(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

>> Announcer: From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The rapid pace of cloud adoption has changed the way organizations approach cybersecurity. Specifically, the cloud is increasingly becoming the first line of cyber defense. As such, along with communicating to the board and creating a security aware culture, the chief information security officer must ensure that the shared responsibility model is being applied properly. Meanwhile, the DevSecOps team has emerged as the critical link between strategy and execution, while audit becomes the free safety, if you will, in the equation, i.e., the last line of defense. Hello, and welcome to this week's, we keep on CUBE Insights, powered by ETR. In this "Breaking Analysis", we'll share the latest data on hyperscale, IaaS, and PaaS market performance, along with some fresh ETR survey data. And we'll share some highlights and the puts and takes from the recent AWS re:Inforce event in Boston. But first, the macro. It's earning season, and that's what many people want to talk about, including us. As we reported last week, the macro spending picture is very mixed and weird. Think back to a week ago when SNAP reported. A player like SNAP misses and the Nasdaq drops 300 points. Meanwhile, Intel, the great semiconductor hope for America misses by a mile, cuts its revenue outlook by 15% for the year, and the Nasdaq was up nearly 250 points just ahead of the close, go figure. Earnings reports from Meta, Google, Microsoft, ServiceNow, and some others underscored cautious outlooks, especially those exposed to the advertising revenue sector. But at the same time, Apple, Microsoft, and Google, were, let's say less bad than expected. And that brought a sigh of relief. And then there's Amazon, which beat on revenue, it beat on cloud revenue, and it gave positive guidance. The Nasdaq has seen this month best month since the isolation economy, which "Breaking Analysis" contributor, Chip Symington, attributes to what he calls an oversold rally. But there are many unknowns that remain. How bad will inflation be? Will the fed really stop tightening after September? The Senate just approved a big spending bill along with corporate tax hikes, which generally don't favor the economy. And on Monday, August 1st, the market will likely realize that we are in the summer quarter, and there's some work to be done. Which is why it's not surprising that investors sold the Nasdaq at the close today on Friday. Are people ready to call the bottom? Hmm, some maybe, but there's still lots of uncertainty. However, the cloud continues its march, despite some very slight deceleration in growth rates from the two leaders. Here's an update of our big four IaaS quarterly revenue data. The big four hyperscalers will account for $165 billion in revenue this year, slightly lower than what we had last quarter. We expect AWS to surpass 83 billion this year in revenue. Azure will be more than 2/3rds the size of AWS, a milestone from Microsoft. Both AWS and Azure came in slightly below our expectations, but still very solid growth at 33% and 46% respectively. GCP, Google Cloud Platform is the big concern. By our estimates GCP's growth rate decelerated from 47% in Q1, and was 38% this past quarter. The company is struggling to keep up with the two giants. Remember, both GCP and Azure, they play a shell game and hide the ball on their IaaS numbers, so we have to use a survey data and other means of estimating. But this is how we see the market shaping up in 2022. Now, before we leave the overall cloud discussion, here's some ETR data that shows the net score or spending momentum granularity for each of the hyperscalers. These bars show the breakdown for each company, with net score on the right and in parenthesis, net score from last quarter. lime green is new adoptions, forest green is spending up 6% or more, the gray is flat, pink is spending at 6% down or worse, and the bright red is replacement or churn. Subtract the reds from the greens and you get net score. One note is this is for each company's overall portfolio. So it's not just cloud. So it's a bit of a mixed bag, but there are a couple points worth noting. First, anything above 40% or 40, here as shown in the chart, is considered elevated. AWS, as you can see, is well above that 40% mark, as is Microsoft. And if you isolate Microsoft's Azure, only Azure, it jumps above AWS's momentum. Google is just barely hanging on to that 40 line, and Alibaba is well below, with both Google and Alibaba showing much higher replacements, that bright red. But here's the key point. AWS and Azure have virtually no churn, no replacements in that bright red. And all four companies are experiencing single-digit numbers in terms of decreased spending within customer accounts. People may be moving some workloads back on-prem selectively, but repatriation is definitely not a trend to bet the house on, in our view. Okay, let's get to the main subject of this "Breaking Analysis". TheCube was at AWS re:Inforce in Boston this week, and we have some observations to share. First, we had keynotes from Steven Schmidt who used to be the chief information security officer at Amazon on Web Services, now he's the CSO, the chief security officer of Amazon. Overall, he dropped the I in his title. CJ Moses is the CISO for AWS. Kurt Kufeld of AWS also spoke, as did Lena Smart, who's the MongoDB CISO, and she keynoted and also came on theCUBE. We'll go back to her in a moment. The key point Schmidt made, one of them anyway, was that Amazon sees more data points in a day than most organizations see in a lifetime. Actually, it adds up to quadrillions over a fairly short period of time, I think, it was within a month. That's quadrillion, it's 15 zeros, by the way. Now, there was drill down focus on data protection and privacy, governance, risk, and compliance, GRC, identity, big, big topic, both within AWS and the ecosystem, network security, and threat detection. Those are the five really highlighted areas. Re:Inforce is really about bringing a lot of best practice guidance to security practitioners, like how to get the most out of AWS tooling. Schmidt had a very strong statement saying, he said, "I can assure you with a 100% certainty that single controls and binary states will absolutely positively fail." Hence, the importance of course, of layered security. We heard a little bit of chat about getting ready for the future and skating to the security puck where quantum computing threatens to hack all of the existing cryptographic algorithms, and how AWS is trying to get in front of all that, and a new set of algorithms came out, AWS is testing. And, you know, we'll talk about that maybe in the future, but that's a ways off. And by its prominent presence, the ecosystem was there enforced, to talk about their role and filling the gaps and picking up where AWS leaves off. We heard a little bit about ransomware defense, but surprisingly, at least in the keynotes, no discussion about air gaps, which we've talked about in previous "Breaking Analysis", is a key factor. We heard a lot about services to help with threat detection and container security and DevOps, et cetera, but there really wasn't a lot of specific talk about how AWS is simplifying the life of the CISO. Now, maybe it's inherently assumed as AWS did a good job stressing that security is job number one, very credible and believable in that front. But you have to wonder if the world is getting simpler or more complex with cloud. And, you know, you might say, "Well, Dave, come on, of course it's better with cloud." But look, attacks are up, the threat surface is expanding, and new exfiltration records are being set every day. I think the hard truth is, the cloud is driving businesses forward and accelerating digital, and those businesses are now exposed more than ever. And that's why security has become such an important topic to boards and throughout the entire organization. Now, the other epiphany that we had at re:Inforce is that there are new layers and a new trust framework emerging in cyber. Roles are shifting, and as a direct result of the cloud, things are changing within organizations. And this first hit me in a conversation with long-time cyber practitioner and Wikibon colleague from our early Wikibon days, and friend, Mike Versace. And I spent two days testing the premise that Michael and I talked about. And here's an attempt to put that conversation into a graphic. The cloud is now the first line of defense. AWS specifically, but hyperscalers generally provide the services, the talent, the best practices, and automation tools to secure infrastructure and their physical data centers. And they're really good at it. The security inside of hyperscaler clouds is best of breed, it's world class. And that first line of defense does take some of the responsibility off of CISOs, but they have to understand and apply the shared responsibility model, where the cloud provider leaves it to the customer, of course, to make sure that the infrastructure they're deploying is properly configured. So in addition to creating a cyber aware culture and communicating up to the board, the CISO has to ensure compliance with and adherence to the model. That includes attracting and retaining the talent necessary to succeed. Now, on the subject of building a security culture, listen to this clip on one of the techniques that Lena Smart, remember, she's the CISO of MongoDB, one of the techniques she uses to foster awareness and build security cultures in her organization. Play the clip >> Having the Security Champion program, so that's just, it's like one of my babies. That and helping underrepresented groups in MongoDB kind of get on in the tech world are both really important to me. And so the Security Champion program is purely purely voluntary. We have over 100 members. And these are people, there's no bar to join, you don't have to be technical. If you're an executive assistant who wants to learn more about security, like my assistant does, you're more than welcome. Up to, we actually, people grade themselves when they join us. We give them a little tick box, like five is, I walk on security water, one is I can spell security, but I'd like to learn more. Mixing those groups together has been game-changing for us. >> Now, the next layer is really where it gets interesting. DevSecOps, you know, we hear about it all the time, shifting left. It implies designing security into the code at the dev level. Shift left and shield right is the kind of buzz phrase. But it's getting more and more complicated. So there are layers within the development cycle, i.e., securing the container. So the app code can't be threatened by backdoors or weaknesses in the containers. Then, securing the runtime to make sure the code is maintained and compliant. Then, the DevOps platform so that change management doesn't create gaps and exposures, and screw things up. And this is just for the application security side of the equation. What about the network and implementing zero trust principles, and securing endpoints, and machine to machine, and human to app communication? So there's a lot of burden being placed on the DevOps team, and they have to partner with the SecOps team to succeed. Those guys are not security experts. And finally, there's audit, which is the last line of defense or what I called at the open, the free safety, for you football fans. They have to do more than just tick the box for the board. That doesn't cut it anymore. They really have to know their stuff and make sure that what they sign off on is real. And then you throw ESG into the mix is becoming more important, making sure the supply chain is green and also secure. So you can see, while much of this stuff has been around for a long, long time, the cloud is accelerating innovation in the pace of delivery. And so much is changing as a result. Now, next, I want to share a graphic that we shared last week, but a little different twist. It's an XY graphic with net score or spending velocity in the vertical axis and overlap or presence in the dataset on the horizontal. With that magic 40% red line as shown. Okay, I won't dig into the data and draw conclusions 'cause we did that last week, but two points I want to make. First, look at Microsoft in the upper-right hand corner. They are big in security and they're attracting a lot of dollars in the space. We've reported on this for a while. They're a five-star security company. And every time, from a spending standpoint in ETR data, that little methodology we use, every time I've run this chart, I've wondered, where the heck is AWS? Why aren't they showing up there? If security is so important to AWS, which it is, and its customers, why aren't they spending money with Amazon on security? And I asked this very question to Merrit Baer, who resides in the office of the CISO at AWS. Listen to her answer. >> It doesn't mean don't spend on security. There is a lot of goodness that we have to offer in ESS, external security services. But I think one of the unique parts of AWS is that we don't believe that security is something you should buy, it's something that you get from us. It's something that we do for you a lot of the time. I mean, this is the definition of the shared responsibility model, right? >> Now, maybe that's good messaging to the market. Merritt, you know, didn't say it outright, but essentially, Microsoft they charge for security. At AWS, it comes with the package. But it does answer my question. And, of course, the fact is that AWS can subsidize all this with egress charges. Now, on the flip side of that, (chuckles) you got Microsoft, you know, they're both, they're competing now. We can take CrowdStrike for instance. Microsoft and CrowdStrike, they compete with each other head to head. So it's an interesting dynamic within the ecosystem. Okay, but I want to turn to a powerful example of how AWS designs in security. And that is the idea of confidential computing. Of course, AWS is not the only one, but we're coming off of re:Inforce, and I really want to dig into something that David Floyer and I have talked about in previous episodes. And we had an opportunity to sit down with Arvind Raghu and J.D. Bean, two security experts from AWS, to talk about this subject. And let's share what we learned and why we think it matters. First, what is confidential computing? That's what this slide is designed to convey. To AWS, they would describe it this way. It's the use of special hardware and the associated firmware that protects customer code and data from any unauthorized access while the data is in use, i.e., while it's being processed. That's oftentimes a security gap. And there are two dimensions here. One is protecting the data and the code from operators on the cloud provider, i.e, in this case, AWS, and protecting the data and code from the customers themselves. In other words, from admin level users are possible malicious actors on the customer side where the code and data is being processed. And there are three capabilities that enable this. First, the AWS Nitro System, which is the foundation for virtualization. The second is Nitro Enclaves, which isolate environments, and then third, the Nitro Trusted Platform Module, TPM, which enables cryptographic assurances of the integrity of the Nitro instances. Now, we've talked about Nitro in the past, and we think it's a revolutionary innovation, so let's dig into that a bit. This is an AWS slide that was shared about how they protect and isolate data and code. On the left-hand side is a classical view of a virtualized architecture. You have a single host or a single server, and those white boxes represent processes on the main board, X86, or could be Intel, or AMD, or alternative architectures. And you have the hypervisor at the bottom which translates instructions to the CPU, allowing direct execution from a virtual machine into the CPU. But notice, you also have blocks for networking, and storage, and security. And the hypervisor emulates or translates IOS between the physical resources and the virtual machines. And it creates some overhead. Now, companies like VMware have done a great job, and others, of stripping out some of that overhead, but there's still an overhead there. That's why people still like to run on bare metal. Now, and while it's not shown in the graphic, there's an operating system in there somewhere, which is privileged, so it's got access to these resources, and it provides the services to the VMs. Now, on the right-hand side, you have the Nitro system. And you can see immediately the differences between the left and right, because the networking, the storage, and the security, the management, et cetera, they've been separated from the hypervisor and that main board, which has the Intel, AMD, throw in Graviton and Trainium, you know, whatever XPUs are in use in the cloud. And you can see that orange Nitro hypervisor. That is a purpose-built lightweight component for this system. And all the other functions are separated in isolated domains. So very strong isolation between the cloud software and the physical hardware running workloads, i.e., those white boxes on the main board. Now, this will run at practically bare metal speeds, and there are other benefits as well. One of the biggest is security. As we've previously reported, this came out of AWS's acquisition of Annapurna Labs, which we've estimated was picked up for a measly $350 million, which is a drop in the bucket for AWS to get such a strategic asset. And there are three enablers on this side. One is the Nitro cards, which are accelerators to offload that wasted work that's done in traditional architectures by typically the X86. We've estimated 25% to 30% of core capacity and cycles is wasted on those offloads. The second is the Nitro security chip, which is embedded and extends the root of trust to the main board hardware. And finally, the Nitro hypervisor, which allocates memory and CPU resources. So the Nitro cards communicate directly with the VMs without the hypervisors getting in the way, and they're not in the path. And all that data is encrypted while it's in motion, and of course, encryption at rest has been around for a while. We asked AWS, is this an, we presumed it