ML on top of Kubernetes. So, you know, the ecosystem is doing what you'd expect it to do once one part of it gets stable. The innovation sort of builds on top of it. And, you know, even though we're virtual, we're still seeing just tons and tons of contributions, different companies different people stepping up and leading. So it's been really cool to watch the last few years. >> Yes, interesting point about the CloudNativeCon. That's an interesting insight, and I totally agree with you. And I think it's worth double clicking on. Let me just ask you, because when you look at like, say Kubernetes, okay, it's enabled a lot. Okay, it's been called the dial tone of Cloud native. I think Pat Gelsinger of VMware used that term. We call it the kind of the interoperability layer it enables more large scale deployments. So you're seeing a lot more Kubernetes enablement on clusters. Which is causing more hybrid cloud which means more Cloud native. So it actually is creating a network effect in and of itself with more Cloud native components and it's changing the development cycle. So the question I want to ask you is one how does a customer deal with that? Because people are saying, I like hybrid. I agree, Multicloud is coming around the corner. And of course, Multicloud is just a subsystem of resource underneath hybrid. How do I connect it all? Now I have multiple vendors, I have multiple clusters. I'm cross-cloud, I'm connecting multiple clouds multiple services, Kubernetes clusters, some get stood up some gets to down, it's very dynamic. >> Yeah, it's very dynamic. It's actually, you know, just coincidentally, you know, our lead architect, a guy named Clayton Coleman, who was one of the Kubernetes founders, is going to give a talk on sort of Kubernetes is this hybrid control plane. So we're already starting to see the tentacles come out of it. So you know how we do cross cloud networking how we do cross cloud provisioning of services. So like, how do I go discover what's in other clouds? You know and I think like you said, it took people a few years to figure out, like how do I use this new thing, this Kubernetes thing. How do I harness it. And, but the demand has since become "I have to do multi-cloud." And that means, you know, hey our company acquires companies, so you know, we don't necessarily know where that next company we acquire is going to run. Are they going to run on AWS? Are they going to, you know, run on Azure I've got to be able to run in multiple places. You know, we're seeing banking industries say, "hey, look cloud's now a viable target for you to put your applications, but you have to treat multiple clouds as if they're your backup domains." And so we're, you know, we're seeing both, you know the way business operates whether it's acquisitions or new things driving it. We're seeing regulations driving hybrid and multi-cloud and, even you know, even if the stalwart were to you know, set for a long time, well the world's only going to be public cloud and sort of you know, legacy data centers even those folks are now coming around to "I've got to bring hybrid to, to these places." So it's been more than just technology. It's been, you know, industries pushing it regulations pushing it, a lot of stuff. So, but like I said, we're going to be talking about kind of our future, our vision on that, our future on that. And, you know Red Hat everything we end up doing is a community activity. So we expect a lot of people will get on board with it >> You know, for all the old timers out there they can relate to this. But I remember in the 80's the OSI Open Systems Interconnect, and I was chatting with Paul Cormier about this because we were kind of grew up through that generation. That disrupted network protocols that were proprietary and that opened the door for massive, massive growth massive innovation around just getting that interoperability with TCP/IP, and then everything else happened. So Kubernetes does that, that's a phenomenal impact. So Cloud native to me is at that stage where it's totally next-gen and it's happening really fast. And a lot of people getting caught off guard, Brian. So you know, I got to to ask you as a product strategist, what's your, how would you give them the navigation of where that North star is? If I'm a customer, okay, I got to figure out where I got to navigate now. I know it's super volatile, changing super fast. What's your advice? >> I think it's a couple of pieces, you know we're seeing more and more that, you know, the technology decisions don't get driven out of sort of central IT as much anymore right? We sort of talk all the time that every business opportunity, every business project has a technology component to it. And I think what we're seeing is the companies that tend to be successful with it have built up the muscle, built up the skill set to say, okay, when this line of business says, I need to do something new and innovative I've got the capabilities to sort of stand behind that. They're not out trying to learn it new they're not chasing it. So that's a big piece of it, is letting the business drive your technology decisions as opposed to what happened for a long time which was we built out technology, we hope they would come. You know, the other piece of it is I think because we're seeing so much push from different directions. So we're seeing, you know people put technology out at the edge. We're able to do some, you know unique scalable things, you know in the cloud and so forth That, you know more and more companies are having to say, "hey, look, I'm not, I'm not in the pharmaceutical business. I'm not in the automotive business, I'm in software." And so, you know the companies that realize that faster, and then, you know once they sort of come to those realizations they realize, that's my new normal, those are the ones that are investing in software skills. And they're not afraid to say, look, you know even if my existing staff is, you know, 30 years of sort of history, I'm not afraid to bring in some folks that that'll break a few eggs and, you know, and use them as a lighthouse within their organization to retrain and sort of reset, you know, what's possible. So it's the business doesn't move. That's the the thing that drives all of them. And it's, if you embrace it, we see a lot of success. It's the ones that, that push back on it really hard. And, you know the market tends to sort of push back on them as well. >> Well we're previewing KubeCon CloudNativeCon. We'll amplify that it's CloudNativeCon as well. You guys bought StackRox, okay, so interesting company, not an open source company they have soon to be, I'm assuring, but Advanced Cluster Security, ACS, as it's known it's really been a key part of Red Hat. Can you give us the strategy behind that deal? What does that product, how does it fit in that's a lot of people are really talking about this acquisition. >> Yeah so here's the way we looked at it, is we've learned a couple of things over the last say five years that we've been really head down in Kubernetes, right? One is, we've always embedded a lot of security capabilities in the platform. So OpenShift being our core Kubernetes platform. And then what's happened over time is customers have said to us, "that's great, you've made the platform very secure" but the reality is, you know, our software supply chain. So the way that we build applications that, you know we need to secure that better. We need to deal with these more dynamic environments. And then once the applications are deployed they interact with various types of networks. I need to better secure those environments too. So we realized that we needed to expand our functionality beyond the core platform of OpenShift. And then the second thing that we've learned over the last number of years is to be successful in this space, it's really hard to take technology that wasn't designed for containers, or it wasn't designed for Kubernetes and kind of retrofit it back into that. And so when we were looking at potential acquisition targets, we really narrowed down to companies whose fundamental technologies were you know, Kubernetes-centric, you know having had to modify something to get to Kubernetes, and StackRox was really the leader in that space. They really, you know have been the leader in enterprise Kubernetes security. And the great thing about them was, you know not only did they have this Kubernetes expertise but on top of that, probably half of their customers were already OpenShift customers. And about 3/4 of their customers were using you know, native Kubernetes services and other clouds. So, you know, when we went and talked to them and said, "Hey we believe in Kubernetes, we believe in multi-cloud. We believe in open source," they said, "yeah, those are all the foundational things for us." And to your point about it, you know, maybe not being an open source company, they actually had a number of sort of ancillary projects that were open source. So they weren't unfamiliar to it. And then now that the acquisition's closed, we will do what we do with every piece of Red Hat technology. We'll make sure that within a reasonable period of time that it's made open source. And so you know, it's good for the community. It allows them to keep focusing on their innovation. >> Yeah you've got to get that code out there cool. Brian, I'm hearing about Platform Plus what is that about? Take us through that. >> Yeah, so you know, one of the things that our customers, you know, have come to us over time is it's you know, it's like, I've been saying kind of throughout this discussion, right? Kubernetes is foundational, but it's become pretty stable. The things that people are solving for now are like, you highlighted lots and lots of clusters, they're all over the place. That was something that our advanced cluster management capabilities were able to solve for people. Once you start getting into lots of places you've got to be able to secure things everywhere you go. And so OpenShift for us really allows us to bundle together, you know, sort of the complete set of the portfolio. So the platform, security management, and it also gives us the foundational pieces or it allows our customers to buy the foundational pieces that are going to help them do multi and hybrid cloud. And, you know, when we bundle that we can save them probably 25% in terms of sort of product acquisition. And then obviously the integration work we do you know, saves a ton on the operational side. So it's a new way for us to, to not only bundle the platform and the technologies but it gets customers in a mindset that says, "hey we've moved past sort of single environments to hybrid and multi-cloud environments. >> Awesome, well thanks for the update on that, appreciate it. One of the things going into KubeCon, and that we're watching closely is this Cloud native developer action. Certainly end users want to get that in a separate section with you but the end user contribution, which is like exploding. But on the developer side there's a real trend towards adding stronger consistency programmability support for more use cases okay. Where it's becoming more of a data platform as a requirement. >> Brian: Right. >> So how, so that's a trend so I'm kind of thinking, there's no disagreement on that. >> Brian: No, absolutely. >> What does that mean? Like I'm a customer, that sounds good. How do I make that happen? 'Cause that's the critical discussion right now in the DevOps, DevSecOps day, two operations. What you want to call it. This is the number one concern for developers and that solution architect, consistency, programmability more use cases with data as a platform. >> Yeah, I think, you know the way I kind of frame this up was you know, for any for any organization, the last thing you want to to do is sort of keep investing in lots of platforms, right? So platforms are great on their surface but once you're having to manage five and six and, you know 10 or however many you're managing, the economies of scale go away. And so what's been really interesting to watch with Kubernetes is, you know when we first got started everything was Cloud native application but that really was sort of, you know shorthand for stateless applications. We quickly saw a move to, you know, people that said, "Hey I can modernize something, you know, a Stateful application and we add that into Kubernetes, right? The community added the ability to do Stateful applications and that got people a certain amount of the way. And they sort of started saying, okay maybe Kubernetes can help me peel off some things of an existing platform. So I can peel off, you know Java workloads or I can peel off, what's been this explosion is the data community, if you will. So, you know, the TensorFlows the PItorches, you know, the Apache community with things like Couchbase and Kafka, TensorFlow, all these things that, you know maybe in the past didn't necessarily, had their own sort of underlying system are now defaulting to Kubernetes. And what we see because of that is, you know people now can say, okay, these data workloads these AI and ML workloads are so important to my business, right? Like I can directly point to cost savings. I can point to, you know, driving innovation and because Kubernetes is now their default sort of way of running, you know we're seeing just sort of what used to be, you know small islands of clusters become these enormous footprints whether they're in the cloud or in their data center. And that's almost become, you know, the most prevalent most widely used use case. And again, it makes total sense. It's exactly the trends that we've seen in our industry, even before Kubernetes. And now people are saying, okay, I can consolidate a lot of stuff on Kubernetes. I can get away from all those silos. So, you know, that's been a huge thing over the last probably year plus. And the cool thing is we've also seen, you know the hardware vendors. So whether it's Intel or Nvidia, especially around GPUs, really getting on board and trying to make that simpler. So it's not just the software ecosystem. It's also the hardware ecosystem, really getting on board. >> Awesome, Brian let me get your thoughts on the cloud versus the power dynamics between the cloud players and the open source software vendors. So what's the Red Hat relationship with the cloud players with the hybrid architecture, 'cause you want to set up the modern day developer environment, we get that right. And it's hybrid, what's the relationship with the cloud players? >> You know, I think so we we've always had two philosophies that haven't really changed. One is, we believe in open source and open licensing. So you haven't seen us look at the cloud as, a competitive threat, right? We didn't want to make our business, and the way we compete in business, you know change our philosophy in software. So we've always sort of maintained open licenses permissive licenses, but the second piece is you know, we've looked at the cloud providers as very much partners. And mostly because our customers look at them as partners. So, you know, if Delta Airlines or Deutsche Bank or somebody says, "hey that cloud provider is going to be our partner and we want you to be part of that journey, we need to be partners with that cloud as well." And you've seen that sort of manifest itself in terms of, you know, we haven't gone and set up new SaaS offerings that are Red Hat offerings. We've actually taken a different approach than a lot of the open source companies. And we've said we're going to embed our capabilities, especially, you know OpenShift into AWS, into Azure into IBM cloud working with Google cloud. So we'd look at them very much as a partner. I think it aligns to how Red Hat's done things in the past. And you know, we think, you know even though it maybe easy to sort of see a way of monetizing things you know, changing licensing, we've always found that, you've got to allow the ecosystem to compete. You've got to allow customers to go where they want to go. And we try and be there in the most consumable way possible. So that's worked out really well for us. >> So I got to bring up the end user participation component. That's a big theme here at KubeCon going into it and around the event is, and we've seen this trend happen. I mean, Envoy, Lyft the laying examples are out there. But they're more end-use enterprises coming in. So the enterprise class I call classic enterprise end user participation is at an all time high in opensource. You guys have the biggest portfolio of enterprises in the business. What's the trend that you're seeing because it used to be limited to the hyperscalers the Lyfts and the Facebooks and the big guys. Now you have, you know enterprises coming in the business model is working, can you just share your thoughts on CloudNativeCons participation for end users? >> Yeah, I think we're definitely seeing a blurring of lines between what used to be the Silicon Valley companies were the ones that would create innovation. So like you mentioned Lyft, or, you know LinkedIn doing Kafka or Twitter doing you know, whatever. But as we've seen more and more especially enterprises look at themselves as software companies right. So, you know if you talk about, you know, Ford or Volkswagen they think of themselves as a software company, almost more than they think about themselves as a car company, right. They're a sort of mobile transportation company you know, something like that. And so they look at themselves as I've got to I've got to have software as an expertise. I've got to compete for the best talent, no matter where that talent is, right? So it doesn't have to be in Detroit or in Germany or wherever I can go get that anywhere. And I think what they really, they look for us to do is you know, they've got great technology chops but they don't always understand kind of the the nuances and the dynamics of open-source right. They're used to having their own proprietary internal stuff. And so a lot of times they'll come to us, not you know, "Hey how do we work with the project?" But you know like here's new technology. But they'll come to us and they'll say "how do we be good, good stewards in this community? How do we make sure that we can set up our own internal open source office and have that group, work with communities?" And so the dynamics have really changed. I think a lot of them have, you know they've looked at Silicon Valley for years and now they're modeling it, but it's, you know, for us it's great because now we're talking the same language, you know we're able to share sort of experiences we're able to share best practices. So it is really, really interesting in terms of, you know, how far that whole sort of software is eating the world thing is materialized in sort of every industry. >> Yeah and it's the workloads of expanding Cloud native everywhere edge is blowing up big time. Brian, final question for you before we break. >> You bet. >> Thanks for coming on and always great to chat with you. It's always riffing and getting the data out too. What's your expectation for KubeCon CloudNativeCon this year? What are you expecting to see? What highlights do you expect will come out of CloudNativeCon KubeCon this year? >> Yeah, I think, you know like I said, I think it's going to be much more on the Cloud native side, you know we're seeing a ton of new communities come out. I think that's going to be the big headline is the number of new communities that are, you know have sort of built up a following. So whether it's Crossplane or whether it's, you know get-ops or whether it's, you know expanding around the work that's going on in operators we're going to see a whole bunch of projects around, you know, developer sort of frameworks and developer experience and so forth. So I think the big thing we're going to see is sort of this next stage of, you know a thousand flowers are blooming and we're going to see probably a half dozen or so new communities come out of this one really strong and you know the trends around those are going to accelerate. So I think that'll probably be the biggest takeaway. And then I think just the fact that the community is going to come out stronger after the pandemic than maybe it did before, because we're learning you know, new ways to work remotely, and that, that brings in a ton of new companies and contributors. So I think those two big things will be the headlines. And, you know, the state of the community is strong as they, as they like to say >> Yeah, love the ecosystem, I think the values are going to be network effect, ecosystems, integration standards evolving very quickly out in the open. Great to see Brian Gracely Senior Director Product Strategy at Red Hat for the cloud business unit, also podcasts are over a million episode downloads for the cloud cast podcast, thecloudcast.net. What's it Brian, what's the stats now. >> Yeah, I think we've, we've done over 500 shows. We're you know, about a million and a half listeners a year. So it's, you know again, it's great to have community followings and, you know, and meet people from around the world. So, you know, so many of these things intersect it's a real pleasure to work with everybody >> You're going to create a culture, well done. We're all been there, done that great job. >> Thank you >> Check out the cloud cast, of course, Red Hat's got the great OpenShift mojo going on into KubeCon. Brian, thanks for coming on. >> Thanks John. >> Okay so CUBE coverage of KubeCon, CloudNativeCon Europe 2021 Virtual, I'm John Furrier with theCUBE virtual. Thanks for watching. (upbeat music)

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are in our Palo Alto studios, COVID is still going on, so all of the interviews continue to be remote, but we're excited to have a Cube alumni, he hasn't been on for a long time, and this guy has been in the weeds of the storage industry for a very very long time and we're happy to have him on and get an update because there continues to be a lot of exciting developments. He's Phil Bullinger, he is the SVP and general manager, data center business unit from Western Digital joining us, I think for Colorado, so Phil, great to see you, how's the weather in Colorado today? >> Hi Jeff, it's great to be here. Well, it's a hot, dry summer here, I'm sure like a lot of places. But yeah, enjoying the summer through these unusual times. >> It is unusual times, but fortunately there's great things like the internet and heavy duty compute and store out there so we can get together this way. So let's jump into it. You've been in the he business a long time, you've been at Western Digital, you were at EMC, you worked on Isilon, and you were at storage companies before that. And you've seen kind of this never-ending up and to the right slope that we see kind of ad nauseum in terms of the amount of storage demands. It's not going anywhere but up, and please increase complexity in terms of unstructure data, sources of data, speed of data, you know the kind of classic big V's of big data. So I wonder, before we jump into specifics, if you can kind of share your perspective 'cause you've been kind of sitting in the Catford seat, and Western Digital's a really unique company; you not only have solutions, but you also have media that feeds other people solutions. So you guys are really seeing and ultimately all this compute's got to put this data somewhere, and a whole lot of it's sitting on Western Digital. >> Yeah, it's a great intro there. Yeah, it's been interesting, through my career, I've seen a lot of advances in storage technology. Speeds and feeds like we often say, but the advancement through mechanical innovation, electrical innovation, chemistry, physics, just the relentless growth of data has been driven in many ways by the relentless acceleration and innovation of our ability to store that data, and that's been a very virtuous cycle through what, for me, has been 30 years in enterprise storage. There are some really interesting changes going on though I think. If you think about it, in a relatively short amount of time, data has gone from this artifact of our digital lives to the very engine that's driving the global economy. Our jobs, our relationships, our health, our security, they all kind of depend on data now, and for most companies, kind of irrespective of size, how you use data, how you store it, how you monetize it, how you use it to make better decisions to improve products and services, it becomes not just a matter of whether your company's going to thrive or not, but in many industries, it's almost an existential question; is your company going to be around in the future, and it depends on how well you're using data. So this drive to capitalize on the value of data is pretty significant. >> It's a really interesting topic, we've had a number of conversations around trying to get a book value of data, if you will, and I think there's a lot of conversations, whether it's accounting kind of way, or finance, or kind of good will of how do you value this data? But I think we see it intrinsically in a lot of the big companies that are really data based, like the Facebooks and the Amazons and the Netflixes and the Googles, and those types of companies where it's really easy to see, and if you see the valuation that they have, compared to their book value of assets, it's really baked into there. So it's fundamental to going forward, and then we have this