>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies creating technologies that drive human progress. (upbeat music) >> Hey everyone, welcome back to theCube's first day of coverage of MWC 23 from Barcelona, Spain. Lisa Martin here with Dave Vellante and Dave Nicholson. I'm literally in between two Daves. We've had a great first day of coverage of the event. There's been lots of conversations, Dave, on disaggregation, on the change of mobility. I want to be able to get your perspectives from both of you on what you saw on the show floor, what you saw and heard from our guests today. So we'll start with you, Dave V. What were some of the things that were our takeaways from day one for you? >> Well, the big takeaway is the event itself. On day one, you get a feel for what this show is like. Now that we're back, face-to-face kind of pretty much full face-to-face. A lot of excitement here. 2000 plus exhibitors, I mean, planes, trains, automobiles, VR, AI, servers, software, I mean everything. I mean, everybody is here. So it's a really comprehensive show. It's not just about mobile. That's why they changed the name from Mobile World Congress. I think the other thing is from the keynotes this morning, I mean, you heard, there's a lot of, you know, action around the telcos and the transformation, but in a lot of ways they're sort of protecting their existing past from the future. And so they have to be careful about how fast they move. But at the same time if they don't move fast, they're going to get disrupted. We heard some complaints, essentially, you know, veiled complaints that the over the top guys aren't paying their fair share and Telco should be able to charge them more. We heard the chairman of Ericsson talk about how we can't let the OTTs do that again. We're going to charge directly for access through APIs to our network, to our data. We heard from Chris Lewis. Yeah. They've only got, or maybe it was San Ji Choha, how they've only got eight APIs. So, you know the developers are the ones who are going to actually build out the innovation at the edge. The telcos are going to provide the connectivity and the infrastructure companies like Dell as well. But it's really to me all about the developers. And that's where the action's going to be. And it's going to be interesting to see how the developers respond to, you know, the gun to the head. If you want access, you're going to have to pay for it. Now maybe there's so much money to be made that they'll go for it, but I feel like there's maybe a different model. And I think some of the emerging telcos are going to say, you know what, here developers, here's a platform, have at it. We're not going to charge you for all the data until you succeed. Then we're going to figure out a monetization model. >> Right. A lot of opportunity for the developer. That skillset is certainly one that's in demand here. And certainly the transformation of the telecom industry is, there's a lot of conundrums that I was hearing going on today, kind of chicken and egg scenarios. But Dave, you had a chance to walk around the show floor. We were here interviewing all day. What were some of the things that you saw that really stuck out to you? >> I think I was struck by how much attention was being paid to private 5G networks. You sort of read between the lines and it appears as though people kind of accept that the big incumbent telecom players are going to be slower to move. And this idea of things like open RAN where you're leveraging open protocols in a stack to deliver more agility and more value. So it sort of goes back to the generalized IT discussion of moving to cloud for agility. It appears as though a lot of players realize that the wild wild west, the real opportunity, is in the private sphere. So it's really interesting to see how that works, how 5G implemented into an environment with wifi how that actually works. It's really interesting. >> So it's, obviously when you talk to companies like Dell, I haven't hit HPE yet. I'm going to go over there and check out their booth. They got an analyst thing going on but it's really early days for them. I mean, they started in this business by taking an X86 box, putting a name on it, you know, that sounded like it was edged, throwing it over, you know, the wall. That's sort of how they all started in this business. And now they're, you know, but they knew they had to form partnerships. They had to build purpose-built systems. Now with 16 G out, you're seeing that. And so it's still really early days, talking about O RAN, open RAN, the open RAN alliance. You know, it's just, I mean, not even, the game hasn't even barely started yet but we heard from Dish today. They're trying to roll out a massive 5G network. Rakuten is really focused on sort of open RAN that's more reliable, you know, or as reliable as the existing networks but not as nearly as huge a scale as Dish. So it's going to take a decade for this to evolve. >> Which is surprising to the average consumer to hear that. Because as far as we know 5G has been around for a long time. We've been talking about 5G, implementing 5G, you sort of assume it's ubiquitous but the reality is it is just the beginning. >> Yeah. And you know, it's got a fake 5G too, right? I mean you see it on your phone and you're like, what's the difference here? And it's, you know, just, >> Dave N.: What does it really mean? >> Right. And so I think your point about private is interesting, the conversation Dave that we had earlier, I had throughout, hey I don't think it's a replacement for wifi. And you said, "well, why not?" I guess it comes down to economics. I mean if you can get the private network priced close enough then you're right. Why wouldn't it replace wifi? Now you got wifi six coming in. So that's a, you know, and WiFi's flexible, it's cheap, it's good for homes, good for offices, but these private networks are going to be like kickass, right? They're going to be designed to run whatever, warehouses and robots, and energy drilling facilities. And so, you know the economics I don't think are there today but maybe they can be at volume. >> Maybe at some point you sort of think of today's science experiment becoming the enterprise-grade solution in the future. I had a chance to have some conversations with folks around the show. And I think, and what I was surprised by was I was reminded, frankly, I wasn't surprised. I was reminded that when we start talking about 5G, we're talking about spectrum that is managed by government entities. Of course all broadcast, all spectrum, is managed in one way or another. But in particular, you can't simply put a SIM in every device now because there are a lot of regulatory hurdles that have to take place. So typically what these things look like today is 5G backhaul to the network, communication from that box to wifi. That's a huge improvement already. So yeah, my question about whether, you know, why not put a SIM in everything? Maybe eventually, but I think, but there are other things that I was not aware of that are standing in the way. >> Your point about spectrum's an interesting one though because private networks, you're going to be able to leverage that spectrum in different ways, and tune it essentially, use different parts of the spectrum, make it programmable so that you can apply it to that specific use case, right? So it's going to be a lot more flexible, you know, because I presume the needs spectrum needs of a hospital are going to be different than, you know, an agribusiness are going to be different than a drilling, you know, unit, offshore drilling unit. And so the ability to have the flexibility to use the spectrum in different ways and apply it to that use case, I think is going to be powerful. But I suspect it's going to be expensive initially. I think the other thing we talked about is public policy and regulation, and it's San Ji Choha brought up the point, is telcos have been highly regulated. They don't just do something and ask for permission, you know, they have to work within the confines of that regulated environment. And there's a lot of these greenfield companies and private networks that don't necessarily have to follow those rules. So that's a potential disruptive force. So at the same time, the telcos are spending what'd we hear, a billion, a trillion and a half over the next seven years? Building out 5G networks. So they got to figure out, you know how to get a payback on that. They'll get it I think on connectivity, 'cause they have a monopoly but they want more. They're greedy. They see the over, they see the Netflixes of the world and the Googles and the Amazons mopping up services and they want a piece of that action but they've never really been good at it. >> Well, I've got a question for both of you. I mean, what do you think the odds are that by the time the Shangri La of fully deployed 5G happens that we have so much data going through it that effectively it feels exactly the same as 3G? What are the odds? >> That's a good point. Well, the thing that gets me about 5G is there's so much of it on, if I go to the consumer side when we're all consumers in our daily lives so much of it's marketing hype. And, you know all the messaging about that, when it's really early innings yet they're talking about 6G. What does actual fully deployed 5G look like? What is that going to enable a hospital to achieve or an oil refinery out in the middle of the ocean? That's something that interests me is what's next for that? Are we going to hear that at this event? >> I mean, walking around, you see a fair amount of discussion of, you know, the internet of things. Edge devices, the increase in connectivity. And again, what I was surprised by was that there's very little talk about a sim card in every one of those devices at this point. It's like, no, no, no, we got wifi to handle all that but aggregating it back into a central network that's leveraging 5G. That's really interesting. That's really interesting. >> I think you, the odds of your, to go back to your question, I think the odds are even money, that by the time it's all built out there's going to be so much data and so much new capability it's going to work similarly at similar speeds as we see in the networks today. You're just going to be able to do so many more things. You know, and your video's going to look better, the graphics are going to look better. But I think over the course of history, this is what's happening. I mean, even when you go back to dial up, if you were in an AOL chat room in 1996, it was, you know, yeah it took a while. You're like, (screeches) (Lisa laughs) the modem and everything else, but once you were in there- >> Once you're there, 2400 baud. >> It was basically real time. And so you could talk to your friends and, you know, little chat room but that's all you could do. You know, if you wanted to watch a video, forget it, right? And then, you know, early days of streaming video, stop, start, stop, start, you know, look at Amazon Prime when it first started, Prime Video was not that great. It's sort of catching up to Netflix. But, so I think your point, that question is really prescient because more data, more capability, more apps means same speed. >> Well, you know, you've used the phrase over the top. And so just just so we're clear so we're talking about the same thing. Typically we're talking about, you've got, you have network providers. Outside of that, you know, Netflix, internet connection, I don't need Comcast, right? Perfect example. Well, what about the over the top that's coming from direct satellite communications with devices. There are times when I don't have a signal on my, happens to be an Apple iPhone, when I get a little SOS satellite logo because I can communicate under very limited circumstances now directly to the satellite for very limited text messaging purposes. Here at the show, I think it might be a Motorola device. It's a dongle that allows any mobile device to leverage direct satellite communication. Again, for texting back to the 2,400 baud modem, you know, days, 1200 even, 300 even, go back far enough. What's that going to look like? Is that too far in the future to think that eventually it's all going to be over the top? It's all going to be handset to satellite and we don't need these RANs anymore. It's all going to be satellite networks. >> Dave V.: I think you're going to see- >> Little too science fiction-y? (laughs) >> No, I, no, I think it's a good question and I think you're going to see fragments. I think you're going to see fragmentation of private networks. I think you're going to see fragmentation of satellites. I think you're going to see legacy incumbents kind of hanging on, you know, the cable companies. I think that's coming. I think by 2030 it'll, the picture will be much more clear. The question is, and I think it's come down to the innovation on top, which platform is going to be the most developer friendly? Right, and you know, I've not heard anything from the big carriers that they're going to be developer friendly. I've heard "we have proprietary data that we're going to charge access for and developers are going to have to pay for that." But I haven't heard them saying "Developers, developers, developers!" You know, Steve Bomber running around, like bend over backwards for developers, they're asking the developers to bend over. And so if a network can, let's say the satellite network is more developer friendly, you know, you're going to see more innovation there potentially. You know, or if a dish network says, "You know what? We're going after developers, we're going after innovation. We're not going to gouge them for all this network data. Rather we're going to make the platform open or maybe we're going to do an app store-like model where we take a piece of the action after they succeed." You know, take it out of the backend, like a Silicon Valley VC as opposed to an East Coast VC. They're not going to get you in the front end. (Lisa laughs) >> Well, you can see the sort of disruptive forces at play between open RAN and the legacy, call it proprietary stack, right? But what is the, you know, if that's sort of a horizontal disruptive model, what's the vertically disruptive model? Is it private networks coming in? Is it a private 5G network that comes in that says, "We're starting from the ground up, everything is containerized. We're going to go find people at KubeCon who are, who understand how to orchestrate with Kubernetes and use containers in microservices, and we're going to have this little 5G network that's going to deliver capabilities that you can't get from the big boys." Is there a way to monetize that? Is there a way for them to be disrupted, be disruptive, or are these private 5G networks that everybody's talking about just relegated to industrial use cases where you're just squeezing better economics out of wireless communication amongst all your devices in your factory? >> That's an interesting question. I mean, there are a lot of those smart factory industrial use cases. I mean, it's basically industry 4.0 use cases. But yeah, I don't count the cloud guys out. You know, everybody says, "oh, the narrative is, well, the latency of the cloud." Well, not if the cloud is at the edge. If you take a local zone and put storage, compute, and data right next to each other and the cloud model with the cloud APIs, and then you got an asynchronous, you know, connection back. I think that's a reasonable model. I think the cloud guys figured out developers, right? Pretty well. Certainly Microsoft and, and Amazon and Google, they know developers. I don't see any reason why they can't bring their model to the edge. So, and that's really disruptive to the legacy telco guys, you know? So they have to be careful. >> One step closer to my dream of eliminating the word "cloud" from IT lexicon. (Lisa laughs) I contend that it has always been IT, and it will always be IT. And this whole idea of cloud, what is cloud? If AWS, for example, is delivering hardware to the edge where it needs to be, is that cloud? Do we go back to the idea that cloud is an operational model and not a question of physical location? I hope we get to that point. >> Well, what's Apex and GreenLake? Apex is, you know, Dell's as a service. GreenLake is- >> HPE. >> HPE's as a service. That's outposts. >> Dave N.: Right. >> Yeah. >> That's their outpost. >> Yeah. >> Well AWS's position used to be, you know, to use them as a proxy for hyperscale cloud. We'll just, we'll grow in a very straight trajectory forever on the back of net new stuff. Forget about the old stuff. As James T. Kirk said of the Klingons, "let them die." (Lisa laughs) As far as the cloud providers were concerned just, yeah, let, let that old stuff go away. Well then they found out, there came a point in time where they realized there's a lot of friction and stickiness associated with that. So they had to deal with the reality of hybridity, if that's the word, the hybrid nature of things. So what are they doing? They're pushing stuff out to the edge, so... >> With the same operating model. >> With the same operating model. >> Similar. I mean, it's limited, right? >> So you see- >> You can't run a lot of database on outpost, you can run RES- >> You see this clash of Titans where some may have written off traditional IT infrastructure vendors, might have been written off as part of the past. Whereas hyperscale cloud providers represent the future. It seems here at this show they're coming head to head and competing evenly. >> And this is where I think a company like Dell or HPE or Cisco has some advantages in that they're not going to compete with the telcos, but the hyperscalers will. >> Lisa: Right. >> Right. You know, and they're already, Google's, how much undersea cable does Google own? A lot. Probably more than anybody. >> Well, we heard from Google and Microsoft this morning in the keynote. It'd be interesting to see if we hear from AWS and then over the next couple of days. But guys, clearly there is, this is a great wrap of day one. And the crazy thing is this is only day one. We've got three more days of coverage, more news, more information to break down and unpack on theCUBE. Look forward to doing that with you guys over the next three days. Thank you for sharing what you saw on the show floor, what you heard from our guests today as we had about 10 interviews. Appreciate your insights and your perspectives and can't wait for tomorrow. >> Right on. >> All right. For Dave Vellante and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE's day one wrap from MWC 23. We'll see you tomorrow. (relaxing music)