was an Arm-based architecture. We wanted to confirm that. Or is it some other type of maybe hybrid using X86 and Arm? They told us the following, and quote, "The SoC, system on chips, for these hardware components are purpose-built and custom designed in-house by Amazon and Annapurna Labs. The same group responsible for other silicon innovations such as Graviton, Inferentia, Trainium, and AQUA. Now, the Nitro cards are Arm-based and do not use any X86 or X86/64 bit CPUs. Okay, so it confirms what we thought. So you may say, "Why should we even care about all this technical mumbo jumbo, Dave?" Well, a year ago, David Floyer and I published this piece explaining why Nitro and Graviton are secret weapons of Amazon that have been a decade in the making, and why everybody needs some type of Nitro to compete in the future. This is enabled, this Nitro innovations and the custom silicon enabled by the Annapurna acquisition. And AWS has the volume economics to make custom silicon. Not everybody can do it. And it's leveraging the Arm ecosystem, the standard software, and the fabrication volume, the manufacturing volume to revolutionize enterprise computing. Nitro, with the alternative processor, architectures like Graviton and others, enables AWS to be on a performance, cost, and power consumption curve that blows away anything we've ever seen from Intel. And Intel's disastrous earnings results that we saw this past week are a symptom of this mega trend that we've been talking about for years. In the same way that Intel and X86 destroyed the market for RISC chips, thanks to PC volumes, Arm is blowing away X86 with volume economics that cannot be matched by Intel. Thanks to, of course, to mobile and edge. Our prediction is that these innovations and the Arm ecosystem are migrating and will migrate further into enterprise computing, which is Intel's stronghold. Now, that stronghold is getting eaten away by the likes of AMD, Nvidia, and of course, Arm in the form of Graviton and other Arm-based alternatives. Apple, Tesla, Amazon, Google, Microsoft, Alibaba, and others are all designing custom silicon, and doing so much faster than Intel can go from design to tape out, roughly cutting that time in half. And the premise of this piece is that every company needs a Nitro to enable alternatives to the X86 in order to support emergent workloads that are data rich and AI-based, and to compete from an economic standpoint. So while at re:Inforce, we heard that the impetus for Nitro was security. Of course, the Arm ecosystem, and its ascendancy has enabled, in our view, AWS to create a platform that will set the enterprise computing market this decade and beyond. Okay, that's it for today. Thanks to Alex Morrison, who is on production. And he does the podcast. And Ken Schiffman, our newest member of our Boston Studio team is also on production. Kristen Martin and Cheryl Knight help spread the word on social media and in the community. And Rob Hof is our editor in chief over at SiliconANGLE. He does some great, great work for us. Remember, all these episodes are available as podcast. Wherever you listen, just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me directly at David.Vellante@siliconangle.com or DM me @dvellante, comment on my LinkedIn post. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well, and we'll see you next time on "Breaking Analysis." (upbeat theme music)

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>from around the globe. It's the Cube presenting enterprise, Digital resilience on hybrid and multi cloud Brought to You by Iota Ho. Okay, let's now get into the next segment where we'll explore data automation. But from the angle of digital resilience within and as a service consumption model, we're now joined by Yusuf Khan, who heads data services for Iota Ho and Shirish County. Up in Who's the vice president and head of U. S. Sales at happiest Minds. Gents, welcome to the program. Great to have you in the Cube. >>Thank you, David. >>Stretch. You guys talk about happiest minds. This notion of born digital, foreign agile. I like that. But talk about your mission at the company. >>Sure. A former in 2011 Happiest minds Up Born digital born a child company. >>The >>reason is that we are focused on customers. Our customer centric approach on delivering digitals and seamless solutions have helped us be in the race. Along with the Tier one providers, our mission, happiest people, happiest customers is focused to enable customer happiness through people happiness. We have Bean ranked among the top 25 I t services company in the great places to work serving hour glass to ratings off 4.1 against the rating off five is among the job in the Indian nineties services company that >>shows the >>mission on the culture. What we have built on the values, right sharing, mindful, integrity, learning and social on social responsibilities are the core values off our company on. That's where the entire culture of the company has been built. >>That's great. That sounds like a happy place to be. Now you have you head up data services for Iot Tahoe. We've talked in the past. Of course you're out of London. What do you what's your day to day focus with customers and partners? What you focused on? >>Well, David, my team work daily with customers and partners to help them better understand their data, improve their data quality, their data governance on help them make that data more accessible in a self service kind of way. To the stakeholders within those businesses on dis is all a key part of digital resilience that will will come on to talk about but later. You're >>right, e mean, that self service theme is something that we're gonna we're gonna really accelerate this decade, Yussef and so. But I wonder before we get into that, maybe you could talk about the nature of the partnership with happiest minds. You know, why do you guys choose toe work closely together? >>Very good question. Um, we see Io Tahoe on Happiest minds as a great mutual fit. A Suresh has said happiest minds are very agile organization. Um, I think that's one of the key things that attracts their customers on Io. Tahoe is all about automation. We're using machine learning algorithms to make data discovery data cataloging, understanding, data, redundancy, uh, much easier on. We're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation with the emphasis on agility, the happiest minds have that. That's a really nice combination. Work works very well together, very powerful. I think the other things that a key are both businesses, a serious have said are really innovative digital native type type companies. Um, very focused on newer technologies, the cloud etcetera, uh, on. Then finally, I think that both challenger brands Andi happiest minds have a really positive, fresh ethical approach to people and customers that really resonates with us that I have tied to its >>great thank you for that. So Russia, Let's get into the whole notion of digital resilience. I wanna I wanna sort of set it up with what I see. And maybe you can comment be prior to the pandemic. A lot of customers that kind of equated disaster recovery with their business continuance or business resilient strategy, and that's changed almost overnight. How have you seen your clients respond to that? What? I sometimes called the forced march to become a digital business. And maybe you could talk about some of the challenges that they faced along the way. >>Absolutely. So, uh, especially during this pandemic times when you see Dave customers have been having tough times managing their business. So happiest minds. Being a digital Brazilian company, we were able to react much faster in the industry, apart from the other services company. So one of the key things is the organizations trying to adopt onto the digital technologies right there has bean lot off data which has been to managed by these customers on. There have been lot off threats and risk, which has been to manage by the CEO Seo's so happiest minds digital resilient technology fight the where we're bringing the data complaints as a service, we were ableto manage the resilience much ahead off other competitors in the market. We were ableto bring in our business community processes from day one, where we were ableto deliver our services without any interruption to the services what we were delivering to our customers. >>So >>that is where the digital resilience with business community process enabled was very helpful for us who enable our customers continue there business without any interruptions during pandemics. >>So, I mean, some of the challenges that that customers tell me they obviously had to figure out how to get laptops to remote workers and that that whole remote, you know, work from home pivot figure out how to secure the end points. And, you know, those were kind of looking back there kind of table stakes, but it sounds like you've got a digital business means a data business putting data at the core, I like to say, but so I wonder if you could talk a little bit more about maybe the philosophy you have toward digital resilience in the specific approach you take with clients? >>Absolutely. They seen any organization data becomes. The key on this for the first step is to identify the critical data. Right. So we this is 1/6 process. What we following happiest minds. First of all, we take stock off the current state, though the customers think that they have a clear visibility off their data. How are we do more often assessment from an external point off view on See how critical their data is? Then we help the customers to strategies that right the most important thing is to identify the most important critical herself. Data being the most critical assault for any organization. Identification off the data's key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure on monitor dearly so that they are consumed well as well as protected from external threats. Then, as 1/4 step, we try to bring in awareness, toe the people we train them at all levels in the organization. That is a P for people to understand the importance off the residual our cells. And then as 1/5 step, we work as a back up plan in terms of bringing in a very comprehensive and the holistic testing approach on people process as well as in technology. We'll see how the organization can withstand during a crisis time. And finally we do a continuous governance off this data, which is a key right. It is not just a one step process. We set up the environment. We do the initial analysis and set up the strategy on continuously govern this data to ensure that they are not only know managed will secure as well as they also have to meet the compliance requirements off the organization's right. That is where we help organizations toe secure on Meet the regulations off the organizations. As for the privacy laws, >>so >>this is a constant process. It's not on one time effort. We do a constant process because every organization goes towards the digital journey on. They have to face all these as part off the evolving environment on digital journey, and that's where they should be kept ready in terms off. No recovering, rebounding on moving forward if things goes wrong. >>So let's stick on that for a minute, and then I wanna bring yourself into the conversation. So you mentioned compliance and governance. When? When your digital business. Here, as you say, you're a data business. So that brings up issues. Data sovereignty. Uh, there's governance, this compliance. There's things like right to be forgotten. There's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these air built in on, and it's not a one shot deal. So do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there, >>so some of way have offered multiple services. Tow our customers on digital race against. On one of the key service is the data complaints. As a service here we help organizations toe map the key data against the data compliance requirements. Some of the features includes in terms off the continuous discovery off data right, because organizations keep adding on data when they move more digital on helping the helping and understanding the actual data in terms off the residents of data, it could be a heterogeneous data sources. It could be on data basis or it could be even on the data lakes. Or it could be or no even on compromise, all the cloud environment. So identifying the data across the various no heterogeneous environment is very key. Feature off our solution. Once we identify, classify this sensitive data, the data privacy regulations on the traveling laws have to be map based on the business rules. So we define those rules on help map those data so that organizations know how critical their digital assets are. Then we work on a continuous marching off data for anomalies because that's one of the key teachers off the solution, which needs to be implemented on the day to day operational basis. So we're helping monitoring those anomalies off data for data quality management on an ongoing basis. And finally we also bringing the automatic data governance where we can manage the sensory data policies on their data relationships in terms off, mapping on manage their business rules on we drive reputations toe also suggest appropriate actions to the customers. Take on those specific data sets. >>Great. Thank you, Yousef. Thanks for being patient. I want to bring in Iota ho thio discussion and understand where your customers and happiest minds can leverage your data automation capability that you and I have talked about in the past. And I'm gonna be great if you had an example is well, but maybe you could pick it up from there. >>Sure. I mean, at a high level, assertions are clearly articulated. Really? Um, Iota, who delivers business agility. So that's by, um, accelerating the time to operationalize data, automating, putting in place controls and ultimately putting, helping put in place digital resilience. I mean, way if we step back a little bit in time, um, traditional resilience in relation to data are often met manually, making multiple copies of the same data. So you have a DB A. They would copy the data to various different places on business. Users would access it in those functional style owes. And of course, what happened was you ended up with lots of different copies off the same data around the enterprise. Very inefficient. Onda course ultimately, uh, increases your risk profile. Your risk of a data breach. Um, it's very hard to know where everything is, and I realized that expression they used David, the idea of the forced march to digital. So with enterprises that are going on this forced march, what they're finding is they don't have a single version of the truth, and almost nobody has an accurate view of where their critical data is. Then you have containers bond with containers that enables a big leap forward so you could break applications down into micro services. Updates are available via a P I s. And so you don't have the same need to build and to manage multiple copies of the data. So you have an opportunity to just have a single version of the truth. Then your challenge is, how do you deal with these large legacy data states that the service has been referring Thio, where you you have toe consolidate, and that's really where I Tahoe comes in. Um, we massively accelerate that process of putting in a single version of the truth into place. So by automatically discovering the data, um, discovering what's duplicate what's redundant, that means you can consolidate it down to a single trusted version much more quickly. We've seen many customers have tried to do this manually, and it's literally taken years using manual methods to cover even a small percentage of their I T estates with a tire. You could do it really very quickly on you can have tangible results within weeks and months. Um, and then you can apply controls to the data based on context. So who's the user? What's the content? What's the use case? Things like data quality validations or access permissions on. Then once you've done there, your applications and your enterprise are much more secure, much more resilient. As a result, you've got to do these things whilst retaining agility, though. So coming full circle. This is where the partnership with happiest minds really comes in as well. You've got to be agile. You've gotta have controls, um, on you've got a drug towards the business outcomes and it's doing those three things together that really deliver for the customer. Thank >>you. Use f. I mean you and I. In previous episodes, we've looked in detail at the business case. You were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time. Thio get to the next step in terms of ultimately getting to the outcome and we talked to a number of customers in the Cube. And the conclusion is really consistent that if you could accelerate the time to value, that's the key driver reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean, how can they take advantage of some of these opportunities that we've discussed >>today? Well, we've tried to make that easy for customers. So with our Tahoe and happiest minds, you can very quickly do what we call a data health check on. Dis is a is a 2 to 3 weeks process are two Really quickly start to understand and deliver value from your data. Um, so, iota, who deploys into the customer environment? Data doesn't go anywhere. Um, we would look at a few data sources on a sample of data Onda. We can very rapidly demonstrate how date discovery those catalog e understanding Jupiter data and redundant data can be done. Um, using machine learning, um, on how those problems can be solved. Um, and so what we tend to find is that we can very quickly as I say in a matter of a few weeks, show a customer how they could get toe, um, or Brazilian outcome on. Then how they can scale that up, take it into production on, then really understand their data state Better on build resilience into the enterprise. >>Excellent. There you have it. We'll leave it right there. Guys. Great conversation. Thanks so much for coming on the program. Best of luck to you in the partnership. Be well. >>Thank you, David. Sorry. Thank you. Thank >>you for watching everybody, This is Dave Volonte for the Cuban Are ongoing Siris on data Automation without Tahoe.