thing called COVID hit, which I'm sure you've seen all the memes on social media. What drove your digital transformation, the CEO, the CMO, the board, or COVID-19? And it became this light switch moment where your opportunities to think about it are no more; you've got to jump in with both feet, and it's really interesting to your point that it's the ability to store this and think about it now differently as an asset driving business value versus a cost that IT has to accommodate to put this stuff somewhere, so it's a really different kind of a mind shift and really changes the investment equation for companies like Western Digital about how people should invest in higher performance and higher capacity and more unified and kind of democratizing the accessibility that data, to a much greater set of people with tools that can now start making much more business line and in-line decisions than just the data scientist kind of on Mahogany Row. >> Yeah, as you mentioned, Jeff, here at Western Digital, we have such a unique kind of perch in the industry to see all the dynamics in the OEM space and the hyperscale space and the channel, really across all the global economies about this growth of data. I have worked at several companies and have been familiar with what I would have called big data projects and fleets in the past. But at Western Digital, you have to move the decimal point quite a few digits to the right to get the perspective that we have on just the volume of data that the world has just relentless insatiably consuming. Just a couple examples, for our drive projects we're working on now, our capacity enterprise drive projects, you know, we used to do business case analysis and look at their lifecycle capacities and we measured them in exabytes, and not anymore, now we're talking about zettabyte, we're actually measuring capacity enterprise drive families in terms of how many zettabyte they're going to ship in their lifecycle. If we look at just the consumption of this data, the last 12 months of industry TAM for capacity enterprise compared to the 12 months prior to that, that annual growth rate was north of 60%. And so it's rare to see industries that are growing at that pace. And so the world is just consuming immense amounts of data, and as you mentioned, the COVID dynamics have been both an accelerant in some areas, as well as headwinds in others, but it's certainly accelerated digital transformation. I think a lot of companies we're talking about, digital transformation and hybrid models and COVID has really accelerated that, and it's certainly driving, continues to drive just this relentless need to store and access and take advantage of data. >> Yeah, well Phil, in advance of this interview, I pulled up the old chart with all the different bytes, kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes, and zettabytes, and just per the Wikipedia page, what is a zettabyte? It's as much information as there are grains of sand in all the world's beaches. For one zettabyte. You're talking about thinking in terms of those units, I mean, that is just mind boggling to think that that is the scale in which we're operating. >> It's really hard to get your head wrapped around a zettabyte of storage, and I think a lot of the industry thinks when we say zettabyte scale era, that it's just a buzz word, but I'm here to say it's a real thing. We're measuring projects in terms of zettabytes now. >> That's amazing. Well, let's jump into some of the technology. So I've been fortunate enough here at theCUBE to be there at a couple of major announcements along the way. We talked before we turned the cameras on, the helium announcement and having the hard drive sit in the fish bowl to get all types of interesting benefits from this less dense air that is helium versus oxygen. I was down at the Mammer and Hammer announcement, which was pretty interesting; big heavy technology moves there, to again, increase the capacity of the hard drive's base systems. You guys are doing a lot of stuff on RISC-V I know is an Open source project, so you guys have a lot of things happening, but now there's this new thing, this new thing called zonedd storage. So first off, before we get into it, why do we need zoned storage, and really what does it now bring to the table in terms of a capability? >> Yeah, great question, Jeff. So why now, right? Because I mentioned storage, I've been in storage for quite some time. In the last, let's just say in the last decade, we've seen the advent of the hyperscale model and certainly a whole nother explosion level of data and just the veracity with which they hyperscalers can create and consume and process and monetize data. And of course with that, has also come a lot of innovation, frankly, in the compute space around how to process that data and moving from what was just a general purpose CPU model to GPU's and DPU's and so we've seen a lot of innovation on that side, but frankly, in the storage side, we haven't seen much change at all in terms of how operating systems, applications, file systems, how they actually use the storage or communicate with the storage. And sure, we've seen advances in storage capacities; hard drives have gone from two to four, to eight, to 10 to 14, 16, and now our leading 18 and 20 terabyte hard drives. And similarly, on the SSD side, now we're dealing with the capacities of seven, and 15, and 30 terabytes. So things have gotten larger, as you expect. And some interfaces have improved, I think NVME, which we'll talk about, has been a nice advance in the industry; it's really now brought a very modern scalable low latency multi-threaded interface to a NAM flash to take advantage of the inherent performance of transistor based persistent storage. But really when you think about it, it hasn't changed a lot. But what has changed is workloads. One thing that definitely has evolved in the space of the last decade or so is this, the thing that's driving a lot of this explosion of data in the industry is around workloads that I would characterize as sequential in nature, they're serial, you can capture it in written. They also have a very consistent life cycle, so you would write them in a big chunk, you would read them maybe in smaller pieces, but the lifecycle of that data, we can treat more as a chunk of data, but the problem is applications, operating systems, vial systems continue to interface with storage using paradigms that are many decades old. The old 512 byte or even Forte, Sector size constructs were developed in the hard drive industry just as convenient paradigms to structure what is an unstructured sea of magnetic grains into something structured that can be used to store and access data. But the reality is when we talk about SSDs, structure really matters, and so what has changed in the industry is the workloads are driving very very fresh looks at how more intelligence can be applied to that application OS storage device interface to drive much greater efficiency. >> Right, so there's two things going on here that I want to drill down on. On one hand, you talked about kind of the introduction of NAND and flash, and treating it like you did, generically you did a regular hard drive. But you could get away and you could do some things because the interface wasn't taking full advantage of the speed that was capable in the NAND. But NVME has changed that, and now forced kind of getting rid of some of those inefficient processes that you could live with, so it's just kind of classic next level step up and capabilities. One is you get the better media, you just kind of plug it into the old way. Now actually you're starting to put in processes that take full advantage of the speed that that flash has. And I think obviously prices have come down dramatically since the first introduction, where before it was always kind of a clustered off or super high end, super low latency, super high value apps, it just continues to spread and proliferate throughout the data center. So what did NVME force you to think about in terms of maximizing the return on the NAND and flash? >> Yeah, NVME, which we've been involved in the standardization, I think it's been a very successful effort, but we have to remember NVME is about a decade old, or even more when the original work started around defining this interface, but it's been very successful. The NVME standard's body is very productive cross company effort, it's really driven a significant change, and what we see now is the rapid adoption of NVME in all of data center architectures, whether it's very large hyperscale to classic on prem enterprise to even smaller applications, it's just a very efficient interface mechanism for connecting SSDs into a server. So we continue to see evolution at NVME, which is great, and we'll talk about ZNS today as one of those evolutions. We're also very keenly interested in NVME protocol over fabrics, and so one of the things that Western Digital has been talking about a lot lately is incorporating NVME over fabrics as a mechanism for now connecting shared storage into multiple post architectures. We think this is a very attractive way to build shared storage architectures of the future that are scalable, that are composable, that really have a lot more agility with respect to rack level infrastructure and applying that infrastructure to applications. >> Right, now one thing that might strike some people as kind of counterintuitive is within the zoned storage in zoning off parts of the media, to think of the data also kind of in these big chunks, is it feels contrary to kind of atomization that we're seeing in the rest of the data center, right? So smaller units of compute, smaller units of store, so that you can assemble and disassemble them in different quantities as needed. So what was the special attribute that you had to think about and actually come back and provide a benefit in actually kind of re-chunking, if you will, in these zones versus trying to get as atomic as possible? >> Yeah, it's a great question, Jeff, and I think it's maybe not intuitive in terms of why zoned storage actually creates a more efficient storage paradigm when you're storing stuff essentially in larger blocks of data, but this is really where the intersection of structure and workload and sort of the nature of the data all come together. If you turn back the clock maybe four or five years when SMR hard drives host managers SMR hard drives first emerged on the scene. This was really taking advantage of the fact that the right head on a hard disk drive is larger than the read head, or the read head can be much smaller, and so the notion of overlapping or shingling the data on the drive, giving the read head a smaller target to read, but the writer a larger write pad to write the data could actually, what we found was it increases aerial density significantly. And so that was really the emergence of this notion of sequentially written larger blocks of data being actually much more efficiently stored when you think about physically how it's being stored. What's very new now and really gaining a lot of traction is the SSD corollary to SMR on the hard drive, on the SSD side, we had the ZNS specification, which is, very similarly where you'd divide up the name space of an SSD into fixed size zones, and those zones are written sequentially, but now those zones are intimately tied to the underlying physical architecture of the NAND itself; the dyes, the planes, the read pages, the erase pages. So that, in treating data as a block, you're actually eliminating a lot of the complexity and the work that an SSD has to do to emulate a legacy hard drive, and in doing so, you're increasing performance and endurance and the predictable performance of the device. >> I just love the way that you kind of twist the lens on the problem, and on one hand, by rule, just looking at my notes here, the zoned storage device is the ZSD's introduce a number of restrictions and limitations and rules that are outside the full capabilities of what you might do. But in doing so, an aggregate, the efficiency, and the performance of the system in the whole is much much better, even though when you first look at it, you think it's more of a limiter, but it's actually opens up. I wonder if there's any kind of performance stats you can share or any kind of empirical data just to give people kind of a feel for what that comes out as. >> So if you think about the potential of zoned storage in general and again, when I talk about zoned storage, there's two components; there's an HDD component of zoned storage that we refer to as SMR, and there's an SSD version of that that we call ZNS. So we think about SMR, the value proposition there is additional capacity. So effectively in the same drive architecture, with roughly the same bill of material used to build the drive, we can overlap or shingle the data on the drive. And generally for the customer, additional capacity. Today with our 18, 20 terabyte offerings that's on the order of just over 10%, but that delta is going to increase significantly going forward to 20% or more. And when you think about a hyperscale customer that has not hundreds or thousands of racks, but tens of thousands of racks. A 10 or 20% improvement in effective capacity is a tremendous TCO benefit, and the reason we do that is obvious. I mean, the economic paradigm that drives large at-scale data centers is total custom ownership, both acquisition costs and operating costs. And if you can put more storage in a square tile of data center space, you're going to generally use less power, you're going to run it more efficiently, you're actually, from an acquisition cost, you're getting a more efficient purchase of that capacity. And in doing that, our innovation, we benefit from it and our customers benefit from it. So the value proposition for zoned storage in capacity enterprise HDV is very clear, it's additional capacity. The exciting thing is, in the SSD side of things, or ZNS, it actually opens up even more value proposition for the customer. Because SSDs have had to emulate hard drives, there's been a lot of inefficiency and complexity inside an enterprise SSD dealing with things like garbage collection and right amplification reducing the endurance of the device. You have to over-provision, you have to insert as much as 20, 25, even 28% additional man bits inside the device just to allow for that extra space, that working space to deal with delete of data that are smaller than the block erase that the device supports. So you have to do a lot of reading and writing of data and cleaning up. It creates for a very complex environment. ZNS by mapping the zoned size with the physical structure of the SSD essentially eliminates garbage collection, it reduces over-provisioning by as much as 10x. And so if you were over provisioning by 20 or 25% on an enterprise SSD, and a ZNS SSD, that can be one or two percent. The other thing I have to keep in mind is enterprise SSD is typically incorporate D RAM and that D RAM is used to help manage all those dynamics that I just mentioned, but with a much simpler structure where the pointers to the data can be managed without all the D RAM. We can actually reduce the amount of D RAM in an enterprise SSD by as much as eight X. And if you think about the MILA material of an enterprise SSD, D RAM is number two on the list in terms of the most expensive bomb components. So ZNS and SSDs actually have a significant customer total cost of ownership impact. It's an exciting standard, and now that we have the standard ratified through the NVME working group, it can really accelerate the development of the software ecosystem around. >> Right, so let's shift gears and talk a little bit about less about the tech and more about the customers and the implementation of this. So you talked kind of generally, but are there certain types of workloads that you're seeing in the marketplace where this is a better fit or is it just really the big heavy lifts where they just need more and this is better? And then secondly, within these hyperscale companies, as well as just regular enterprises that are also seeing their data demands grow dramatically, are you seeing that this is a solution that they want to bring in for kind of the marginal kind of next data center, extension of their data center, or their next cloud region? Or are they doing lift and shift and ripping stuff out? Or do they enough data growth organically that there's plenty of new stuff that they can put in these new systems? >> Yeah, I love that. The large customers don't rip and shift; they ride their assets for a long lifecycle, 'cause with the relentless growth of data, you're primarily investing to handle what's coming in over the transom. But we're seeing solid adoption. And in SMRS you know we've been working on that for a number of years. We've got significant interest and investment, co-investment, our engineering, and our customer's engineering adapting the application environment's to take advantage of SMR. The great thing is now that we've got the NVME, the ZNS standard gratified now in the NVME working group, we've got a very similar, and all approved now, situation where we've got SMR standards that have been approved for some time, and the SATA and SCSI standards. Now we've got the same thing in the NVME standard, and the great thing is once a company goes through the lift, so to speak, to adapt an application, file system, operating system, ecosystem, to zoned storage, it pretty much works seamlessly between HDD and SSD, and so it's not an incremental investment when you're switching technologies. Obviously the early adopters of these technologies are going to be the large companies who design their own infrastructure, who have mega fleets of racks of infrastructure where these efficiencies really really make a difference in terms of how they can monetize that data, how they compete against the landscape of competitors they have. For companies that are totally reliant on kind of off the shelf standard applications, that adoption curve is going to be longer, of course, because there are some software changes that you need to adapt to enable zoned storage. One of the things Western Digital has done and taken the lead on is creating a landing page for the industry with zoned storage.io. It's a webpage that's actually an area where many companies can contribute Open source tools, code, validation environments, technical documentation. It's not a marketeering website, it's really a website built to land actual Open source content that companies can use and leverage and contribute to to accelerate the engineering work to adapt software stacks to zoned storage devices, and to share those things. >> Let me just follow up on that 'cause, again, you've been around for a while, and get your perspective on the power of Open source. And it used to be the best secrets, the best IP were closely guarded and held inside, and now really we're in an age where it's not necessarily. And the brilliant minds and use cases and people out there, just by definition, it's more groups of engineers, more engineers outside your building than inside your building, and how that's really changed kind of a strategy in terms of development when you can leverage Open source. >> Yeah, Open source clearly has accelerated innovation across the industry in so many ways, and it's the paradigm around which companies have built business models and innovated on top of it, I think it's always important as a company to understand what value ad you're bringing, and what value ad the customers want to pay for. What unmet needs in your customers are you trying to solve for, and what's the best mechanism to do that? And do you want to spend your RND recreating things, or leveraging what's available and innovating on top of it? It's all about ecosystem. I mean, the days where a single company could vertically integrate top to bottom a complete end solution, you know, those are fewer and far between. I think it's about collaboration and building ecosystems and operating within those. >> Yeah, it's such an interesting change, and one more thing, again, to get your perspective, you run the data center group, but there's this little thing happening out there that we see growing, IOT, in the industrial internet of things, and edge computing as we try to move more compute and store and power kind of outside the pristine world of the data center and out towards where this data is being collected and processed when you've got latency issues and all kinds of reasons to start to shift the balance of where the compute is and where the store and relies on the network. So when you look back from the storage perspective in your history in this industry and you start to see basically everything is now going to be connected, generating data, and a lot of it is even Opensource. I talked to somebody the other day doing kind of Opensource computer vision on surveillance video. So the amount of stuff coming off of these machines is growing in crazy ways. At the same time, it can't all be processed at the data center, it can't all be kind of shipped back and then have a decision and then ship that information back out to. So when you sit back and look at Edge from your kind of historical perspective, what goes through your mind, what gets you excited, what are some opportunities that you see that maybe the laymen is not paying close enough attention to? >> Yeah, it's really an exciting time in storage. I get asked that question from time to time, having been in storage for more than 30 years, you know, what was the most interesting time? And there's been a lot of them, but I wouldn't trade today's environment for any other in terms of just the velocity with which data is evolving and how it's being used and where it's being used. A TCO equation may describe what a data center looks like, but data locality will determine where it's located, and we're excited about the Edge opportunity. We see that as a pretty significant, meaningful part of the TAM as we look three to five years. Certainly 5G is driving much of that, I think just any time you speed up the speed of the connected fabric, you're going to increase storage and increase the processing the data. So the Edge opportunity is very interesting to us. We think a lot of it is driven by low latency work loads, so the concept of NVME is very appropriate for that, we think, in general SSDs deployed and Edge data centers defined as anywhere from a meter to a few kilometers from the source of the data. We think that's going to be a very strong paradigm. The workloads you mentioned, especially IOT, just machine-generated data in general, now I believe, has eclipsed human generated data, in terms of just the amount of data stored, and so we think that curve is just going to keep going in terms of machine generated data. Much of that data is so well suited for zoned storage because it's sequential, it's sequentially written, it's captured, and it has a very consistent and homogenous lifecycle associated with it. So we think what's going on with zoned storage in general and ZNS and SMR specifically are well suited for where a lot of the data growth is happening. And certainly we're going to see a lot of that at the Edge. >> Well, Phil, it's always great to talk to somebody who's been in the same industry for 30 years and is excited about today and the future. And as excited as they have been throughout their whole careers. So that really bodes well for you, bodes well for Western Digital, and we'll just keep hoping the smart people that you guys have over there, keep working on the software and the physics, and the mechanical engineering and keep moving this stuff along. It's really just amazing and just relentless. >> Yeah, it is relentless. What's exciting to me in particular, Jeff, is we've driven storage advancements largely through, as I said, a number of engineering disciplines, and those are still going to be important going forward, the chemistry, the physics, the electrical, the hardware capabilities. But I think as widely recognized in the industry, it's a diminishing curve. I mean, the amount of energy, the amount of engineering effort, investment, that cost and complexity of these products to get to that next capacity step is getting more difficult, not less. And so things like zoned storage, where we now bring intelligent data placement to this paradigm, is what I think makes this current juncture that we're at very exciting. >> Right, right, well, it's applied AI, right? Ultimately you're going to have more and more compute power driving the storage process and how that stuff is managed. As more cycles become available and they're cheaper, and ultimately compute gets cheaper and cheaper, as you said, you guys just keep finding new ways to move the curve in. And we didn't even get into the totally new material science, which is also coming down the pike at some point in time. >> Yeah, very exciting times. >> It's been great to catch up with you, I really enjoy the Western Digital story; I've been fortunate to sit in on a couple chapters, so again, congrats to you and we'll continue to watch and look forward to our next update. Hopefully it won't be another four years. >> Okay, thanks Jeff, I really appreciate the time. >> All right, thanks a lot. All right, he's Phil, I'm Jeff, you're watching theCUBE. Thanks for watching, we'll see you next time.