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

(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)

>>Hey, welcome back to live coverage in San Francisco, California, the cubes coverage of 80 west summit, 2022 here in SF and NYC New York city. Summit's coming up in the summer. We'll be there as well. Check it out. Okay. We've got a great guest here. C Bhan co and CEO RAI systems. Welcome to the cube, hot startup and growing company. And Kubernetes is great to see you. >>Yeah, John, thanks for having me. Appreciate it. >>Great to have you on. So Cubans coming up, you got cloud native here at AWS. You guys in the middle of it, take a minute to explain what your company does. Sure. >>So 50,000 enterprises are going to modernize in the next five to 10 years. They're all going to run into the exact same problem, which is they're gonna choose Kubernetes as the orchestra platform. And then they're gonna invest in building a platform essentially on top of Kubernetes so that their internal consumers, that developers can consume it. That requires a lot, a lot of effort. We, lot of people, a lot of time, a lot of effort, what we did was we thought about entire journey for Kubernetes operations that a team would go through and we package that as an offering. It's a SaaS product that you can consume. You can make it work with Amazon's Kubernetes, Azures Kubernetes, Googles, Kubernetes, upstream, Kubernetes, but then you can move significantly faster so that the goal of modernization can be achieved now versus two years more. >>What's the big, uh, opportunity that Kubernetes brings. And what are some of the pain points that are being removed or solved or blockers being removed and pain being reduced? Is it standing up Kubernetes? Is it running it in production? Is it the new revisions? I mean, honestly, it's huge. Yeah. What's the pain point. The customers that you guys solve. >>Yeah. Look, the, the paradox with Kubernetes is when it's working. It's awesome. It's great. And we can move it fast, but to get there, it's hard. Yeah. So simple things as a starting point, how do I provision my infrastructure repeatedly in the same way with the right blueprints? How do I make sure they all look the same? How do I make sure John can access certain things? And he cannot, how do I make sure the right policies are set up? How do I make sure consistent deployment is happening? Can I watch every we think, and I measure everything and we are not beyond basic things, right? Yeah. I need to back this up, you know, on and on. I need to do cost management. I need to network policy management. I need service management. You already built the team now. Right? Each of these is, is, is multiple people's jobs sometimes. Right? So it's really complicated. But again, everybody is investing. This is complexity. It's complexity. Yes. But people are investing in this because everybody understands now that once this is all working, the beauty, the, the P the pace at which you can run is exactly what we were promised five, six years ago when we were all told about modernization. Yeah. So the, when you get there, it's awesome. And we are helping companies get there significantly faster. Then they would've had, were they not working with a company? >>It's it really is a holy grail kind of orchestration layer if it works. And a lot of people, even myself, which a big fan of Kubernetes, caution, cautions over the oo problem, which is the clusters are up. I can't find talent to run them. They're too hard. Um, that's kind of in the back of people's minds and there's a lot of scar tissue around that. Uh, and then a little bit of open stack, you know, is it too hard, too hard? So the question is, is that what needs to happen to be successful with Kubernetes to make that go faster? So that's easy to deploy. Exactly. Yeah. And what what's your product do? Is it software open source? Yeah. What's, what's your product? >>The, the key here is repeatably usable automation. It's automation that it can use again and again, and it's flexible enough that it solves for many companies problem. You know, the funny thing is, and this is something that took me a while to figure out whether we have a financial services customer or an insurance company, or a healthcare company, or a high tech company, you know, what their problems are exactly the same. <laugh> when it comes to Kubernetes, it's all the same, right? So we figured out what it takes to build that automation in a repeatable fashion so that we could essentially sell it as a product. Our product is a SaaS product. Um, and once you have the right automation in place that you can ideally consume as a service, then now the beauty is that the people who are using it on a day to day basis, they don't need to be as expert at Kubernetes as today. Yeah. And that's the issue today? The issue is, you know, people, I've seen ads now where people say, you know, looking for Kubernetes expertise, 10 years, minimum experience, okay, that's ridiculous. Right. But you see these ads out there, right? Because people are rude about it, a tool like this makes it easy for you to take your existing skillset, existing resources and allow them to become Kubernetes. >>That's the key. I think that's the key in my mind is like hiring talent for these, I call DevOps glass eating projects, cuz it's hard. Yep. Some of this stuff's hard when you get down to the early stuff. And even in the hyperscalers, you look at the early hyperscalers, they were rolling their own and they were rock stars. And they were like the 1% of top developers. Right? Yep. And now you have general audiences who just want to code. Yep. They want abstractions. They want Kubernetes as a service. Uh, and they want all the benefits. And even if they could hire the Oddsly hiring the low level core people yeah. Is hard. Yeah. >>It takes time. Yeah. >>Absolutely. That's a core problem you guys solve. >>Absolutely. I think look, the, the one thing that every enterprise you think about is when the, the big companies, the hyperscale is that you mentioned that build this themselves when they us out 5, 6, 7, whatever years ago, when, you know, even some, some of them pre Kubernetes, it was a competitive advantage to roll this out because nobody else was doing it now as, as an enterprise who is trying to use software to move faster. Yeah. It's actually a competitive disadvantage because now you're building your own product. And now you're building this thing called Kubernetes that doesn't make any sense, focus your application, focus on your products, roll them out faster, and then essentially reuse the learnings from the market. Right. That's what we are doing. Really? What, what are we doing? We're taking the best practices of this industry and packaging that up into an easy to consume platform. That's awesome. That's it? >>Well, we'll see you in Cooper, Cuban, not Kubernetes contest in Valencia. Yep. Uh, and thanks for coming on. I know we didn't have a lot of time to drill into it, um, here, but great to meet you and the company. Final question as a co-founder what's your north star, you got, you got a company to run in. Bill got employees, you're managing and hiring inspiring. What's the north star for the company. >>So I'd say, I mean, the phrase that I, that, that I think about when, when you say north star is, is loyalty with urgency. >>So loyalty to whom? Yeah. It's to my team, right? My team comes first beyond before anything else. Right? And then my customers, right? My customers, many, many of our, our customers even now, right? We a four old company, they have my cell phone number and people call me at odd hours and I will show up. I will get people on a call. I will show up. Right. That's critical. But with urgency now my customer needs help. It needs to happen now. Not tomorrow, not next week. My team has heard me say this a thousand times, by the way, not tomorrow, not next week now. And this, if you do this in a startup, you will be successful. >>Yeah. I mean, you gotta make the market as the founder, inspiring people, product market fits huge. Yep. Getting that scale point. Yep. Where you're got the value proposition in position you're in mode to scale, you got visibility on unit economics. It's hard. Yep. It's super hard look. Good news is you get in a good area. Cloud native Kubernetes, automation, cloud, native modernization of apps. Super hot right now. Yeah. Big >>Time. Yeah. Look, I mean, you know, of course you, you want your teams to be topnotch. Right. But I gotta tell you there's a lot of luck and timing to everything. >>Exactly. >>Timing is in hindsight, nobody times anything. Right. So we have, time is perfect, but it's luck. Yeah. Right. We're very lucky. We're we have the right team. We're doing a great job. I think our customers are very happy. What we've rebuilt and uh, you know, look forward >>To Steve. You're humble. And you're a humble person. I can tell. I don't believe in luck. I think you make luck. I think luck is just part of the hustle, making those phone calls, doing those calls, doing the right things, grinding. And then when you get the shot, you're ready. Yeah. Yeah. So congratulations. Thanks for coming the queue. Appreciate it. Appreciate your time, sir. Nice to meet you coverage here in San Francisco, back with more day, two coverage. After this short break, stay with us.

>> From "theCUBE" studios in Palo Alto, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo 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)

>>from around the globe. It's the Cube with digital coverage of next level network experience event brought to you by info blocks. >>Welcome back to our coverage. The Cube. I'm John Furrier, your host. We're here with a virtual event with info blocks on next level networking. It's a virtual event hosted with the Cube of great guests Kenya Asuni, who is the EVP of products and corporate development with info blocks today. Thank you for coming on. Appreciate it. You guys are the theme of this is next level networking, which I love. Next level, it really kind of illustrates we are going to the next level with Cove in 19. We're seeing it everywhere security DNS topic that most people aren't familiar with. An i t. You know all about it. You guys are leading and reinventing d I for the folks that I want to know what that is. It's DNS de HCP and I p address management for the hybrid cloud and borderless enterprise, which is basically everything. Now, um, this is super super important. As we see every single company living this right now, which is workforce is working from home workplaces that are transforming the surface area is huge. You still got to connect to the Internet. You still need to go to a website and you still do. E commerce needs to run your business. This is a huge, huge problem that's been highlighted. Secure access there you guys are in the forefront for next gen or networking. Tell us what you define as next level. >>So, John, I think one of the things you'll see is if you, if you look at the train, is happening in our business, that is, there's an increasing adoption of SAS services, whether it's infrastructures of service being consumed from AWS, azure, Google or all the idea applications moving into SAS, you're already seeing a shift away from this data center. Being the center of the university in the Enterprise, I t infrastructure to more of a cloud edge world where a lot of the applications now sit in the cloud some in your private cloud still but a lot in the public cloud. And then you have your enterprise edge from where you want to get to these applications directly instead of back calling all the traffic into your traditional data center. We're also seeing a big push into the number of devices coming into the infrastructure, whether it be by Odie Iot G five GS or more devices coming into the infrastructure. As you said, that perimeter and the surface area of the enterprise has exploded. So you have to You have to start to think about security from a different standpoint. So all of these trends are starting to play out in the market. I think what you're going to see is over the next couple of years that the the network inside the Enterprise is gonna look very different from ordered yesterday. Today, everything gets back to the data center, and that's where all the action's. I think what you're going to see is a big shift towards what we call a hybrid multi cloud enterprise, where you may have some workloads sitting in your data center. Some workloads sitting in public clouds, some in your private cloud, and then you want the ability to move these workloads around and you're utilizing everything all your applications. You're actually continue rising all your applications, and you want all this stuff to move around so it poses a very interesting challenge. And that's why we say you need a next level network experience to deal with all the changes that you, their enterprises, are going to it. >>That's a great point. This is our top story that we've been reporting for a long time but rose recently with code 19. This notion of multiple networks, multiple environments, multiple clouds. Certainly hybrid cloud has been ratified. Everyone pretty much acknowledges that cloud operations on premises to the cloud of their. But you got to still move packets from A to B moving around, and now you're storing them and all kinds of things are happening. But I want to get your thoughts on a trend that even makes what you just said even more complex because the complexity is crazy. Right now, there's a trend of managed services. Cloud explosion comes on. You mentioned SAS more coming or deploying a managed services, sometimes multi tenant, sometimes pure instances in the cloud or on premises and data center that's causing access. I still want to integrate that into a Web presence. So, you know, I gotta integrate all these things. It's not that easy. Now. Again, DNS has been a big part of the Web presence But now you have a new dimension of hosted applications. You have managed services that that are easy to stand up. But now I gotta integrate them. This is one of the hardest challenge is that we're here, and I want to get your thoughts in reaction to that. Yeah, >>and I think Google has certainly accelerated the shift that we talked about. So I think a good point there in terms of your school reacting is there is a big accelerant in terms of the shift of the cloud. I think one of the the key role that we play as the enterprise gets much more dynamic is you need three elements you need the element to be to get visibility into everything that's going on in your cluster, you need to provide a layer of security of foundational security in your infrastructure and you need automation because then you have workloads moving around. You need to automate all your idea. Simple flows around allocating. I p address system is VMS or containers on moving as containers. Moving our retaining I P addresses assigning your i P addresses managing DNS records for them. So the work we do that dd I there really becomes the life blood of how this hybrid multi cloud enterprise comes along. And as you get to a much more distributed I T infrastructure, you are not going to be able to manage this entire infrastructure yourself the traditional. So if you have an enterprise idea administrator, you cannot sit there and say, Look, I'm gonna do the traditional model of deploying software on premise or appliances on premise, and I love my guys going out there and managing the administration of that software every six months after do a software upgrade and I'll do all that. What you need, because the enterprise has become so distributed in dynamic, is you need a cloud managed or a managed services. In either case, basically, what you see what you're looking at is a centralized management more and the ability to spin up and down the services Dynamically. We are strong believers in sass or a cloud managed approach and a cloud native architecture being the right architecture for the next level network. And that is something from a delivery standpoint and MSP can use. A managed service provider can leverage this flower manage architecture that we have to offer the services to enterprise customers and take away the whole headache off, managing and administering their own infrastructure. >>I like how you said dd I layer because there's an abstraction you can create the take away that complexity that was pretty straight forward. The best yet. DNS dhc p I p I p addresses. Okay, you manage those cases? No problem Naming whatnot. Now. You have a dynamic environment. That's key. I want to get back to and follow up what you said about the I t folks, your customers in the Enterprise. They're sitting there saying, Hey, I'm used to the on premises world and I have cloud What's the difference in your mind between on premises and