>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, welcome back to the cube. As we continue our coverage of AWS reinvent 2020, you know, I know you're familiar with Moneyball, the movie, Brad Pitt, starting as Billy Bean, the Oakland A's general manager, where the A's were all over data, right. With the Billy Bean approach, it was a very, uh, data driven approach to building his team and a very successful team. Well, AWS is taking that to an extraordinary level and with us to talk about that as Matt Hearst, who was the head of global sports marketing and communications at AWS and Matt, thanks for joining us here on the queue. >>John is my pleasure. Thanks so much for having me. You >>Bet. Um, now we've already heard from a couple of folks, NFL folks, uh, at re-invent, uh, about the virtual draft. Um, but for those of our viewers who maybe aren't up to speed on that, or having a chance to see, uh, what those folks had to say, uh, let's just talk about that as an opener, um, about your involvement with the NFL and particularly with, with the draft and, and what that announcement was all about. >>Sure. We, we saw, we've seen a great evolution with our work with the NFL over the past few years. And you mentioned during the infrastructure keynote where Michelle McKenna who's, the CIO for the NFL talks about how they were able to stage the 2020 virtual draft, which was the NFL is much most watched ever, uh, you know, over 55 million viewers over three days and how they were unable to do it without the help and the power of AWS, you know, utilizing AWS is reliability, scalability, security, and network connectivity, where they were able to manage thousands of live feeds to flow to the internet and go to ESPN, to airline. Um, but additionally, Jennifer LinkedIn, who's the SVP of player health and innovation at the NFL spoke during the machine learning keynote during reinvent. And she talked about how we're working with the NFL, uh, to co-develop the digital athlete, which is a computer simulation model of a football player that can replicate infinite scenarios in a game environment to help better foster and understanding of how to treat and rehabilitate injuries in the short term and in the long-term in the future, ultimately prevent, prevent and predict injuries. >>And they're using machine learning to be able to do that. So there's, those are just a couple of examples of, uh, what the NFL talked about during re-invent at a couple of keynotes, but we've seen this work with the NFL really evolve over the past few years, you know, starting with next gen stats. Those are the advanced statistics that, uh, brings a new level of entertainment to football fans. And what we really like to do, uh, with the NFL is to excite, educate, and innovate. And those stats really bring fans closer to the game to allow the broadcasters to go a little bit deeper, to educate the fans better. And we've seen some of those come to life through some of our ads, uh, featuring Deshaun Watson, Christian McCaffrey, um, these visually compelling statistics that, that come to life on screen. Um, and it's not just the NFL. AWS is doing this with some of the top sports leagues around the world, you know, powering F1 insights, Buddhist league, and match facts, six nations, rugby match stats, all of which utilize AWS technology to uncover advanced stats and really help educate and engage fans around the world in the sports that they love. >>Let's talk about that engagement with your different partners then, because you just touched on it. This is a wide array of avenues that you're exploring. You're in football, you're in soccer, you're in sailing, uh, you're uh, racing formula one and NASCAR, for example, all very different animals, right? In terms of their statistics and their data and of their fan interest, what fans ultimately want. So, um, maybe on a holistic basis first, how are you, uh, kind of filtering through your partner's needs and their fans needs and your capabilities and providing that kind of merger of capabilities with desires >>Sports, uh, for AWS and for Amazon are no different than any other industry. And we work backwards from the customer and what their needs are. You know, when we look at the sports partners and customers that we work with and why they're looking to AWS to help innovate and transform their sports, it's really the innovative technologies like machine learning, artificial intelligence, high performance computing, internet of things, for example, that are really transforming the sports world and some of the best teams and leagues that we've talked about, that you touched on, you know, formula one, NASCAR, NFL, Buena, Sligo, six nations, rugby, and so on and so forth are using AWS to really improve the athlete and the team performance transform how fans view and engage with sports and deliver these real-time advanced statistics to give fans, uh, more of that excitement that we're talking about. >>Let me give you a couple of examples on some of these innovative technologies that our customers are using. So the Seattle Seahawks, I built a data Lake on AWS to use it for talent, evaluation and acquisition to improve player health and recovery times, and also for their game planning. And another example is, you know, formula and we talk about the F1 insights, those advanced statistics, but they're also using AWS high-performance computing that helped develop the next generation race car, which will be introduced in the 2022 season. And by using AWS F1 was able to reduce the average time to run simulations by 70% to improve the car's aerodynamics, reducing the downforce loss and create more wheel to wheel racing, to bring about more excitement on the track. And a third example, similar to, uh, F1 using HPC is any of those team UK. So they compete in the America's cup, which is the oldest trophy in international sports. And endosteum UK is using an HPC environment running on Amazon, easy to spot instances to design its boat for the upcoming competition. And they're depending on this computational power on AWS needing 2000 to 3000 simulations to design the dimension of just a single boat. Um, and so the power of the cloud and the power of the AWS innovative technologies are really helping, uh, these teams and leagues and sports organizations around the world transform their sport. >>Well, let's go back. Uh, you mentioned the Seahawks, um, just as, uh, an example of maybe, uh, the kind of insights that that you're providing. Uh, let's pretend I'm there, there's an outstanding running back and his name's Matt Hearst and, uh, and he's at a, you know, a college let's just pretend in California someplace. Um, what kind of inputs, uh, are you now helping them? Uh, and what kind of insights are you trying to, are you helping them glean from those inputs that maybe they didn't have before? And how are they actually applying that then in terms of their player acquisition and thinking about draft, right player development, deciding whether Matt Hertz is a good fit for them, maybe John Wallace is a good fit for them. Um, but what are the kinds of, of, uh, what's that process look like? >>So the way that the Seahawks have built the data Lake, they built it on AWFs to really, as you talk about this talent, evaluation and acquisition, to understand how a player, you know, for example, a John Walls could fit into their scheme, you know, that, that taking this data and putting it in the data Lake and figuring out how it fits into their schemes is really important because you could find out that maybe you played, uh, two different positions in high school or college, and then that could transform into, into the schematics that they're running. Um, and try to find, I don't want to say a diamond in the rough, but maybe somebody that could fit better into their scheme than, uh, maybe the analysts or others could figure out. And that's all based on the power of data that they're using, not only for the talent evaluation and acquisition, but for game planning as well. >>And so the Seahawks building that data Lake is just one of those examples. Um, you know, when, when you talk about a player, health and safety, as well, just using the NFL as the example, too, with that digital athlete, working with them to co-develop that for that composite NFL player, um, where they're able to run those infinite scenarios to ultimately predict and prevent injury and using Amazon SageMaker and AWS machine learning to do so, it's super important, obviously with the Seahawks, for the future of that organization and the success that they, that they see and continue to see, and also for the future of football with the NFL, >>You know, um, Roger Goodell talks about innovation in the national football league. We hear other commissioners talking about the same thing. It's kind of a very popular buzz word right now is, is leagues look to, uh, ways to broaden their, their technological footprint in innovative ways. Again, popular to say, how exactly though, do you see AWS role in that with the national football league, for example, again, or maybe any other league in terms of inspiring innovation and getting them to perhaps look at things differently through different prisms than they might have before? >>I think, again, it's, it's working backwards from the customer and understanding their needs, right? We couldn't have predicted at the beginning of 2020, uh, that, you know, the NFL draft will be virtual. And so working closely with the NFL, how do we bring that to life? How do we make that successful, um, you know, working backwards from the NFL saying, Hey, we'd love to utilize your technology to improve Clare health and safety. How are we able to do that? Right. And using machine learning to do so. So the pace of innovation, these innovative technologies are very important, not only for us, but also for these, uh, leagues and teams that we work with, you know, using F1 is another example. Um, we talked about HPC and how they were able to, uh, run these simulations in the cloud to improve, uh, the race car and redesign the race car for the upcoming seasons. >>But, uh, F1 is also using Amazon SageMaker, um, to develop new F1 insights, to bring fans closer to the action on the track, and really understand through technology, these split-second decisions that these drivers are taking in every lap, every turn, when to pit, when not to pit things of that nature and using the power of the cloud and machine learning to really bring that to life. And one example of that, that we introduced this year with, with F1 was, um, the fastest driver insight and working F1, worked with the Amazon machine learning solutions lab to bring that to life and use a data-driven approach to determine the fastest driver, uh, over the last 40 years, relying on the years of historical data that they store in S3 and the ML algorithms that, that built between AWS and F1 data scientists to produce this result. So John, you and I could sit here and argue, you know, like, like two guys that really love F1 and say, I think Michael Schumacher is the fastest drivers. It's Lewis, Hamilton. Who's great. Well, it turned out it was a arts incentive, you know, and Schumacher was second. And, um, Hamilton's third and it's the power of this data and the technology that brings this to life. So we could still have a fun argument as fans around this, but we actually have a data-driven results through that to say, Hey, this is actually how it, how it ranked based on how everything works. >>You know, this being such a strange year, right? With COVID, uh, being rampant and, and the major influence that it has been in every walk of global life, but certainly in the American sports. Um, how has that factored into, in terms of the kinds of services that you're looking to provide or to help your partners provide in order to increase that fan engagement? Because as you've pointed out, ultimately at the end of the day, it's, it's about the consumer, right? The fan, and giving them info, they need at the time they want it, that they find useful. Um, but has this year been, um, put a different point on that for you? Just because so many eyeballs have been on the screen and not necessarily in person >>Yeah. T 20, 20 as, you know, a year, unlike any other, um, you know, in our lifetimes and hopefully going forward, you know, it's, it's not like that. Um, but we're able to understand that we can still bring fans closer to the sports that they love and working with, uh, these leagues, you know, we talk about NFL draft, but with formula one, we, uh, in the month of may developed the F1 Pro-Am deep racer event that featured F1 driver, uh, Daniel Ricardo, and test driver TA Sianna Calderon in this deep racer league and deep racers, a one 18th scale, fully autonomous car, um, that uses reinforcement learning, learning a type of machine learning. And so we had actual F1 driver and test driver racing against developers from all over the world. And technology is really playing a role in that evolution of F1. Um, but also giving fans a chance to go head to head against the Daniel Ricardo, which I don't know that anyone else could ever say that. >>Yeah, I raced against an F1 driver for head to head, you know, and doing that in the month of may really brought forth, not only an appreciation, I think for the drivers that were involved on the machine learning and the technology involved, but also for the developers on these split second decisions, these drivers have to make through an event like that. You know, it was, it was great and well received. And the drivers had a lot of fun there. Um, you know, and that is the national basketball association. The NBA played in the bubble, uh, down in Orlando, Florida, and we work with second spectrum. They run on AWS. And second spectrum is the official optical provider of the NBA and they provide Clippers court vision. So, uh, it's a mobile live streaming experience for LA Clippers fans that uses artificial intelligence and machine learning to visualize data through on-screen graphic overlays. >>And second spectrum was able to rely on, uh, AWS is reliability, connectivity, scalability, and move all of their equipment to the bubble in Orlando and still produce a great experience for the fans, um, by reducing any latency tied to video and data processing, um, they needed that low latency to encode and compress the media to transfer an edit with the overlays in seconds without losing quality. And they were able to rely on AWS to do that. So a couple of examples that even though 2020 was, uh, was a little different than we all expected it to be, um, of how we worked closely with our sports partners to still deliver, uh, an exceptional fan experience. >>So, um, I mean, first off you have probably the coolest job at AWS. I think it's so, uh, congratulations. I mean, it's just, it's fascinating. What's on your want to do less than in terms of 20, 21 and beyond and about what you don't do now, or, or what you would like to do better down the road, any one area in particular that you're looking at, >>You know, our, our strategy in sports is no different than any other industry. We want to work backwards from our customers to help solve business problems through innovation. Um, and I know we've talked about the NFL a few times, but taking them for, for another example, with the NFL draft, improving player health and safety, working closely with them, we're able to help the NFL advance the game both on and off the field. And that's how we look at doing that with all of our sports partners and really helping them transform their sport, uh, through our innovative technologies. And we're doing this in a variety of ways, uh, with a bunch of engaging content that people can really enjoy with the sports that they love, whether it's, you know, quick explainer videos, um, that are short two minute or less videos explaining what these insights are, these advanced stats. >>So when you see them on the screening and say, Oh yeah, I understand what that is at a, at a conceptual level or having blog posts from a will, Carlin who, uh, has a long storied history in six nations and in rugby or Rob Smedley, along story history and F1 writing blog posts to give fans deeper perspective as subject matter experts, or even for those that want to go deeper under the hood. We've worked with our teams to take a deeper look@howsomeofthesecometolifedetailingthetechnologyjourneyoftheseadvancedstatsthroughsomedeepdiveblogsandallofthiscanbefoundataws.com slash sports. So a lot of great rich content for, uh, for people to dig into >>Great stuff, indeed. Um, congratulations to you and your team, because you really are enriching the fan experience, which I am. One of, you know, hundreds of millions are enjoying that. So thanks for that great work. And we wish you all the continued success down the road here in 2021 and beyond. Thanks, Matt. Thanks so much, Sean.