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought >>leaders all around the world. This is a cube conversation. >>Hey, welcome back, everybody. Jeff Frick here with the Cube. We're in our Palo Alto Studios Cove. It is still going on. So, uh, all of our all of the interviews continue to be remote, but we're excited to have Ah, Cube alumni hasn't been on for a long time, but this guy has been in the weeds of the storage industry for a very, very long time, and we're happy to, uh, I have a mon and get an update because there continues to be a lot of exciting developments. He's Phill Bollinger. Ah, he is the SVP and general manager Data center business unit from Western Digital. Joining us, I think from Colorado. So, Phil, great to see you. How is the weather in Colorado today? >>Hi, Jeff. It's great to be here. Well, it's It's a hot, dry summer here. I'm sure like a lot of places. Yeah, enjoying enjoying this summer through these unusual times it >>is. It is unusual times, but fortunately, there's great things like the Internet and heavy duty. Ah, compute and store out there so we can we can get together this way. So let's jump into it. You've been in the business a long time. You've been a Western digital, your DMC you worked on I salon and you were at storage companies before that. And you've seen kind of this never ending up into the right slope that we see, you know, kind of ad nauseam. In terms of the amount of storage demands. It's not going anywhere but up in police. Increased complexity in terms of unstructured data, sources of data, speed of data, you know, the kind of classic big V's of big data. So I wonder before we jump into specifics if you can kind of share your perspective because you've been kind of sitting in the catbird seat. And Western Digital's a really unique company. You not only have solutions, but you also have media that feeds other people solutions. So you guys are really, you know, seeing. And ultimately all this computes gotta put this data somewhere, and a whole lot of it's in our western digital. >>Yeah, it's It's a great a great intro there. Yeah, it's been interesting, you know, through my career. I've seen a lot of advances in storage technology. Uh, you know, speeds and feeds like we often say, But you know, the advancement through mechanical innovation, electrical innovation, chemistry, physics, you know, just the relentless growth of data has been, has been driven in many ways by the relentless acceleration and innovation of our ability to store that data. And that's that's been a very virtuous cycle through you know what for me has been more than 30 years and in enterprise storage there are some really interesting changes going on that I think if you think about it in a relatively short amount of time, data has gone from, you know, just kind of this artifact of our digital lives, um, to the very engine that's driving the global economy, um, our jobs, our relationships, our health, our security. They all depend on data on for most companies, kind of irrespective of size. How you use data, how you how you store it, how you monetize it, how you use it to make better decisions to improve products and services. You know, it becomes not just a matter of whether your company's going to thrive and I bet in many industries it's it's almost an existential question. Is, is your company going to be around in the future? And it and it depends on how well you're using data. So this this drive toe capitalize on the value of data is is pretty significant. >>It's Ah, it's a really interesting topic. We've had a number of conversations around trying to get, like a book value of data, if you will. And I think there's a lot of conversations, whether it's accounting, kind of way or finance or kind of of good will of how do you value this data? But I think we see it intrinsically in a lot of the big companies that are really database, like the Facebooks and the Amazons and the Netflix and the Googles and those >>types >>of companies where it's really easy to see. And if you see you know the valuation that they have compared to their book value of assets, right, it's really baked into there. So it's it's it's fundamental to going forward. And then we have this thing called Covet Hit, which, you know, >>you've >>seen on the media on social media, right? What drove your digital transformation. The CEO CIO, the CMO, the board Rick over 19. And it became this light switch moment where your opportunities to think about it or no more, you've got to jump in with both feet. And it's really interesting to your point that it's the ability to store this and think about it differently as an asset driving business value versus a cost that I t has >>to >>accommodate to put this stuff somewhere. So it's a really different kind of a mind shift and really changes the investment equation for companies like Western Digital about how people should invest in higher performance and higher capacity and more unified it in kind of democratizing the accessibility that data to a much greater set of people with tools that can now start making much more business line and in line decisions than just the data scientists you know, kind of on mahogany row. >>Yeah, like as you mentioned Jeff Inherit Western Digital. We have such a unique kind of perch in the industry to see all the dynamics in the ODM space and the hyper scale space and the channel really across all the global economy's about this this growth of data. I have worked at several companies and have been familiar with what I would have called big data projects and and, ah, fleets in the past. But the Western digital you have to move the decimal point, you know, quite a few digits to the right to get to get the perspective that that we have on just the volume of data, that the world is just relentlessly, insatiably consuming. Just a couple examples for for our Dr Projects we're working on now, our capacity enterprise Dr. Projects. You know, we used to do business case analyses and look at their life cycle. Pass it ease and we measure them and exabytes and not anymore. Now we're talking about Zeta Bytes were actually measuring capacity Enterprise drive families in terms of how many's petabytes they're gonna ship in their life cycle. And if we look at just the consumption of this data the last 12 months of Industry tam for capacity enterprise, compared to the 12 months prior to that, that annual growth rate was north of 60%. So it's it's rare to see industries that are that are growing at that pace. And so the world is just consuming immense amounts of data. And as you mentioned, the dynamics have been both an accelerant in some areas as well as headwinds and others. But it's certainly accelerated digital transformation. I think a lot of companies were talking about digital transformation and and, um, hybrid models. And Covert has really accelerated that. And it's certainly driving continues to drive just this relentless need toe to store and access and take advantage of data. Yeah, >>well, filling In advance of this interview, I pulled up the old chart right with with the all the different bytes, right, kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes and petabytes. And just just for the Wikipedia page. What is is that a byte, a zoo? Much information as there are grains of sand in all the world's beaches. For one fight, you're talking about thinking in terms of those units. I mean, that is just mind boggling to think that that is the scale in which we're operating. >>It's really hard to get your head wrapped around a set amount of storage. And, you know, I think a lot of the industry thinks when we say that a byte scale era that It's just a buzzword. But I'm here to say it's a real thing where we're measuring projects and in terms of petabytes, that's >>amazing. Let's jump into some of the technology. So I've been fortunate enough here at the Cube toe to be there at a couple of major announcements along the way. We talked before we turned the cameras on the helium announcement and having the hard drive sit in the in the fish bowl, um, to get off types of interesting benefits from this less dense air that is helium versus oxygen. I was down at the mammary and hammer announcement, which was pretty interesting. Big, big, heavy technology moves there to again increase the capacity of the hard drive based systems. You guys are doing a lot of stuff on. This five I know is an open source projects. You guys have a lot of things happening, but now there's this new thing, this new thing called zoned storage. So first off before we get into, why do we need zone storage? And really, what does it now bring to the table in terms of ah, capability? >>Yeah, Great question, Jeff. So why now, right. I as I mentioned, you know, storage. I've been in storage for quite some time in the last. Let's just say, in the last decade we've seen the advent of the hyper scale model and certainly the, you know, a whole another explosion, level of, of data and just the veracity with which the hyper scaler is can create and consume and process and monetize data. And, of course, with that has also come a lot of innovation, frankly, in the compute space around had a process that data and moving from, you know, what was just a general purpose CPU model to GP use and DP use. And so we've seen a lot of innovation on that. But you know, frankly, in the storage side, we haven't seen much change at all in terms of how operating systems applications, final systems, how they actually use the storage or communicate with the storage. And sure we've seen, you know, advances in storage capacities. Hard drives have gone from 2 to 4 to 8 to 10 to 14 16 and now are leading 18 and 20 terabyte hard drives and similarly on the SSD side, you know, now we're dealing with the complexities of seven and 15 and 30 terabytes. So things have gotten larger, as you would expect, but and and some interfaces have improved, I think Envy Me, which we'll talk about, has been nice advance in the industry. It's really now brought a very modern, scalable, low latency, multi threaded interface to a NAND flash to take advantage of the inherent performance of transistor based, persistent storage. But really, when you think about it hasn't changed a lot and so but what has changed his workloads? One thing that definitely has evolved in the space of the last decade or so is this. The thing that's driving a lot of this explosion of data and industry is around workloads that I would characterize as a sequential in nature there, see, really captured and written. They also have a very consistent lifecycle, so you would write them in a big chunk. You would read them, uh, maybe in smaller pieces, but the lifecycle of that data we can treat more as a chunk of data, but the problem is applications. Operating systems. File systems continue to interface with storage, using paradigms that are, you know, many decades old, they'll find 12 bite or even four K sectors. Size constructs were developed in, you know, in the hard drive industry, just as convenient paradigms to structure what is unstructured sea of magnetic grains into something structured that can be used to store and access data. But the reality is, you know, when we talk about SSD is structured really matters. And so these what has changed in the industry as the workloads are driving very, very fresh looks at how more intelligence could be applied to that application OS storage device interface to drive much greater officials. >>Right? So there's there's two things going on here that I want to drill down on one hand. You know, you talked about kind of the introduction of NAND flash Ah, and treating it like you did generically. You did a regular hard drive, but but you could get away and you could do some things because the interface wasn't taking full advantage of the speed that was capable in the nan. But envy me has changed that and forced kind of getting getting rid of some of those inefficient processes that you could live with. So it's just kind of classic. Next next level step up and capabilities. One is you got the better media. You just kind of plug it into the old way. Now, actually, you're starting to put in processes that take full advantage of the speed that that flash has. And I think you know, obviously, prices have come down dramatically since the first introduction. And for before, we always kind of clustered offer super high end, super low latency, super high value APS. You know, it just continues to Teoh to spread and proliferate throughout the data center. So, you know what did envy me force you to think about in terms of maximizing, you know, kind of the return on the NAND and flash? >>Yeah, yeah, in envy me, which, you know, we've been involved in the standardization after I think it's been a very successful effort, but we have to remember Envy me is is about a decade old, you know, or even more When the original work started around defining this this interface and but it's been very successful, you know, the envy, any standards, bodies, very productive, you know, across company effort, it's really driven a significant change. And what we see now is the rapid adoption of Envy Me in all data center architectures. Whether it's a very large hyper scale to, you know, classic on prim enterprise to even, you know, smaller applications. It's just a very efficient interface mechanism for connecting SSD, ease and Teoh into a server, you know, So the we continue to see evolution and envy me, which is great, and we'll talk about Z and s. Today is one of those evolutions. We're also very keenly interested in VM e protocol over fabrics. And so one of the things that Western Digital has been talking about a lot lately is incorporating Envy me over fabrics as a mechanism for now connecting shared storage into multiple post architectures. We think this is a very attractive way to build shared storage architectures in the future that are scalable, that air compose herbal that really are more have a lot more agility with respect two rack level infrastructure and applying that infrastructure to applications. Right >>now, one thing that might strike some people it's kind of counterintuitive is is within the zone, um, storage and zoning off parts of the media to think of the data also kind of in these big chunks, is it? It feels contrary to kind of optimization that we're seeing in the rest of the data center. Right? So smaller units of compute smaller units of store so that you can assemble and disassemble them in different quantities as needed. So what was the special attributes that you had to think about and and actually come back and provide a benefit in actually kind of re chunking, if you will in the zones versus trying to get as atomic as possible? >>Yeah, It's a great question, Jeff, and I think it's maybe not intuitive in terms of why zone storage actually creates a more efficient storage paradigm when you're storing stuff essentially in larger blocks of data. But if this is really where the intersection of structure and workload and sort of the nature of the data all come together, uh, if you turn back the clock, maybe 45 years when SMR hard drives host managers from our hard drives first emerged on the scene, this was really taking advantage of the fact that the right head on a hard describe is larger than the reader can't reach. It could be much smaller, and so then the notion of overlapping or singling the data on the drive giving the read had a smaller target to read. But the writer a larger right pad to write the data I could. Actually, what we found was it increases areal density significantly, Um, and so that was really the emergence of this notion of sequentially written larger blocks of data being actually much more efficiently stored. When you think about physically how it's being stored, what is very new now and really gaining a lot of traction is is the the SSD corollary to tomorrow in the hard drive. On the SSD side, we have the CNS specification, which is very similarly where you divide up a name space of an SSD and two fixed size zones, and those zones are written sequentially. But now those zones are are intimately tied to the underlying physical architecture of the NAND itself. The dies, the planes, the the three pages, the the race pages so that in treating data as a black, you're actually eliminating a lot of the complexity and the work that an SSD has to do to emulate a legacy hard drive. And in doing so, you're increasing performance and endurance and and the predictable performance of the device. >>I just love the way that that, you know, you kind of twist the lens on the problem and and on one hand, you know, by rule just looking at my notes of his own storage devices, the CS DS introduced a number of restrictions and limitations and and rules that are outside the full capabilities of what you might do. But in doing so in aggregate, the efficiency and the performance of the system in the hole is much, much better, even though when you first look at you think it's more of a limiter, but it's actually opens up. I wonder if there's any kind of performance stats you can share or any kind of empirical data, just to >>get people kind >>of a feel for what? That what that comes out as >>so if you think about the potential of zone storage in general, when again, When I talk about zone storage, there's two components. There's an HDD component of zone storage that we that we refer to as S. Some are, and there's an SSD version of that that we call Z and s So you think about SMR. The value proposition. There is additional capacity so effectively in the same Dr architecture with with, you know, roughly the same bill of material used to build the drive. We can overlap or single the data on the drive and generate for the customer additional capacity. Today with our 18 20 terabyte offerings, that's on the order of just over 10% but that Delta is going to increase significantly, going forward 20% or more. And when you think about ah, hyper scale customer that has not hundreds or thousands of racks but tens of thousands of racks, a 10 or 20% improvement and effective capacity is a tremendous TCO benefit, and the reason we do that is obvious. I mean, the the the the economic paradigm that drives large scale data centers is total cost of ownership, the acquisition costs and operating costs. And if you can put more storage in a square, you know, style of data center space, you're going to generally use less power. You're gonna run it more efficiently. You're actually from an acquisition cost. You're getting a more efficient purchase of that capacity. And in doing that, our innovation, you know, we benefit from it and our customers benefit from it so that the value proposition pours. Don't storage in in capacity. Enterprise HDD is very clear. It's it's additional capacity. The exciting thing is in the SSD side of things for Z and as it actually opens up even more value proposition for the customer. Um, because SSD is have had to emulate hard drives. There's been a lot of inefficiency in complexity inside an enterprise. SSD dealing with things like garbage collection and write amplification, reducing the endurance of the device. You have to over provision. You have to insert as much as 2025 28% additional NAND bits inside the device just too allow for that extra space, that working space to deal with with delete of the you know that that are smaller than the the a block of race that that device supports. And so you have to do a lot of reading and writing of data and cleaning up it creates for a very complex environment. Z and S by mapping the zone size with the physical structure of the SSD, essentially eliminates garbage collection. It reduces over provisioning by as much as 10% are 10 x And so if you were over provisioning by 20 or 25% in an enterprise SSD and Xeon SSD, that could be, you know, one or 2%. The other thing we have to keep in mind is enterprise. SSD is typically incorporate D RAM and that D RAM is used to help manage all those dynamics that I that I just mentioned, but with a very much simpler structure where the pointers to the data can be managed without all that d ram, we can actually reduce the amount of D ram in an enterprise SSD by as much as eight X. And if you think about the bill of material of an enterprise, SSD d ram is number two on the list in terms of the most expensive bomb components. So Z and S and SSD is actually have a significant customer. Total cost of ownership impact. Um, it's it's an exciting it's an exciting standard. And now that we have the standard ratified through the Envy me working group, um, you can really accelerate the development of the software ecosystem around >>right. So let's shift gears and talk a little bit about less about the tech and more about the customers and the implementation of this. So, you know, are there you talked to kind of generally, but are there certain certain types of workloads that you're seeing in the marketplace where this is, you know, a better fit? Or is it just really the big heavy lifts? Um, where they just need more and this is better. And then secondly, within you know, these both hyper scale companies, um, as well as just regular enterprises that are also seeing their data demands grow dramatically. Are you seeing you know, that this is a solution that they want to bring in for kind of the marginal kind of next data center extension data center or their next ah, cloud region? Or are they doing you know, lift and shift and ripping stuff out? Or do they have enough? Do they have enough data growth organically? >>Then >>there's plenty of new stuff that they can. They can put in these new systems. >>Yeah, well, the large customers don't don't rip and shift. They they write their assets for a long life cycle because with the relentless growth of data. You're primarily investing to handle what's what's coming in over the transom, but we're seeing we're seeing solid adoption in SMR. As you know, we've been working on that for a number of years. We've we've got, you know, significant interest in investment co investment, our engineering and our customers engineering, adapting the the application environments. Let's take advantage of SMR. The great thing is, now that we've got the envy me, the Xeon s standard ratified now, in the envy of the working group, um, we've got a very similar and all approved now situation where we've got SMR standards that have been approved for some time in the sand and scuzzy standards. Now we've got the same thing in the envy, any standard. And that's the great thing is once a company goes through the lifts, so it's B to adapt an application file system, operating system, ecosystem to zone storage. It pretty much works seamlessly between HDD and SSD. And so it's not. It's not an incremental investment when you're switching technologies and for obviously the early adopters of these technologies are going to be the large companies who designed their own infrastructure. You have you know, mega fleets of racks of infrastructure where these efficiencies really, really make a difference in terms of how they can monetize that data, how they compete against, you know, the landscape of competitors They have, um, for companies that are totally reliant on kind of off the shelf standard applications. That adoption curve is gonna be longer, of course, because there are there are some software changes that you need to adapt to to enable zone storage. One of the things Western Digital is has done, and taking the lead on is creating a landing page for the industry with zone storage. Not Iot. It's a Web page that's actually an area where, where many companies can contribute open source tools, code validation environments, technical documentation it's not. It's not a marketeering website. It's really a website bill toe land, actual open source content that companies can and use and leverage and contribute to. To accelerate the engineering work to adapt software stacks his own storage devices on to share those things. >>Let me just follow up on that, because again you've been around for a while and get your perspective on the power of open source and you know, it used to be, you know, the the best secrets, the best I p were closely guarded and held inside. And now really, we're in an age where it's not necessarily and you know, the the brilliant minds and use cases and people out there. You know, just by definition, it's a It's a more groups of engineers, more engineers outside your building than inside your building and how that's really changed. You know, kind of the strategy in terms of development when you can leverage open source. >>Yeah, Open source clearly has has accelerated innovation across the industry in so many ways. Um, and it's ah, you know, it's the paradigm around which, you know companies have built business models and innovated on top of it. I think it's always important as a company to understand what value add, you're bringing on what value add that customers want to pay for what unmet needs and your customers are you trying to solve for and what's the best mechanism to do that? And do you want to spend your R and D recreating things or leveraging what's available and and innovating on top of it? It's all about ecosystems in the days where the single company can vertically integrate. I talked about him a complete end solution. You know those air few and far between. I think it's It's about collaboration and building ecosystems and operating within those. >>Yeah, it's it's It's such an interesting change. And one more thing again, to get your perspective, you run the data center group. But there's this little thing happening out there that we see growing in I o T Internet of things and the industrial Internet of things and edge computing. As we, you know, try to move more, compute and store and power, you know, kind of outside the pristine world of the data center and out towards where this data is being collected and processed when you've got latency issues and and in all kinds of reasons to start to shift the balance of where the computers aware that store Ah, and the reliance on the network. So when you look back from a storage perspective in your history in this industry and you start to see that basically everything is now going to be connected, generating data and and and a lot of it is even open source. I talked to somebody the other day doing, you know, kind of open source, computer vision on surveillance, you know, video. So, you know, the amount of stuff coming off of these machines is growing like crazy ways at the same time, you know, it can't all be processed at the data center. It can all be kind of shift back and then have you have a decision and then ship that information back out to. So when you sit back and look at the edge from your kind of historical perspective, what goes through your mind? What gets you excited? You know, what are some of the opportunities that you see that maybe the Lehman is not paying close enough attention to? >>Yeah, it's It's really an exciting time in storage. I get asked that question from time to time, having been in storage for more than 30 years, you know what was the most interesting time, and there's been a lot of them, but I wouldn't trade today's environment for any other in terms of just the velocity with which data is is evolving and how it's being used and where it's being used. You know that the TCO equation made describe what a data center looks like. But data locality will determine where it's located and we're excited about the edge opportunity. We see that as a pretty significant, meaningful part of the TAM. As we look out 3 to 5 years, certainly five G is driving much of that. I think just anytime you speed up the speed of the connected fabric, you're going to increase storage and increase the processing of the data. So the edge opportunity is very interesting to us. We think a lot of it is driven by low latency workloads. So the concept of envy any, um is very appropriate for that. We think in general SSD is deployed in in edge data centers defined as anywhere from a meter to a few kilometres from the source of the data. We think that's going to be a very strong paradigm. Um, the workloads you mentioned especially I O. T just machine generated data in general now I believe, has eclipse human generated data in terms of just the amount of data stored, and so we think that curve is just going to keep going in terms of machine generated data, much of that data is so well suited for zone story because it's sequential, it's sequentially written, it's captured, it's it has a very consistent and homogeneous lifecycle associated with it. So we think what's going on with with Zone storage in general and and Z and S and SMR specifically are well suited for where a lot of the data growth is happening. And certainly we're going to see a lot of that at the edge. >>Well, Phil, it's always great to talk to somebody who's been in the same industry for 30 years and is excited about today and the future on as excited as they have been throughout the whole careers. That really bodes well for you both. Well, for for Western Digital. And we'll just keep hoping the smart people that you guys have over there keep working on the software and the physics, Um, and then in the mechanical engineering to keep moving this stuff along. It's really ah, it's just amazing and just relentless. >>Yeah, it is. It is relentless. What's what's exciting to me in particular, Jeff is we've we've we've driven storage advancements, you know, largely through. As I said, a you know a number of engineering disciplines, and those are still going to be important going forward the chemistry of the physics, the electrical, the hardware capabilities. But I think, as you know, is widely recognized in the industry that it's a diminishing curve. I mean, the amount of energy, the amount of engineering, effort, investment, the cost and complexity of these products to get to that next capacity step, um, is getting more difficult, not less. And so things like zone storage where we now bring intelligent data placement to this paradigm is what I think makes this current juncture that we're at a very exciting >>right, Right. Well, it is applied ai, right. Ultimately, you're gonna have, you know, more more compute, you know, compute power. You know, driving the storage process and how that stuff is managed. And, you know, as more cycles become available and they're cheaper and ultimately compute, um gets cheaper and cheaper. You know, as you said, you guys just keep finding new ways to ah, to move the curve. And we didn't even get into the totally new material science, which is also, you know, come down the pike at some point in time. Well, >>very exciting. >>It's been great to catch up with you. I really enjoy the Western Digital story. I've been fortunate to to sit in on a couple chapters. So again, congrats to you. And, uh, we'll continue to watch and look forward to our next update. Hopefully, it won't be another four years. >>Okay. Thanks, Jeff. I really appreciate the time. All >>right. Thanks a lot. Alright. He's Phill. I'm Jeff. You're watching the Cube. Thanks for watching. We'll see you next time. Yeah, Yeah, yeah, yeah.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