cloud managed d D I and why does it matter? >>Look, I think in the traditional world, all the i t infrastructure it again was sitting in one or more regional or or regional or centralized data centers and that it was easy to manage. You could appliances from info blocks and now and it was easy. You had the folks sitting in these data centers and they could manage the entire infrastructure using someone premise management tools and things of that nature. But now I think about it. If you're if you're Walmart and you have 4500 stores right now, if you want to push DNS d A T v i p address management software into all these 5500 locations, it is very difficult to do that by deploying individual appliances or by deploying sort of shrink wrap software that has to sit in every every one of these locations. It's just from an idea administration standpoint. It's a it's a much heavier lift. But if I could take all the management and all the policy management that the policy framework and pull that up into a SAS lower that you can access from anywhere on the planet and I'll leave the protocol serving engines, if you will, on premise. So you have a container that gets spun up that can sit on any third party hardware that's sitting at your infrastructure. But it is all managed through the cloud it zero touch provisioning Andi, completely orchestrator. Now you're sitting at us at a central dashboard, and if you're in a corporate environment, you're sitting at home and just accessing our SAS service and managing your entire infrastructure from from from your from your home from your our checked at your home. Right? So it just becomes so much easier for idea administrators to operate. And I >>have so much free time on their hands to be the Watches virtual event. So be fun. There certainly >>do Stash stash. That's a great >>point. I want to get your thoughts because I like how you know I love the term next level. Anything going, the next level has been something that you talk about, whether you're a technical person and an entrepreneur or a business person. Let's go the next level. It means go the next level. But you add the word experience in there, and I want to get your thoughts on that because it is about the user experience. What >>do you >>guys do to provide that what info blocks provide specifically to provide that next level experience? >>Yeah, that's a great question. We are formed believers again that the future of networking and security in I T. Is going to shift to a cloud managed cloud native paradigm, which means you should be able to just like the hyper skaters. AWS is the Googles and Amazons of the world, right? If you look at how they build out their cloud infrastructure, it's all about separating the infrastructure layers of the compute layer from the applications that sit on top of them. So the compute nodes can scale at a difference at a different pace from that from the applications. That same mindset needs to come into into managing networking and security services as well. So if you have 1000 different educations, lets you can decide through a centralized policy framework what services you want to spin up a lease 1000 locations. Today you would have to buy a box, a small medium large box from info blocks or any one of the networking guys out there, and you would have to deploy that. And most likely, you will end up over provisioning each site because you don't want to run out of capacity. The next level experience would say, Just tell me what side you're deploying. The sites will call home. They will download the number of services needed based on some centralized policy that was defined, and you would get a right size deployment off services at that particular site. You need more services because, say, the user profile, that the profile of the users at that site change, which means you need to spend a Let's, say, a couple of additional security services. Well, that gets automatically imported from the cloud and gets incense created in that particular site. If you need more capacity because it's end of the quarter and you're doing a whole bunch of peer some financial contractions for closing the books, you need more capacity for some of the security applications. Those additional containers with those security applications can can get spun up, so you're starting to scale out as you need and scale back when you don't need the capacity. But this whole thing becomes a very dynamic experience in terms of how services get spun up. They get on down, and it's all driven by. There's this whole notion off the users that are sitting in a location, the context of the users of what devices they're trying to access these applications from what, what is the time of the day? How is the security profile of that device you bring all that know how into the house services get provisioned and how services get operationalized at any particular site in any particular enterprise. Rights are very simple experience when it comes to networking and security, and how do you deploy it at scale? >>And the thing that that sets up is what you're saying really about automation, because once you're in this mode in this experience, the environment lends itself well to automation because it is downloading the right services you need. But since it's dynamic and it needs to be ready, how does automation fit into that piece? >>Absolutely, if you disaster management is already automated for you now if you want to drive further automation and orchestration through integration with your Dev ops, SEC ops, Net ops tools, we have public FBI's through which this this can be driven. There's two ways to manage this right. We have a Cloud Services portals. If somebody wanted to go in and leverage our porter to manage their infrastructure, they can't do that. If they wanted this to be completely programmatic and driven through their their dev ops SEC ops tools, then through the public AP guys, we will tightly integrated into all the tools they have, whether it's sensible data forms some of the Dev ops tools or on the security side. If you want to integrate us into your store platform security orchestration, platforms, you can do that. And your entire workflow for networking as well as security can be completely, completely automated. >>That's awesome. I want to get as we get limited time left and you got to go. We have to hard stop with segment here. Customer example. I'll see customers have a need for this. You're in business to do this. Can you give an example of a customer? That kind of illustrates the next level networking >>we have. We have 6000 plus active customers. We have over 50% share when it comes to this DNS DCP eye Pam market. So you will see has deployed and have you deployed in 95. Out of the Fortune 100 enterprises in four blocks is some someone you will see in any customer that you that you go through. We have some public references such as Adobe, a great customer of ours on our website. They, their entire global network, runs on the foundational layer of D. I. We have some very large customers that are not as comfortable being public references, but we have again. If you have 95 of the Fortune 100 enterprises want you, you can imagine how sticky VR how broadly deployed we are. Typically, what happens is we would go in and we would go in as the FBI there for them to control and manage that I p address space and their DNS infrastructure. Then they take on more off. They take on a security lens at this and say, Look through the http and eye Pam, I know everything that is sitting in my infested toe, DNS. I have full visibility into all the communication happening from that employer. So that's a great data source for me to leverage as a first layer of defense from a security stand. So then they start to bring in security into the into the mix in terms of how they leverage our products and then through our SAS platforms and SAS offerings. They take that and extended as they're driving this edge transformation. So they push these services now to the edge of the infrastructure so and that the new infant, the new offerings are blocks one platform is our SAS platform and blocks one based applications on our new offerings that integrates very nicely with some of our traditional offerings. So you get a very comprehensive single pane of glass in terms of how you can manage your entire enterprise footprint, whether it's it's on prim at the edge, in the public cloud at the cloud edge, right? >>You know, having a good business model that puts abstractions and reduces complexity is is a great one. We've seen the innovation with DNS and anything that needs an Internet address. You got to connect, and I o. T only creates more need for connection. This is the key enterprises know DNS. They know it differently that it's the plumbing we all know. But every time there's an innovation inflection point, a new abstraction layer emerges for simplicity, ease of use. >>DNS is the phone book of the end of off the Internet. Right, So you want to call anywhere you have to first, your DNS. Look up and you brought up I o t. That's a great example. You're not going to be able to put in these eye ot sensors. You're not going to be able to put endpoint security software, but they're going to call home so you can leverage DNS and do some behavioral analysis of the DNS. Traffic coming out of those Iot. The sensors are I ot endpoints and say, Hey, look, is there something militias going on? Why is my thermostat talking to a server in China? You can detect that to a DNS based security earlier that this foundational >>and to your point, whether it's a light bulb or anything untested device, they're being turned on and turned off all the time at massive scale. There's no other way to handle it, but having abstraction and automation. Absolutely. Thank you. Thank you very much for your time. Great segment. We're here at the info blocks. Virtual event. This is the cube coverage. I'm John Furrier. Thanks for watching. Thank you, John. Yeah, Yeah, yeah, yeah.

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hello everyone, welcome to theCUBE Conversation here in Palo Alto, in our studios of theCUBE, I'm John Furrier, your host. We're here during the crisis of COVID-19 doing remote interviews. I come into the studio, we've got a quarantine crew are here, getting the interviews, getting the stories out there and of course, the story we're going to continue to talk about is the impact of COVID-19, and how we're all getting back to work, either working at home or working remotely and virtually certainly, but as things start to change, we're going to start to see events, mostly digital events, and we're here to talk about an event that's coming up called the Failover Conference from Gremlin which is now gone digital because it's April 21st. But I think what's important about this conversation that I want to get into is, not only talk about the event that's coming up, but talk about the scale problems that are being highlighted by this change in work environment, working at home. We've been talking about the at-scale problems that we're seeing whether it's a flood of surge of traffic and the chaos that's ensuing across the world and with this pandemic. So I'm excited, I've two two great guests, Alberto Fernando, senior vice president of marketing in Gremlin and Tammy Butow, principal site reliability engineer, or SRE. Guys thanks for coming on. Appreciate it, thank you. >> Thanks. >> Thanks for having me. >> Alberto, I want to get to you first. We've know each other before. You've been in this industry. We've been all talking about the cloud native, cloud scale for some time. It's kind of inside the ropes, it's inside baseball. Tammy, you're a site reliability engineer. Everyone knows Google, knows how cloud works. This is large scale stuff. Now with the COVID-19, we're starting to see the average person, my brother, my sister, our family members and people around the world go, "Oh my God, this is really a high impact." This change of behavior, this surge of web, whether it's traffic on the internet or work at home tools that are inadequate, you start to see (laughs) the statistical things that were planned for, not working well, and this actually maps the things that we've been talking about in our industry. Alberto, you've been on this. How are you guys doing? >> Yeah. >> And what's your take on this situation we're in right now? >> Yeah, we're doing pretty well as a company. We were born as a distributed organization to begin with, so for us working in a distributed environment from all over the world is common practice day-to-day. Personally, I'm originally from Italy, my parents, my family, is Milan and Bergamo of all places, so I have to follow the news with extra care and it becomes so much clear nowadays that the technology is not just a powerful tool to enable our businesses but it also is so critical for our day-to-day life, and thanks to video calls, I can easily talk to my family back there every day. So that's really important. So yes, we've been talking for a long time as you mentioned about complex systems at scale and reliability often in the context of mission critical applications, but more and more of these systems need to be reliable also when it comes to back office systems that enable people to continue to work on a daily basis. >> Yeah, well our hearts go out to your family and your friends in Italy, and I hope everyone stays safe there (speaks faintly) a tough situation continues to be a challenge. Tammy, I want to get your thoughts. How's life going for you? You're a site reliable engineer. What you deal with on the tech side is now (laughs) happening in the real world. It's mind blowing to me that we're seeing these things happen, it's a paradigm that needs attention. How do you look at it as a SRE, dealing with mostly on the tech side now seeing it play out in real life? >> It's been such an interesting situation, obviously really terrible for everybody to have to go through and deal with, so one of the things that I specialize in as a site reliability engineer is incident management and so for example, I previously worked at Dropbox where I was the incident manager on call for 500 million customers, it's like 24/7 shift. These large scale incidents, you really need to be able to act fast. There are two very important metrics that we track and care about as a site reliability engineer. The first one is mean time to detection. How fast can you detect that something is happening? Obviously, if we detect an issue faster then you've got a better chance of making the impact lower so you can contain the blast radius. I like to explain it to people like, if you have a fire in your sauce bin in your kitchen, and you put it out, that's way better than waiting until your entire house is on fire. And the other metric is mean time to resolution. So how long does it take you to recover from the situation? So yeah, this is a large scale, global incident right now that we're in. >> Yeah, I know you guys do a lot, talk about chaos, theory and that applies. A lot of math involved, we all know that, but I think we need to look at the real world. This is now going to be table stakes and there's now a line in the sand here, pre-pandemic, post-pandemic, and I think you guys have an interesting company, Gremlin, in the sense that this is a complex system and that if you think about the world we're going to be living in, whether it's digital events that you guys have one coming up or how to work at home or tools that humans are going to be using, it's going to be working with systems, right? So you have this new paradigm going to be upon us pretty quickly and it's not just buying software mechanisms or software, it's a complex system, it's distributed computing, it's an operating system. I mean this is kind of the world. Can you guys talk about the Gremlin situation of how you guys are attacking these new problems and these new opportunities that are emerging? >> Sure, I can talk about that. So yeah, one of the things I've always specialized in over the last ten years is chaos engineering. And so the idea of chaos engineering is that your injecting failure on purpose to uncover weaknesses. So that's really important in distributed systems, with distributed cloud computing, all these different services that you're kind of putting together. But the idea is if you can inject failure, you can actually figure out what happens when I inject that small failure? And then you can actually go ahead and fix it. One of the things I like to say to people is focus on what you're top five critical systems are. Let's fix those first. Don't go for low hanging fruit. Fix the biggest problems first, get rid of the biggest amount of pain that you have as a company, and then you can go ahead and actually... If you think about Pareto principle, the 80/20 rule, if you fix 20% of your biggest problems, you'll actually solve 80% of your issues. That always works. It's something that I've done while working at the National Australia Bank doing chaos engineering. Also at Gremlin, at Dropbox and I help a lot of our customers do that too. >> Alberto, talk about the mindset involved. It's the most counter intuitive. Whoa! Whoa! Risk! The biggest system. >> Yeah >> I don't want to touch those. They're working fine right now. And then these problems just gestate, they kind of hang around to the bin in the kitchen fire, this is okay, I don't want to touch it. The house is still working. So this is kind of a new mindset. Could you talk about what your take is on that? Is the industry there? I mean, it was a kind of a corner case, you had Netflix, you had the Chaos Monkey those days and then now it's a DevOps practice, for a lot of folks, you guys are involved in that. What's the appetite and what's the progress of chaos engineering in mainstream case? >> Yeah, it's interesting that you mentioned DevOps, and recently Gartner came up with a new, revisited DevOps framework that has chaos engineering in the middle of the lifecycle management of your application. And the reality is that systems have become so complex in infrastructure, so many layers of abstractions. You have hundreds of services if you're doing microservices, but even if you're not doing microservices, you have so many applications connected to each other, build really complex workflows and automation flows. It's impossible for traditional QA to really understand where the vulnerability are in terms of resiliency, in terms of quality. Too often the production environment is also too different from the staging environment, and so you need a fundamentally different approach to go and find where your weaknesses are and find them before they happen, before you end up finding yourself in a situation like the one we're into today and you are not prepared. And so, so much of what we talk about is giving a tool and the methodology for people to go and find these vulnerabilities. Not so much about creating chaos, but it's about managing chaos that is built into our current system and exposing those vulnerabilities before they create problem. And so that's a very scientific methodology and tooling that we bring to market and we help customers well. >> Tammy, I want to get your thoughts on something. We used to riff a lot with our 10th unit CUBE, we've had a lot of conversation we've riffed over the years, but you know when the surge of Amazon web services came out it was pretty obvious that cloud's amazing and look at the startups that were born, you mentioned Dropbox, you worked there. These companies, all these born on the cloud, these hyper scale, companies built from scratch, great way to scale up. And we used to joke about Google, people would say, "I would like a cloud like Google," but no one has Googles use cases. And Google really pioneered the SRE concept, and you got to give 'em a lot of props for that. But now we're kind of getting to a world where it's becoming Google-like. There's more scale now than ever before. It's not a corner case, it's becoming more popular and more of a preferred architecture, this large scale. What's your assessment of the main stream enterprises, how far are they in your mind, are they there with chaos? Are they close? Are they doing it? How does someone develop an SRE practice to get the Google-like scale? 