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hello everyone, David Vellante here and welcome to the special CUBE conversation with a colleague and friend of mine, Randy Seidl is a accomplished CEO, he's an executive, sales pro, and he's a founder of the Sales Community, this newly formed social network, Randy, good to see you again, welcome. >> Hey, great to see you, it's been a lot of great years, great relationship with you and congratulations with all your success with SiliconANGLE and theCUBE. I was remembering back, I think it's been probably since 1985, so 35 years ago when we were both Cub Scouts, I was at EMC, and you were at IDC. >> Yeah, I mean, first of all, I love where you are, your man-cave there, we heard you held a great little networking event that you do periodically with some of our joint colleagues. And yeah, wow, we were both in our twenties, I was a young pop and Dicky Eagan, and Jack and Mike, and they would have me talk to you guys, you know, sort of brief you on the market, what little I knew now looking back. But wow, Randy, I mean. >> We knew! >> Right, I mean, and then just the whole thing just took off, but we had a good instinct, that storage was going to matter, everything back then was mainframe and IBM was the king of the world, and then you guys just crushed it. Wow, what a run, amazing. >> Yeah, absolutely. >> So tell me about Sales Community. What are you trying to accomplish with this new social network? >> Well, it was kind of really my COVID moment. I was talking to Peter Bell I know, you know well as well, and it was right in the beginning of COVID we were kind of comparing notes and long story short, he said, hey Randy, you do all this work with these technology companies, and channel partners, and use your customers, CIO, CTO, CSOs, but you're really not doing much for those that you know the best, which are really technology sales professionals, CROs, STRs kind of up and down the food chain. And that really got me thinking, then he introduced me to one of his companies that sells to CROs and I was going through with them and they were kind of calling me on the carpet saying, okay, do I really know these people? I'm like, oh my gosh! They basically just said, I'm a dope, I haven't really done anything here. So, one thing led to another and ended up developing a Sales Community, a big thing and big help for me was talking to probably 150 or so during the course of the summer, CROs, VPs of sales, Reps STRs to really kind of help get some feedback from them in terms of I caught now they call product-market fit, but kind of what they think it's missing, what's needed, what are their teams need, what do they want? So, it's kind of all a perfect storm, which to be honest without COVID probably wouldn't have created Sales Community. >> Well, I joined and it was a great onboarding experience and love participating with colleagues. I mean, sales is hard, I mean, you've got your ups and your downs and you just got to keep pressing on, but who's participating in Sales Community. >> We're targeting STRs on up to CROs and the kind of the tagline is learn more so you can sell more. We have a lot of great different kind of content areas and we're going to kind of bob and weave based on the feedback that we get, but we've got some great virtual events and interviews. We have an executive coach, Tony Jerry, who's doing nine sessions on designing your life. We did a recording, a live session last week on personal goal setting. We did one yesterday, it was a live session that'll be posted shortly on strategic health. Next one's on branding, so that's not necessarily specific to tech sales, but kind of adding value. We also have Dave Knorr, another executive coach doing a weekly interview series that we're calling tech sales insights with some of the leading CROs, CEOs, Jim Sullivan, who I know you know well, he's going to be the first one, it's going to be next Wednesday, he runs a NWN and he's done a lot of great things and a lot of other great leaders from there. Also still on the interview virtual events side, Michael Cotoia from Tech Target he's going to do a CMO insights series. His Tech Target International editors are also going to do regional ones. So CIO interviews from AMEA, Asia Pac, Latin America, Australia, also on the CSO side, we have somebody focused on doing a CSO interviews, Paul Salamanca of channel interviews, I think this channel, by and large gets missed a lot. CEO's and then Steve Duplessie, I know you know well as well is going to do and focus on CIO, sub-CIO insights, but basically creating virtual events and interview series that are really targeted at people that we sell to. So that covers the kind of virtual event and interview side. And I maybe more quickly go through some of the other key segments. So another one is a content library. There's the guy who's a STR at ServiceNow went through, send me note the other day that said, hey, I found out you have some great feedback on prospecting cold calling, I shared it with my team helped me a lot. So a lot of good things in terms of content library, also opportunity to network. So you could be say selling to Fidelity, you could send a note to the community and members and say anybody else trying to sell the Fidelity, let's network, let's compare notes, also great opportunities for channel partners. So channel partner could raise their hand and say, hey, I know Fidelity, let me help with you. A lot of sharing of best practices. And also just in terms of communication, slack channels, and then opportunities to create round tables. So you might have CROs from startups that want to have maybe six to 10 of them get together. So they can kind of commiserate, ask questions, you could have CROs, companies that are maybe transforming going from on-prem to kind of SAS model. So a lot of different great things, ultimately really to serve the folks in the tech Sales Community. >> Yeah, it sounds like, I mean, first of all tons of content, the other thing I like about it is we all read books on sales, some of them are so like gimmicky, some of them are inspirational. Some of them have really great suggestions. Some of them can be life changing, but what's always been missing in my opinion, is this notion of a network, a social network, if you will, where people can help each other, you just gave a ton of good examples. So you're really trying to differentiate from a lot of the things that have worked over the years, but have really sort of one way communication, some sales guru either training or you're reading his or her book. >> Yes, and we're also fortunate on the content side, we have some of the best kind of consulting sales methodology companies that love what we're doing. So they're likewise providing a lot of content and as you said, it's crazy. You think of any other industry, restaurant, hotel, lawyers, landscape, they have these big, kind of user groups, even technology companies user groups within the larger field of technology sales enterprise B2B sales, there's really nothing that looks like this that exists. So far the feedback's been great. >> Well, so just to what you're describing, I mean, I've known you for a long, long time, and one of the principles of great salespeople is, you help others, right? You make as many friends as you can, and you're the master of that. But essentially you're bringing a lot of the things that have worked, a lot of the principles that have worked in your career to this community. Maybe talk about that a little bit. >> Yeah, I mean, especially I think some of the younger sales folks, it's not kind of off the cuff as we know, but it's really kind of training, being disciplined, being prepared, what are you going to do, how are you going to do it in this COVID moment? You know, I'm seeing lots of friends where the companies that have great relationships, they can do really well and kind of lean in a lot. If you're kind of cold calling and this environment, and it's tough, so kind of, how can you be best prepared, how can you do the best homework? How can you have the kind of right agenda, when you're going to do the sales calls? And then it's not really as much follow up, but really follow through in terms of what you do afterwards. So kind of what is the training? What can you do, how can you do it? And, you know, it's crazy, a lot of companies spend lots of money on training, but if you think about it they're really tied in specifically to tech sales, hopefully this will be great. Plus being able to just kind of throw out questions here and there works out well as well. >> Well that's what I'm looking forward to, say, hey, I got some challenges, how do others deal with this? You know, one of the things that is, I think, paramount to being a great salesperson is the attitude you hear it all the time. How do you stay pumped up? (laughing) Like I said before, we've all been through ups and downs, and what do you tell people there? >> In terms of staying pumped up, interestingly enough, the session we did yesterday on strategic health, probably plays a key role. So yeah, there's the work aspects and how are you going to focus and wake up and get fired up. But ultimately, I think you really got to take several steps back and saying are you taking care of yourself? Are you sleeping, are you eating and drinking correctly? Are you drinking enough water, are you exercising? So, in this moment, I think that's probably something that gets missed a lot in terms of getting fired up. And then ultimately just being excited about kind of what you're doing, how are you doing it, taking care of the customers and serving those around you. And you had mentioned in terms of giving it back, but a lot of us that have been around, love the idea of kind of paying it forward, helping out others and seeing a lot of the great younger folks really rise up and become stars. >> I think that's one of the most exciting things is somebody has been around for awhile. Like (laughing) we all get cold calls and say, hey, how you doing today? You know, (laughing) you really had that dead air, and you actually want to reach out and help these individuals. A lot of times they'll call you, they have no idea what you do, well I've read your website, and I think we'd be a great fit for, you know, something that would not be a great fit. So, there's a level of preparation we always talk about in sales, you got to be prepared, but there's also sometimes... I was talking to a sales pro the other day, you know, sometimes you can over prepare he said, I've been on sales calls, I prepare for hours and hours and hours, and then they get there, and it was just a lot of wasted hours. I probably could have done it in 15 minutes. I mean, so there's a really a balance there. And it comes with experience, I guess. >> Yeah, I mean, I don't know how anybody could prepare hours and hours, so that's a whole different subject to think. >> Well, he said, my technique now is just 15 minutes before the call I'll jump on and just, you know, cram as much as I can. And it actually, it worked for him. So, different approaches, right? >> Yeah, absolutely. The other thing I'd like to mention is the advisory board I'm fortunate to have a work with, and be friends with several of the best in industry like you. So if anybody goes to the website, you can click on an advisory board and there's a 200 plus and haven't count them exactly. But you know, some of the best in technology, we've got them sorted on the sales side and the channel side, the consulting side, the coaching side, analyst side, but, really just such a tremendous each head of talent that can really help us continue to go and grow and pivot and you're making sure that we are serving our Sales Community and making sure everybody's learning more so they can sell more. And then I guess I should add onto that also, earning more and making more money. >> So I got to ask you where you land on this. I mean, you're a sports fan, I am too and for a while there once the "Moneyball" came out, you saw Billy Bean and it was this sort of formulaic approach. The guy, you know, we would joke the team with the best nerds would win. But it seems like there's an equilibrium. It used to be all gut feel and experience, and then it became the data nerds. And it seems like in our industry, it's following a similar pattern, the marketing ops, Martech, becoming very, very data driven. But it feels to me, Randy, especially in these COVID times that there really is this equilibrium, this balance between experience, and tribal knowledge, gut feel, network, which is something you're building and the data. How do you see that role, that CRO role, that sales role evolving, especially in the context of what I just talked about with the data nerds? (laughing) >> Yeah, absolutely, I think I heard two points there since you brought up Billy Bean, I forgot the guy's name, but in the movie is kind of nerd. I've got Jesse and Tucker who have been tremendously helpful for us putting together a Sales Community. But to answer the question on the CMOs side, the CMOs out there frankly not going to like this answer, but I think more and more, you see CMOs and CROs kind of separated and it's kind of different agendas, my belief is that eventually the CMO function or marketing is really going to come under sales and sales are really going to take a much more active role in driving and leveraging that marketing function in terms of what's the best bang for the buck, what are they doing, how are they doing it? And I've got a lot of friends, I won't name names, but they're not on the sales side and they're doing what they can, but they just see what I'd call it kind of wasted money or inefficiencies on the marketing side. So, if I maybe I spin that a different way, I think given kind of analytics and those companies that do have best practices, and I write things on the marketing side, you know, they're going to continue to go and grow, you know, on cert with the right sales team. So I think that you bring up a great point and that area is going to continue to evolve a lot. >> Does that principle apply to product marketing? In other words do you feel like product marketing should be more aligned with engineering or sales and maybe sales and finance, where do you land on that? >> Yeah, I mean, I'm kind of old school, so I go back to Dick and Jack and Roger and Mike Rutgers, and you all in terms of, hey, you have those silos, but you get everybody at the table, kind of what we're working well together. It is interesting though in today's world, the PLG, Product-Led Growth models, where a lot of companies now are trying to get in maybe almost like a VMware, maybe BMC did in the early days where you're kind of getting into the low level developers and then kind of things bubble up so that you think Product-Led Growth model, having a lower cost insight sales model, works when I'll say the kind of the product sells itself. But I would argue, that I think some of those PLG led companies really miss out on leveraging the high end enterprise relationships, to kind of turbocharge and supersize and expedite larger sales deals, larger (indistinct). >> Well, and you mentioned earlier a channel you said a lot of times that's overlooked and I couldn't agree more, channel increasingly important. That's where a lot of the relationships live, it gives you scale, it just gives you a lot of leverage, maybe you talk about the importance of channel and how it relates to Sales Community. >> Yeah, I mean, it's interesting they're really unto themselves, there's some things that are channel channel, but if you think about, you know, go to market tech sales, pick the company on average is probably half of the business goes through the channel. And it used to be way back when just kind of fulfillment, but now the best companies really are those that have the right relationships, that are adding value, that can help on the pre sales, that can help on the post sales, that can help kind of cross sale. You know, if I'm a customer, I don't want to deal with whatever five or 10 different vendors if I can have a one stop shop with one bar solution provider, partner, SI, or whatever you want to call them, you know, that certainly makes life a lot easier. And I think a lot of companies almost been kind of a second class citizen, but I think those companies that really bring them into the fold as really