(vibrant music) >> Everyone, welcome to our Palo Alto studio. I'm John Furrier host of theCUBE. We're here for a digital conversation. Part of our new digital events, part of our new structure of bringing people into the studio and also doing remotes. We'd love to do that in the era of the travel bans, but it's always great to have local Silicon Valley executives and startups here. Milin Desai, CEO of Sentry IO is here with me. Former VM-ware industry executive, CEO of Sentry IO hot startup. Thanks for coming in. >> Thank you for having me. >> So you can drive in. You don't have to fly anywhere. It's all good. No wearing masks. The coronavirus is crazy. I'm so glad we have you at this studio and get this content acquisition. Thanks for coming in. I want to get your take on your company before we get into the industry thing. I think you look at some of the most successful categories that just came out of nowhere. You know, you look at AIOps for instance in driving, you know, observability. But what is observability? That beginning, that comes with public page or do the list just goes on and on. The cloud has created this agile market where real time and then a lot of automation is going on so whether it's error logs like a Splunk does and that's scaled up. You get to doing something variation with software code that's not just something breaks, a phone rings. There's a lot a going on. You're this really kind of the tailwind here for you with cloud scale. What does Sentry doing? What's their secret sauce? >> So, the simplest way I would put it is we help you measure and monitor your code in production in close to real time. So what does that mean? You look at all, all of the companies that we talk about, whether it's a John Deere on one end or a Spotify on the other. They're all getting more digital in nature, which means they all trying to interact with their customers more often, building apps with an interface with an API. And as we all know, through our own personal experiences, if you don't get a great experience, you simply move on. So, you pull up your app, you pull up Uber, it's not working, let me look at Lyft. Right? That's the kind of consumer behavior that's starting to take in. >> So-- >> Meaning you don't really know as the owner of the app if they're abandoning or not, it's just down sales or? >> Correct. And so, what we do is we help developers monitor how the usages of their code in production. So, as users hit editors, a checkout button is not working or a user is having a bad experience on a mobile phone, whereas the same application on a browser looks fine. We in real time giving notification saying X number of users on this type of device, on this type of interface are having issues. And not just that, it's an alert, it's an alert that says this is the issue, this is the line of code where the issue's taking place, this is the potential commit that you did in your getRepository, which is causing it. So, it's the full kind of metadata around the issue. Which typically would be, what, two days? I take it as filed. Support me, look at it. Hey, customer has an issue, let's reproduce it. Well the customer is gone. So this is all done in real-- >> Or it could be a complete blindspot too. You don't know, right? This is the thing. This is why I love this whole digital transformation role where instrumentation is re-imagining how everything's being done. So for instance, you could see a code push and you go, okay, it's in production. And then why are sales down? Why is usage down? And then you've got to do a postmortem. >> Correct. >> No one called, just going what the hell happened? Fingers are blaming. He did it! Here you're trying to get to the point where you can see that error earlier or before or after, during as it work. >> It's almost in real time. Close to real time. As the user has the error immediately through either PagerDuty, Slack, email, whichever your communication medium is. You get to know a user or a set of users are having an issue. You click it, you go to this portal. All the metadata is right there. So, it's in real time. And so to exactly your point, it's not after the fact. >> Yeah. >> Right, it's happening. And so, the CTO of tackled.io, said it best, it's a startup that helps companies get on to marketplaces. He said, "Hey, we found issues before our customers even filed a issue against us." So, you know, this helps us deliver true customer experience, as a development team. >> So, on the developers that target profile get that and they're coding away. They don't have time to do research. They'll be like, "Oh, I better bolt on some instrumentation here." That's been the successful move. Look at like what Datadog has done in DevOps. Just the easy onboarding, free use it. Is that the same model you guys are taking this free land, adopt then expand. So, is it a freemium, could you explain the business model? >> Yeah, so, a Sentry is a open source. And so customers can take the piece of software that we have as is, fully functional and run it themselves on their data center on their cloud, or they can choose a SaaS version from us and we offer kind of like a free version and then you pay for the plan. So, what we typically see is customers turn it on, developers turn it on and they like it. And then, the best score I got recently was, one CEO who said, "Hey, you know, I don't send you that many events, but I see the value of what you do, so I decided to pay you." Right, so, they went from free to paid. And that's kind of typical pattern that we see. And the best thing about this is, it takes you approximately four lines of code to get started. Four lines of code in your code and you get started getting the benefits of Sentry. >> What's good sign for monetization when you got the paying it forward literally with cash. I want to ask you the difference between the open source version because I saw in the origination story it's really interesting. They were at jobs and they saw this side project grow into a real opportunity. And it's always good to see the open source not die, right. So, this been maintain the project. When would someone use the open sources? Is that the hardcore folks or, so SaaS, obviously makes sense. It's easier if you're doing a lot of the extra support and whatnot on top of it. But what's the use case for the folks who are going to bring it in house loaded on their cloud? >> I think we'll leave it to our customers to decide that. And we've seen, folks who say, "Hey, you know, we have, we're going to try it out, it's a small, we have got a good DevOps practice. We're going to get it up and running." Here's what happened with one of my teams at VMware. The engineer in charge looked at it and said, "It's not worth my time given what the price on SaaS is." Right, so, like our smallest plan is $29, which satisfies most startups or small software projects. And his point was like, "Hey, you know, it's almost better for me to start and using that versus--" >> Well they weren't using NSX. I'm sure Pat Gels would be like, "Get shipped the next product." Well this is the trade off, right? I mean, so that's what's beautiful of open source. You want to bring it in and make it work for yourself. That trade off has to be economically there. >> Correct. >> So you have a nice balance of if you're hardcore, no problem. >> Please use-- >> Use it, contribute, be part of the team. But if you want ease of use and all the bells and whistles and the speed. >> I think it comes down to what we are starting to see, which is, how much do you care about getting to value faster and where is your value? Is it in kind of running and operating all these pieces of software or is it in, you know, getting value to your end customer? So, if you are focused on building your business, we are this value add that kind of gets you there faster. So, stop focusing on kind of building the infrastructure. Start delivering kind of the value to the business. >> So I'm going to ask you, so, are you the CEO? So the founders who I've not met. I look forward to interviewing them. They seem pretty cool. I'm sure they probably say, "Oh this guy from VMware, he's probably the big company guy." 'Cause they were like, we're going to Dropbox now. Engineers, I could almost imagine their, what they're like. Probably skeptical, this is VMware guy. How did you get through the interview process? Obviously, you're the CEO, you made it. Were they skeptical ? What worked? Why you, why'd you go there? >> You know, the best thing about this transition is Chris and David. So, David was the CEO. He is now the CTO. He's the founder creator along with Chris. And it was his decision, to bring someone into the company, given that we are seeing this, you know, we are now at 20000 plus customers and he felt like he wanted to kind of go back to building and creating and bring a partner in crime. So, that was the good part. I would say like, we started talking and we are at the same energy level, you know? So, I think it just worked out in the way we communicated. And you've known me for a bit. I'm kind of hands on. I like, you know, to kind of get into things and build businesses. So, I think the profile matched out and both of us took our time. So it was, a long dating process, where we got to know each other. Not just as, you know, what we do for work. But, you know, how we operate and had coffee and lunch and dinner and--- >> Well, it is a dating, dating and marriage is always thinking, but the founders are, it's a tough move to make. I mean, for founders to be self-aware, to bring in someone else. But also the fit has to be there. And a lot of entrepreneurs just check the box and try to hire someone too fast that could fail or gets jammed down by the VCs, you know. So, the founders are pretty kind of reluctant. So, that's interesting that you did that. >> Yeah, he's been thinking. You know, the thing about David is he's super thoughtful and hopefully you'll get to see him soon. He's been thinking about this for a bit. And he took his time. And he worked through the process and that's why I said it felt like we were not just talking about, me joining as a CEO, as much as us getting to know each other and building this for the long run. And so we really took our time on both ends--- >> And he want to to get back on the engine of the business? He's a developer, right? He's like the code. >> Just don't want to, >> It was-- >> 20000 customers, you going to get hiring people. It's HR issues. This probably, I don't want to do that. >> That and you know it was kind of the personality thing, right? Grit and grind, you know. We kind of, can somebody come in and have the passion, the same that he believes in what we do. And he saw that and I saw that in him and I'm like, this is a great opportunity that I cannot forego. >> So talk about the, I say love modern, the modern startups because, you know, you're on the right side of history when you got cloud at your tailwind and kind of DevOps, like vibe you get going on with, I know it's not DevOps, but it's common like cloud scale and the agility. How are you guys organized? You guys have virtual teams. You have a central office. Is there a physical place? Do people come in? What's the, how is the company's philosophy on work environment? >> So, we actually have three locations. One in San Francisco, which is the headquarters, where we are located. And then in Vienna, Austria, where one of the early engineers and pioneers live. And so we built around that person and that location. >> No one's complaining about that. >> No. >> Vienna's not a bad place there-- >> Not a bad place. I haven't visited yet. (laughs) I am looking forward to it. I was supposed to be there in April, but, given the circumstances, I'm postponing it. And we recently started this past year in Toronto. And so, we are--- >> So three strong areas for tech talent for sure. >> And then we do have some employees working from home. So, we try and hire the best, and then we accommodate. But we do try to kind of cluster around these three locations. >> So, I got to get your take as the CEO, obviously we're all grappling with this, work at home, Covid 19, the coronavirus, is impacting. Everything's being canceled here in Silicon Valley. I would say Seattle has more of a hotspot than our area. Mostly China as China. What's the view that you guys are taking right now? You're telling people who work at home. Obviously, events are being canceled. Places where people doing Biz Dev, KubeCon was canceled, Dell Technology World is can-- I mean everything's being canceled. How's that affecting your business and what's your philosophy? How are you guys are executing through this tough time? >> I think as a company we've kind of taken the step for having people work from home and we did it on a location by location basis. So, for folks in San Francisco, especially because folks who are commuting on public transportation and other things. We wanted to make our team feel comfortable. And so we've instituted a work from home policy, for, I think we said two weeks, but I think it's going to keep going until we get a clear signal from the government, both locally and at the federal level. So that's kind of where we are as a team. And then what we noticed was the Austrian government kind of had similar regulations of everyone's working from home. Slack, you know, Google Hangouts. We spending a lot of time on video, making sure we are connected as a team. And you know, just that spirit of how we operate and talk to each other continues. As a business, we are a bottoms up business. So, what I mean by that is folks sign up, they use the product. And developers are right now globally still fully functional. The only difference being they're now working from home. So we feel like as a business, we'll be fine. And we are ensuring that our customers through this transition and through this period of kind of unknowns are able to continue to be successful for their customers. >> It's funny, I was talking with someone, it's like there's going to be some, obviously, sectors, like events are going to take a big hit. South by got canceled, Coachella's being canceled. All the tech events are being canceled. That's why we're going to be doing our stuff at the studio with virtual events, for theCUBE. But certain things are going to be different. You going to see pregnancy, boom. You know, nine months later, people are going to be having kids cause they're home alone or divorces depending on how you look at it. But productivity, developer wise has been talked about as actually developers want to just crank out some code. They don't have to come into the office. You can be more, I mean you can still be productive. Developers have been doing this for decades. >> I think-- >> At least if they are more. >> You know, I think you, you know, I think there might be a scenarios of adjustment, a period of adjustment. And then folks will get comfortable. So, it's super important to create that engagement model. Whether, do you have the tooling to keep the team engaged. And there companies that are completely remote. And so we're making sure we learn from their best practices around that. But I do believe that, for tech companies or even for manufacturing companies focused on building software, developers are going to be productive. >> Okay, so a baby boom's coming, divorce rate's going to go up and productivity is skyrocketing. (both laugh) >> For developers. >> For developers. Well, I mean it's a good time. Okay, can I get your take on the industry now. Honestly, putting all the coronavirus aside, we saw a surge in public cloud check. Done. And ask you when your VMware with NSX coming in and becoming the engine with software defined networking as part of the Series piece. You're starting to see hybrid clear as day. It's going to happen. Multi clouds on the horizon. So, you now have a three wave cloud game going on. Wave one, done. Wave two is hybrid. Wave three maybe bigger than them all with multicloud. Do you agree with that trend analysis and what's your take on that? >> So, this is where I'll probably kind of look back at my time at VMware. I think, you know, definitely see the multicloud wave catching on. But I would use the word multicloud as in, not a app spread across three clouds as much as, you know, a company choosing to have a certain assets in AWS, certain assets in Azure, certain in Google. So, I don't see yet this idea of an app being stretched across the three clouds but definitely, while I was-- >> VMware tried that. (both laugh) >> While I was at VMware and in talking to customers, we definitely saw adoption of multiple clouds. And that's where when I was working with the cloud health team, this idea of managing cost and security across three clouds became very common as a pattern that came up. You definitely see that as a kind of directional thing that a lot of organizations are doing. >> Yeah, the idea of just rapidly shifting up workloads based on pricing, all that stuff. I think it's aspirational at best because development teams are now just getting their groove on with hybrid and operation, cloud operations. So, I can see a day where if you can manage the latency network issues, maybe some day, but I mean, come on, really? I think about how hard that is, just latency alone. >> And the issue is like, architecturally you have to make really good choices to get there. So, I think you might see that in like kind of tech software firms. We're thinking about, how do I stay cloud neutral? But for the most part, if you want to take the full value of AWS or full value of GCP, you want to go deeper in there. And use all their services. >> Yeah, I think that's great insight. Let's riff on that a little bit because one of the things I was talking to Dave Alante and Stu Miniman about was, if you look at the multicloud, I don't think it's going to come from a vendor. I think if you look at the success of the Facebooks of the world, even Dropbox where your founders came from, early on, they had to just basically build it from cloud native, from ground up. And all the hyper scalers use open source. They built all their stuff. No one was selling them anything. They just did it. So, I think you'll see smart architectural moves, but that'll be the unicorn. That'll not be the standard. That'll be the exception, not the rule. I don't think you can sell multicloud, in my opinion, yet, or I don't think that'll even be possible. But I think someone will come out and say, make those architectural decisions saying, "I have an architecture that works multicloud because we architect it that way." >> Yup, yup. And I think that's kind of the more, kind of from an engineering standpoint, I think you'll see more of that. I think from a, you know, from a kind of solution standpoint, you will see folks saying, "I will help you manage or secure or build into each of the clouds and give you kind of common pattern versus the latter of it." And engineering team says, "Here's a way to architect for multicloud." >> You know, we pay a lot of attention to the next gen kind of psychologies. Obviously, we do a lot of coding on with our cube cloud that's coming out now. But, how do you see the founders you're working with and that in this new peer group that's developing. I call it, the next gen entrepreneur, technical entrepreneur. As they look at the vast resources of cloud and all of the data opportunities there and mobility, internet things and all this stuff going on. What is the general mindset right now of these kinds of entrepreneurs from a technology perspective? How are they looking at the problem space? What's your take on this new landscape as an entrepreneur? >> Yeah, I'll give you kind of what got me super excited about Sentry. Like how, why did I think about that? Which is if you look at 2000 to 2010, we did software defined infrastructure. Things started moving into software. 2010 to 2020 was, as you correctly wanted a cloud, hybrid, everything became kind of as a service. I think this next decade will be about data. So, companies using the data to get a competitive advantage or figuring out, you know, how to stay ahead, whether it's competitively or even to win a market. And the other aspect of this is because everything is so, as a service, API centric, I think it's going to explode how we develop things. And I think this is going to be truly now the decade for the developer, who's going to make deeper choices, greater choices, buying decisions. And so, with data kind of exploding, and the management of it and getting insights out of it is one aspect of it. And, you know, as somebody who's looking at Sentry, we do a lot of that, right? Which is how are customers using it? What are they using? What languages? And everything else that goes with that. But on the other end, developers are going to start kind of using things and create a whole new set of use cases that's going to change the way we think about it. So I think there's a whole set of elements around how to use this infrastructure to build new applications, creative products, that is going to be a massive boom. >> I think that's a great point. I think that's great insight. Because you think about observability, which I was just joking earlier on about, but I think the relevance observability is network management applied to value real time, right? Because if you can instrument everything, the smart people are going to saying, "Hey, I can just instrument this and get the data I need rather than dealing with this hassle process we had before." So, it brings up that kind of philosophy of kill the old to bring in the new or something new that kills the old. So, it's an interesting phenomenon. I think it's very relevant. But I want to get your, question as a CEO now, you've got, you're at the helm, helm of a company is technical. And talking about architecture, what's your architecture for the venture? What's your plans? How do you see the, you said you're going to come and build this next level growth. What's your architecture look like? Are you going to, do more of the same? Any new things that we see? What are you going to... What's your plan? >> Fundamentally, you know, we as a kind of set of users in the world today, have spent a lot of time monitoring, as I told you earlier, machines, systems and applications, right? And so there's a lot of successful companies doing that. But if you fundamentally believe that this is the decade where you're going to write more code than we've ever before or refresh more applications than we've ever before. Our focus is code and how it does whether it's in a staging environment, in a canary deployment, or in production. How do we measure code and monitor code in production. And the impact of that code to the end users. So it could be errors and now increasingly code performance. So you will see us kind of venture into this idea of helping developers. Not only find issues that they run into production like we talked about before, but also be able to say, looks like over the past three releases, our logins per second have gone down progressively by 10%. Why is that happening? Where is that happening? Which team made that change? So, you will see us kind of really double down on this idea of measuring and monitoring code going forward, complimenting how we measure monitor systems, machines and applications today. >> Yeah, I mean, code has got to be managed, as people more, people contribute. It's like a compiler for the compiler. (laughs) >> It's like if code fails, your business-- >> Code for the code. >> Yeah. >> Meta three meta meta as they say, but code for the code. But that's, it's basically code management in a way, right? It's the code data. You're leveraging that code relationship to the application. >> And so we talk about applications a lot. And so we write code, we store code, you know, in a getRepository. Now there's a whole set of elements around securing it. We deploy it. What about measuring and monitoring it? That is the element where we focus and kind of bring that whole cycle together. Helping that application developer be successful. >> What's it like for you going from VMware to the startup? What's the biggest, coolest thing that's happened? >> It's been a great transition. You know, and I always say this to folks who ask me for career advice. They say, always choose the people you work with and the people you work for. And I've been fortunate enough to do that and I think this transition has been great for that reason alone. Which is I've had the time to get to know the team at Sentry. They got to know me and it's just been, it's been fantastic. I think the velocity of and the pace at which I can make changes, has been the most fun part of it. >> And you've got like 25, 20000 paying customers 50000 total customers roughly in that range. Pretty sizeable. Employee count, how many employees do you have? >> 100 plus employees and-- >> Still small, still small. >> Yeah, still small. And we're going to probably double this year, give or take. And you know, it's 20000 customers from every startup. I've spoken to a startups, over 100 startups in two months. And it's amazing to see their reaction and their love for Sentry. >> And funding, how many rounds of funding have you guys done? >> We just finished Series C, in September of last year. 40 million, any Accel growth. So, we feel really good about where we are. With the revenue ramp that we've seen, we're in great shape. >> And pretty good numbers in terms of a head count too, very leveraged SaaS model. Get the developers. >> Yes. >> Great. Well, we're going to be entertaining a lot of developers at DockerCon this year. DockerCon used to be an event for Docker. Now they sold half the business to Mirantis. They're focusing on Docker developers. We have an event here. We're doing a virtual event. So, a lot more developer action coming. We'll talk more about that. Love to meet your founders, have them come in too. We want to thank you for coming on. >> Thank you. >> Milin Desai, CEO of sentry.io, former VMware executive with a great hot startup, Series C funded, growing here in Silicon Valley, San Francisco and in Austria. I'm John Furrier with theCUBE. Thanks for watching. (vibrant music)