'Cause Google has an amazing network, they got large scale cloud, they have SRE's, they've been doing it for years. How does a company that's transforming their IT (laughs) have SRE's? >> That's a great question. I get asked this a lot as well. One of our goals at Gremlin is to help make the internet more reliable for everybody. Everyone using the internet, all of the engineers who are trying to build reliable services, and so I'm often asked by companies all over the world, how do we create an SRE practice and how do we practice chaos engineering? But you can get started actually rolling out your SRE program. Based on my experiences, I've done it. So when I worked at Dropbox, I worked with a lot of people who had been at Google, they've been at YouTube, they were there when SRE was rolled out across those companies, and then they brought those learnings to Dropbox, and I learned from them. But also the interesting thing is if you look at enterprise companies, so large banks. Say for example, I worked at the National Australia Bank for six years, we actually did a lot of work that I would consider chaos engineering and SRE practices. So for example, we would do large scale disaster recovery, and that's where you'd fail over an entire data center to a secret data center in an unknown location, and the reason is 'cause you're checking to make sure that everything operates okay if there's a nuclear blast. That's actually what you have to do and you have to do that practice every quarter. But if you think about it, it's not very good to only do it once a quarter. You really want to be practicing chaos engineering and injecting failure on purpose. I think actually, I prefer to do it three times a week, so I do it a lot. But I'm also someone who likes to work out a lot and be fit all the time so I know that if you do something regularly, you get great results. So that's what I always tell everyone. >> Yeah, get the reps in, as we say, get stronger, get the muscle memory. >> Yep, exactly. >> Guys, talk about the event that's coming up. You've got an event that was scheduled, physical event and then you were right in the planning mode and then the crisis hits. You're going digital, going virtual, it's really digital, but it's digital. It's on the internet. So how are you guys thinking about this? I know its out there. It's April 21st. Can you share some specifics around the event? Who should be attending and how do they get involved online? >> Yeah, the event really came together about a month ago when we started to see all the cancellations happening across the industry because of COVID-19 and we were extremely engaged in the community and we have a lot of talks and we were seeing a lot of conferences just dropping and so speakers losing their opportunity to really share their knowledge with respect with how you do reliability and topics that we focus on. And so we quickly pivoted as a company and created a new online event to give everyone in the community the opportunity to just failover to a new event as the conference name says and have those speakers who'll have lost their speaking slots have a new opportunity to go share their knowledge. And so that came together really quickly, we shared the idea with a dozen of our partners and everyone liked it and all the sudden this thing took off like crazy and just a month where we are approaching 4,000 registrations, we have over 30 partners signed up and supporting the initiative. A lot of past partners as well covering the event. So it was impressive to see the amount of interest that we were able to generate in such a short amount of time. And really, this is a conference for anybody who is interested in resiliency. If you want to know from the best on how to build business continuity across systems, people and processes, this is a great opportunity at no cost really. It's a free conference. >> And the target persona and the audience you want to have attend is what? SREs or folks doing architectural work? What's the target >> Yeah >> person to attend? >> Architects, SREs, developers, business leaders who care about the quality and the reliability of their applications, who need to help create a framework and a mindset for their organizations that speaks to what Tammy was saying a minute ago. Having that constant practice on a daily basis about go and finding how to improve things. >> You know, Tammy we've been going to physical events with theCUBE and extracting the signal from the noise and distributed it digitally for 10 years and I got to ask you because now that those events have gone away, you talk about chaos and injecting failure. Doing these digital events is not as easy as just live streaming, it's hard to replicate the value of a physical event, years of experience and standards, roles and responsibilities to digital. A different consumption environment, it's asynchronous, you're trying to create a synchronous environment. It's its own complex system, so I think a lot of people who are experimenting and learning (laughs) from these events because it's pretty chaotic. So, I'd love to get your thoughts on how you look at these digital events as a chaos engineer. How should people be looking at these events? How are you guys looking at... I mean, obviously you want to get the program going, get people out there, get the content, but to iterate on this, how do you view this? >> It is really different. So I actually like to compare it to fire drills in SRE. So often what you do there is you actually create a fake incident or a fake issue, so you just, you were saying, "Let's have a fire drill." Similar to when you're in a building and you have a fire drill that goes off and you have wardens and everything and you all have to go outside. So we can do that in this new world that we're all in all of the sudden. A lot people have never run an online event and now all of a sudden they have to. So what I would say is like, do a fire drill. Run a fake one before you do the actual one to make sure that everything does work okay. My other tip is make sure that you have backup plans. Backup plans on backup plans on backup plans. As an SRE, I always have at least three to five backup plans. I'm not just saying plan A and plan B, but there's also a C, D, and E and I think that's very important and even when you're considering technology, one of the things we say with chaos engineering is, if you're using one service, inject failure and make sure that you can fail over to a different alternative servers in case something goes wrong. >> Yeah, hence the Failover Conference, which is the name of the conference. (chuckles) >> Exactly! >> Yeah, well we certainly are going to be sending a digital reporter there, virtually. If you need any backup plans, obviously we have the remote interviews here. If you need any help, let us know, really appreciate it. Great to see you guys. And thanks for sharing. Any final thoughts on the conference? What happens when we get through the other side of this? I'll give you guys a final word. We'll start with Alberto, with you first. >> Yeah, I think when we are on the other side of this, we'll understand even more the importance of effective resilience, architecting and testing. As a provider of tools and methodologies for that, we think we will be able to help customers when we do a significant leap forward on that side. And the conference is just super exciting. I think it's going to be a great event. I encourage everyone to participate. We have tremendous lineup of speakers that have incredible reputation in their field so I'm really happy and excited about the work that the team has been able to do with our partners put together at this type of event. >> Okay, Tammy. >> Yeah, for me, I'm actually going to be doing the opening keynote for the conference and the topic that I'm speaking about is that reliability matters more now than ever. And I'll be sharing some, bizarre, weird incidents that I have worked on myself that I have experienced, really critical strange issues that have come up. But yeah, I'm really looking forward to sharing that with everybody else, so please come along, it's free. You can join from your own home and we can all be there together to support each other. >> You got a great community support and there's a lot of partners, Press Media and ecosystem and customers, so congratulations Gremlin, having a conference on April 21st called the Failover Conference. TheCUBE and SiliconANGLE have a digital reporter there that will be covering the news. Thanks for coming on and sharing. I appreciate the time. I'm John Furrier in the Palo Alto studio with remote interview with Gremlin around their Failover Conference, April 21st. It's really demonstrating, in my opinion, the at scale problems that we've been working on the industry, now more applicable than ever before as we get post-pandemic with COVID-19. Thanks for watching. Be back. (calm music)

>>Live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Welcome back to the Cube's live coverage here at Cisco Live 2020 in Barcelona, Spain. I'm jumpers student of cube coverage. We've got a lot of stuff going on in Cisco Multi cloud and cloud technology. Quantification of Cisco's happening in real time is happening right now. Cloud is here here to stay. We got two great guests unpack what's going on in cloud native and networking and applications as the modern infrastructure and software evolves. We got you. Gene Kim, global product marketing. Compute Storage at Cisco Global marketing manager and Rob Gori, senior director. Cloud Solution Marketing Guys come back. Thanks for coming back. Appreciate it. Great to see you Barcelona guys. So, Bobby, we've had multiple conversations and you see that from the sales force given kind of the the discussion in the motivation Cloud is big. It's here. It's here to stay. It's changing. Cisco AP I first week here in all the products, it's changing everything. What's the story now? What's going on? >>I would say you know the reason why we're so excited about the launch here in Barcelona is because this time it's all about the application of spirits. I mean, the last two years we've being announcing some really exciting stuff in the cloud space where I think about all the announcements with AWS is the Googles the azure, so the world. But this time it really boils down to making sure that is incredibly hyper distributive world. There is an application explosion. Ultimately, we will help for the right operation stools and infrastructure management tools to ensure that the right application experience will be guaranteed for the end customer. And that's incredibly important because at the end, what really really matters is that you will ensure the best possible digital experience to your customer. Otherwise, ultimately nothing's gonna work. And, of course, you're gonna lose your brand and your customers. >>One of the main stories that we're covering is the transformation of the industry. Also, Cisco and one of the highlights to me was the opening keynote. You had APP dynamics first, not networking. Normally it's like what's in the hood? Routers and the gear. No, it was about the applications. This is the story we're seeing. It's kind of a quiet unveiling. Its not get a launch, but it's evolving very quickly. Can you share what's going on behind this? All this? >>Absolutely. It's exactly along the lines of what I was saying a second ago, in the end that the reason why we're driving the announcement, if you want from the application experience side of the House, is because with Appdynamics, we already have very, very powerful application performance management, which it's evolving extremely rapidly. First of all, Appdynamics can correlate not just the application for four months to some technology, maybe eyes, but through actual business KP eyes. So app dynamics can give you, for instance, serial time visibility off, say, a marketing funnel conversion rates transactions that you're having in your in your business operation. Now we're introducing an incredibly powerful new capability that takes the bar to a whole new level. And that's the Appdynamics experience. Journey maps. What are those? It's actually the ability off, focusing not so much on front ends and back ends and the business performances, but really focusing on what the user is seen in front of his or her screen. And so what really matters is capturing the journey that given user of your application is being and understanding whether the experience is the one that you want to deliver or you have, like, a sudden drop off somewhere. And you know why this is important because in the end we've been talking about is the problem of the application, performance issues or performance. It could be a badly designed page. How do you know? And so this is a very precious information they were giving to application developers know, just through the idea. Ops, guys, that is incredibly gracious. >>Okay, you want to get this in. So you just brought up that journey. So that's part of the news. Just break down real quick. One minute what the news is. >>Yeah, so we have three components. The 1st 1 as you as you correctly pointed out, is really the introduction of the application. The journey maps, right. The experience journey maps. That's very, very important. The second he's way are actually integrating Appdynamics with the inter site. Actually, inter site the optimization manager, the workload optimization, workload, optimizer. And so because there is exchange of data between the two now, you are in a position to immediately understand whether you have an application problem. We have a worker problem for structure problem, which is after me, where you really need to do as quickly as you can. And thirdly, way have introduced a new version of our hyper flex platform, which is hyper converge flagship platform for Cisco with a fully containerized version, the tax free if you want as well, that is a great platform for containerized applications. >>So you do and what I've been talking to customers last few years. When they go through their transformational journey, there's the modernization they need to do. The pattern I've seen most successful is first, modernize the platform often HD I is, you know, an option for that. It really simplifies the environment, reduces the silos on, has more of that operational model that looks closer to what the cloud experience is. And then, if I've got a good platform, then I can modernize the applications on top of it. But often those two have been a little bit disconnected. It feels like the announcements now that they are coming together. What are you seeing? What're you hearing? How your solutions at solving this issue >>exactly. I mean, as we've been talking to our customers, a lot of them are going through a different application. Modernizations and kubernetes and containers is extremely important to them. And to build a container cloud on Prem is extremely one of their needs. And so there's three distinctive requirements that they've kind of talk to us about. A lot of it has to be ableto it's got to be very simple, very turnkey, fully integrated, ready to turn on the other. One is something that's very agile, right? Very