partners at the table, whether it be an account planning sessions, whether you're doing sales calls, but kind of leveraging that I call it a variable cost kind of off balance sheet, sales force really is where the future is going to continue to go. >> So you've been a successful individual sales contributor. You've been a CEO, you've run large sales organizations. I mean, you basically ran sales at HP for Donna Telly, and so you've seen it all, and you've been helping startups. When you look at hiring sales people, what are the attributes that you look for? Is it intelligence, is it hard work, is it coach ability? What are some of the things that are most important to you, and do you apply different attributes in different situations? What are your thoughts on that? >> Great question in a little plug, maybe for a recruiting business, top talent recruiting, (laughing) but one of the key things that we do, which I think is different from others in the recruiting side is the relationships. So a lot of people don't dig in, when we're talking to candidates, they say, well, nobody really asked me this before. And I would argue a key differentiator, and this is way before COVID, but especially now with COVID is okay, who do you have relationships with? So I could be talking to a candidate that maybe somebody is hiring, wants to cover financial services in New York. And then I'll say, okay, well, who do you know what City JPB Bay and I'll know more people than they know. And I'll probably say, just so you know, that's weird me up in Boston. I know more than the council you probably know the best. So really trying to unearth, really kind of who has the right relationships and then separate from that in terms of a reference check, being able to reference checks sooner in the process with somebody that know well firsthand, as opposed to second hand. And a lot of times I've seen even some of the larger, more expensive recruiting firms, you're kind of wait until somebody is the final say, when do an offer, then they do a reference check and they do the reference check with somebody that they don't know. And to me, I mean, that's totally useless which quite with LinkedIn today, I could be say if we're looking at you for candidate, maybe a bad example, but I don't know, we probably have a 1000 in common, and from those, we probably have 200 that we both know, well, that I could check. And when you do reference checking, it's not a maybe it's either, hey, the person is a yes, or the person's a no. So trying to do that early in the process, I think is a big differentiator. And then last and probably third piece I'd highlight is, if it's a startup company, you can't get somebody that's just from a big company. If it's a big company role, you can't get somebody that just from a small company, you got to really make sure you kind of peel back the onions and see where they're from. And you could have somebody from a big company, but they were kind of wearing a smaller division. So again, you have to kind of, you can't judge a book by the cover. You got to kind of peel back the onion. >> So Randy, how do people learn more about Sales Community? Where do they go to engage, sign up, et cetera? >> Absolutely, it's salescommunity.com. So it should be pretty straight forward. A lot of great information there. You can go subscribe, and if you like it spread the word and a lot of great content and you can ping me there. And if not I'm randy@salescommunity.com. So love to get any feedback, help out in any way we can. >> Well, I think it's critical that you're putting this network together and you are probably the best networker that I know I've seen you in action at gatherings and you really have been a great inspiration and a friend. So, Randy, thanks so much for doing the Sales Community and coming on theCUBE and sharing your experience with us. >> Great, thanks Dave, appreciate it. >> All right you're very welcome and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of smart data. Marketplace is brought to You by Io Tahoe Digital transformation is really gone from buzzword to a mandate. Additional businesses, a data business. And for the last several months, we've been working with Iot Tahoe on an ongoing content. Serious, serious, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're gonna do a deeper dive on automating data Discovery. And one of the thought leaders in this space is a J ahora who is the CEO of Iot. Tahoe's once again joining Me A J Good to see you. Thanks for coming on. >>A great to be here, David. Thank you. >>So let's start by talking about some of the business realities. And what are the economics that air? That air driving, automated data Discovery? Why is that so important? >>Yeah, and on this one, David, it's It's a number of competing factors we've got. The reality is data which may be sensitive, so this control on three other elements are wanting to drive value from that data. So innovation, you can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those costs, overheads and data discovery is at the roots of managing that in an automated way to classify that data in sets and policies to put that automation in place. >>Yeah. Okay, look, we have a picture of this. We could bring it up, guys, because I want oh, A j help the audience. Understand? Unaware data Discovery fits in here. This is as we talked about this, a complicated situation for a lot of customers. They got a variety of different tools, and you really laid it out nicely here in this diagram. So take us through. Sort of where that he spits. >>Yeah. I mean, where at the right hand side, This exchange. You know, we're really now in a data driven economy that is, everything's connected through AP, eyes that we consume on mine free mobile relapse. And what's not a parent is the chain of activities and tasks that have to go into serving that data two and eight p. I. At the outset, there may be many legacy systems, technologies, platforms on premise and cloud hybrids. You name it. Andi across those silos. Getting to a unified view is the heavy lifting. I think we've seen Cem some great impacts that be I titles such as Power Bi I tableau looker on DSO on in Clear. Who had Andi there in our ecosystem on visualising Data and CEO's managers, people that are working in companies day to day get a lot of value from saying What's the was the real time activity? What was the trend over this month? First his last month. The tools to enable that you know, we here, Um, a lot of good things are work that we're doing with snowflake mongo db on the public cloud platforms gcpd as your, um, about enabling building those pay planes to feed into those analytics. But what often gets hidden is have you sauce that data that could be locked into a mainframe, a data warehouse? I ot data on DPA, though, that all of that together that is the reality of that is it's it's, um, it's a lot of heavy lifting It z hands on what that, um, can be time consuming on the issue There is that data may have value. It might have potential to have an impact on the on the top line for a business on outcomes for consumers. But you never any sure unless you you've done the investigation discovered it unified that Onda and be able to serve that through to other technologies. >>Guys have. You would bring that picture back up again because A. J, you made a point, and I wanna land on that for a second. There's a lot of manual curating. Ah, an example would be the data catalogue if they decide to complain all the time that they're manually wrangling data. So you're trying to inject automation in the cycle, and then the other piece that I want you to addresses the importance of AP eyes. You really can't do this without an architecture that allows you to connect things together. That sort of enables some of the automation. >>Yeah, I mean, I don't take that in two parts. They would be the AP eyes so virtual machines connected by AP eyes, um, business rules and business logic driven by AP eyes applications. So everything across the stack from infrastructure down to the network um, hardware is all connected through AP eyes and the work of serving data three to an MP I Building these pipelines is is often, um, miscalculated. Just how much manual effort that takes and that manual ever. We've got a nice list here of what we automate down at the bottom. Those tasks of indexing, labeling, mapping across different legacy systems. Um, all of that takes away from the job of a data scientist today to engineer it, looking to produce value monetize data on day two to help their business day to conceive us. >>Yes. So it's that top layer that the business sees, of course, is a lot of work that has to go went into achieving that. I want to talk about some of the key tech trends that you're seeing and one of the things that we talked about a lot of metadata at the importance of metadata. It can't be understated. What are some of the big trends that you're seeing metadata and others? >>Yeah, I'll summarize. It is five. There's trains now, look, a metadata more holistically across the enterprise, and that really makes sense from trying. Teoh look across different data silos on apply, um, a policy to manage that data. So that's the control piece. That's that lever the other side's on. Sometimes competing with that control around sense of data around managing the costs of data is innovation innovation, being able to speculate on experiment and trying things out where you don't really know what the outcome is. If you're a data scientist and engineer, you've got a hypothesis. And now, before you got that tension between control over data on innovation and driving value from it. So enterprise wide manage data management is really helping to enough. Where might that latent value be across that sets of data? The other piece is adaptive data governance. Those controls that that that stick from the data policemen on day to steer its where they're trying to protect the organization, protect the brand, protect consumers data is necessary. But in different use cases, you might want to nuance and apply a different policy to govern that data run of into the context where you may have data that is less sensitive. Um, that can me used for innovation. Andi. Adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively and working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known up front objective on direct that data discovery to towards that. And that means applying around with Dems run technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes do winds down what changes are happening with data over time flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity that's really giving a day drops team the ability to to manage and calibrate how they're applying policies and controls today. There, in the last two David that we're seeing is this huge drive towards self service so reimagining how to play policy data governance into the hands off, um, a day to consumer inside a business or indeed, the consumer themselves. The South service, um, if their banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptive Lee, um, serve those data marketplaces that, um when they're involved in creating, >>I want to ask you about the autonomous data discovering the adaptive data. Governance is the is the problem where addressing their one of quality. In other words, machines air better than humans are doing this. Is that one of scale that humans just don't don't scale that well, is it? Is it both? Can you add some color to that >>yet? Honestly, it's the same equation that existed 10 years ago, 20 years ago. It's It's being exacerbated, but it's that equation is how do I control both things that I need to protect? How do we enable innovation where it is going to deliver business value? Had to exchange data between a customer, somebody in my supply chains safely. And all of that was managing the fourth that leg, which is cost overheads. You know, there's no no can checkbook here. I've got a figure out. If only see io and CDO how I do all of this within a fixed budget so that those aspects have always been there. Now, with more choices. Infrastructure in the cloud, um, NPR driven applications own promise. And that is expanding the choices that a a business has and how they put mandated what it's also then creating a layer off management and data governance that really has to now, uh, manage those full wrath space control, innovation, exchange of data on the cost overhead. >>That that top layer of the first slide that we showed was all about business value. So I wonder if we could drill into the business impact a little bit. What do your customers seeing you know, specifically in terms of the impact of all this automation on their business? >>Yeah, so we've had some great results. I think view the biggest Have Bean helping customers move away from manually curating their data in their metadata. It used to be a time where for data quality initiatives or data governance initiative that be teams of people manually feeding a data Cavallo. And it's great to have the inventory of classified data to be out to understand single version of the trees. But in a having 10 15 people manually process that keep it up to date when it's moving feet. The reality of it is what's what's true about data today? and another few sources in a few months. Time to your business on start collaborating with new partners. Suddenly the landscape has changed. The amount of work is gonna But the, um, what we're finding is through automating creating that data discovery feeding a dent convoke that's releasing a lot more time for our CAS. Mr Spend on innovating and managing their data. A couple of others is around cell service data and medics moving the the choices of what data might have business value into the hands of business users and and data consumers to They're faster cycle times around generating insights. Um, we really helping that by automating the creation of those those data sets that are needed for that. And in the last piece, I'd have to say where we're seeing impacts. A more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital to rewire their business processes. Andi. Everything from an r p a initiative. Teoh automation involving digital transformation is having, um, see iose Chief data officers Andi Enterprise architects rethink how do they how they re worthy pipelines? But they dated to feed that additional transformation. >>Yeah, to me, it comes down to monetization. Of course, that's for for profit in industry, from if nonprofits, for sure, the cost cutting or, in the case of healthcare, which we'll talk about in a moment. I mean, it's patient outcomes. But you know, the the job of ah, chief data officer has gone from your data quality and governance and compliance to really figuring out how data and be monetized, not necessarily selling the data, but how it contributes for the monetization of the company and then really understanding specifically for that organization how to apply that. And that is a big challenge. We chatted about it 10 years ago in the early days of a Duke. And then, you know, 1% of the companies had enough engineers to figure it out. But now the tooling is available, the technology is there and the the practices air there, and that really to me, is the bottom line. A. J is it says to show me the money. >>Absolutely. It's is definitely then six sing links is focusing in on the saying over here, that customer Onda, where we're helping there is dio go together. Those disparities siloed source of data to understand what are the needs of the patient of the broker of the if it's insurance? Ah, one of the needs of the supply chain manager If its manufacturing onda providing that 3 60 view of data, um is helping to see helping that individual unlock the value for the business. Eso data is providing the lens, provided you know which data it is that can God assist in doing that? >>And you know, you mentioned r p A. Before an r p A customer tell me she was a six Sigma expert and she told me we would never try to apply six segment to a business process. But with our P A. We can do so very cheaply. Well, what that means is lower costs means better employee satisfaction and, really importantly, better customer satisfaction and better customer outcomes. Let's talk about health care for a minute because it's a really important industry. It's one that is ripe for disruption on has really