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to MIT CDOIQ. The CDO Information Quality Conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host, Paul Gillin. This is our day two of our two day coverage. Jean Ross is here. She's the principle research scientist at MIT CISR, Jean good to see you again. >> Nice to be here! >> Welcome back. Okay, what do all these acronyms stand for, I forget. MIT CISR. >> CISR which we pronounce scissor, is the Center for Information Systems Research. It's a research center that's been at MIT since 1974, studying how big companies use technology effectively. >> So and, what's your role as a research scientist? >> As a research scientist, I work with both researchers and with company leaders to understand what's going on out there, and try to present some simple succinct ideas about how companies can generate greater value from information technology. >> Well, I guess not much has changed in information technology since 1974. (laughing) So let's fast forward to the big, hot trend, digital transformation, digital business. What's the difference between a business and a digital business? >> Right now, you're hoping there's no difference for you and your business. >> (chuckling) Yeah, for sure. >> The main thing about a digital business is it's being inspired by technology. So in the past, we would establish a strategy, and then we would check out technology and say, okay, how can technology make us more effective with that strategy? Today, and this has been driven a lot by start-ups, we have to stop and say, well wait a minute, what is technology making possible? Because if we're not thinking about it, there sure are a lot of students at MIT who are, and we're going to miss the boat. We're going to get Ubered if you will, somebody's going to think of a value proposition that we should be offering and aren't, and we'll be left in the dust. So, our digital businesses are those that are recognizing the opportunities that digital technologies make possible. >> Now, and what about data? In terms of the role of digital business, it seems like that's an underpinning of a digital business. Is it not? >> Yeah, the single biggest capability that digital technologies provide, is ubiquitous data that's readily accessible anytime. So when we think about being inspired by technology, we could reframe that as inspired by the availability of ubiquitous data that's readily accessible. >> Your premise about the difference between digitization and digital business is interesting. It's more than just a sematic debate. Do companies now, when companies talk about digital transformation these days, in fact, are most of them of thinking of digitization rather than really transformative business change? >> Yeah, this is so interesting to me. In 2006, we wrote a book that said, you need to become more agile, and you need to rely on information technology to get you there. And these are basic things like SAP and salesforce.com and things like that. Just making sure that your core processes are disciplined and reliable and predictable. We said this in 2006. What we didn't know is that we were explaining digitization, which is very effective use of technology in your underlying process. Today, when somebody says to me, we're going digital, I'm thinking about the new value propositions, the implications of the data, right? And they're often actually saying they're finally doing what we thought they should do in 2006. The problem is, in 2006, we said get going on this, it's a long journey. This could take you six, 10 years to accomplish. And then we gave examples of companies that took six to 10 years. LEGO, and USAA and really great companies. And now, companies are going, "Ah, you know, we really ought to do that". They don't have six to 10 years. They get this done now, or they're in trouble, and it's still a really big deal. >> So how realistic is it? I mean, you've got big established companies that have got all these information silos, as we've been hearing for the last two days, just pulling their information together, knowing what they've got is a huge challenge for them. Meanwhile, you're competing with born on the web, digitally native start-ups that don't have any of that legacy, is it really feasible for these companies to reinvent themselves in the way you're talking about? Or should they just be buying the companies that have already done it? >> Well good luck with buying, because what happens is that when a company starts up, they can do anything, but they can't do it to scale. So most of these start-ups are going to have to sell themselves because they don't know anything about scale. And the problem is, the companies that want to buy them up know about the scale of big global companies but they don't know how to do this seamlessly because they didn't do the basic digitization. They relied on basically, a lot of heroes in their company to pull of the scale. So now they have to rely more on technology than they did in the past, but they still have a leg up if you will, on the start-up that doesn't want to worry about the discipline of scaling up a good idea. They'd rather just go off and have another good idea, right? They're perpetual entrepreneurs if you will. So if we look at the start-ups, they're not really your concern. Your concern is the very well run company, that's been around, knows how to be inspired by technology and now says, "Oh I see what you're capable of doing, "or should be capable of doing. "I think I'll move into your space". So this, the Amazon's, and the USAA's and the LEGO's who say "We're good at what we do, "and we could be doing more". We're watching Schneider Electric, Phillips's, Ferovial. These are big ole companies who get digital, and they are going to start moving into a lot of people's territory. >> So let's take the example of those incumbents that you've used as examples of companies that are leaning into digital, and presumably doing a good job of it, they've got a lot of legacy debt, as you know people call it technical debt. The question I have is how they're using machine intelligence. So if you think about Facebook, Amazon, Microsoft, Google, they own horizontal technologies around machine intelligence. The incumbents that you mentioned, do not. Now do they close the gap? They're not going to build their own A.I. They're going to buy it, and then apply it. It's how they apply it that's going to be the difference. So do you agree with that premise, and where are they getting it, do they have the skill sets to do it, how are they closing that gap? >> They're definitely partnering. When you say they're not going to build any of it, that's actually not quite true. They're going to build a lot around the edges. They'll rely on partners like Microsoft and Google to provide some of the core, >> Yes, right. >> But they are bringing in their own experts to take it to the, basically to the customer level. How do I take, let me just take Schneider Electric for an example. They have gone from being an electrical equipment manufacturer, to a purveyor of energy management solutions. It's quite a different value proposition. To do that, they need a lot of intelligence. Some of it is data analytics of old, and some of it is just better representation on dashboards and things like that. But there is a layer of intelligence that is new, and it is absolutely essential to them by relying on partners and their own expertise in what they do for customers, and then co-creating a fair amount with customers, they can do things that other companies cannot. >> And they're developing a software presumably, a SAS revenue stream as part of that, right? >> Yeah, absolutely. >> How about the innovators dilemma though, the problem that these companies often have grown up, they're very big, they're very profitable, they see disruption coming, but they are unable to make the change, their shareholders won't let them make the change, they know what they have to do, but they're simply not able to do it, and then they become paralyzed. Is there a -- I mean, looking at some of the companies you just mentioned, how did they get over that mindset? >> This is real leadership from CEO's, who basically explain to their boards and to their investors, this is our future, we are... we're either going this direction or we're going down. And they sell it. It's brilliant salesmanship, and it's why when we go out to study great companies, we don't have that many to choose from. I mean, they are hard to find, right? So you are at such a competitive advantage right now. If you understand, if your own internal processes are cleaned up and you know how to rely on the E.R.P's and the C.R.M's, to get that done, and on the other hand, you're using the intelligence to provide value propositions, that new technologies and data make possible, that is an incredibly powerful combination, but you have to invest. You have to convince your boards and your investors that it's a good idea, you have to change your talent internally, and the biggest surprise is, you have to convince your customers that they want something from you that they never wanted before. So you got a lot of work to do to pull this off. >> Right now, in today's economy, the economy is sort of lifting all boats. But as we saw when the .com implosion happened in 2001, often these breakdown gives birth to great, new companies. Do you see that the next recession, which is inevitably coming, will be sort of the turning point for some of these companies that can't change? >> It's a really good question. I do expect that there are going to be companies that don't make it. And I think that they will fail at different rates based on their, not just the economy, but their industry, and what competitors do, and things like that. But I do think we're going to see some companies fail. We're going to see many other companies understand that they are too complex. They are simply too complex. They cannot do things end to end and seamlessly and present a great customer experience, because they're doing everything. So we're going to see some pretty dramatic changes, we're going to see failure, it's a fair assumption that when we see the economy crash, it's also going to contribute, but that's, it's not the whole story. >> But when the .com blew up, you had the internet guys that actually had a business model to make money, and the guys that didn't, the guys that didn't went away, and then you also had the incumbents that embrace the internet, so when we came out of that .com downturn, you had the survivors, who was Google and eBay, and obviously Amazon, and then you had incumbent companies who had online retailing, and e-tailing and e-commerce etc, who thrived. I would suspect you're going to see something similar, but I wonder what you guys think. The street today is rewarding growth. And we got another near record high today after the rate cut yesterday. And so, but companies that aren't making money are getting rewarded, 'cause they're growing. Well when the recession comes, those guys are going to get crushed. >> Right. >> Yeah. >> And you're going to have these other companies emerge, and you'll see the winners, are going to be those ones who have truly digitized, not just talking the talk, or transformed really, to use your definition. That's what I would expect. I don't know, what do you think about that? >> I totally agree. And, I mean, we look at industries like retail, and they have been fundamentally transformed. There's still lots of opportunities for innovation, and we're going to see some winners that have kind of struggled early but not given up, and they're kind of finding their footing. But we're losing some. We're losing a lot, right? I think the surprise is that we thought digital was going to replace what we did. We'd stop going to stores, we'd stop reading books, we wouldn't have newspapers anymore. And it hasn't done that. Its only added, it hasn't taken anything away. >> It could-- >> I don't think the newspaper industry has been unscathed by digital. >> No, nor has retail. >> Nor has retail, right. >> No, no no, not unscathed, but here's the big challenge. Is if I could substitute, If I could move from newspaper to online, I'm fine. You don't get to do that. You add online to what you've got, right? And I think this right now is the big challenge. Is that nothing's gone away, at least yet. So we have to sustain the business we are, so that it can feed the business we want to be. And we have to make that transition into new capabilities. I would argue that established companies need to become very binary, that there are people that do nothing but sustain and make better and better and better, who they are. While others, are creating the new reality. You see this in auto companies by the way. They're creating not just the autonomous automobiles, but the mobility services, the whole new value propositions, that will become a bigger and bigger part of their revenue stream, but right now are tiny. >> So, here's the scary thing to me. And again, I'd love to hear your thoughts on this. And I've been an outspoken critic of Liz Warren's attack on big tech. >> Absolutely. >> I just think if they're breaking the law, and they're really acting like monopolies, the D.O.J and F.T.C should do something, but to me, you don't just break up big tech because they're good capitalists. Having said that, one of the things that scares me is, when you see Apple getting into payment systems, Amazon getting into grocery and logistics. Digital allows you to do something that's never happened before which is, you can traverse industries. >> Yep. >> Yeah, absolutely >> You used to have this stack of industries, and if you were in that industry, you're stuck in healthcare, you're stuck in financial services or whatever it was. And today, digital allows you to traverse those. >> It absolutely does. And so in theory, Amazon and Apple and Facebook and Google, they can attack virtually any industry and they kind of are. >> Yeah they kind are. I would certainly not break up anything. I would really look hard though at acquisitions, because I think that's where some of this is coming from. They can stop the overwhelming growth, but I do think you're right. That you get these opportunities from digital that are just so much easier because they're basically sharing information and technology, not building buildings and equipment and all that kind of thing. But I think there all limits to all this. I do not fear these companies. I think there, we need some law, we need some regulations, they're fine. They are adding a lot of value and the great companies, I mean, you look at the Schneider's and the Phillips, yeah they fear what some of them can do, but they're looking forward to what they provide underneath. >> Doesn't Cloud change the equation here? I mean, when you think of something like Amazon getting into the payments business, or Google in the payments business, you know it used to be that the creating of global payments processing network, just going global was a huge barrier to entry. Now, you don't have nearly that same level of impediment right? I mean the cloud eliminates much of the traditional barrier. >> Yeah, but I'll tell you what limits it, is complexity. Every company we've studied gets a little over anxious and becomes too complex, and they cannot run themselves effectively anymore. It happens to everyone. I mean, remember when we were terrified about what Microsoft was going to become? But then it got competition because it's trying to do so many things, and somebody else is offering, Sales Force and others, something simpler. And this will happen to every company that gets overly ambitious. Something simpler will come along, and everybody will go "Oh thank goodness". Something simpler. >> Well with Microsoft, I would argue two things. One is the D.O.J put some handcuffs on them , and two, with Steve Ballmer, I wouldn't get his nose out of Windows, and then finally stuck on a (mumbles) (laughter) >> Well it's they had a platform shift. >> Well this is exactly it. They will make those kind of calls . >> Sure, and I think that talks to their legacy, that they won't end up like Digital Equipment Corp or Wang and D.G, who just ignored the future and held onto the past. But I think, a colleague of ours, David Moschella wrote a book, it's called "Seeing Digital". And his premise was we're moving from a world of remote cloud services, to one where you have to, to use your word, ubiquitous digital services that you can access upon which you can build your business and new business models. I mean, the simplest example is Waves, you mentioned Uber. They're using Cloud, they're using OAuth.in with Google, Facebook or LinkedIn and they've got a security layer, there's an A.I layer, there's all your BlockChain, mobile, cognitive, it's all these sets of services that are now ubiquitous on which you're building, so you're leveraging, he calls it the matrix, to the extent that these companies that you're studying, these incumbents can leverage that matrix, they should be fine. >> Yes. >> The part of the problem is, they say "No, we're going to invent everything ourselves, we're going to build it all ourselves". To use Andy Jassy's term, it's non-differentiated heavy lifting, slows them down, but there's no reason why they can't tap that matrix, >> Absolutely >> And take advantage of it. Where I do get scared is, the Facebooks, Apples, Googles, Amazons, they're matrix companies, their data is at their core, and they get this. It's not like they're putting data around the core, data is the core. So your thoughts on that? I mean, it looks like your slide about disruption, it's coming. >> Yeah, yeah, yeah, yeah. >> No industry is safe. >> Yeah, well I'll go back to the complexity argument. We studied complexity at length, and complexity is a killer. And as we get too ambitious, and we're constantly looking for growth, we start doing things that create more and more tensions in our various lines of business, causes to create silos, that then we have to coordinate. I just think every single company that, no cloud is going to save us from this. It, complexity will kill us. And we have to keep reminding ourselves to limit that complexity, and we've just not seen the example of the company that got that right. Sooner or later, they just kind of chop them, you know, create problems for themselves. >> Well isn't that inherent though in growth? >> Absolutely! >> It's just like, big companies slow down. >> That's right. >> They can't make decisions as quickly. >> That's right. >> I haven't seen a big company yet that moves nimbly. >> Exactly, and that's the complexity thing-- >> Well wait a minute, what about AWS? They're a 40 billion dollar company. >> Oh yeah, yeah, yeah >> They're like the agile gorilla. >> Yeah, yeah, yeah. >> I mean, I think they're breaking the rule, and my argument would be, because they have data at their core, and they've got that, its a bromide, but that common data model, that they can apply now to virtually any business. You know, we're been expecting, a lot of people have been expecting that growth to attenuate. I mean it hasn't yet, we'll see. But they're like a 40 billion dollar firm-- >> No that's a good example yeah. >> So we'll see. And Microsoft, is the other one. Microsoft is demonstrating double digit growth. For such a large company, it's astounding. I wonder, if the law of large numbers is being challenged, so. >> Yeah, well it's interesting. I do think that what now constitutes "so big" that you're really going to struggle with the complexity. I think that has definitely been elevated a lot. But I still think there will be a point at which human beings can't handle-- >> They're getting away. >> Whatever level of complexity we reach, yeah. >> Well sure, right because even though this great new, it's your point. Cloud technology, you know, there's going to be something better that comes along. Even, I think Jassy might have said, If we had to do it all over again, we would have built the whole thing on lambda functions >> Yeah. >> Oh, yeah. >> Not on, you know so there you go. >> So maybe someone else does that-- >> Yeah, there you go. >> So now they've got their hybrid. >> Yeah, yeah. >> Yeah, absolutely. >> You know maybe it'll take another ten years, but well Jean, thanks so much for coming to theCUBE, >> it was great to have you. >> My pleasure! >> Appreciate you coming back. >> Really fun to talk. >> All right, keep right there everybody, Paul Gillin and Dave Villante, we'll be right back from MIT CDOIQ, you're watching theCUBE. (chuckles) (techno music)

>> from our studios in the heart of Silicon Valley. PALO ALTO, California It is a cute conversation, >> living welcome to this special Cuba conversation in Palo Alto, California We're here with our friends on Twitter and influences in the cloud computing edge and open source game. We have our distinguished power panel here talking about if every tech company, every company should be a tech company. And what does it mean in the air of a modern infrastructure? Police to have my kale with ct of everest dot org's from most Gatto's California Rob Hirschfeld, founder and CEO of Rock n Calling in From Where You Calling in from >> Austin, Texas. >> Austin, Texas. Good to have you and Mark Theo Who's with EJ Gravity brand New opportunity. Congratulations calling in Las Vegas. Thanks for coming in, guys. Thanks for spending the time on this cube power panel from the influencers. Always great to see you guys on Twitter with this morning. I woke up, was very active at a Crouch said earlier this morning. And Mark, you wrote a post that got my attention. So I think you hit a nerve that has been sparking around the Internets around the role of technology as couples, they're starting to rethink and building out there enterprise architectures in their businesses. And we're seeing some signals around cybersecurity. Dev Ops certainly has been kind of banging on this drum with cloud computing, and that is that the role of technology plays as a percentage of the business part of the business. And your tweet was simply put, you said every bit. If every business needs to become a tech business, it business has to decide to own its own infrastructure something of that effect, which which triggered me because it's like That's a good question. It isn't just a part of an organization supporting it. Tech is becoming much more instrumental. So I want to get your reaction. What was the motivation behind that tweet? What's your what's your What was your point around it? >> Yeah, I mean, like many of my tweets, they're poorly worded and rushed out, so you know, it's not as clear as it could have been. But the real point of the message wasn't Thio highlight that a technology company has to be all in the cloud or has to own its infrastructure, but rather as a company makes a change towards becoming a technology company. I mean, if we go back Thio you know, 1995 or 1996 when we wanted a library, we went to the library. But now we have Google. We didn't know that Google was gonna become an online the equivalent of a library. But it became a digital company before anybody asked for that solution or anybody was running that kind of solution in some sort of company format and then changed it over. But, you know, Google Facebook, Microsoft's into it. Adobe PayPal. We could go down the long list there. All I t cos in the end, whether you call the technology that they built to run their businesses engineering with a CTO or I t. Is the material. They are in fact, large giant I t organizations that do what they do to make money. And so, as more companies look to make the change as digital transformation takes hold as more efforts are presented to try to get a closer handle on customers to build loyalty with customers, create new engagement models, maybe at the edge, even in traditional application environments, then companies have to make a decision about how they're going toe oh, nightie and whether they're goingto own any portion of the infrastructure of I T. And if they're going to do that, then I don't think that there's any question that they have to own it. Atleast following a model of the way the large providers and the facebooks, et cetera have provided for us cannot continue. In other words, what I've been known to say before, we can't continue to throw more hardware and people at the problem. >> My mike, I want to get your thoughts on this because one of the things that I know you have been involved a lot with security on dhe I t. As well in security, which which is a canary in the coal mine. For a lot of these architectural decisions are all kind of looking at how they hire and build on premise in house around tech stacks. And one of the things that became apparent to me at Amazon Aws reinforce, which is their Amazons first cloud security conference, was most of the ceases. When I talk privately was saying, we don't really believe in multi cloud. We have multiple clouds, but We're investing in people on certain stacks that fit our guiding principles of what we're building as a company. And they said we then go to the suppliers and saying, Here's the AP eyes we want you to support So you start to see the shift from being hiring the general purpose software vendors to come in and supply them with I t stuff Were hardware. As Mark pointed out, too much more, the customer saying No, no, this is our spec build that we built it. And so the trend that points to the trend of a reinvestment of building tech at the core of the business, which would imply to Mark's point around their tech companies. What's your thoughts on this? >> So a nuance. My answer. I think their tech enabled companies more than tech companies like Tech is enabling, whether it's Google or into it or pay power of the other companies. Mark mentioned technologies the base of their companies stack, um, then to go into your security portion, security has to be architected and embedded into the core solutions not bolted on after the fact with vendor solutions like it is today, and I think we've proven time and time again, including the capital one issue as a day or two ago that the current approaches are not working. And, uh, I agree with whomever See says you've been talking thio like being driving a P I integrations and be consumptive of them and telling what you need to build is a much better approach. Would you want to build a custom house with that actually talking to your builder and finding out later? What? What features and pictures have been installed in your home. But what do you wanna have a hand in that from the ground up? I think that's the mischief. >> Well, I want to come back to the capital. One point that's gonna be a separate talk track. So let's hold that thought. Rob, I want to go to you. Because StarBeat Joel, whose prolific on these threads you know, posting is nice Twitter cards on their um, he said, If you know, talk about leasing out extra capacity in a private data centers question Mark, you know, teasing out the question. And then Ben Haines responded and said, Why the hell would you want to be in that business when you have a real business to run again to what Mark was saying about, You know, Tech is going to be everywhere. Why should I even be in the data center? Because I don't want to be in that business. I gotta figure out Tech for the business. So Ben kind of brings that practitioner perspective. What's your thought? Because you're in the middle of this with the devil's movement. Bare metal, big part of it, Your thoughts. >> Yeah, And that's why we really focus on fixing the bear mental problem. Andi, I want to come back to where a bear metal fits with all