Dev Ops friendly and the third being a very economic container cloud on prim. So as you mentioned, High Flex Application Platform takes our hyper converge system and build on top of it a integrated kubernetes platform to deliver a container as a service type capability. And it provides a full stack, fully supported element platform for our customers, and one of the best great aspects of it is it's all managed from inter site, from the physical infrastructure to the hyper converge layer to all the way to the container management. So it's very exciting to have that full stack management and inter site as well. >>It's great to see you, John and I have been following this kubernetes wave since the early early days. Fabio mentioned integrations with the Amazons and Googles of the world because, you know, a few years ago you talk to customers and they're like, Oh, well, I'm just going to build my own community. Nobody ever said that is easy now. Just delivering as a service seems to be the way most people want it. So if I'm doing it on Amazon or Google, they've got their manage service that I could do that or that there partners we're working with. So explain what you're doing to make it simpler in the data center environment. Because on Prem absolutely is a piece of that hybrid equation that customers need. >>Yes, so, essentially from the customer experience perspective, as I mentioned, very fairly turnkey right from the hyper flex application platform we're taking are happening for software were integrating a application virtualization layer on top of it analytics k VM based. And then on top of that, we're integrating the kubernetes stack on top of as well. And so, in essence, right? It's a fully curated kubernetes stack that has all the different elements from the networking from the storage elements and provide that in a very turnkey way. And as I mentioned, the inter site management is really providing that simplicity that customers need for that management. >>Fabio This is the previous announcements you've made with the public clouds. This just ties into those hybrid environments. That's exactly a few years ago. People like, Oh, is there going to be a distribution that wins in kubernetes? We don't think that's the answer, but still, I can't just move between kubernetes. You know seamlessly yet. But this is moving toward that >>direct. Absolutely. A lot of customers want to have a very simple implementation. At the same time, they weren't off course a multi cloud approach and I really care about marking the difference between multi cloud hybrid Cloud has been a lot of confusion. But if you think about a multi cloud is re routed into the business need or harnessing innovation from wherever it comes from, you know the different clouds capability from things, and you know what they do today. Tomorrow it could even change, so people want optionality, so they want a very simple implementation that's integrated with public cloud providers that simplifies their life in terms of networking, security and application of workload management. And we've been executing towards that goal so fundamentally simplify the operations of these pretty complex kind of hybrid apartments. >>And once you nail that operations on hybrid, that's where multi cloud comes in. That's really just a connection point. >>Absolutely, you know, you might know is an issue. So in order to fulfill your business, your line of business needs you. Then you have a hybrid problem, and you want to really kind of have a consistent production grade environment between things on Prem that you own and control versus things that you use and you want to control better. Now, of course, they're different school thoughts. But most of the customers who are speaking with really want to expand their governance and technology model right to the cloud, as opposed to absorb in different ways of doing things from each and every time. >>I want to unpack a little bit of what you said earlier about the knowing where the problem is, because a lot of times it's a point, the finger at the other first, it's the application promising the problem, so I want to get into that. But first I want to understand the hyper flex application platform. Eugene, if you could just share the main problem that you guys solve, what are some of the pain points that customers had? What problem does the AP solved? >>Yeah, as I mentioned, it's really the platform for our customers to modernize the applications on right, and it addresses those things that they're looking for as far as the economics right, really? The ability to provide a full stack container experience without having to, you know, but bringing any third party hyper visor licenses as well support costs that's well integrated. There you have your integrated, hyper converged storage capability. You have the cloud based management, and that's really developing. You provide that developer dev ops simplicity from that agility that they're looking for internally as well as for their production environments. And then the other aspect is the simplicity to manage all this right and the entire life cycle management >>as well. So it's the operational side of the hole in under the covers hobby on the application side where the problem is because this is where I'm a bit skeptical, Normal rightfully so. But I can see a problem where it's like Whose fault is it? Applications, problem or the network? I mean, it runs on where? Sears Workloads, Banking app. It's having trouble. How do you know where the problem is? And how do you solve that problem with what's going on for that specific issue? >>Absolutely. And you know, the name of the game here is breaking down this operational side, right? And I love what are appdynamics VP? GM Any? Whitaker said. You know, he has this terminology. Beast develops, which it may sound like an interesting acrobatics, but it's absolutely too. The business has to be part of this operational kind of innovation because, as you said, you know, developer just drops their containers and their code to the I T. Ops team, but you don't really know whether the problem a certain point is going to be in the code or in the application is actually deployed. Or maybe a server that doesn't have enough CPU. So in the end, it boils down to one very important thing. You have to have visibility, insights and take action at every layer of the stack. Instrumentation. Absolutely. There are players that only do it in their software overlay domain. The problem is, very often these kind of players assume they're underneath. Things are fine, and very often they're not. So in the end, this visibility inside in action is the loop that everybody's going after these days, too, Really get to the next. If you want a generational operation, where you gotta have a constant feedback loop and making it more faster and faster because in the end you can only win in the marketplace, right? So your I T ops, if you're faster than your competitors, >>will still still questioning the GM of APP Dynamics. Run, observe, ability. And he's like, No, it's not a feature, it's everywhere. So he's comment was observe. Abilities don't really talk about it because it's a big in. You agree with that? >>Absolutely. It has to be at every layer of the stack, and only if you have visibility inside an action through the entire stock, from the software all the way to the infrastructure level that you can solve the problems. Otherwise, the finger pointing quote unquote will continue, and you will not be able to gain the speed you need. >>Okay, so The question on my mind I want to get both of you guys could weigh in on this is that if you look at Cisco as a company, you got a lot going on. You guys huge customer base core routers to know applications. There's a lot going on a lot of a lot of complexity. You got I o. T. Security members talking about that. You got the WebEx rooms totally popular. It's got a lot of glam, too, and having the WebEx kind of, I guess, what virtual presence was telepresence kind of model. And then you get cloud. Is there a mind share within the company around how cloud is baked into everything? Because you can't do I ot edge without having some sort of cloud operational things. Stuff we're talking about is not just a division. It's kind of it's kind of threads everywhere across Cisco. What's the what's the mind share right now within the Cisco teams and also customers around cloud ification? >>Well, I would say it's it's a couple of dimensions. The 1st 1 is the cloud is one of the critical domains of this multi domain architecture. That, of course, is the cornerstone of Cisco's. The knowledge is strategy, right? If you think about it, it's all about connecting users to applications wherever they are and not just the users to the applications themselves. Like if you look at the latest US from I. D. C. 58% of workloads is heading to a public cloud, and the edge is like the data center is exploding many different directions. So you have this highly distributed kind of fabric. Guess what sits in between. All these applications and micro services is a secure network, and that's exactly what we're executing upon. Now that's the first kind of consideration. The second is if you look at the other civil line. Most of the Cisco technology innovation is also going a direction of absorbing cloud as a simplified way of managing all the components or the infrastructure. You look at the hyper flex. AP is actually managed by Inter site, which is a SAS kind of component. This journey started long time ago with Cisco Iraqi on then, of course, we have sass properties like WebEx. Everything else absolutely migrate borders. >>We've been reporting Eugene that five years ago we saw the movement where AP, eyes were starting to come in when you go back five years ago. Not a lot of the gear and stuff that Cisco had AP eyes. Now you got AP eyes building in all the new products that you see the software shift with you intent based networking to APP dynamics. It's interesting. It's you're seeing kind of the agile mindset. This is something you and I talk all the time. But agile now is the new model. Is it ready for customers? I mean, the normal enterprises still have the infrastructure and separated, and they're like, Okay, how do I bring it together? What do you guys see in the customer base? What's going on with that early adopters, Heavy duty hardcore pioneers out there. But you know, the general mainstream enterprise. Are they there yet? Have they had that moment of awakening? >>Yeah, I mean, I think they they are there because fundamentally, it's all about ensuring that application experience. And you could only ensure the application experience right by having your application teams and infrastructure teams work together. And that's what's exciting. You mentioned Ap eyes and what we've done. They were with APP dynamics, integrating with inner sight workload. Optimizer as you mentioned all the visibility inside in action and what APP Dynamics has provides. Provide that business and end user application performance experience. Visibility Inter site. It's giving you visibility on the underlining workload, and the resource is whether it's on prim in your private data center environment or in a different type of cloud providers. So you get that full stack visibility right from the application all the way down to the bottom and then inter site local optimizer is then also optimizing the resource is to proactively ensure that application experience. So before you know, if we talk about someone at a check out and they're about there's of abandonment because the function is not working, we're able to proactively prevent that and take a look at all that. So, you know, in the end, I think it's all about ensuring that application experience and what we're providing with APP Dynamics is for the application team is kind of that horizontal visibility of how that application performing and at the same time, if there's an issue, the infrastructure team could see exactly within the workload topology, where the issue is and entertain safely, whether it be manual intervention or even automatically our ops capability. Go ahead and provide that action so the action could be, you know, scaling out the VM that's on Prem or looking at new, different type of easy to template in the cloud. That's a very exciting about this. It's really the application experience is now driving and optimize the infrastructure in real >>time. And let me flip your question like, Do you even have a choice, John, when you think about in the next two years 50% more applications? If you're a large enterprise here, 5 to 7000 apps you have another 2 3000 applications just coming into into the and then 50% of the existing ones that are going to be re factor lifted and shifted the replace or retired by SAS application. It's just like a tsunami that's that's coming on you and oh, by the way, because again the micro services kind of effect the number of dependencies between all these applications is growing incredibly rapidly, Like last year, we were eight average interdependencies for applications. Now we have 20 so in Beijing imaginable happens as you are literally flooded with this can really you have to ensure that your application infrastructure fundamentally will get tied up as quickly as you can >>see. You and I have been talking for at least five years now, if not longer. Networking has been the key kind of last change over clarification. I would agree with you guys. I think last question because I wanted to get your perspective. But think about it. It's 13 years since the iPhone so mobile has shown people that mobile app can change business. But now you get the pressure of the networks. Bringing that pressure on the network or the pressure of the network to be better than programmable is the rise of video and data. I mean, you got mobile check now you got it. Video. I mean more people doing video now than ever before. Videos of consumer. Well, it's streaming. You got data? These two things absolutely forced customers to deal with it. >>But what really tipped the balance? John is actually the SAS effect is the cloud effect because, as you know, it's an I t. So the inflection points. Nothing gets a linear right. So once you reach a certain critical mass of cloud apps, and we're absolutely they're already all of a sudden your traffic pattern on your network changes dramatically. So why in the world are you continuing? Kind of, you know, concentrating all of your traffic in your data center and then going to the Internet. You have to absolutely open the floodgates at the branch level and as close to the users this possible, and that it implies a radical change of the >>way I would even add to that. And I think you guys are right on where you guys are going. It may be hard to kind of tease out with all the complexity with Cisco, but in the keynote, the business model shifts come from SAS. So you got all this technical stuff going on. You have the sass ification, or cloud changes the business models so new entrants can come in and existing players get better. So I think that whole business model conversation never was discussed at Cisco Live before in depth. Okay, run your business, connect your hubs campus move packets around Dallas applications in business model, >>but also the fact that there is increasing number off software capabilities and so fundamental. You want to simplify the life of your customers through subscription models that help the customer buying a using what they really need the right at any given point in time, all the way to having enterprise agreements. >>I also think that's about delivering these application experiences free for small, different experience. That's really what's differentiating you from your competitors, right? And so that's a different type of >>shift as well. Well, you guys have got a good That's a good angle on this cloud. I love it. I got to ask the question. What can we expect next from Cisco? More progression along cloud ification? What's next? >>Well, I would say we've been incredibly consistent, I believe in the last few years in executing on our cloud strategy, which again is sent around helping customers really gluing this mix, set off data centers and clouds to make it work as one right as much as possible. And so what we really deliver is networking security and application performance management, and we're integrating this more and more on the two sides of the equation, right? The data center side and the public cloud side and more more integrated in between all of these layers again, to fundamentally give you this operational capability to get faster and faster. We'll continue doing so and >>we'll get you set up before we came on camera that you were talking to sales teams. What are they? What's the vibe with sales team? They get excited by this. What's the >>oh yeah, feedback. And absolutely, from the inter site work optimizer and the app Dynamics side. It's very exciting for them. Switch the conversation they're having with their customers, really from that application experience and proactively ensuring it. And on the hyper flex application platform side, this is extreme exciting with providing a container cloud to our customers. And you know what's coming down is more and more capabilities for our customers to modernize the applications on hyper >>flex. You guys are riding a pretty big waves here at Cisco in a cloud way to get the i o t. Security wave. Great stuff. Thanks for coming in. Thanks for sharing the insights. Appreciate it. >>Thank you for having >>coverage here in Barcelona. I'm John. First, Minutemen back with more coverage. Fourth day of four days of cube coverage. Be right back after this short break. >>Yeah, yeah, yeah.