been up until recently, pretty slow. Teoh adopt ah, lot of the major technologies that have been made available, but come, what are you seeing in terms of this theme, we're using a putting data to work in health care. Specific. >>Yeah, I mean, healthcare's Havlat thrown at it. There's been a lot of change in terms of legislation recently. Um, particularly in the U. S. Market on in other economies, um, healthcare ease on a path to becoming more digital on. Part of that is around transparency of price, saying to be operating effectively as a health care marketplace, being out to have that price transparency, um, around what an elective procedure is going to cost before taking that that's that forward. It's super important to have an informed decision around there. So we look at the US, for example. We've seen that health care costs annually have risen to $4 trillion. But even with all of that on cost, we have health care consumers who are reluctant sometimes to take up health care if they even if they have symptoms on a lot of that is driven through, not knowing what they're opening themselves up to. Andi and I think David, if you are, I want to book, travel, holiday, maybe, or trip. We want to know what what we're in for what we're paying for outfront, but sometimes in how okay, that choice, the option might be their plan, but the cost that comes with it isn't so recent legislation in the US Is it certainly helpful to bring for that tryst price, transparency, the underlying issue there? There is the disparity. Different formats, types of data that being used from payers, patients, employers, different healthcare departments try and make that make that work. And when we're helping on that aspect in particular related to track price transparency is to help make that date of machine readable. So sometimes with with data, the beneficiary might be on a person. I've been a lot of cases now we're seeing the ability to have different systems, interact and exchange data in order to process the workflow. To generate online at lists of pricing from a provider that's been negotiated with a payer is, um, is really a neighboring factor. >>So, guys, I wonder if you bring up the next slide, which is kind of the Nirvana. So if you if you saw the previous slide that the middle there was all different shapes and presumably to disparage data, this is that this is the outcome that you want to get. Everything fits together nicely and you've got this open exchange. It's not opaque as it is today. It's not bubble gum band aids and duct tape, but but but described this sort of outcome the trying to achieve and maybe a little bit about what gonna take to get there. >>Yeah, that's a combination of a number of things. It's making sure that the data is machine readable. Um, making it available to AP eyes that could be our ph toes. We're working with technology companies that employ R P. A full health care. I'm specifically to manage that patient and pay a data. Teoh, bring that together in our data Discovery. What we're able to do is to classify that data on having made available to eight downstream tour technology or person to imply that that workflow to to the data. So this looks like nirvana. It looks like utopia. But it's, you know, the end objective of a journey that we can see in different economies there at different stages of maturity, in turning healthcare into a digital service, even so that you could consume it from when you live from home when telling medicine. Intellicast >>Yes, so And this is not just health care but you wanna achieve that self service doing data marketplace in virtually any industry you working with TCS, Tata Consultancy Services Toe Achieve this You know, if you are a company like Iota has toe have partnerships with organizations that have deep industry expertise Talk about your relationship with TCS and what you guys are doing specifically in this regard. >>Yeah, we've been working with TCS now for room for a long while. Andi will be announcing some of those initiatives here where we're now working together to reach their customers where they've got a a brilliant framework of business for that zero when there re imagining with their clients. Um, how their business cause can operate with ai with automation on, become more agile in digital. Um, our technology, the dreams of patients that we have in our portfolio being out to apply that at scale on the global scale across industries such as banking, insurance and health care is is really allowing us to see a bigger impact on consumer outcomes. Patient outcomes And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data. Friction. Um, I think that's a polite term for the the image that we just saw with the disparity technologies that the legacy that has built up. So if we want to create a transformation, Um, having a partnership with TCS across Industries is giving us that that reach and that impacts on many different people's day to day jobs and knives. >>Let's talk a little bit about the cloud. It's It's a topic that we've hit on quite a bit here in this in this content Siri's. But But you know, the cloud companies, the big hyper scale should put everything into the cloud, right? But but customers are more circumspect than that. But at the same time, machine intelligence M. L. A. The cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the cloud? Ah, putting dated to work, if you will, with machine learning stuff you're doing with aws. What? You're fit there? >>Yeah, we we and David. We work with all of the cloud platforms. Mike stuffed as your G, c p IBM. Um, but we're expanding our partnership now with AWS Onda we really opening up the ability to work with their Greenfield accounts, where a lot of that data that technology is in their own data centers at the customer, and that's across banking, health care, manufacturing and insurance. And for good reason. A lot of companies have taken the time to see what works well for them, with the technologies that the cloud providers ah, are offered a offering in a lot of cases testing services or analytics using the cloud to move workloads to the cloud to drive Data Analytics is is a real game changer. So there's good reason to maintain a lot of systems on premise. If that makes sense from a cost from a liability point of view on the number of clients that we work with, that do have and we will keep their mainframe systems within kobo is is no surprise to us, but equally they want to tap into technologies that AWS have such a sage maker. The issue is as a chief data officer, I don't have the budget to me, everything to the cloud day one, I might want to show some results. First upfront to my business users Um, Onda worked closely with my chief marketing officer to look at what's happening in terms of customer trains and customer behavior. What are the customer outcomes? Patient outcomes and partner at comes I can achieve through analytics data signs. So I, working with AWS and with clients to manage that hybrid topology of some of that data being, uh, in the cloud being put to work with AWS age maker on night, I hope being used to identify where is the data that needs to bay amalgamated and curated to provide the data set for machine learning advanced and medics to have an impact for the business. >>So what are the critical attributes of what you're looking at to help customers decide what what to move and what to keep, if you will. >>Well, what one of the quickest outcomes that we help custom achieve is to buy that business blustery. You know that the items of data that means something to them across those different silos and pour all of that together into a unified view once they've got that for a data engineer working with a a business manager to think through how we want to create this application. There was the turn model, the loyalty or the propensity model that we want to put in place here. Um, how do we use predictive and medics to understand what needs are for a patient, that sort of innovation is what we're looking applying the tools such a sagemaker, uh, night to be west. So they do the the computation and to build those models to deliver the outcome is is across that value chain, and it goes back to the first picture that we put up. David, you know the outcome Is that a P I On the back of it, you've got the machine learning model that's been developed in That's always such as data breaks. But with Jupiter notebook, that data has to be sourced from somewhere. Somebody has to say that yet you've got permission to do what you're trying to do without falling foul of any compliance around data. Um, it'll goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down two hours and days. >>Yeah, it's the it's the innovation part of your data portfolio, if you will, that you're gonna put into the cloud. Apply tools like sage maker and others. You told the jury. Whatever your favorite tool is, you don't care. The customer's gonna choose that and hear the cloud vendors. Maybe they want you to use their tool, but they're making their marketplaces available to everybody. But it's it's that innovation piece, the ones that you where you want to apply that self service data marketplace to and really drive. As I said before monetization. All right, give us your final thoughts. A. J bring us home. >>So final thoughts on this David is that at the moment we're seeing, um, a lot of value in helping customers discover that day the using automation automatically curating a data catalogue, and that unified view is then being put to work through our A B. I's having an open architecture to plug in whatever tool technology our clients have decided to use, and that open architecture is really feeding into the reality of what see Iose in Chief Data Officers of Managing, which is a hybrid on premise cloud approach. Do you suppose to breed Andi but business users wanting to use a particular technology to get their business outcome having the flexibility to do that no matter where you're dating. Sitting on Premise on Cloud is where self service comes in that self service. You of what data I can plug together, Dr Exchange. Monetizing that data is where we're starting to see some real traction. Um, with customers now accelerating becoming more digital, uh, to serve their own customers, >>we really have seen a cultural mind shift going from sort of complacency. And obviously, cove, it has accelerated this. But the combination of that cultural shift the cloud machine intelligence tools give give me a lot of hope that the promises of big data will ultimately be lived up to ah, in this next next 10 years. So a J ahora thanks so much for coming back on the Cube. You're you're a great guest. And ah, appreciate your insights. >>Appreciate, David. See you next time. >>All right? And keep it right there. Very right back. Right after this short break

>>from around the globe. It's the Cube with coverage of Coop con and cloud, native con Europe 2020 Virtual brought to you by Red Hat, The Cloud Native Computing Foundation and its ecosystem partners >>Welcome back. This is the Cube coverage of Cube Con Cloud, native con, the European show for 2020. I'm your host to Minuteman. And when we talk about the container world, we talk about what's happening in cloud. Native storage has been one of those sticking points. One of those things that you know has been challenging, that we've been looking to mature and really happy to welcome back to the program two of our cube alumni to give us the update on the state of storage for the container world. Both of them are oh, founders and CEOs. First of all, we have Xiang Yang from Rancher Labs, of course, was recently acquired by Sue Save it and the intention to acquire on and also joining us from early the relay. Who is with port works? Shang Amerli. Thanks so much for joining us. Thank you. Thank you. Alright. So early. I actually I'm going to start with you just cause you know we've seen, you know, a couple of waves of companies working on storage. In this environment, we know storage is difficult. Um, And when we change how we're building things, there's architectural things that can happen. Eso maybe if you could just give us a snapshot, you know, Port works, you know, was created to help unpack this. You know, straight on here in 2020 you know, where you see things in the overall kind of computer storage landscape? >>Absolutely. Still, before I kind of jump into port works. I just want to take a minute to publicly congratulate the the whole rancher team, and and Shang and Shannon And will China have known those folks for a while there? They're kind of true entrepreneurs. They represent the serial entrepreneur spirit that that so many folks know in the valley, and so, you know, great outcome for them. We're very happy for them and ah, big congrats and shout out to the whole team. What works is is a little over five years old, and we've been kind of right from the inception of the company recognized that to put containers in production, you're gonna have to solve, not just the orchestration problem. But the issue of storage and data orchestration and so in a natural kubernetes orchestrates containers and what works orchestrates storage and data. And more specifically, by doing that, what we enable is enterprises to be able to take APS that are containerized into production at scale and and have high availability. Disaster recovery, backup all of the things that for decades I t has had to do and has done to support application, reliability and availability. But essentially we're doing it for purpose with the purpose build solution for containerized workloads. >>Alright, shaming. Of course, storage is a piece of the overall puzzle that that ranchers trying to help with. Maybe if you could just refresh our audience on Longhorn, which your organization has its open source. It's now being managed by the CN. CF is my understanding. So help us bring Longhorn into the discussion >>thanks to. So I'm really glad to be here. We've I think rancher and port work started about the same time, and we started with a slightly different focus. More is exactly right to get containers going, you really need both so that the computer angle orchestrating containers as well as orchestrating the storage and the data. So rancher started with, ah, it's slightly stronger focus on orchestrating containers themselves, but pretty quickly, we realized, as adoption of containers grow, we really need it to be able to handle ah, storage feather. And like any new technology, you know, uh, Kubernetes and containers created some interesting new requirements and opportunities, and at the time, really, they weren't. Ah, a lot of good technologies available, you know, technologies like rook and SEF at the time was very, very premature, I think, Ah, the You know, we actually early on try to incorporate ah, the cluster technology. And it was just it was just not easy. And And at the time I think port Works was, ah, very busy developing. Ah, what turned out to be there flagship product, which we end up, end up, uh, partnering very, very closely. But but early on, we really had no choice but to start developing our own storage technology. So Long horn. As a piece of container storage technology, it's actually almost as oh, there's rancher itself. When about funding engineers, we hired he he ended up, you know, working on it and Then over the years, you know the focus shift that I think the original version was written in C plus plus, and over the years it's now being completely re written in Golan. It was originally written more for Docker workload. Now, of course, everything is kubernetes centric. And last year we you know, we we decided to donate the Longhorn Open Source project to CN CF. And now it's a CN CF sandbox project, and the adoption is just growing really quickly. And just earlier this year, we we finally ah decided to we're ready to offer a commercial support for it. So So that's that's where rancher is. And with longhorn and container storage technology. >>Yeah, it has been really interesting to watch in this ecosystem. A couple of years ago, one of the Q con shows I was talking to people coming out of the Believe It was the Sigs, the special interest group for storage, and it was just like, Wow, it was heated. Words were, you know, back and forth. There's not a lot of agreement there. Anybody that knows the storage industry knows that you know standards in various ways of doing