this because you really can't get away from bare metal. I think the first question is really is every day to send is every business in I t business. And you know, not every business is a Google and strictly a nighty business. But what we're seeing with machine learning and Internet of things and just extension of what was traditionally siloed I t or data center, I t into everyday operations. You can't get away from the fact that if you're not able to take in the data, work with the data, manipulate and understand what your customers were doing. Then you are going to be behind. That's That's how you're gonna lose. You're gonna be out of business on. So I think that what we're doing is we're redefining business into not just a product that you're selling, but understanding how your customers air interacting with that product, what value they're getting from it. We really redefined supply chain in a very transformative way compared to anything else. And that's an I T enabled transformation. >> Ben brings up a good point, but the Brent wanted Friends Point is essentially teasing out mark and yourself a bare metal. All this stuff is complicated. Cut and make investments. Ben's teasing as What the hell business do you want to be in? I think that becomes a lot of this digital transformation. Conversation is Hey, Cloud is an easy decision. We were start up 10 years ago. We don't have I t. We have 50 plus people on growing. We're all in the cloud. That's fine for us. Dropbox started in the cloud. All these guys started class. It's easy as hell to do it. No, no debate there. But as you start thinking, Maurin Maur integration as a big enterprise which wasn't born in the cloud. This is where the transformations happening is what business? What the hell they doing? What's what's the purpose of their >> visit? Yeah, but the reality of you, a cloud infrastructure and how cloud infrastructure is structured does not really take you away from owning how you operate and run that infrastructure, right Amazons than an amazing marketing job of telling everybody that they're not smart enough to run their own infrastructure. And it's just not true way definitely let operations get very lax. We built up a lot of technical debt that we we need to be able to fix. An Amazon walked in and said, This is too hard for you. Let us take it off your plate. But the reality is people using Amazon still have toe owned their operations of that infrastructure. The capital one didn't doesn't get to just get a pass and say, I used Amazon. Oh, well, Too bad. Talk to them. You still own your infrastructure. >> Technically, it wasn't Amazons fall, so let's get the capital. One is this brings up a good point. Converged infrastructure was the Holy Grail, savior for the I t If you go back when we started doing Cuba interviews, stupidity and I would talk about converged is awesome. You got Nutanix kicked ass and grew like crazy. And so then you have the converge kind of meat's maker. When it sees the cloud, it's like, OK, I got great converged infrastructure, but yet the breach on capital one had nothing to do with a W s. It was basically an s three bucket that the firewall Miss configured. So it was really Amazon was a victim of its simplicity there. I mean, there's a >> I mean, this is this is what we're talking about with. To me with this tweet is that we need to look, we need to be better at operating the infrastructure we have, whether it's Amazon or physical assets on your premises. What we've really done is we've eroded our ability to manage those pieces well and do it in a way that builds on itself. And so as soon as we can get on improvement there, I mean, this this is where I went with this threat is if we can really improve our operational efficiency with the infrastructure we have, whether it's in the cloud on premises. You create benefits there than everything you build on top of that is gonna have a nim prove mint, right. We're gonna change the way we look at infrastructure. Amazons already done that on. We think about infrastructure in cloud terms, but I don't think that what they've done is the end destination. They just taught us how to be better running infrastructure. >> Well, it brings up that it brings up the point, and I have so Mike shaking his head to get his thought and mark on this. If I is that I tease problem our operational technologies problem because the world's not as simple as it used to be. It was not. It wasn't. It's not simple. You got edge. You get externally incest cloud players now multi cloud. So information technology teams and operational technology teams whose fault is it? Who is responsible thing? Could you just had a AI bots managing the the filtering and access to history buckets that could have been automated away? What, Whose problem was it? Operations, technology or I t. >> So that I think, to touch upon what Rob was talking about. There's my chain and technology, uh, from the classic sound byte is people process and technology. The core cause of literally every security breach, including capital one is a lack of sophisticated process and the root cause being people, and there's no amount of a I currently that can fix that. So you have to start focusing on your operational supply chain processes, which has, Rob said. Amazon has really solidified, and the company should look to emulate that forces trying to emulate the cloud infrastructure and some of your processed and your people challenges first. And then you can leverage the technology. >> Great point. Totally agree with you on that one >> market. Yeah, I would agree with everything that both Mike and Rob just said, and I would just add that we we don't have any choice but to face the future. That is, I t. And in order to provide the best possible service to our customers for our applications that even haven't been built yet, we have to look at the service is that are available to us and utilize them the best way possible and then find appropriate management and, like so correctly put it supply chain processes for managing them. So I've talked to people who are building unique cloud platforms internally to solve a specific business problem in ways that the individual clouds offered by the Big Three is an example can't do or can't do as well or can't do is cheaply. And the same thing applies to customers who are just using more than one of the big cloud providers. Even for some in some cases, for workloads. That might seem similar because each of the clouds provide a different opportunity associated with that specific set of requirements. And so we don't have any choice but to manage it better. And whether it's we make a choice to use it in our data center because it's more cost effective long term. And that's our single most important driver. Or whether we decide to leverage every tool in our tool belt, which includes a handful of cloud providers. And some we do our own, um, or we put it all in one cloud. It doesn't change our responsibility for owning it correctly, right? And my simple message really was that you have to figure out how to own and I'll steal from Mike again. You have to figure out how to own that supply chain. But more lower down more base is ifs. Part of that supply chain is delivering compute into a data center or environment that you own. Then you have to find the tools capabilities to ensure that you're not making the kind of mistakes that were made with capital or >> or, if you have tools are networks and tools you don't know and look at the quotes. So called scare with the China hack from Super Micro. That's a silly why chain problems? Well, it's on the silicon. So again, back to the process, people an equation. I think that's right on this brings us kind of through the next talking track. I want to get your thoughts on, which is cloud two point. Oh, I mean, I'm putting that term out there on Lee is a provocative way. Remember, Web to point. It works so well in debating about what it what it was. If one if cloud one data was Amazon Web service is, thank you very much. Public cloud. You could say cloud two point. Oh, our second inning would be just what happens next because you're seeing now a confluence of different dynamics edge, um, security, industrial edge. And then you know this all coming into on premises, which is hybrid and public, all working together. And then you throw multi cloud in there from a complexity standpoint. Do you wanna have support Microsoft's Stack, Azure Stack, Google and Amazon? This is this is the fundamental 2.0 question. Because things are more real time. Things are data specific. This costs involved. There's really network innovation needed what you guys thoughts on cloud to point out. >> I think the basic cloud 2.0, is moving to the shared responsibility model. And we should stop blaming people for teams for breaches as architectures become much more complex, including network computing, storage and in service orchestration layers like kubernetes, no one team or individual, individual or one team and manage all of that. So you're all responsible for infrastructure, scalability, performance and security. So I think it's the cultural movement more than the technology movement at the base of >> Rob. What's your definition? Cloud 2.0, from your perspective. >> Oh boy, I've been calling it Post Cloud Is my feeling on this? Yeah, it to me. It's it's about rethinking the way we automate. Um, you know, we really learned that we had to interact with infrastructure via automation and eliminate the human risk elements of. This doesn't mean that we have an automation is foolproof either It's not, but what? What I think we've seen is that people have really understood that we have to bring the type of automation and power that we're seeing in clouding the benefits because they're very riel. But back into everything that we do. There's no doubt in my mind that infrastructure is moving back into the environment. Where is what? Which is EJ from my perspective, and we'll see computing in a much more distributed way and those benefits and getting that right in the automation. Is this necessary to run autonomous zero touch infrastructure in environmental situations. That is gonna be justice transformative, freighted that that environment makes the cloud look easy. Frankly, >> Mark, what's your take? I want to get because, you know, security houses, one element get self driving cars. You got kind of a new front end of of EJ devices, whether it's a Serie Buy Me a song on iTunes, which has to go out to a traditional system and purchase a song. But that that Siri priest is different than what? The back end? Does this simply database, Get it? Moving over self driving cars, You're seeing all kinds of EJ industrial activity. You know, the debate of moving compute to the data. You got Amazon with ground station, all these new infrastructure physical activities going on that needs software to power it. What, you're in cloud to point. It seems to be a nice place not just for analytics, but for operational thing. Your thoughts on cloud to point out >> Well, I mean you you describe the opportunity relatively well. I could certainly go in. I've spent a lot of time going into detail about what EJ might mean and what might populate edge and why people would use it. But I think from if we just look at it from a cloud 2.0, standpoint, maybe I'm oversimplifying. But I would say, you know, if you add on to what Mike and Rob already so well pointed out is that it's best fit right, it's best fit from compute location, Thio CPU type Thio platform on, and historically, for I t they've always had to make pragmatic choice is that I believe, limit their ability on Helped to create Maur you know, legacy Tech that they have to manage, um on and create overhead tech debt, as they call it on DSO. I think judo. And in my book the best case for two Dato is that I can put best fit work where I need it when I need it for as long as I need it. >> That's that's really kind of gasp originals. Well, people got to get the software stood up. That's where I think Kubernetes has shown a nice position. I want to extend this track to another thought, another topic around networking. So if you look at the three pillars of computing computing mean industry, compute storage and networking, cloud one daughter, you can say pretty much compute storage did a good job. Amazon has a C two as three. Everything went great. Networking always got taken to the wood shed. You know, networking was getting, you know, people were pissing and moaning about networking. But if you look at kind of things were just talking about networking seems to be an area that this cloud 2.0, could innovate on. So wanna get each of your thoughts on? If you could throw the magic wand out there around the network doesn't take the same track as Dev ops that gets abstracted away because you see VM wear now doing deals. All the cloud providers they got they're going after Cisco with the networking PCC Cisco trying to be relevant. The big guys you got edge, which is power and network connection. You need those things. So what is the role of the network? And two point If you guys could wave the magic wand and have something magically happen or innovate, what would it be? >> Oh, wait, it's part complaining. It's your world. You know, it's ironic that I said this Thio competitors to my most previous company. Ericsson Company was away. They asked me after an event in San everything was a cloud expo. I just got off stage and the gentleman came up to me and asked me So mark you the way you talked about Cloud. I appreciate the comments you made yada, yada, yada. But what do you think about networking? And I said Well, network big problem right now is that you can't follow cloud assumptions as faras usage characteristics and deployment characteristics with networking. When that problem is solved, will have moved light years ahead in how people can use and deploy i t. Because it doesn't matter if you can define workload opportunity in 30 minutes on an edge device somewhere or on a new set of data centers belonging to Google or 10 Cent or anybody else. If you can't treat the network with same functionality and flexibility and speed to value that, you can the cloud then, um, it's Unfortunately, you're really reducing your opportunity and needlessly lengthening the time to value for whatever activity it is. You're really >> so network, certainly critical in 2.0, terms have absolutely that Mike any any thoughts there? >> So I think you know, there's there's easy answers to this that are actually the answer. You know, I P v six was the answer from a couple years ago, and that hasn't solved in the fantasy of the solved. All the problems, just like five G is not gonna magically transform our edge infrastructure into this brilliant network. The reality is, networking is hard and it's hard because there's a ton of legacy embedded stuff that still has to keep working. You can't just, you know, install a new container on container system and say, I've now fixed networking. You have to deal with the globally interconnected MASH insistence. I think when we look at networking, we have to do it in a way that respects the legacy and figures out migration strategies. One of the biggest problems I see that a lot of our technology stacks here is that they just assume we're gonna pave over the problems of yesteryear, nor them and with network, when you don't get that benefit, what you described with cloud networking, never living up the potential, it's because cloud networking isn't club networking. It's it's, you know, early days of the Internet. Networking is still what we use today. It's not. It's not something you can just snap your fingers and disrupt. >> Well, I mean, networking had two major things that were big parts of a networking and who build networks knows you provisioned them and you have policy stuff that runs on them, right? You moving paintings from A to B, then you got networks you don't own right so that's kind of pedestrian, old thinking. But if you want to make networks programmable to me, it just seems like they just seem to be so much more there that needs to be developed, not just moving package. Well, >> you just said it's traditional. Networks were built first, and the infrastructure was then built around them or leveraging them, so you need to take like in zero. Trust paper. When Bugsy Siegel built Las Vegas, he built the town first and then put the roads around the infrastructure. So you need to take that approach with networking. You need to have the core infrastructure of first and then lay down the networking around to support it. And, as Mark said, that needs to be much more real time or programmable. So moving from ah, hardware to find to a software to find model, I think, is how you fix networking. It's not gonna be fixed by a new protocol or set of protocols or adding more policies or complexity to it, >> so you see a lot of change then, based on that, I'd take away that you see change coming to networking in a big way because Vegas we're gonna build >> our if it has to happen. The current way is not working. And that's why we need the bottlenecks. Wherever >> Mark you live in is the traffic's brutal. But, you know, still e gotta figure out, You know, they got some more roads. The bill change coming. What are your thoughts on the change coming with this networking paradigm >> show? I mean, there are a few companies in the space already. I'm going to refuse to name anyway at this point because one of them is a partner of my new company, not my new company, but the new company I work for and I don't want to leave them out of the discussion. But there are several companies in the space right now that are attempting to do just then just that from centralized locations, helping customers to more rapidly deploy network services to and from cloud or two and from other data centers in a chain of data centers. Programmatically as we've talked about. But in the long run, your ability to lay down networking from your office without having to create new firewall rules and spend months on on contract language and things like that on being able to take a slice of the network you already have and deploy it on DDE, not have to go through the complex Mpls or Or VPN set ups that are common today on defectively reroute destinations when you want to or make new connections when you need to. Is far as I'm concerned, that's vital to the success of anything we would call a cloud two point. Oh, >> well, we're gonna try tracks when he's hot startups. So you guys see anyone around this area? I love this topic. I think it's worth talking a lot more about love. Love to continue on with you guys on that another. Another time. Final five minutes. I'd love to spend with you guys talking about the the digital transformation paradox. Rob, we're talking before we came on camera. He loved this paradox because it's simply not as easy to saying Kill the old man, bringing the new and everything's gonna be hunky dorey. It's not that simple, but but it also brings up the fact that in all these major waves, the hype outlives the reality, too. So you're seeing so I want to get your thoughts on digital transformation. Each of you share your thoughts on what's come home to be realistic in digital transformation, which what hasn't showed up yet in terms of benefits and capability. >> I mean, this is this to me is one of the things that we see happen in every wave. They people jump on that bandwagon really hard, and then they tell everybody who's doing the current stuff, that they're doing it wrong. Um, and that that to me, actually does a lot more heart. What we what we've seen in places where people said, burn the boats, you know, we don't care. They have actually not managed to get traction and not create the long term sustainability that you would get if you created ways to bring things forward. Networking is a good example for that, right? Automating a firewall configuration and creating a soft firewall or virtual network function is just taking something that people understand and moving it into a much more control perspective in a lot of ways. That's what we saw with Cloud Cloud took working I t infrastructure that people understood added some change but also kept things that people 1% and so the paradox. Is that you? Is it the more you tell people, they just have to completely disrupt and break everything they've done and walk away from their no nighty infrastructure, the less actually you create these long term values. And I know there are people who really know you got totally changed everything that disrupted value. But a lot of the disrupted value comes from creating these incremental changes and then building something on top of that. So what? So >> what did what Indigenous in digital transformation, what has happened? That's positive and what hasn't happened that was supposed to happen. >> So when I look att Dev ops on what people thought we were going to do, just automate all things that turned out to be a much bigger lift than people expected. But when we started looking at pipelines and deployment pipelines and something very concrete for that which let people start in one or two places and then expand, I think I think, uh, pipelines and build deploy pipelines are transformative, right? Going from a continuously integrated system all the way to a continuously integrated data center. Yeah, that's transformative. And it's very concrete just telling people automate everything is not been as effective >> guys. Other thoughts there on the digital >> transformation dream. I agree with everything that Rob just said, and I would just add just because, you know, it's the boarding piece that someone always has to say, and nobody in Tech everyone is he here? But you know, every corporation at one point or another in its Kurt in its life span faces a transformative period of time because of product change or a new competitor that's doing things differently, or has figured out a way to do it cheaper or whatever it is. And they usually make or break that transformation not because of technology, not because of whether they have smart people, not because of whether they implemented the newest solution, but because of culture and organizational motivation and the vast majority of like Everything, Rob said doesn't just apply to I. T. A lot of the best I T frameworks around Agile and Dev ops apply to how the rest of the organization can and should react to opportunity so that if I t can be and should be really time, then it only makes sense that the business should be able to be real time in responding to what is being created through I t systems. And right now I would argue that the vast majority of the 80% of transformations that don't see the benefit that they're looking for have nothing to do with whether they could have gotten the right technology or done the technology correctly. But it has to do with institutional culture and motivation. And if you can fix that, then the only piece all add on to that. That again I vociferously, really agree with Robin is that if you want to lower the barrier to entry and you want to get more people into this market, you won't get more people to buy more of your stuff and grow what they own. Then you have to be able to show them a path to taking, getting the most value out of what they already have. There is no doubt in my mind that that's the only way forward, and that's where some of the tools that we're talking about and what we're talking about today on Twitter or so important >> Mike final stops on the >> docks >> on your thoughts on the transmission paradox, >> so the paradox that Robb describe think is set, the contact is set incorrectly by calling it digital transformation should be digital revolution, where the evolution process doesn't end. Transformation makes people think that there's some end state, which means let's burn the votes. That's let's get rid of all over all on prime infrastructure moved to cloud and we're done. And really, that's only the beginning. Which is why we're talking about Cloud two point. Oh, do you have to take that approach that you want to have continuous evolution and improvements, which Segways into what Rob said about de box and automating all the things you don't automate your tasks and processes and you're done? You want to keep improving upon them. Figuring out how to improve the process is and then change the automation five that the is, Mark said. It's a cultural and mental shift versus trying to get to this Holy Grail and state of transforming transformation. >> Awesome. Well, why I got you guys here first off. Thanks for spending the time and unpacking these big issue. Well, two more of it. I'd >> love to just get >> your thoughts real quick on just your opinion of Capital One. The breach, survivability and impact of the industry. Since it's still in the news, who wants to jump for us? We'll start with Mike. Mike, start with you will go down the line. Mike, Robin Mara. >> I mean, the good news for Capital One is I don't think any personal information was breached that hasn't already been exposed by the various other massive reaches. Like I do my so security number as a throw away at this point which never should have been used for identity. But I want All >> right, So there were Do you think >> it's recoverable is not gonna be as critical, say, Equifax, which was brutal. >> It doesn't sound like there was negligence where Equifax seemed like it was Maura negligent driven than just ah ah, bad process or bad hygiene around a user or roll account and access to a certain subset of data. >> I mean, this was someone who stumbled upon open history bucket and said, >> Well, well, look at this >> bragging about it on Twitter and the user groups. I mean, this >> was like from from what the press said, I think there's other companies that may or may not be affected by this as well, so that it's just capital one, which will probably defuse the attention on them and lessen the severity or backlash. >> Rob your thoughts on Capital One. >> Yeah, I wish it would move the needle. I think that we have become so used to the security of breach of the week or the hardware. Very. You know, it is we We need to really think through what it's really gonna take toe treat security as a primary thing, which means actually treating operations and infrastructure and the human processes piece of this, um, and slowed down a little bit. Um, and I always saw >> 11 lawmaker, one congressman's woman said, More regulation. >> Yeah, they don't want this. I don't think regulation is the right is the right thing. I don't know exactly what it is because I think >> regularly, we don't understand. That's Washington, DC, >> But but we're building a very, very, very fragile I T infrastructure. And so this is not a security problem. It's a It's a fact that we've built this Jenga tower of I t infrastructure, and we don't actually understand how it's built, Um, and that I don't see that slowing down. Unfortunately, >> unlike Las Vegas is, Mike pointed out, it's was built with purpose. They built the roads around the town. Mark, you live there now What's your thoughts on this capital? One piece ends and >> I have been said I would say that what I'm hoping sort of like when you have, ah, a lack of employees for a specific job type. Like right now in United States, it's incredibly difficult to find a truck driver if you're a trucking company, So what does that mean? But that means it's gonna accelerate automation and truck driving because that's the best alternative, right? If you can't solve it the old way, then you find a new way to solve it. And we have an enormous number of opportunity. He's from a process standpoint, but also, from a technology standpoint, did not build on this. Pardon my French crap that we have already >> they were digital. Then, when I ruled by the FCC, >> had build it the right way from the start. >> Well, you know what was soon? How about self driving security? We needed guys. Thanks for spending the time this cube talk. Keep conversation. Appreciate time. Mike, Rob mark. Thanks for kicking it off. Thanks. >> Thank you. >> You're watching Cute conversation with promote guests. Panel discussion Breaking down. How businesses should look at technology as part of their business. Cloud 2.0, security hacks and digital transformation Digital evolution. I'm John free. Thanks for watching.