>> From the Silicon Angle Media Office in Boston Massachusetts, it's "The Cube." (groovy techno music) Now, here's your host, Dave Vellante. >> Hello, everyone. The rise of open source is really powering the digital economy. And in a world where every company is essentially under pressure to become a software firm, open source software really becomes the linchpin of digital services for both incumbents and, of course, digital natives. Here's the challenge, is when developers tap and apply open source, they're often bringing in hundreds, or even thousands of lines of code that reside in open sourced packages and libraries. And these code bases, they have dependencies, and essentially hidden traps. Now typically, security vulnerabilities in code, they're attacked after the software's developed. Or maybe thrown over the fence to the sec-ops team and SNYK is a company that set out to solve this problem within the application development life cycle, not after the fact as a built-on. Now, with us to talk about this mega-trend is Peter McKay, a friend of The Cube and CEO of SNYK. Peter, great to see you again. >> Good to see you, dude. >> So I got to start with the name. SNYK, what does it mean? >> SNYK, So Now You Know. You know, people it's sneakers sneak. And they tend to use the snick. So it's SNYK or snick. But it is SNYK and it stands for So Now You Know. Kind of a security, so now you know a lot more about your applications than you ever did before. So it's kind of a fitting name. >> So you heard my narrative upfront. Maybe you can add a little color to that and provide some additional background. >> Yeah, I mean, it's a, you know, when you think of the larger trends that are going on in the market, you know, every company is going through this digital transformation. You know, and every CEO, it's the number one priority. We've got to change our business from, you know, financial services, healthcare, insurance company, whatever, are all switching to digital, you know, more of a software company. And with that, more software equals more software risk and cybersecurity continues to be, you know, a major. I think 72% of CEOs worry about cybersecurity as a top issue in protecting companies' data. And so for us, we've been in the software in the security space for the four and a half years. I've been in the security space since, you know, Watchfire 20 years ago. And right now, with more and more, as you said, open source and containers, the challenge of being able to address the cybersecurity issues that have never been more challenging. And so especially when you add the gap between the need for security professionals and what they have. I think it's four million open positions for security people. So you know, with all this added risk, more and more open source, more and more digitization, it's created this opportunity in the market where you're traditional approaches to addressing security don't work today, you know? Like you said, throwing it over the fence and having someone in security, you know, check and make sure and finding all these vulnerabilities, and throw it back to developers to fix is very slow and something at this point is not driving to success. >> So talk a little bit more about what attracted you to SNYK early. I mean, you've been with the company, you're at least involved in the company for a couple years now. What were the trends that you saw, and what was it about SNYK that, you know, led you to become an investor and ultimately, CEO? >> Yeah, so four years involved in the business. So you know, I've always loved the security space. I've been in it for a number, almost 20 years. So I enjoy the space. You know, I've watched it. The founder, Guy Podjarny, one of the founders of SNYK, has been a friend of mine for 16 years from back in the Watchfire days. So we've always stayed connected. I've always worked well together with him. And so when you started, and I was on the board, the first board member of the company, so I could see what was going on, and it was this, you know, changing, kind of the right place at the right time in terms of developer first security. Really taking all the things that are going on in the security space that impacts a developer or can be addressed by the developer, and embedding it into the software into that developer community, in a way that developers use, the tools that they use. So it's a developer-first mindset with security expertise built-in. And so when you look at the market, the number of open source container evolution, you know, it's a huge market opportunity. Then you look at the business momentum, just took off over the past, you know, four years. That it was something that I was getting more and more involved in. And then when Guy asked me to join as the CEO, it was like, "Sure, what took you so long?" (Dave laughing) >> We had Guy on at Node JS Summit. I want to say it was a couple years ago now. And what he was describing is when you package, take the example of Node. When you package code in Node, you bring in all these dependencies, kind of what I was talking about there, but the challenge that he sort of described was really making it seamless as part of the development workflow. It seems like that's unique to SNYK. Maybe you could talk about-- >> Yeah, it is. And you know, we've built it from the ground up. You know, it's very difficult. If it was a security tool for security people, and then say, "Oh, let's adapt it for the developer," that is almost impossible. Why I think we've been so successful from the 400,000 developers in the community using Freemium to paid, was we built it from the ground up for developer, embedded into the application-development life cycle. Into their process, the look and feel, easy for them to use, easy for them to try it, and then we focused on just developer adoption. A great experience, developers will continue to use it and expand with it. And most of our opportunities that we've been successful at, the customers, we have over 400 customers. That had been this try, you know, start it with the community. They used the Freemium, they tried it for their new application, then they tried it for all their new, and then they go back and replace the old. So it was kind of this Freemium, land and expand has been a great way for developers to try it, use it. Does it work, yes, buy more. And that's the way we work. >> We're really happy, Peter, that you came on because you've got some news today that you're choosing to share with us in our Cube community. So it's around financing, bring us up to date. What's the news? >> Yeah so you know, I'd say four months ago, five months ago, we raised a $70 million round from great investors. And that was really led by one of our existing investors, who kind of knew us the best and it was you know, Excel Venture, and then Excel Growth came in and led the $70 million round. And part of that was a few new investors that came in and Stripes, which is you know a very large growth equity investor were part of that $70 million round said you know, preempted it and said, "Look it, we know you don't need the money, but we want to," you know, "We want to preempt. We believe your customer momentum," here we did, you know, five or six really large deals. You know, one, 700, seven million, 7.4 million, one's 3.5 million. So we started getting these bigger deals and we doubled since the $70 million round. And so we said, "Okay, we want to make money not the issue." So they led the next round, which is $150 million round, at a valuation of over a billion. That really allows us now to, with the number of other really top tier, (mumbles) and Tiger and Trend and others, who have been part of watching the space and understand the market. And are really helping us grow this business internationally. So it's an exciting time. So you know, again, we weren't looking to raise. This was something that kind of came to us and you know, when people are that excited about it like we are and they know us the best because they've been part of our board of directors since their round, it allows us to do the things that we want to do faster. >> So $150 million raise this round, brings you up to the 250, is that correct? >> Yes, 250. >> And obviously, an up-round. So congratulations, that's great. >> Yeah, you know, I think a big part of that is you know, we're not, I mean, we've always been very fiscally responsible. I mean, yes we have the money and most of it's still in the bank. We're growing at the pace that we think is right for us and right for the market. You know, we continue to invest product, product, product, is making sure we continue our product-led organization. You know, from that bottoms up, which is something we continue to do. This allows us to accelerate that more aggressively, but also the community, which is a big part of what makes that, you know, when you have a bottoms up, you need to have that community. And we've grown that and we're going to continue to invest aggressively and build in that community. And lastly, go to market. Not only invest, invest aggressively in the North America, but also Europe and APJ, which, you know, a lot of the things we've learned from my Veeam experience, you know how to grow fast, go big or go home. You know, are things that we're going to do but we're going to do it in the right way. >> So the Golden Rule is product and sales, right? >> Yes, you're either building it or selling it. >> Right, that's kind of where you're going to put your money. You know, you talk a lot about people, companies will do IPOs to get seen, but companies today, I mean, even software companies, which is a capital-efficient industry, they raise a lot of dough and they put it towards promotion to compete. What are your thoughts on that? >> You know, we've had, the model is very straightforward. It's bottoms up, you know? Developers, you know, there's 28 million developers in the world, you know? What we want is every one of those 28 million to be using our product. Whether it's free or paid, I want SNYK used in every application-development life cycle. If you're one developer, or you're a sales force with standardized on 12,000 developers, we want them using SNYK. So for us, it's get it in the hands. And that, you know, it's not like-- developers aren't going to look at Super Bowl ads, they're not going to be looking. It's you know, it's finding the ways, like the conference. We bought the DevSecCon, you know, the conference for developer security. Another way to promote kind of our, you know, security for developers and grow that developer community. That's not to say that there isn't a security part. Because, you know, what we do is help security organizations with visibility and finding a much more scalable way that gets them out of the, you know, the slows-down, the speed bump to the moving apps more aggressively into production. And so this is very much about helping security people. A lot of times the budgets do come from security or dev-ops. But it's because of our focus on the developer and the success of fixing, finding, fixing, and auto-remediating that developer environment is what makes us special. >> And it's sounds like a key to your success is you're not asking developer to context switch into a new environment, right? It's part of their existing workflow. >> It has to be, right? Don't change how they do their job, right? I mean, their job is to develop incredible applications that are better than the competitors, get them to market faster than they can, than they've ever been able to do before and faster than the competitor, but do it securely. Our goal is to do the third, but not sacrifice on one and two, right? Help you drive it, help you get your applications to market, help you beat your competition, but do it in a secure fashion. So don't slow them down. >> Well, the other thing I like about you guys is the emphasis is on fixing. It's not just alerting people that there's a problem. I mean, for instance, a company like Red Hat, is that they're going to put a lot of fixes in. But you, of course, have to go implement them. What you're doing is saying, "Hey, we're going to do that for you. Push the button and then we'll do it," right? So that, to me, that's important because it enables automation, it enables scale. >> Exactly, and I think this has been one of the challenges for kind of more of the traditional legacy, is they find a whole bunch of vulnerabilities, right? And we feel as though just that alone, we're the best in the world at. Finding vulnerabilities in applications in open source container. And so the other part of it is, okay, you find all them, but prioritizing what it is that I should fix first? And that's become really big issue because the vulnerabilities, as you can imagine, continue to grow. But focusing on hey, fix this top 10%, then the next, and to the extent you can, auto-fix. Auto-remediate those problems, that's ultimately, we're measured by how many vulnerabilities do we fix, right? I mean, finding them, that's one thing. But fixing them is how we judge a successful customer. And now it's possible. Before, it was like, "Oh, okay, you're just going to show me more things." No, when you talk about Google and Salesforce and Intuit, and all of our customers, they're actually getting far better. They're seeing what they have in terms of their exposure, and they're fixing the problems. And that's ultimately what we're focused on. >> So some of those big whales that you just mentioned, it seems to me that the value proposition for those guys, Peter, is the quality of the code that they can develop and obviously, the time that it takes to do that. But if you think about it more of a traditional enterprise, which I'm sure is part of your (mumbles), they'll tell you, the (mumbles) will tell you our biggest problem is we don't have enough people with the skills. Does this help? >> It absolutely-- >> And how so? >> Yeah, I mean, there's a massive gap in security expertise. And the current approach, the tools, are, you know, like you said at the very beginning, it's I'm doing too late in the process. I need to do it upstream. So you've got to leverage the 28 million developers that are developing the applications. It's the only way to solve the problem of, you know, this application security challenge. We call it Cloud Dative Application Security, which all these applications usually are new apps that they're moving into the Cloud. And so to really fix it, to solve the problem, you got to embed it, make it really easy for developers to leverage SNYK in their whole, we call it, you know, it's that concept of shift left, you know? Our view is that it needs to be embedded within the development process. And that's how you fix the problem. >> And talk about the business model again. You said it's Freemium model, you just talked about a big seven figure deals that you're doing and that starts with a Freemium, and then what? I upgrade to a subscription and then it's a land and expand? Describe that. >> Yeah we call it, it's you know, it's the community. Let's get every developer in a community. 28 million, we want to get into our community. From there, you know, leverage our Freemium, use it. You know, we encourage you to use it. Everybody to use our Freemium. And it's full functionality. It's not restricted in anyway. You can use it. And there's a subset of those that are ready to say, "Look it, I want to use the paid version," which allows me to get more visibility across more developers. So as you get larger organization, you want to leverage the power of kind of a bigger, managing multiple developers, like a lot of, in different teams. And so that kind of gets that shift to that paid. Then it goes into that Freemium, land, expand, we call it explode. Sales force, kind of explode. And then renew. That's been our model. Get in the door, get them using Freemium, we have a great experience, go to paid. And that's usually for an application, then it goes to 10 applications, and then 300 developers and then the way we price is by developer. So the more developers who use, the better your developer adoption, the bigger the ultimate opportunity is for us. >> There's a subscription service right? >> All subscription. >> Okay and then you guys have experts that are identifying vulnerabilities, right? You put them into a database, presumably, and then you sort of operationalize that into your software and your service. >> Yeah, we have 15 people in our security team that do nothing everyday but looking for the next vulnerability. That's our vulnerability database, in a large case, is a lot of our big companies start with the database. Because you think of like Netflix and you think of Facebook, all of these companies have large security organizations that are looking for issues, looking for vulnerabilities. And they're saying, "Well okay, if I can get that feed from you, why do I have my own?" And so a lot of companies start just with the database feed and say, "Look, I'll get rid of mine, and use yours." And then eventually, we'll use this scanning and we'll evolve down the process. But there's no doubt in the market people who use our solution or other solution will say our known the database of known vulnerabilities, is far better than anybody else in the market. >> And who do you sell to, again? Who are the constituencies? Is it sec-ops, is it, you know, software engineering? Is it developers, dev-ops? >> Users are always developers. In some cases dev-ops, or dev-sec. Apps-sec, you're starting to see kind of the world, the developer security becoming bigger. You know, as you get larger, you're definitely security becomes a bigger part of the journey and some of the budget comes from the security teams. Or the risk or dev-ops. But I think if we were to, you know, with the user and some of the influencers from developers, dev-ops, and security are kind of the key people in the equation. >> Is your, you have a lot of experience in the enterprise. How do you see your go to market in this world different, given that it's really a developer constituency that you're targeting? I mean, normally, you'd go out, hire a bunch of expensive sales guys, go to market, is that the model or is it a little different here because of the target? >> Yeah, you know, to be honest, a lot of the momentum that we've had at this point has been inbound. Like most of the opportunities that come in, come to us from the community, from this ground up. And so we have a very large inside sales team that just kind of follows up on the inbound interest. And that's still, you know, 65, 70% of the opportunities that come to us both here and Europe and APJ, are coming from the community inbound. Okay, I'm using 10 licenses of SNYK, you know, I want to get the enterprise version of it. And so that's been how we've grown. Very much of a very cost-effective inside sales. Now, when you get to the Googles and Salesforces and Nordstroms of the world, and they have already 500 licenses us, either paid or free, then we usually have more of a, you know, senior sales person that will be involved in those deals. >> To sort of mine those accounts. But it's really all about driving the efficiency of that inbound, and then at some point driving more inbound and sort of getting that flywheel effect. >> Developer adoption, developer adoption. That's the number one driver for everybody in our company. We have a customer success team, developer adoption. You know, just make the developer successful and good things happen to all the other parts of the organization. >> Okay, so that's a key performance indicator. What are the, let's wrap kind of the milestones and the things that you want to accomplish in the next, let's call it 12 months, 18 months? What should we be watching? >> Yeah, so I mean it continues to be the community, right? The community, recruiting more developers around the globe. We're expanding, you know, APJ's becoming a bigger part. And a lot of it is through just our efforts and just building out this community. We now have 20 people, their sole job is to build out, is to continue to build our developer community. Which is, you know, content, you know, information, how to learn, you know, webinars, all these things that are very separate and apart from the commercial side of the business and the community side of the business. So community adoption is a critical measurement for us, you know, yeah, you look at Freemium adoption. And then, you know, new customers. How are we adding new customers and retaining our existing customers? And you know, we have a 95% retention rate. So it's very sticky because you're getting the data feed, is a daily data feed. So it's like, you know, it's not one that you're going to hook on and then stop at any time soon. So you know, those are the measurements. You look at your community, you look at your Freemium, you look at your customer growth, your retention rates, those are all the things that we measure our business by. >> And your big pockets of brain power here, obviously in Boston, kind of CEO's prerogative, you got a big presence in London, right? And also in Israel, is that correct? >> Yeah, I would say we have four hubs and then we have a lot of remote employees. So, you know, Tel Aviv, where a lot of our security expertise is, in London, a lot of engineering. So between London and Tel Aviv is kind of the security teams, the developers are all in the community is kind of there. You know, Boston, is kind of more go to market side of things, and then we have Ottawa, which is kind of where Watchfire started, so a lot of good security experience there. And then, you know, we've, like a lot of modern companies, we hired the best people wherever we can find them. You know, we have some in Sydney, we've got some all around the world. Especially security, where finding really good security talent is a challenge. And so we're always looking for the best and brightest wherever they are. >> Well, Peter, congratulations on the raise, the new role, really, thank you for coming in and sharing with The Cube community. Really appreciate it. >> Well, it's great to be here. Always enjoy the conversations, especially the Patriots, Red Sox, kind of banter back and forth. It's always good. >> Well, how do you feel about that? >> Which one? >> Well, the Patriots, you know, sort of strange that they're not deep into the playoffs, I mean, for us. But how about the Red Sox now? Is it a team of shame? All my friends who were sort of jealous of Boston sports are saying you should be embarrassed, what are your thoughts? >> It's all about Houston, you know? Alex Cora, was one of the assistant coaches at Houston where all the issues are, I'm not sure those issues apply to Boston, but we'll see, TBD. TBD, I am optimistic as usual. I'm a Boston fan making sure that there isn't any spillover from the Houston world. >> Well we just got our Sox tickets, so you know, hopefully, they'll recover quickly, you know, from this. >> They will, they got to get a coach first. >> Yeah, they got to get a coach first. >> We need something to distract us from the Patriots. >> So you're not ready to attach an asterisk yet to 2018? >> No, no. No, no, no. >> All right, I like the optimism. Maybe you made the right call on Tom Brady. >> Did I? >> Yeah a couple years ago. >> Still since we talked what, two in one. And they won one. >> So they were in two, won one, and he threw for what, 600 yards in the first one so you can't, it wasn't his fault. >> And they'll sign him again, he'll be back. >> Is that your prediction? I hope so. >> I do, I do. >> All right, Peter. Always a pleasure, man. >> Great to see you. >> Thank you so much, and thank you for watching everybody, we'll see you next time. (groovy techno music)