things often are contentious and there's there's differences of opinion. Look at the storage industry. You know, there's a reason why there's so many different solutions out there. So maybe it love to hear from early. From your standpoint, things are coming to get a little bit more. There are still a number of options out there. So you know, why is this kind of coop petition? I actually good for the industry? >>Yeah, I think this is a classic example of Coop petition. Right? Let's let's start with the cooperation part right? The first part of time the you know, the early days of CN, CF, and even sort of the Google Communities team, I think, was really very focused on compute and and subsequent years. In the last 34 years, there's been a greater attention to making the whole stack works, because that's what it's going to take to take a the enterprise class production and put it in, you know, enterprise class application and put it in production. So extensions like C and I for networking and CS I container storage interface. We're kind of put together by a working group and and ah ah you know both both in the CN CF, but also within the kubernetes Google community. That's you talked about six storage as an example. And, you know, as always happens, right? Like it It looks a little bit in the early days. Like like a polo game, right where folks are really? Ah, you know, seemingly, uh, you know, working with each other on on top of the pool. But underneath they're kicking each other furiously. But that was a long time back, and we've graduated from then into really cooperating. And I think it's something we should all be proud of. Where now the CS I interface is really a A really very, very strong and complete solution tow, allowing communities to orchestrate storage and data. So it's really strengthened both communities and the kubernetes ecosystem. Now the competition part. Let's kind of spend. I want to spend a couple of minutes on that too, right? Um, you know, one of the classic things that people sometimes confuse is the difference between an overlay and an interface. CSC is wonderful because it defines how the two layers off essentially kind of old style storage. You know, whether it's a san or ah cloud, elastic storage bucket or all of those interact with community. So the the definition of that interface kind of lay down some rules and parameters for how that interaction should happen. However, you still always need an overlay like Port Works that that actually drives that interface and enables Kubernetes to actually manage that storage. And that's where the competition is. And, you know, she mentioned stuff and bluster and rook and kind of derivatives of those. And I think those have been around really venerable and and really excellent products for born in a different era for a different time open stack, object storage and all of that not really meant for kind of primary workloads. And they've been they've been trying to be adapted for, for for us, for this kind of workload. Port Works is really a built from right from the inception to be designed for communities and for kubernetes workloads at enterprise scale. And so I think, you know, as I as I look at the landscape, we welcome the fact that there are so many more people acknowledging that there is a vital need for data orchestration on kubernetes right, that that's why everybody and their brother now has a CS I interface. However, I think there's a big difference between having an interface. This is actually having the software that provides the functionality for H. A, D R. And and for backup, as as the kind of life cycle matures and doing it not just at scale, but in a way that allows kind of really significant removal or reduction off the storage admin role and replaces it with self service that is fully automated within communities. Yeah, if I >>can, you know, add something that that I completely agree. I mean, over the Longhorns been around for a long time. Like I said, I'm really happy that over the years it hasn't really impacted our wonderful collaborative partnership with what works. I mean, Poll works has always been one of our premier partners. We have a lot of, ah, common customers in this fight. I know these guys rave about what works. I don't think they'll ever get out for works. Ah, home or not? Uh huh. Exactly. Like Morissette, you know, in the in the storage space, there's interface, which a lot of different implementations can plugging, and that's kind of how rancher works. So we always tell people Rancher works with three types of storage implementations. One is let we call legacy storage. You know, your netapp, your DMC, your pure storage and those are really solid. But they were not suddenly not designed to work with containers to start with, but it doesn't matter. They've all written CS I interfaces that would enable containers to take advantage of. The second type is some of the cloud a block storage or file storage services like EBS, GFS, Google Cloud storage and support for these storage back and the CS I drivers practically come with kubernetes itself, so those are very well supported. But there's still a huge amount of opportunities for the third type of you know, we call container Native Storage. So that is where Port Works and the Longhorn and other solutions like open EBS storage OS. All these guys fitting is a very vibrant ecosystem of innovation going on there. So those solutions are able to create basically reliable storage from scratch. You know, when you from from just local disks and they're actually also able to add a lot of value on top of whatever traditional or cloud based, persistent storage you already have. So so the whole system, the whole ecosystem, is developing very quickly. A lot of these solutions work with each other, and I think to me it's really less of a competition or even Coop petition. It's really more off raising the bar for for the capabilities so that we can accelerate the amount of workload that's been moved onto this wonderful kubernetes platform in the end of the benefit. Everyone, >>Well, I appreciate you both laying out some of the options, you know, showing just a quick follow up on that. I think back if you want. 15 years ago was often okay. I'm using my GMC for my block. I'm using my netapp for the file. I'm wondering in the cloud native space, if we expect that you might have multiple different data engine types in there you mentioned you know, I might want port works for my high performance. You said open EBS, very popular in the last CN CF survey might be another one there. So is do we think some of it is just kind of repeating itself that storage is not monolithic and in a micro service architecture. You know, different environments need different storage requirements. >>Yeah, I mean quick. I love to hear more is view as well, especially about you know, about how the ecosystem is developing. But from my perspective, just just the range of capabilities that's now we expect out of storage vendors or data management vendors is just increased tremendously. You know, in the old days, if you can store blocks to object store file, that's it. Right. So now it's this is just table stakes. Then then what comes after that? There will be 345 additional layers of requirements come all the way from backup, restore the our search indexing analytics. So I really think all of this potentially off or in the in the bucket of the storage ecosystem, and I just can't wait to see how this stuff will play out. I think we're still very, very early stages, and and there, you know what? What, what what containers did is they made fundamentally the workload portable, but the data itself still holds a lot of gravity. And then just so much work to do to leverage the fundamental work load portability. Marry that with some form of universal data management or data portability. I think that would really, uh, at least the industry to the next level. Marie? >>Yeah. Shanghai Bean couldn't. Couldn't have said it better. Right? Let me let me let me kind of give you Ah, sample. Right. We're at about 160 plus customers now, you know, adding several by the month. Um, just with just with rancher alone, right, we are. We have common customers in all common video expedient Roche March X, Western Asset Management. You know, charter communications. So we're in production with a number off rancher customers. What are these customers want? And why are they kind of looking at a a a Port works class of solution to use, You know, Xiang's example of the multiple types, right? Many times, people can get started with something in the early days, which has a CS I interface with maybe say, $10 or 8 to 10 nodes with a solution that allows them to at least kind of verify that they can run the stack up and down with, say, you know, a a rancher type orchestrator, workloads that are containerized on and a network plug in and a storage plugging. But really, once they start to get beyond 20 notes or so, then there are problems that are very, very unique to containers and kubernetes that pop up that you don't see in a in a non containerized environment, right? Some. What are some of these things, right? Simple examples are how can you actually run 10 to hundreds of containers on a server, with each one of those containers belonging to a different application and having different requirements? How do you actually scale? Not to 16 nodes, which is sort of make typically, maybe Max of what a San might go to. But hundreds and thousands of notes, like many of our customers, are doing like T Mobile Comcast. They're running this thing at 600 thousands of notes or scale is one issue. Here is a critical critical difference that that something that's designed for Kubernetes does right. We are providing all off the storage functions that Shang just described at container granted, granularity versus machine granularity. One way to think about this is the old Data center was in machine based construct. Construct everything you know. VM Ware is the leader, sort of in that all of the way. You think of storage as villains. You think of compute and CPUs, everything. Sub sub nets, right? All off. Traditional infrastructure is very, very machine centric. What kubernetes and containers do is move it into becoming an app defined control plane, right? One of the things were super excited about is the fact that Kubernetes is really not just a container orchestrator, but actually a orchestrator for infrastructure in an app defined way. And by doing that, they have turned, uh, you know, control off the infrastructure via communities over to a kubernetes segment. The same person who uses rancher uses port works at NVIDIA, for example to manage storage as they use it, to manage the compute and to manage containers. And and that's marvellous, because now what has happened is this thing is now fully automated at scale and and actually can run without the intervention off a storage admin. No more trouble tickets, right? No more requests to say, Hey, give me another 20 terabytes. All of that happens automatically with the solution like port works. And in fact, if you think about it in the world of real time services that we're all headed towards right Services like uber now are expected in enterprises machine learning. Ai all of these things analytics that that change talk about are things that you expect to run in a fully automated way across vast amounts of data that are distributed sometimes in the edge. And you can't do that unless you're fully automated and and not really the storage admin intervention. And that's kind of the solution that we provide. >>Alright, well, we're just about out of time. If I could just last piece is, you know, early and saying to talk about where we are with long for and what we should expect to see through the rest of this year and get some early for you to you know, what differentiates port works from Just, you know, the open source version. So And maybe if we start with just kind of long or in general and then really from from your standpoint, >>yeah, so it's so so the go along one is really to lower the bar for folks to run state for workloads on on kubernetes we want you know, the the Longhorn is 100% open source and it's owned by CN cf now. So we in terms of features and functionalities is obviously a small subset of what a true enterprise grade solution like Port Works or, um, CEO on that that could provide. So there's just, you know, the storage role. Ah, future settle. The roadmap is very rich. I don't think it's not really Ranchers go Oh, our Longhorns goal to, you know, to try to turn itself into a into a plug in replacement for these enterprise, great storage or data management solutions. But But they're you know, there's some critical critical feature gaps that we need address. And that's what the team is gonna be focusing on, perhaps for the rest of the year. >>Yeah, uh, still, I would I would kind of, you know, echo what Chang said, right? I think folks make it started with solutions, like longer or even a plug in connector plug in with one of their existing storage vendors, whether it's pure netapp or or EMC from our viewpoint, that's wonderful, because that allows them to kind of graduate to where they're considering storage and data as part of the stack. They really should that's the way they're going to succeed by by looking at it as a whole and really with, You know, it's a great way to get started on a proof of concept architecture where your focus initially is very much on the orchestration and the container ization part. But But, as Xiang pointed out, you know what what rancher did, what I entered it for Kubernetes was build a simple, elegant, robust solution that kind of democratized communities. We're doing the same thing for communities storage right? What Port works does is have a solution that is simple, elegant, fully automated, scalable and robust. But more importantly, it's a complete data platform, right? We we go where all these solutions start, but don't kind of venture forward. We are a full, complete lifecycle management for data across that whole life cycle. So there's many many customers now are buying port works and then adding deal right up front, and then a few months later they might come back and I'd backup from ports. So two shanks point right because of the uniqueness of the kubernetes workload, because it is an app defined control plane, not machine to find what is happening is it's disrupting, Just like just like virtualization day. VM exist today because because they focused on a VM version off. You know, the their backup solution. So the same thing is happening. Kubernetes workloads are district causing disruption of the D r and backup and storage market with solutions like sports. >>Wonderful. Merlin Chang. Thank you so much for the updates. Absolutely. The promise of containers A Z you were saying? Really, is that that Atomic unit getting closer to the application really requires storage to be a full and useful solution. So great to see the progress that's being made. Thank you so much for joining us. >>Welcome, Shannon. We look forward to ah, working with you as you reach for the stars. Congratulations again. We look >>forward to the containing partnership morally and thank you. Still for the opportunity here. >>Absolutely great talking to both of you And stay tuned. Lots more coverage of the Cube Cube Con cloud, native con 2020 Europe. I'm stew minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah, yeah