>> live from Washington D. C. It's the Cube covering a ws public sector summit by Amazon Web services. >> Hello, everyone. Welcome back to the cubes Live coverage of a ws Public Sector summit here in our nation's capital. I'm your host, Rebecca Knight. We're joined by Raghu Rahman. He is the director of Fin Row, the Financial Industry Regulatory Authority. Thank you so much for coming on the Cube >> fighter back. Good afternoon, but happy to be here. >> So we're angry. This is the 10th annual public sector. Somebody should have said so Tell us a little bit about Finn Ra and what you do. They're >> sure Fender itself is the financial industry Regulatory authority way our private sector, not for profit institutions. Our mission is investor protection on market integrity. Way our member funded on DH. We have a member driven board board of directors and we engage in ensuring that all the stock market operations in the U. S. Capital markets play with rules. So that's the essence of who we are. >> And all of those stakeholders have a vested interest in making sure their rivals are also playing bythe. So you're here giving a presentation on fraud detection, using machine learning and artificial intelligence. That's right. What was So what were you saying? >> So, Brenda, we have a very deliberate technology strategy on We constantly keep pace with technology in order to affect our business in the best possible way, way. Always are looking for a means to get more efficient and more effective and use our funding for the best possible business value so to that, and wear completely in the cloud for a lot off our market regulation operations. All the applications are in the clouds. We, in fact, we were one of the early adopters of the cloud. From that perspective, all of our big data operations were fully operational in the cloud by 2016 itself. That was itself a two year project that we started in 40 14 then from 2016 were being working with machine language on recently. Over the past six months or so, we've been working with neural networks. So this was an opportunity for us to share what? Where we have bean, where we're coming from, where we're going with the intent that whatever we do by way of principles can be adopted by any other enterprise. We're looking to share our journey on to encourage others to adopt technology. That's really what why we do this >> and I want to dig into the presentation a little bit. But can you just set the scene for our viewers about what kinds of how big a problem fraud is with these financial institutions and how much money is on the table here? >> Well, I don't want to get you to the actual dollar figures, because each dimension off it comes up with a different aspect to it. Waken say that in full in federal, we have a full caseload year after year, decade after decade that end up with multiple millions of dollars worth of fines just on the civil cases alone. And then there are, of course, multibillion dollar worth problems that we read in the media cases going as far back as Bernie Madoff. Case is going through the different banking systems so that our various kinds of fraud across the different financial sectors, of course, we're focused on the capital markets alone. We don't do anything with regard to banking or things of that nature, But even in our own case, we franchise composed of nearly 33 100 people on all of us, engaging the fulltime task of ensuring that markets are fair for the investors on for the other participants, it's a big deal. >> So in your in your presentation, you told the story of two of your colleagues who are facing different kinds of challenges to sort to make your story come alive. Tell our viewers a little bit about about their challenges. >> We spoke about Brad, who is an expert. He's an absolute wizard when it comes to market regulation, and he's being doing this for a long time on DH What I shared with the members of the audience earlier today. Wass He can probably look ATT market, even data on probably tell you what the broker had for breakfast. >> That >> scary good on. We also shared the story about Jamie, who is in the member supervision division offender, a wicked, smart and extensive experience. So these are the kind of dedicated people that we have a fender on guy took up to Rhea life use cases sort of questions that they face. So in the case of Brad, it is always a question of Hey, we're good. But how do we get better? What is the unknown unknown there? The volume of transactions in the market keeps going up. How do we then end up with a situation where we can do effective surveillance in the market on detect the behaviors that are not off interest that are not for doctor? That might be even. Don't write manipulated. How do we make sure that way? Got it all, so to speak? That's Brad's thing. >> That idea about these? No, these unknown nun note Because we know we have no no known unknowns with the unknown unknowns are even scarier. >> Exactly. They are, and we want to shed light on that for ourselves and make sure that the markets are really fair for everybody to operate him. That is where use of the latest technologies helps us get better and better at it. To reduce the number of unknown unknowns to shed light on the entirety of market activities on toe, perform effective surveillance. So that was a just off our conversation today. How we have gotten better in the past 45 years, how machine language machine learning based technologies have helped us how artificial intelligence that we started working with specifically, neural networks have started helping us even further. >> Okay, okay. And then Jamie had a problem, too. >> In Jimmy's case. Member supervision, if you will. The problem is off a different context and character. They're still volumes of data. We still receive more than 1,000,000 individual pieces of document every year that we work with. But in her case, the important aspect of it is that it is unstructured data. It makes sense to humans. It is in plain English, but the machines, it's really difficult. So over the past two years, way have created an entirely new text analytics platform on that helps us parts through hundreds of thousands of different documents. Those could come from e mails it to come from war documents, spreadsheets, evenhanded and documents. We can go through all of those extract meaningful information, automatically summarized them, even have measures off confidence that the machine will imprint upon it to say how confident I am. I that this is off relevance to you. It will imprint that. And then it represented Jamie for her toe. Use her judgment and expertise to make a final call. One thing that we are really conscious about is way. Don't let algorithms completely take everything through. We always have a human. So we think of a I as really assistive intelligence on. We bring that to a fact for our business, >> and I think that that's a really key there, too, for the for the employees is to know that this is this is this's taking away some of their more manual, more boring tests and actually freeing them up to do the more creative, analytical problem solving >> you hit you. I think you hit that nail right on the head. All the tedious work the machine bus on. Then it leaves humans to do like you said, Absolutely the creative, the inter toe on the final judgment call. I think that's a great system. >> How much to these solutions cost way >> generally are not pricing these things individually, however overall, one of the things that we did with the cloud was actually reduce our overall cost ofthe technology. So from that perspective, we don't look at Costas, the primary driver, although many times these things do end up costing less than the prior system that we would be in. However, the benefits that offer to our clientele, the benefit that it offers to our business, to the people that are investors in the stock market, that is tremendous, and that has a lot of value for us. >> So what is next for Finneran? I mean, this is This is a really moment for so many industries in terms of the the rise of cyber threats, the end and fraud being such a huge problem. Privacy thes air the financial services industry more than, I guess maybe is equal to healthcare. This's really sensitive stuff we're talking about here. What what are some of the things that you have on the horizon? What are some of the things that you're hearing from your members? >> So all of our members treat data security really, really special on really carefully on wear, very deliberate and very conscious about how we treat the data that is interested to us way have to obligations. One is to treat it securely. The other is to extract appropriate insights from it because that's the purpose of why we're being interested with the data. Wait, take both of those dimensions very seriously. Way have an entire infrastructure organization. It's composed off experts in the field way, headed by a chief information security officer with a large team that looks at multi layered security right from the application defending itself all the way to perimeter security. We go off that we have extensive identity and access management systems. We also have an extensive program to combat insider tracks. So this type of multi layer security is what helps us keep the data secure. >> And >> every day we do notice that there are additional track factors that get exposed. So we keep ourselves on the edge in terms ofthe working with all the vendors that we partner with in working with the latest technologies to protect our data as an example, all of our data in the cloud is completely encrypted with high encryption, and it is encrypted both at rest. I'm during flight so that even in the rare case that someone has access to something is gibberish. So that's the intent of the encryption himself. So that is the extent to which we take things very seriously. >> I want to ask you to, but the technology backlash that we're seeing so much and you're you live here so you really know about the climate that does that technology industries, air facing for so long. They were our national treasure and they still are considered it all in a lot of ways. The Amazons, the Googles, the facebooks of the world. But now we have a presidential candidates calling for the break up of big tech and and they And there's been a real souring on the part of the public of concerns about privacy. How What are your thoughts? What are you seeing? What are you hearing on the ground here in D. C? >> With specifically with regard to where we operate from Infanta? We've tried not to access or use any data. That is not for regulatory purpose. Wear Very careful about it. Way don't sprawl across and crawl across social media just on a general fishing expedition. We try not to do that. All of the data that we take in store on operate technology upon we are entitled to use it for by policy are my rules are my regulation for the specific purpose off our regulator activities. We take that very seriously. We try not to access data outside off what we have need for on. So we limit ourselves to the context and that, if you look at, is really what the public is trying to tell us, don't take our data and use it in ways that we did not really authorize you to do. So So the other thing is that franchise on our profit, not for not for profit institutions. We really have absolutely no interest beyond regulatory capability to use the data. We absolutely shut it down for any other use way are not so that way. We are very clear about what our mission is. Where we use our data, why we use it and stop. >> Great. Well, Raghu, thank you so much for coming on the Cube. It's been a pleasure talking to you. >> Thank you. Thank >> you. I'm Rebecca Knight. Please stay tuned for more of the cubes. Live coverage of the es W s public Sector summit here in Washington. D c. Stay tuned. >> Oh,

(orchestral music) >> Hi, I'm Peter Burris and welcome to another digital community event. As we do with all digital community events, we're gonna start off by having a series of conversations with real thought leaders about a topic that's pressing to today's enterprises as they try to achieve new classes of business outcomes with technology. At the end of that series of conversations, we're gonna go into a crowd chat and give you an opportunity to voice your opinions and ask your questions. So stay with us throughout. So, what are we going to be talking about today? We're going to be talking about the challenge that businesses face as they try to apply AI, ML, and new classes of analytics to their very challenging, very difficult, but nonetheless very value-producing outcomes associated with data. The challenge that all these businesses have is that often, you spend too much time in the infrastructure and not enough time solving the problem. And so what's required is new classes of technology and new classes of partnerships and business arrangements that allow for us to mask the underlying infrastructure complexity from data science practitioners, so that they can focus more time and attention on building out the outcomes that the business wants and a sustained business capability so that we can continue to do so. Once again, at the end of this series of conversations, stay with us, so that we can have that crowd chat and you can, again, ask your questions, provide your insights, and participate with the community to help all of us move faster in this crucial direction for better AI, better ML and better analytics. So, the first conversation we're going to have is with Anant Chintamaneni. Anant's the Vice President of Products at BlueData. Anant, welcome to theCUBE. >> Hi Peter, it's great to be here. I think the topic that you just outlined is a very fascinating and interesting one. Over the last 10 years, data and analytics have been used to create transformative experiences and drive a lot of business growth. You look at companies like Uber, AirBnB, and you know, Spotify, practically, every industry's being disrupted. And the reason why they're able to do this is because data is in their DNA; it's their key asset and they've leveraged it in every aspect of their product development to deliver amazing experiences and drive business growth. And the reason why they're able to do this is they've been able to leverage open-source technologies, data science techniques, and big data, fast data, all types of data to extract that business value and inject analytics into every part of their business process. Enterprises of all sizes want to take advantage of that same assets that the new digital companies are taking and drive digital transformation and innovation, in their organizations. But there's a number of challenges. First and foremost, if you look at the enterprises where data was not necessarily in their DNA and to inject that into their DNA, it is a big challenge. The executives, the executive branch, definitely wants to understand where they want to apply AI, how to kind of identify which huge cases to go after. There is some recognition coming in. They want faster time-to-value and they're willing to invest in that. >> And they want to focus more on the actual outcomes they seek as opposed to the technology selection that's required to achieve those outcomes. >> Absolutely. I think it's, you know, a boardroom mandate for them to drive new business outcomes, new business models, but I think there is still some level of misalignment between the executive branch and the data worker community which they're trying to upgrade with the new-age data scientists, the AI developer and then you have IT in the middle who has to basically bridge the gap and enable the digital transformation journey and provide the infrastructure, provide the capabilities. >> So we've got a situation where people readily acknowledge the potential of some of these new AI, ML, big data related technologies, but we've got a mismatch between the executives that are trying to do evidence-based management, drive new models, the IT organization who's struggling to deal with data-first technologies, and data scientists who are few and far between, and leave quickly if they don't get the tooling that they need. So, what's the way forward, that's the problem. How do we move forward? >> Yeah, so I think, you know, I think we have to double-click into some of the problems. So the data scientists, they want to build a tool chain that leverages the best in-class, open source technologies to solve the problem at hand and they don't want, they want to be able to compile these tool chains, they want to be able to apply and create new algorithms and operationalize and do it in a very iterative cycle. It's a continuous development, continuous improvement process which is at odds with what IT can deliver, which is they have to deliver data that is dispersed all over the place to these data scientists. They need to be able to provide infrastructure, which today, they're not, there's an impotence mismatch. It takes them months, if not years, to be able to make those available, make that infrastructure available. And last but not the least, security and control. It's just fundamentally not the way they've worked where they can make data and new tool chains available very quickly to the data scientists. And the executives, it's all about faster time-to-value so there's a little bit of an expectation mismatch as well there and so those are some of the fundamental problems. There's also reproducibility, like, once you've created an analytics model, to be able to reproduce that at scale, to be then able to govern that and make sure that it's producing the right results is fundamentally a challenge. >> Audibility of that process. >> Absolutely, audibility. And, in general, being able to apply this sort of model for many different business problems so you can drive outcomes in different parts of your business. So there's a huge number of problems here. And so what I believe, and what we've seen with some of these larger companies, the new digital companies that are driving business valley ways, they have invested in a unified platform where they've made the infrastructure invisible by leveraging cloud technologies or containers and essentially, made it such that the data scientists don't have to worry about the infrastructure, they can be a lot more agile, they can quickly create the tool chains that work for the specific business problem at hand, scale it up and down as needed, be able to access data where it lies, whether it's on-prem, whether it's in the cloud or whether it's a hybrid model. And so that's something that's required from a unified platform where you can do your rapid prototyping, you can do your development and ultimately, the business outcome and the value comes when you operationalize it and inject it into your business processes. So, I think fundamentally, this start, this kind of a unified platform, is critical. Which, I think, a lot of the new age companies have, but is missing with a lot of the enterprises. >> So, a big challenge for the enterprise over the next few years is to bring these three groups together; the business, data science world and infrastructure world or others to help with those problems and apply it successfully to some of the new business challenges that we have. >> Yeah, and I would add one last point is that we are on this continuous journey, as I mentioned, this is a world of open source technologies that are coming out from a lot of the large organizations out there. Whether it's your Googles and your Facebooks. And so there is an evolution in these technologies much like we've evolved from big data and data management to capture the data. The next sort of phase is around data exploitation with artificial intelligence and machine learning type techniques. And so, it's extremely important that this platform enables these organizations to future proof themselves. So as new technologies come in, they can leverage them >> Great point. >> for delivering exponential business value. >> Deliver value now, but show a path to delivery value in the future as all of these technologies and practices evolve. >> Absolutely. >> Excellent, all right, Anant Chintamaneni, thanks very much for giving us some insight into the nature of the problems that enterprises face and some of the way forward. We're gonna be right back, and we're gonna talk about how to actually do this in a second. (light techno music) >> Introducing, BlueData EPIC. The leading container-based software platform for distributed AI, machine learning, deep learning and analytics environments. Whether on-prem, in the cloud or in a hybrid model. Data scientists need to build models utilizing various stacks of AI, ML and DL applications and libraries. However, installing and validating these environments is time consuming and prone to errors. BlueData provides the ability to spin up these environments on demand. The BlueData EPIC app store includes, best of breed, ready to run docker based application images. Like TensorFlow and H2O driverless AI. Teams can also add their own images, to provide the latest tools that data scientists prefer. And ensure compliance with enterprise standards. They can use the quick launch button. which provides pre configured templates with the appropriate application image and resources. For example, they can instantly launch a new Sandbox environment using the template for TensorFlow with a Jupyter Notebook. Within just a few minutes, it'll be automatically configured with GPUs and easy access to their data. Users can launch experiments and make GPUs automatically available for analysis. In this case, the H2O environment was set up with one GPU. With BlueData EPIC, users can also deploy end points with the appropriate run time. And the inference run times can use CPUs or GPUs. With a container based BlueData Platform, you can deploy fully configured distributed environments within a matter of minutes. Whether on-prem, in the public cloud, or in a hybrid a architecture. BlueData was recently acquired by Hewlett Packward Enterprise. And now, HPE and BlueData are joining forces to help you on your AI journey. (light techno music) To learn more, visit www.BlueData.com >> And we're back. I'm Peter Burris and we're continuing to have this conversation about how businesses are turning experience with the problems of advance analytics and the solutions that they seek into actual systems that deliver continuous on going value and achieve the business capabilities required to make possible these advanced outcomes associated with analytics, AI and ML. And to do that, we've got two great guests with us. We've got Kumar Sreekanti, who is the co-founder and CEO of BlueData. Kumar, welcome back to theCUBE. >> Thank you, it is nice to be here, back again. >> And Kumar, you're being joined by a customer. Ramesh Thyagarajan, is the executive director of the Advisory Board Company which is part of Optum now. Ramesh, welcome to theCUBE. >> Great to be here. >> Alright, so Kumar let's start with you. I mentioned up front, this notion of turning technology and understanding into actual business capabilities to deliver outcomes. What has been BlueData's journey along, to make that happen? >> Yeah, it all started six years ago, Peter. It was a bold vision and a big idea and no pun intended on big data which was an emerging market then. And as everybody knows, the data was enormous and there was a lot of innovation around the periphery. but nobody was paying attention to how to make the big data consumable in enterprise. And I saw an enormous opportunity to make this data more consumable in the enterprise and to give a cloud-like experience with the agility and elasticity. So, our vision was to build a software infrastructure platform like VMware, specially focused on data intensity distributed applications and this platform will allow enterprises to build cloud like experiences both on enterprise as well as on hybrid clouds. So that it pays the journey for their cloud experience. So I was very fortunate to put together a team and I found good partners like Intel. So that actually is the genesis for the BlueData. So, if you look back into the last six years, big data itself has went through a lot of evolution and so the marketplace and the enterprises have