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a Cube Conversation. >> Hi, welcome to another Wikibon digital community event, this one sponsored by BMC Software. Every organization faces the challenge of how to do service management and operations management better. The ideal is to start bringing them together, but traditionally, they've been undertaken by different groups, often utilizing different tools. And that's what we're going to talk about today in today's digital community event. What can we do to improve our digital business operations, competitiveness, and customer experience, by doing a better job of bringing together those core resources that handle service management and operations management activities. As with all digital community events, this one's going to feature some upfront conversations with a number of thought leaders in this crucial space, and then we're going to run a crowd chat, which will be your opportunity to share your insights, ask your questions, and ultimately, communicate with others like you in the community that focuses on this important issue. So stay through to the end and help us participate in that digital community event. Now, recently, I had an opportunity to attend BMC Helix's Immersion Day, and while there, theCUBE was able to conduct a number of different interviews. One of the best ones we had was a great conversation with Nayaki Nayyar, who is the president of service management and operations management at the BMC Helix division, and Mihir Shukla, who is the CEO of Automation Anywhere. Let's hear what they had to say about the potential of bringing service management and operations management together. >> So, Nayaki, I want to start with you. A year ago, we started on this journey of how this new digital services platform is going to evolve to do more types of work for people. How has BMC's Helix platform evolved in that time? >> So, if you remember last time, it was almost a year back when we launched Helix, which was all around taking the service management capability that we had on prem, made it available in Cloud, containerized, so customers can run in cloud of their choice, and provided experience through various channels, bought as a channel of that customer experience. This is what we had released last time, we call it the three C's for Helix, everything in cloud, containerized, with cognitive capabilities, so customers can transform their experience. In this version, what we are extending Helix is with the operations side, so all the ITOM capabilities that we have in our platform are now a part of Helix, so we have one end-to-end platform, so that customers can discover every asset that they have on prem and cloud, monitor those assets, detect any anomalies, service both for lines of business and for IT, remediate any issues that happen, vulnerabilities that are there in the system, and automatically optimize capacity and cost and holistically, this whole closed loop of operations and service coming together is what this next wave of innovations that we are launching with BMC Helix. >> So, Mihir, Nayaki's talked about, very successfully, and Helix has been a very successful platform for improving user experience, but up front I noted that we're not just talking about human beings as users anymore, we're now talking about software as users. RPA, robotic process automation, is a central feature of some of these new trends. Tell us a little bit about how robotic process automation is driving an increased need for this kind of digital service in operations management capability. >> Sure, think in a high level, you have to think of the new organization as augmented organization that are human and bots working side by side, each doing what they're best at. And so in a specific example of a service organization, where BMC Helix is taking this is, think of this as a utility, where the way you plug it into an electricity outlet and switch on the light and you get the electricity, you plug into the BMC Helix, and behind it you have augmented workflows of chart bots, RPA bots, human beings, each doing what they're best at, and giving a far superior customer experience, unlike any other. That is happening now, and that's the future of service industry. >> But when you plug a human, so to speak, metaphorically, into that system, there's a certain amount of time, there's a certain amount of training, and as a consequence you can have a little bit more predictable scale. That doesn't mean that you don't end up with a lot of complexity, but RPA seems, the potential of RPA seems that you're going to increase the rate at which these users, in this case, digital users, are going to enter into the system, you don't have a training regimen you can attach to them, they have to be tested, they have to be discovered, they have to be put in operation with reliability, how is that ultimately driving the need for some of these new capabilities? >> I think if you think of these bots as digital workers, you almost have to go through the same process that you would go through human beings. You onboard them, in terms of, you configure them, you train them with cognitive capabilities, and then the one difference is they monitor themselves, without any bias, they can give their own performance rating card. But the beauty of this is when human and bots work together, because there are some functions that the bots can do well and then at some point they can hand off to the human beings, and human beings do some of the more interesting work that is based on judgment call, customer service, all of that. So that the combination is the end goal for everybody. >> And to add to what Mihir said, right, that customer experience, whether you're providing an experience to employees or consumers or end customers, that is the ultimate goal, that's the ultimate result of what you want to get, and the speed at which you provide that experience is the accuracy at which you provide experience, the cost at which you provide that experience becomes a comparative differentiation, which is where all this automation, this augmentation that they're doing with humans and bots, is what enables us to do that, right? For all large enterprise customers, major service organizations trying to transform into that future goal. >> But increasingly it seems as though the things that we have to do, to orchestrate and administrate, more users, digital and human, undertaking more complex tasks where each is best applied, is really driving a lot of new data, as I mentioned upfront, an enormous amount of new software, and you said new experiences, but those experiences have to be reliable, have to be secure, they have to be predictable. So that suggests this overwhelming impact of all of these capabilities. You talk about a digital tsunami. What are some of the key things that you think enterprises are going to have to do to start engaging that? >> Yeah, and whether we call it revolution, whether we call it digital transformation, I think what we all are experiencing is a tsunami, tech tsunami, right, tsunami of clouds where you have professional clouds, private clouds, hybrid clouds, managed clouds. Tsunami of devices, not just the mobile devices, but also as everything is getting connected, IoT devices. Tsunami of channels, as an end user, I want to experience that in the channel of my preference, Slack as a channel, SM as a channel. A tsunami of bots, of conversation bots and RPA bots, so in this tsunami, I think what everyone is trying to figure out is, how do they manage this explosion? It's humanly impossible to do it all manually you have to augment it, with of course, intelligence AIML, but then of course bots become a big part of that augmentation to orchestrate all of that back to back process. >> I would say that this is no longer nice to have, because if you look at it from a more consumer's perspective, last 20 years of digital technologies from Amazons and Googles of the world, Netflix and others, they have created this mindset of instant customer gratification. And we all been trained for it, so what was acceptable five years ago is no longer acceptable in our own lives. And so this new standard of instant result, instant outcome, instant respond, instant delivery, we just expect it, right? Once your end consumer begins to do that, we as a business no longer have a choice, that's writing on the wall. And so what these new platforms are doing, like with BMC Helix and Automation Anywhere, is delivering that instant gratification, right? And when you think about it more and more of the new customers that are millennials, they don't know any other way. So for them, this is the only experience they will relate to, so again, this is not nice to have. It is the only way world will operate, right? >> We're going to turn back to the conversation that I had with Nayaki and Mihir shortly, but first, let's see what BMC's actually doing as they try to bring together service management and operations management, by watching a quick demo that they've prepared. (techno music) (music continues) >> Great demonstration of how these technologies are coming together in a real world sense. Now let's hear more of the conversation I had with Nayaki and Mihir about bringing together service management and operations management, but specifically focusing on how this class of technology is going to be extended, and made even more powerful for business as they think about not just IT, but other classes of automation. Let's hear what they had to say. >> So if you look at large organizations, they have vast amount of applications. Sometimes 400, 800, few thousand. And what we have been doing historically is using people as a human bridges between these applications, and we have operated that way for too long, and that's the world today. >> So humans are the interface, they're the system interfaces. >> Humans are the bridges between applications, and we often call it a swivel chair operations, that's an easiest way to describe it. So what Automation Anywhere does, is it offers this technology platform, robotic process automation, AI in an RTX platform, that integrates all of it together into a seamless automation bot that can go across, and with AI it can make intelligent choices. And so now we can take that, combined with the BMC Helix, and you have a seamless service platform that can deliver a superior experience. >> So we've got now the swivel chair users, now being software, which means that we can discover them more easily, we can monitor them more easily, and that feeds Helix. >> Absolutely, so you know in our consumer world, in our day to day life, we are used to a certain experience of how we consume data or consume experiences with our TVs and all the channels. That experience that we have in our day to day life is what people expect when they walk into the company, right, walk into the enterprise, which every IT organization is trying to figure out how do they get to that level of maturity. So this is what the combination of what we are doing with Helix and Automation Anywhere, brings that consumer grid experiences into an enterprise world. >> So Mihir, when we think about RPA, we're applying it in interesting and innovative ways, no question about it. But there are certain patterns of success, give us some visibility into what you are seeing leads to success, and then what's the future of RPA, how's that going to evolve over the next few years? >> Sure, so RPA has been deployed across virtually every industry and virtually every department. So there are many ways to get started and all of them are right. But often we find is that you can either start in a central organization wherein that organization is doing everything centrally. It is a great way to get started, but eventually we learn that the federated way's the best way to end. Where hundreds of offices all over the world, if you're especially a large organization, each business unit is doing it with IT providing governance and central security and policies, and actual bots running and being implemented all over the world. Eventually for a large-scale transformation, there is a common pattern we have seen among successful customers. >> And where do you think this pattern going to evolve, as enterprises gain more familiarity with it, innovate in new and interesting ways, and as Automation Anywhere and others advance the state of the art, where do you think it's going to end up? >> The rate it's going is, is I define it as an app store experience or a Google Play experience. So if you think about how we operate our mobile devices today, if you want something on your device, you will look for an app that does that. We are getting to a point where there is bot for everything, and a digital worker for everything, so if you need certain job done, you first go to a bot store, that is an Automation Anywhere website, look for a bot that does something, hire or download that bot, get the work done, and it comes prebuilt like many there are works with BMC Helix, and many others. So that is your first way you will look for getting your work done in a new bot economy, and if there's no bot available, then you look for other options. It will transform how we work and how we think of work. >> In many respects, it's the gig economy with perfect contractor, right? And it leads to some very interesting challenges, ultimately, when we start thinking about services. So Nayaki, based on what Mihir just talked about, where does digital services go as RPA joins other classes of users in creating those new experiences at new profit points and new value propositions? >> It becomes a compare of how you provide that service, can become a big competitive differentiation for financial institutions, for Telcos, which is a service industry, right, you provide that service, and like to Mihir's point, when the user hits that switch, they expect the light to come on, so if I'm an end user, the consumer, wanting a service from my Telco provider or from my financial institution, I expect that service to be instantaneous, and the highest accuracy, accuracy at which you provide is going to start driving competitive differentiation from financial institution to financial institution, Telco to Telco, and that's how I see companies differentiating and really surviving or thriving in the long term. >> Now let's hear from a really important partner, a CDO, someone who's thinking about how these technologies are going to be applied to the front lines of business change. Sanjay Srivastava is the CDO at Genpact, and he and I had a great conversation at BMC Immersion Days about what this means to digital business transformation. How will service management and operations management in combination accelerate and make more successful businesses' efforts to transform digitally. Let's hear what Sanjay had to say. >> So tell us a little bit about, what is a digital service outcome and why is it so important? >> Yeah, well I think the reality is that what technology is doing is it's disintermediating the ecosystem, so many of the industries are clients-operated, and they have to go back and reimagine their value proposition at the core of what they do with the use of new, innovative technologies, and it's that intersection of new capabilities, of new innovative business models that really use emerging technologies, but intersect them with their business models, with their business processes, and the requirements of their clients, and help them rethink, reimagine, and deliver their new value proposition. That's really what it's all about. >> So a digital service outcome would then be the things that the business must do and must do well, but ideally, with a different experience or with a different degree of flexibility and agility, or with a different cost profile, have I got that right? >> Correct. >> So when we think about that, what are some of the key elements of a digital service success? >> We like to think about three critical success factors in driving any digital transformation. The first one is the notion of experience, and what I mean by that is not user interface for a piece of software, but the journey of a customer, an employee, a provider, a partner, in engaging with you and your business model. When we think about journey mapping that scientifically, we think about design, thinking on the back of that, and we think about re-imagining what the new experience looks like. One of the largest things we've got in the industry is digital transformation on the back of cost take out of productivity or efficiency is insufficient drive and optimize the value that digital can bring. And using experience as the compass, as sort of the north star in that journey is a meaningful differentiator and driver of business benefit, so that's number one. I think the second area that's become increasingly apparent is the intersection of domain with digital. And the thinking there is that to materialize the benefit of digital in an enterprise, you have to intersect it with the specifics of that business, how users interact, what clients seek, how does business actually happen? We talk about artificial intelligence a lot, we do a lot of work in AI as an example, and the key thing about machine learning is goal orientation, and what is goal orientation? It's about understanding the specifics of the environments, you can actually orient the goal of the machine learning algorithm to deliver high accuracy results. And it's something that can often easily get overlooked, so indexing on the two halves of the whole, the yin and the yang, the piece around digital, and the innovative technologies, and being able to leverage and take advantage of them, but equally, be founded in domain, understand the environment, and use that knowledge to drive the right materialization of the end outcome. And that's the second critical success factor, I think, to get it right. I think the third one is the notion of how do you build a framework for innovation? You know, it's not the sort of thing where a large fortune company, Fortune 500 companies can necessarily experiment and it's a little bit of a go happy go lucky strategy, doesn't really work, you have to innovate at scale, you have to do it in a fundamental fashion, you have to do it as a critical success factor. And so one of the biggest things we focus on is how do you innovate at the edge? Innovation must be at the edge, this is where the rubber meets the road. But governance has to be at the core. >> Well let me build on that for a second, 'cause you said innovation's at the edge, so basically that means where the brand promise is being enacted for the customer, and that could be at an industrial automation setting or it could be in just making a recommendation, it could be any number of things, but it's where the value proposition is realized for the customer. >> Correct, that's exactly right, and that's where innovation must happen. So as a large corporation, you must be able, it's important to set up a framework that allows you to do innovation at the edge, otherwise it's not meaningful innovation if you, "Well, it's just a lot of busy work." And yet as you do that, and as you change your business model, as you bring new components to the equation, how do you drive governance, and it's increasingly becoming more important, you think about, we're going to be in a AI first world increasingly, more and more that's the reality of the world we're going in, and in that AI first world, I work here in Palo Alto, walk into my office, a couple of hundred people any given day. If tomorrow morning I walked in and 100 people didn't show up for work, I would know right away, because I can see them. Now fast forward to an environment where we have digital workers, we have automation bots, we have conversational AI Chatbots. And in that world, understanding which of my AI components are on, which ones are off, which ones showed up for work today, which ones fell sick, and really being able to understand that governance, and that's just the productivity piece of it. Then you think about data and security, AI changes complete dimensions on that. And you think about bias and explainability, it just become increasingly important, a notion of a digital ethics board, and thinking about ethics more pervasively. So I think that companies and clients we serve that do really well in digital transformation are those that key in on those three things, the notion of experience is the true compass for how you drive transformation. The ability to intermix domain and digital in a meaningfully intersecting fashion. And to be thoughtful, proactive, and get governance right up front in the journey to come. >> So let me again build on that a little bit, 'cause people are increasingly recognizing that we're not going to centralize with cloud, we're going to greater distribute. We're going to distribute data more, we're going to distribute function more, but you just added another dimension, that some of us have been thinking about for a long time, and that's this notion of distributing authorities so that an individual at the edge can make the decision based on the data and the resources that are available, with the appropriate set of authorities, and that has to be handled at a central, in a overall coherent governant way. So that leads to the next question. >> And just before you go there, I mean I think the best example of that, is we do that, most corporations do that really well in the financial scheme of things. Businesses at the edge make decisions on a day to day basis on pricing and relationships and so on and so forth, and yet there's a central other committee that looks through the financials and makes sure it meets the right requirements and has the right framework, and much in the same way, we're going to start seeing digital ethics committees that become part of these large corporations as they think about digitizing the business. >> Governance at the end of the day is how do you orchestrate multiple divergent claims against a common set of assets, and being able to do that is absolutely essential, and it leads to this notion of we've got these ideas of digital business, digital services and operations management. How are we going to weave them together utilizing some of these new technologies, new fabrics that are now possible to both achieve the outcomes we're talking about at scale and at speed? >> Yeah, well the technology capabilities are improving really well in that area, and so the good news is they're the set of tools that are now available that give you the ingredients, the components of the recipe that's required to make dinner, if you will. The work that needs to happen is actually how to orchestrate that, to figure out which components need to come in, and how do you pull together a vertical stack that has the right components to meet your needs today, and more importantly, to address the needs of the future, because this is changing like no other time in history. >> You want options with everything you do now, you want to make sure that you have a string of options for the future, and it's especially important here. >> That's right, that's exactly right. And the quick framework we've established there is sort of the three-legged stool of, how do you integrate quickly, how do you modularize your investments and then how do you govern them into one integrated whole, and those become really important. I'll give you examples, much of the work we do, we'll work with a consumer bank for instance, and they'll want to do a robotic process automation engagement, we'll run them for nine months, they'll get 1800 robots up and running. And the next question becomes, well now we have all this data that we didn't really have, because now we have an RPA running, how do I learn some machine learning insights from there, and so we then work with them to actually derive some insights and get these questions answered. And then the engagement changes to, well now that we have this pattern recognition then we understand more questions are going to be asked, how do I respond to those questions, A, automatically, and before they get asked, this notion of next best action. And so you think about that journey of a traditional client, the requirements change from robotics to machine learning to conversational AI to something else, and keeping that string of investments, that innovative sort of streak true, and yet being able to manage, govern, and protect the investments, that's the key role. >> We want to thank all the thought leaders that participated in preparing their thoughts for this digital community event, especially the folks at BMC Software. But now here's your opportunity to weigh in on how you see service management and operations management coming together in your business. How's it going to affect your IT organization, your IT organization's ability to serve your business, and your business overall? This is your opportunity to participate in a crowd chat where the community comes together and shares insights, asks each other questions, and engages with these thought leaders to try to get the answers that you need to move forward on the journey to bring together service management and operations management in your shop. Let's crowd chat!

>> from our studios in the heart of Silicon Valley, Palo ALTO, California It is a cute conversation. >> Hello and welcome to this Special Cube conversation here in Palo Alto, California Cubes Studios Jon for your host of the Q. We retreat embodies the founder and CEO of H 20 dot ay, ay, Cuba Lem hot. Start up right in the action of all the machine learning artificial intelligence with the democratization, the role of data in the future, it's all happening with the cloud 2.0, Dev Ops 2.0, great to see you, The test. But the company What's going on, you guys air smoking hot? Congratulations. You got the right formally here with a I explain what's going on. It started about seven >> years ago on Dottie. I was was just a new fad that arrived into Silicon Valley. Today we have thousands of companies in the eye and we're very excited to be partners in making more companies becoming I first. And our region here is to democratize the eye and we've made simple are open source made it easy for people to start adapting data signs and machine learning and different functions inside their large and said the large organizations and apply that for different use cases across financial service is insurance healthcare. >> We leapfrog in 2016 and build our first closer. It's chronic traveler >> C I. We made it on GPS using the latest hardware software innovations Open source. I has funded the rice off automatic machine learning, which >> further reduces the need for >> extraordinary talent to build machine learning. >> No one has time >> today and then we're trying to really bring that automatic mission learning a very significant crunch. Time free, I so people can consuming. I better. >> You know, this is one of the things I love about the current state of the market right now. Entrepreneur Mark, as well as start of some growing companies Go public is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something like provisioning like the old eyes. I get a phD and we're seeing this in data science. I mean, you don't have to be a python coder. This democratisation is not just a tagline. It's actually the reality is of a business opportunity of whoever can provide the infrastructure and the systems four people to do. It is an opportunity. You guys were doing that. This is a real dynamic. This isn't a new way, a new kind of dynamic in the industry. The three real character >> sticks on ability to adopt. Hey, Iris Oneness Data >> is a team, a team sport, which means that you gotta bring different dimensions within your organization to be able to take advantage of data and the I and, um, you've got to bring in your domain. Scientists work closely with your data. Scientists were closely with your data. Engineers produce applications that can be deployed and then get your design on top of it. That can convince users are our strategist to make those decisions. That delays is showing up, so that takes a multi dimensional workforce to work closely together. So the rial problem, an adoption of the AI today is not just technology, it's also culture. And so we're kind of bringing those aspects together and form of products. One of our products, for example, explainable. Aye, aye. It's helping the data. Scientists tell a story that businesses can understand. Why is the model deciding? I need to take discretion. This'll direction. Why's this moral? Giving this particular nurse a high credit score? Even though she is, she has a very she doesn't have a high school graduation. That kind of figuring out those Democratic democratization goes all the way down there. It's wise, a mortal deciding what's deciding and explaining and breaking that down into English, which which building trust is a huge aspect in a >> well. I want to get to the the talent in the time and the trust equation on the next talk track, but I want to get the hard news out there. You guys are have some news driverless a eyes, your one of your core things. What's the hard Explain the news. What's the big news? >> The big news has Bean, that is, the money ball from business and money Ball, as it has been played out, has been. The experts >> were left out of the >> field and all garden is taking over and there is no participation between experts, the domain scientists and the data scientists and what we're bringing with the new product in travel see eyes, an ability for companies to take away I and become a I companies themselves. The rial air races not between the Googles and the Amazons and Microsoft's and other guy companies, software companies. The relay race is in the word pickles. And how can a company, which is a bank or an insurance giant or a health care company take a I platforms and become, take the data, monetize the data and become a I companies themselves? >> You know, that's a really profound state. I would agree with 100% on that. I think we saw that early on in the big data world round Doop doop kind of died by the wayside. But day Volonte and we keep on team have observed and they actually predicted that the most value was gonna come from practitioners, not the vendors, because they're the ones who have the data. And you mentioned verticals. This is another interesting point. I want to get more explanation from you on Is that APS are driven by data data needs domain specific information. So you can't just say I have data. Therefore, magic happens. It's really at the edge of the domain speak or the domain feature of the application. This is where the data is this kind of supports your idea that the eyes with the company's not that are using it, not the suppliers of the technology. >> Our vision has always being hosted by maker customer service for right to be focused on the customer, and through that we actually made customer one of the product managers inside the company. And the way that the doors that opened from working where it closed with some of our leading customers was that we need to get them to participate and take a eyes, algorithms and platforms that can tune automatically. The algorithms and the right hyper parameter organizations, right features and amend the right data sets that they have. There's a whole data lake around there on their data architecture today, which data sets them and not using in my current problem solving. That's a reasonable problem in looking at that combination of these Berries. Pieces have been automated in travel a, C I. A. And the new version that we're not bringing to market is able to allow them to create their own recipes, bring your own transformers and make that automatic fit for their particular race. Do you think about this as a rebuilt all the components of a race car. They're gonna take it and apply for that particular race to win. >> So that's where driverless comes in its travels in the sense of you don't really need a full operator. It kind of operates on its own. >> In some sense, it's driver less, which is in some there taking the data scientists giving them a power tool that historically before automatic machine learning your valises in the umbrella automatic machine learning they would find tune learning the nuances off the data and the problem, the problem at hand, what they're optimizing for and the right tweaks in the algorithm. So they have to understand how deep the streets are gonna be home, any layers off, off deep learning they need what particular variation and deploying. They should put in a natural language processing what context they need to the long term, short term memory. All these pieces, they have to learn themselves. And they were only a few Grand masters are big data scientist in the world who could come up with the right answer for different problems. >> So you're spreading the love of a I around. So you simplifying that you get the big brains to work on it and democratization. People can then participate in. The machines also can learn both humans and machines between >> our open source and the very maker centric culture we've been able to attract on the world's top data scientists, physicists and compiler engineers to bring in a form factor that businesses can use. And today it one data scientist in a company like Franklin Templeton can operate at the level of 10 or hundreds of them and then bring the best in data science in a form factor that they can plug in and play. >> I was having a cautious We can't Libby, who works with being our platform team. We have all this data with the Cube, and we were just talking. Wait higher data science and a eye specialist and you go out and look around. You get Google and Amazon all these big players, spending between 3 to $4,000,000 per machine learning engineer, and that might be someone under the age of 30. And with no experience or so the talent war is huge. I mean the cost to just hire these guys. We can't hire these people. It's a >> global war. >> There's no there's a talent shortage in China. There's talent shortage in India. There stand shortage in Europe and we have officers in in Europe and in India. The talent shortage in Toronto and Ottawa writes it is. It's a global shortage off physicists and mathematicians and data scientists. So that's where our tools can help. And we see that you see travelers say I as a wave you can drive to New York or you can fly to me >> off. I started my son the other days taking computer science classes in school. I'm like, Well, you know, the machine learning at a eyes kind like dog training. You have dog training. You train that dog to do some tricks that some tricks. Well, if you're a coder, you want to train the machines. This is the machine training. This is data science is what a. I possibilities that machines have to be taught. Something is a base in foot. Machines just aren't self learning on their own. So as you look at the science of a I, this becomes the question on the talent gap. Can the talent get be closed by machines and you got the time you want speed low, latent, see and trust. All these things are hard to do. All three. Balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's where we brought a I to help the day >> I travel A. C. I's concept that bringing a I to simplify it's an export system to do a I better so you can actually give it to the hands of a new data scientists so you can perform it the power off a Dead ones data centers if you're not disempowering. The data sent that he is a scientist, the park's still foreign data scientist, because he cannot be stopped with the confusion matrix, false positives, false negatives. That's something a data scientists can understand. What you're talking about featured engineering. That's something a data scientists understand. And what travelers say is really doing is helping him may like do that rapidly and automated on the latest hardware. That's what the time is coming into GPS that PTSD pews different form off clouds at cheaper, faster, cheaper and easier. That's the democratization aspect, but it's really targeted. Data Scientist to Prevent Excrement Letter in Science data sciences is a search for truth, but it's a lot of extra minutes to get the truth and law. If you can make the cost of excrement really simple, cheaper on dhe prevent over fitting. That's a common problem in our science. Prevent by us accidental bites that you introduced because the data is last right, trying to kind of prevent the common pitfalls and doing data science leakage. Usually your signal leaks. And how do you prevent those common those pieces? That's kind of weird, revolutionize coming at it. But if you put that in the box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> Aye aye for creative people, for instance. They want infrastructure. They don't wanna have to be an expert. They wanted that value. That's the consumer ization, >> is really the co founder for someone who's highly imaginative and his courage right? And you don't have to look for founders to look for courage and imagination that a lot of intra preneurs in large companies were trying to bring change to that organization. >> You know, we always say that it's intellectual property game's changing from you know I got the protocol. This is locked and patented. Two. You could have a workflow innovation change. One little tweak of a process with data and powerful. Aye, aye, that's the new magic I P equation. It's in the workforce, in the applications, new opportunities. Do you agree with that? >> Absolutely. That the leapfrog from here is businesses will come up with new business processes that we looked at. Business process optimization and globalization can help there. But a I, as you rightfully said earlier, is training computers, not just programming them. Their schooling most of computers that can now with data, think almost at the same level as a go player. Right there was leading Go player. You can think at the same level off an expert in that space. And if that's happening now, I can transform. My business can run 24 by seven at the rate at which I can assembled machines and feed a data data creation becomes making new data becomes the real value that hey, I can >> h 20 today I announcing driverless Aye, aye. Part of their flagship problem product around recipes and democratization. Ay, ay, congratulations. Final point take a minute to explain for the folks just the product, how they buy it. What's it made of? What's the commitment? How did they engage with you >> guys? It's an annual license recruit. License this software license people condone load on our website, get a three week trial, try it on their own retrial. Pretrial recipes are open source, but 100 recipes built by then Masters have been made open source and they could be plugged and tried and taken. Customers, of course, don't have to make their software open source. They can take this, make it theirs. And our region here is to make every company in the eye company. And and that means that they have to embrace it. I learn it. Ticket. Participate some off. The leading conservation companies are giving it back so you can access in the open source. But the real vision here is to build that community off. A practitioners inside large formulations were here or teams air global. And we're here to support that transformation off some of the largest customers. >> So my problem of hiring an aye aye person You could help you solve that right today. Okay, So it was watching. Please get their stuff and come get a job opening here. That's the goal. But that's that's the dream. That is the dream. And we we want to be should one day. I have watched >> you over the last 10 years. You've been an entrepreneur. The fierce passion. We want the eye to be a partner so you can take your message to wider audience and build monetization or on the data you have created. Businesses are the largest after the big data warlords we have on data. Privacy is gonna come eventually. But I think I did. Businesses are the second largest owners of data. They just don't know how to monetize it. Unlock value from it. I will have >> Well, you know, we love day that we want to be data driven. We want to go faster. I love the driverless vision travel. Say I h 20 dot ay, ay here in the Cuban John for it. Breaking news here in Silicon Valley from that start of h 20 dot ay, ay, thanks for watching. Thank you.