gone from offline analytics to AI, ML based work loads that are actually giving them predictive and descriptive analytics. What BlueData has done is by making the infrastructure invisible, by making the tool set completely available as the tool set itself is evolving and in the process, we actually created so many game changing software technologies. For example, we are the first end-to-end content-arised enterprise solution that gives you distributed applications. And we built a technology called DataTap, that provides computed data operation so that you don't have to actually copy the data, which is a boom for enterprises. We also actually built multitenancy so those enterprises can run multiple work loads on the same data and Ramesh will tell you in a second here, in the healthcare enterprise, the multitenancy is such a very important element. And finally, we also actually contributed to many open source technologies including, we have a project called KubeDirector which is actually is our own Kubernetes and how to run stateful workloads on Kubernetes. which we have actually very happy to see that people like, customers like Ramesh are using the BlueData. >> Sounds like quite a journey and obviously you've intercepted companies like the advisory board company. So Ramesh, a lot of enterprises have mastered or you know, gotten, understood how to create data lakes with a dupe but then found that they still weren't able to connect to some of the outcomes that they saw. Is that the experience that you had. >> Right, to be precise, that is one of the kind of problems we have. It's not just the data lake that we need to be able to do the workflows or other things, but we also, being a traditional company, being in the business for a long time, we have a lot of data assets that are not part of this data lake. We're finding it hard to, how do we get the data, getting them and putting them in a data lake is a duplication of work. We were looking for some kind of solutions that will help us to gather the benefits of leaving the data alone but still be able to get into it. >> This is where (mumbles). >> This is where we were looking for things and then I was lucky and fortunate to run into Kumar and his crew in one of the Hadoop conferences and then they demonstrated the way it can be done so immediately hit upon, it's a big hit with us and then we went back and then did a POC, very quickly adapt to the technology and that is also one of the benefits of corrupting this technology is the level of contrary memorization they are doing, it is helping me to address many needs. My data analyst, the data engineers and the data scientists so I'm able to serve all of them which otherwise wouldn't be possible for me with just this plain very (mumbles). >> So it sounds as though the partnership with BlueData has allowed you to focus on activities and problems and challenges above the technology so that you can actually start bringing data science, business objectives and infrastructure people together. Have I got that right? >> Absolutely. So BlueData is helping me to tie them all together and provide an excess value to my business. We being in the healthcare, the importance is we need to be able to look at the large data sets for a period of time in order to figure out how a patient's health journey is happening. That is very important so that we can figure out the ways and means in which we can lower the cost of health care and also provide insights to the physician, they can help get people better at health. >> So we're getting great outcomes today especially around, as you said that patient journey where all the constituents can get access to those insights without necessarily having to learn a whole bunch of new infrastructure stuff but presumably you need more. We're talking about a new world that you mentioned before upfront, talking about a new world, AI, ML, a lot of changes. A lot of our enterprise customers are telling us it's especially important that they find companies that not only deliver something today but demonstrate a commitment to sustain that value delivery process especially as the whole analytics world evolves. Are you experiencing that as well? >> Yes, we are experiencing and one of the great advantage of the platform, BlueData platform that gave me this ability to, I had the new functionality, be it the TensorFlow, be it the H2O, be it the heart studio, anything that I needed, I call them, they give me the images that are plug-and-play, just put them and all the prompting is practically transparent to nobody need to know how it is achieved. Now, in order to get to the next level of the predictive and prescriptive analytics, it is not just you having the data, you need to be able to have your curated data asset set process on top of a platform that will help you to get the data scientists to make you. One of the biggest challenges that are scientist is not able to get their hands on data. BlueData platform gives me the ability to do it and ensure all the security meets and all the compliances with the various other regulated compliances we need to make. >> Kamar, congratulations. >> Thank you. >> Sounds like you have a happy customer. >> Thank you. >> One of the challenges that every entrepreneur faces is how did you scale the business. So talk to us about where you are in the decisions that you made recently to achieve that. >> As an entrepreneur, when you start a company, odds are against you, right? You're always worried about it, right. You make so many sacrifices, yourself and your team and all that but the the customer is the king. The most important thing for us to find satisfied customers like Rameshan so we were very happy and BlueData was very successful in finding that customer because i think as you pointed out, as Ramesh pointed out, we provide that clean solution for the customer but as you go through this journey as a co-founder and CEO, you always worry about how do you scale to the next level. So we had partnerships with many companies including HPE and we found when this opportunity came in front of me with myself and my board, we saw this opportunity of combining the forces of BlueData satisfied customers and innovative technology and the team with the HPs brand name, their world-class service, their investment in R&D and they have a very long, large list of enterprise customers. We think putting these two things together provides that next journey in the BlueData's innovation and BlueData's customers. >> Excellent, so once again Kumar Sreekanti, co-founder and CEO of BlueData and Ramesh Thyagarajan who is the executive director of the advisory board company and part of Optum, I want to thank both of you for being on theCUBE. >> Thank you >> Thank you, great to be here. >> Now let's hear a little bit more about how this notion of bringing BlueData and HPE together is generating new classes of value that are making things happen today but are also gonna make things happen for customers in the future and to do that we've got Dave Velante who's with Silicon Angle Wiki Bond joined by Patrick Osbourne who's with HPE in our Marlborough studio so Dave over to you. >> Thanks Peter. We're here with Patrick Osbourne, the vice president and general manager of big data and analytics at Hewlett Packard Enterprise. Patrick, thanks for coming on. >> Thanks for having us. >> So we heard from Kumar, let's hear from you. Why did HPE purchase, acquire BlueData? >> So if you think about it from three angles. Platform, people and customers, right. Great platform, built for scale addressing a number of these new workloads and big data analytics and certainly AI, the people that they have are amazing, right, great engineering team, awesome customer success team, team of data scientists, right. So you know, all the folks that have some really, really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side, great logos, major fortune five customers in the financial services vertical, healthcare, pharma, manufacturing so a huge opportunity for us to scale that within HP context. >> Okay, so talk about how it fits into your strategy, specifically what are you gonna do with it? What are the priorities, can you share some roadmap? >> Yeah, so you take a look at HPE strategy. We talk about hybrid cloud and specifically edge to core to cloud and the common theme that runs through that is data, data-driven enterprises. So for us we see BlueData, Epic platform as a way to you know, help our customers quickly deploy these new mode to applications that are fueling their digital transformation. So we have some great plans. We're gonna certainly invest in all the functions, right. So we're gonna do a force multiplier on not only on product engineering and product delivery but also go to market and customer success. We're gonna come out in our business day one with some really good reference architectures, with some of our partners like Cloud Era, H2O, we've got some very scalable building block architectures to marry up the BlueData platform with our Apollo systems for those of you have seen that in the market, we've got our Elastic platform for analytics for customers who run these workloads, now you'd be able to virtualize those in containers and we'll have you know, we're gonna be building out a big services practice in this area. So a lot of customers often talk to us about, we don't have the people to do this, right. So we're gonna bring those people to you as HPE through Point Next, advisory services, implementation, ongoing help with customers. So it's going to be a really fantastic start. >> Apollo, as you mentioned Apollo. I think of Apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in to mainstream, is that what you're seeing? >> Yeah absolutely, I mean we know that a lot of our customers have traditional workloads, you know, they're on the path to almost completely virtualizing those, right, but where a lot of the innovation is going on right now is in this mode two world, right. So your big data and analytics pipeline is getting longer, you're introducing new experiences on top of your product and that's fueling you know, essentially commercial HPC and now that folks are using techniques like AI and modeling inference to make those services more scalable, more automated, we're starting to bringing these more of these platforms, these scalable architectures like Apollo. >> So it sounds like your roadmap has a lot of integration plans across the HPE portfolio. We certainly saw that with Nimble, but BlueData was working with a lot of different companies, its software, is the plan to remain open or is this an HPE thing? >> Yeah, we absolutely want to be open. So we know that we have lots of customers that choose, so the HP is all about hybrid cloud, right and that has a couple different implications. We want to talk about your choice of on-prem versus off-prem so BlueData has a great capability to run some of these workloads. It essentially allows you to do separation of compute and storage, right in the world of AI and analytics we can run it off-prem as well in the public cloud but then we also have choice for customers, you know, any customer's private cloud. So that means they want to run on other infrastructure besides HPE, we're gonna support that, we have existing customers that do that. We're also gonna provide infrastructure that marries the software and the hardware together with frameworks like Info Site that we feel will be a you know, much better experience for the customers but we'll absolutely be open and absolutely have choice. >> All right, what about the business impact to take the customer perspective, what can they expect? >> So I think from a customer perspective, we're really just looking to accelerate deployment of AI in the enterprise, right and that has a lot of implications for us. We're gonna have very scalable infrastructure for them, we're gonna be really focused on this very dynamic AI and ML application ecosystems through partnerships and support within the BlueData platform. We want to provide a SAS experience, right. So whether that's GPUs or accelerators as a service, analytics as a service, we really want to fuel innovation as a service. We want to empower those data scientists there, those are they're really hard to find you know, they're really hard to retain within your organization so we want to unlock all that capability and really just we want to focus on innovation of the customers. >> Yeah, and they spend a lot of time wrangling data so you're really going to simplify that with the cloud (mumbles). Patrick thank you, I appreciate it. >> Thank you very much. >> Alright Peter, back to you in Palo Alto. >> And welcome back, I'm Peter Burris and we've been talking a lot in the industry about how new tooling, new processes can achieve new classes of analytics, AI and ML outcomes within a business but if you don't get the people side of that right, you're not going to achieve the full range of benefits that you might get out of your investments. Now to talk a little bit about how important the data science practitioner is in this equation, we've got two great guests with us. Nanda Vijaydev is the chief data scientists of BlueData. Welcome to theCUBE. >> Thank you Peter, happy to be here. >> Ingrid Burton is the CMO and business leader at H2O.AI, Ingrid, welcome to the CUBE. >> Thank you so much for having us. >> So Nanda Vijaydev, let's start with you. Again, having a nice platform, very, very important but how does that turn into making the data science practitioner's life easier so they can deliver more business value. >> Yeah thank you, it's a great question. I think end of the day for a data scientist, what's most important is, did you understand the question that somebody asked you and what is expected of you when you deliver something and then you go about finding, what do I need for them, I need data, I need systems and you know, I need to work with people, the experts in the process to make sure that the hypothesis I'm doing is structured in a nice way where it is testable, it's modular and I have you know, a way for them to go back to show my results and keep doing this in an iterative manner. That's the biggest thing because the satisfaction for a data scientist is when you actually take this and make use of it, put it in production, right. To make this whole thing easier, we definitely need some way of bringing it all together. That's really where, especially compared to the traditional data science where everything was monolithic, it was one system, there was a very set way of doing things but now it is not so you know, with the growing types of data, with the growing types of computation algorithms that's available, there's a lot of opportunity and at the same time there is a lot of uncertainty. So it's really about putting that structure and it's really making sure you get the best of everything and still deliver the results, that is the focus that all data scientists strive for. >> And especially you wanted, the data scientists wants to operate in the world of uncertainty related to the business question and reducing uncertainty and not deal with the underlying some uncertainty associated with the infrastructure. >> Absolutely, absolutely you know, as a data scientist a lot of time used to spend in the past about where is the data, then the question was, what data do you want and give it to you because the data always came in a nice structured, row-column format, it had already lost a lot of context of what we had to look for. So it is really not about you know, getting the you know, it's really not about going back to systems that are pre-built or pre-processed, it's getting access to that real, raw data. It's getting access to the information as it came so you can actually make the best judgment of how to go forward with it. >> So you describe the world with business, technology and data science practitioners are working together but let's face it, there's an enormous amount of change in the industry and quite frankly, a deficit of expertise and I think that requires new types of partnerships, new types of collaboration, a real (mumbles) approach and Ingrid, I want to talk about what H2O.AI is doing as a partner of BlueData, HPE to ensure that you're complementing these skills in pursuit or in service to the customer's objectives. >> Absolutely, thank you for that. So as Nanda described, you know, data scientists want to get to answers and what we do at H2O.AI is we provide the algorithms, the platforms for data scientist to be successful. So when they want to try and solve a problem, they need to work with their business leaders, they need to work with IT and they actually don't want to do all the heavy lifting, they want to solve that problem. So what we do is we do automatic machine learning platforms, we do that with optimizing algorithms and doing all the kind of, a lot of the heavy lifting that novice data scientists need and help expert data scientists as well. I talk about it as algorithms to answers and actually solving business problems with predictions and that's what machine learning is really all about but really what we're seeing in the industry right now and BlueData is a great example of kind of taking away some of the hard stuff away from a data scientist and making them successful. So working with BlueData and HPE, making us together really solve the problems that businesses are looking for, it's really transformative and we've been through like the digital transformation journey, all of us have been through that. We are now what I would term an AI transformation of sorts and businesses are going to the next step. They had their data, they got their data, infrastructure is kind of seamlessly working together, the clusters and containerization that's very important. Now what we're trying to do is get to the answers and using automatic machine learning platforms is probably the best way forward. >> That's still hard stuff but we're trying to get rid of data science practitioners, focusing on hard stuff that doesn't directly deliver value. >> It doesn't deliver anything for them, right. They shouldn't have to worry about the infrastructure, they should worry about getting the answers to the business problems they've been asked to solve. >> So let's talk a little bit about some of the new business problems that are going to be able to be solved by these kinds of partnerships between BlueData and H2O.AI. Start, Nanda, what do you, what gets you excited when we think about the new types of business problems that customers are gonna be able to solve. >> Yeah, I think it is really you know, the question that comes to you is not filtered through someone else's lens, right. Someone is trying an optimization problem, someone is trying to do a new product discovery so all this is based on a combination of both data-driven and evidence-based, right. For us as a data scientist, what excites me is that I have the flexibility now that I can choose the best of the breed technologies. I should not be restricted to what is given to me by an IT organization or something like that but at the same time, in an organization, for things to work, there has to be some level of control. So it is really having this type of environments or having some platforms where some, there is a team that can work on the control aspect but as a data scientist, I don't have to worry about it. I have my flexibility of tools of choice that I can use. At the same time, when you talk about data, security is a big deal in companies and a lot of times data scientists don't get access to data because of the layers and layers of security that they have to go through, right. So the excitement of the opportunity for me is if someone else takes care of the problem you know, just tell me where is the source of data that I can go to, don't filter the data for me you know, don't already structure the data for me but just tell me it's an approved source, right then it gives me more flexibility to actually go and take that information and build. So the having those controls taken care of well before I get into the picture as a data scientist, it makes it extremely easy for us to focus on you know, to her point, focus on the problem, right, focus on accessing the best of the breed technology and you know, give back and have that interaction with the business users on an ongoing basis. >> So especially focus on, so speed to value so that you're not messing around with a bunch of underlying infrastructure, governance remaining in place so that you know what are the appropriate limits of using the data with security that is embedded within that entire model without removing fidelity out of the quality of data. >> Absolutely. >> Would you agree with those? >> I totally agree with all the points that she brought up and we have joint customers in the market today, they're solving very complex problems. We have customers in financial services, joint customers there. We have customers in healthcare that are really trying to solve today's business problems and these are everything from, how do I give new credit to somebody? How do I know what next product to give them? How do I know what customer recommendations can I make next? Why did that customer churn? How do I reach new people? How do I do drug discovery? How do I give a patient a better prescription? How do I pinpoint disease than when I couldn't have seen it before? Now we have all that data that's available and it's very rich and data is a team sport. It takes data scientists, it takes business leaders and it takes IT to make it all work together and together the two companies are really working to solve problems that our customers are facing, working with our customers because they have the intellectual knowledge of what their problems are. We are providing the tools to help them solve those problems. >> Fantastic conversation about what is necessary to ensure that the data science practitioner remains at the center and is the ultimate test of whether or not these systems and these capabilities are working for business. Nanda Vijaydev, chief data scientist of BlueData, Ingrid Burton CMO and business leader, H2O.AI, thank you very much for being on theCUBE. >> Thank you. >> Thank you so much. >> So let's now spend some time talking about how ultimately, all of this comes together and what you're going to do as you participate in the crowd chat. To do that let me throw it back to Dave Velante in our Marlborough studios. >> We're back with Patrick Osbourne, alright Patrick, let's wrap up here and summarize. We heard how you're gonna help data science teams, right. >> Yup, speed, agility, time to value. >> Alright and I know a bunch of folks at BlueData, the engineering team is very, very strong so you picked up a good asset there. >> Yeah, it means amazing technology, the founders have a long lineage of software development and adoption in the market so we're just gonna, we're gonna invested them and let them loose. >> And then we heard they're sort of better together story from you, you got a roadmap, you're making some investments here, as I heard. >> Yeah, I mean so if we're really focused on hybrid cloud and we want to have all these as a services experience, whether it's through Green Lake or providing innovation, AI, GPUs as a service is something that we're gonna be you know, continuing to provide our customers as we move along. >> Okay and then we heard the data science angle and the data science community and the partner angle, that's exciting. >> Yeah, I mean, I think it's two approaches as well too. We have data scientists, right. So we're gonna bring that capability to bear whether it's through the product experience or through a professional services organization and then number two, you know, this is a very dynamic ecosystem from an application standpoint. There's commercial applications, there's certainly open source and we're gonna bring a fully vetted, full stack experience for our customers that they can feel confident in this you know, it's a very dynamic space. >> Excellent, well thank you very much. >> Thank you. Alright, now it's your turn. Go into the crowd chat and start talking. Ask questions, we're gonna have polls, we've got experts in there so let's crouch chat.