>> from Bahrain. It's the Q covering AWS Public sector Bahrain brought to you by Amazon Web service is >> Okay Welcome back, everyone to the cube coverage We are hearing by rain for a W s summit where cloud computing is changing the games. The Fintech panel discussion Yasmine el Sharif, head of Fintech Innovation Unit, Central Bank of Rain Thank you for joining >> us. Thank you for having me >> Elmo Yacht. Whose founder and CEO of Ammonia Technologies Thank you for coming on. Thank you for having so We're very robust Conversation before they turn on the cameras Fit in tech is hot. I'll see in global fintech Everyone knows what that is, but it's interesting because entrepreneurship and innovation is not just for start ups. It's for countries and hearing by rain, this ecosystem and the mandate to go cloud first has had a ripple effect. We were talking about open banking, mandate, open banking versus regulation, chasing innovation, holding it back. You guys here taking a different approach. Take a minute to explain the philosophy. >> Yeah, I think there's there's benefits to being late adopters to the game. I think in the case of behind it's been a very interesting journey. I think the we started with the whole AWS. But if you look at the prerequisites of technical adoption and creating Data Pool's for analytics to run on, I think the what's interesting about Bahrain is it's really led by regulation. If you look at the prerequisites of creating a digital economy, what's happening in financial service is, or the digitization or openness of financial service. Is it really one context off the bigger picture of Bahrain's digitization plan or the economic strategy? And really, what happens here is if you look at first built the data fools and or the data centers bring a W. A s in and create the data centers. Number two is creator data or cloud First policy. Move the entire government onto the cloud and then give the ownership of the data to the people by implementing the Bahrain personal data protection laws. Once you've done that, then you've given the ownership to the people and you've created what we have is we started with a unique identifies. So the citizens of the country or the residents of the country have a unique identify our number where they're known by once you've done that and then you start mandating certain sectors to open up with a P I integrations. You're creating a very, very interesting value proposition. It creates a much faster you leap frog, a generation of technology. You're going from the classic screen scraping technologies or whatever to a very a completely open infrastructure and open a P I. Where things air cryptographic Lee signed. People are in control of their data, people can control the mobility of their date, and you're really creating a very robust data pool for a lot of algorithms to sit on. >> You know what I love about this has me were talking before he came on cameras that you guys are thinking holistically as a knocking operating system is being in a geek that I am. I love that. But it's not just one thing you're doing, it's a it's a system and it's it's a modernization view. Now we all know that financial systems, power economies and fin tech innovation unit, but you're in. This is important. You gotta have that. That leg of the stool, that pillar that's working absolutely sandbox. You have technology mechanisms to roll in tech, move things quickly moving fast. What's the strategy? What if some of the key things What's the sandbox? >> Let me start by saying The Kingdom of Bahrain has always been considered as a centre of excellence as a financial centre of excellence. And we do realize at the Central Bank in order for us to maintain that position, we have to innovate. We have to remain dynamic and agile enough to make the necessary reforms within our regulations to meet the dynamics off the digital economy. Technology is changing the paradigm off the financial system on the changes happening extremely fast. Regulators have had to come up with a mechanism whereby they can harness and test the feasibility of these innovations whilst putting the risks in a controlled environments as regulators were not typically assigned to host incubators to host startups. However, because of all this change in technology, it has become extremely essential that we come up with a regulatory approach to enable startups as well as existing financial institutions to test out their innovative financial solutions in a controlled environment. So a sandbox is really a controlled live bounds time bounds environment, enabling startups as well as existing financial institutions to test out their innovative solutions under the strict supervision off the regulator, without being required to abide by full regulatory requirements directly with volunteer customers. >> You have to put this trick standards now but means sandboxes. What developers? No, it's a collaborative approach, absolutely not being an incubator. But you're setting up a rules of engagement, Senator startups to take what they know how to do >> exactly >> end up sandboxes in the cloud. That's what everyone does >> absolutely, and our journey with the sandbox has been very successful. We've launched our sandbox back in 2000 and 17. Up to date, we have 35 companies that have been admitted into the sun box. We have been able to graduate to companies successfully. One of them has been licensed as a crypto acid provider, the other as an open biking service provider. We have four other companies in the pipeline ready to graduates. I think all in all, our experience with Sun Box has enabled us to grow and develop his regulators. It has enabled us to maintain open communication with animators, to come tea, to learn the needs of innovators and to enable innovators to live, get familiar realized. With the regulatory environment of the Kingdom of Bahrain, >> you know, you guys are doing some really pioneering work. I wouldn't want to say it's really commendable. I know it's fast and new, but if you look at the United States with Facebook there now asking to be regulated regulation if it comes too late is bad because you know things got out of control and if you're too early, you can put a clamp down and stifle innovation. So the balance between regulation and innovation has always been an art, if you will. >> Exactly. >> What do you guys, How do you view that? What's the philosophy? >> So from a regular perspective, we think that regulation and innovation goes hand in hand, and we have to embrace innovation open heartedly. However, having said that, regulators have to run all common sense checks, meaning that we don't accept an innovation that will potentially pulls more harm to the financial stability of the economy as opposed to the advantages that puzzles. We've passed the number of different regulations to support innovation in the financial services sector dating back to 2014 when we first issued our payment service provider licenses allowing more competition and innovation within the payments sector. We've issued CROWDFUNDING regulations. We've issued robo advisory regulations. We've issued insurance aggregator regulations, crypto asset service provider regulations, open banking regulations, Justin in a few. And I think that each of the regulations that we have issued solves a specific pain point, whether it's to enhance financial inclusion, whether it's to empower customers by retaining ownership back, uh, of their financial information and data, Whether it's too also empower startups and to enable them to get it gain access to funding through digital platforms. >> Have dual. I want to get you in here because as an entrepreneur, like I love all that great, I just wanna get funded. I want my product to market. I need a capital market that's going to be robust. And I need to have that's capital providers state venture capital for private equity supporting their limited partners. So I want to see that I don't wanna be standing there when I need gas for my car. I need fuel. I got to get to the next level. This is what I want And he bought >> on. I think, the one thing John that is very important that people look at in the context of fintech today. Raising money investing into fintech Regulatory uncertainty is one that defines scalability today. Once your technology is proven, where you go next really is dependent on the regulator that you'll be dealing with in the context of that specific activity that you'll be performing. In the case of Bahrain, I must say we were blown away by the receptiveness. We in what way? Yes, yes, mean mentioned open banking, for example. We got into the regulatory sandbox, which you hear a lot about sandboxes all around the world. We got into the sandbox. We got into the sandbox with contact with with with an idea of building and accounts aggregator direct FBI integration to these banks. And we got into the sandbox. We There were no regulations at the time. They like the idea. We started bouncing ideas back and forth on how to develop it. We developed the technology. We started piloting the technology. We integrated to 15 banks in the country on a sandbox environment. The consul, the white paper on open banking, was listed. They sent it out for consultation. We integrated on a production environment to more than 70% of the banks in it in the country. The central Bank of Bahrain mandated open banking across the entire nation. With every retail bank all in a period of less than 18 months. That's insane. That's the kind of context. So as a no Vester exactly so as an investor or as an entrepreneur that looks at the sector. The question is here. If anything, I think the regulator in Bahrain is the one that's leading the innovation and these air the benefits of being late adopters. We get to test out and see what's going on in the rest of the world and really develop great regulations that will embrace and and foster innovation. >> You know, I love the liquidity conversation because this neck goes to the next level. Liquidity is a wonderful thing started. Wanna go public? If that's what happens in the U. S. Mergers and acquisitions, we have an incubator that we're gonna interview here flat Six labs just had to come. One of their companies got sold to match dot com. So you're seeing a lot of cross border liquidity. Yeah, this is a new dynamic. It's only gonna get stronger, more come. He's gonna come out of my reign in the region. Liquid is important. Absent. So how do you guys want to foster that? What's the strategy? Continue to do the same. >> So from a regular perspective again, we don't really holds. Thank you. Beaters are actually two accelerators, but what we do as we refined our regulations to support startups to gain access to liquidity, for example, are crowdfunding regulations that have been passed in 2017 and they support both. Equity is one of financing crowdfunding, including conventional as well as Sharia compliant. Crowdfunding transactions were also currently working on refining our regulations for enabling venture capitalists to take roots and marine and to support these startups. >> Yeah, I think John, you mentioned two things you mentioned regulation leading. When you mandate something like open banking, you are ultimately pushing the entire sector forward, saying you better innovators fastest possible. And there's a gap that you need to you need to basically bridge, and that really loosens up a lot of liquidity when it comes to partnerships. When it comes to acquisitions, when it comes to these banks ultimately looking for better solutions, so they that's the role of the regulator. Here we are seeing a lot of VC activity come to the region right now, the region is only starting to open up. AWS just went live a few months ago. We're seeing the cloud adoption start to really take effect, and this is where you'll start seeing real scalability. But I think the most compelling thing here is Previously people would look at the Middle East with a boot with a bit of skepticism. How much innovation can really take place and the reality is here. There are a few prerequisites that have been put in place. Foreign ownership is at 100% cloud. First policy. There's a lot of things that can really foster innovation. And we're, I mean, where as an entrepreneur, where living proof off this whole Team Bahrain initiative of the fact that you can get in you can build in accounts aggregator in a country that never even had the regulations to adopted to mandate it and to be Ultimately, I think Bahrain will become the global reference point for open banking very soon because it has mandated a regulation of open AP eyes with cryptographic signatures ultimate security frameworks with a robust infrastructure across an entire nation. And don't forget, we still have a population of below the age of 30 70% of our population below. So it gives a very compelling story t test your technology. And then what we end up saying is, once you're on AWS or any cloud for that matter than the scalability of the technology just depends on where you want to go in there. >> No doubt the demographics are solid here, and I love the announcement here. The bachelor's degree. Yeah, cloud computing. We've seen some data science degrees, so new skills are coming on. My vision is interesting. I think that would interest me about the region of Amazon. Being here is these regions create revitalisation? >> Yeah, you >> guys are in perfect position with this Modernization trend is beautiful, not only to be a template for the world but a center for global banking. So I think to me, is that, you know is I'm trying to put together and connect the dots of where this goes in the next two decades. I mean, if crypto currency market continues to get matured and stabilized, that's still flowing with a lot of money. A lot of money in the relay >> absolutely >> was not just the region business to do here for couples to come here. It's you guys playing a role in global financial system. That's of interest to me. What's your vision? >> Absolutely. I think that regulators around the world are starting to realize the importance of collaborating together, to try and work on policy challenges in line with innovation within the financial service of sector and to share experiences to share lessons learned at the Central Bank of Bahrain were a member of the Global Financial Innovation Network, which is an initiative that has Bean passed by the F C A in the UK Again, we're also a member of the authentic working group of the GCC and through these two different initiatives, we work alongside other regulators to collaborate on solving policy issues, to solve, to share experiences and knowledge and to try and harmonize our regulations. Because of the end of the day, startups and innovators ultimately will want to scale up and want to serve customers across the friend jurisdictions. So it's important to have that kind of harmonization in terms of regulations to foster innovation as well as to safeguard the overall security of the international financial. Um, >> keep partnerships. Do you guys need to do to kind of go global on this 20 year vision? Is there other things they have to fall into place? That needs to happen? >> I think >> 20 years is a long time, I say in the next. Let's take five years, for example. If you say in the next five years and where I see this going, the question is, what do entrepreneurs and startups need to look at a jurisdiction and say That's where I want to test my technology. You need a robust infrastructure. You need a regulator than embraces you. You need technical subsidies and financial subsidies that are available, and then you need an independent arm that can really hand hold you and take you to that >> thrust. Its critical trust, money making absolutely ability. >> Just add to that and Byron, we take great pride in our human capital, which we believe is one of our biggest assets. And today, with having your Amazon web service is in Bahrain, this has enabled training of young Bahrainis for the data and knowledge economies which is expected Thio greet around 5000 jobs within becoming five years through different schemes such as Amazon education. For example. >> This is super exciting, which we had more time. Congratulations. Love the vision again. Occupiers like to make money. They wanted environments could be trustworthy and some scalability on behind it. So good luck. We're behind you. We'll keep following up. Thanks for having a cube coverage here and by rain for AWS. I'm John Ferrier. Stay tuned for more after this short break.

>> 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.

>> Hi, I'm Stu Miniman, and this is special On the Ground of theCUBE here at the VTUG Summer Slam 2019. We've had the pleasure of knowing the VTUG team for quite awhile back actually, when it was the New England VMUG was when I started attending. When it switched to the VTUG at Gillette Stadium's when we started doing theCUBE there. And happy to bring back to the program first, Chris Harney, who is the one who created this as a true user event. And joining him is his wife Dawn Harney, who we know is behind the scenes organizing all of this event. So, Dawn and Chris, thank you so much for joining us and thank you for sharing this community and educational process with all of us. >> Thanks Stu, it's been a pleasure. >> All right, so, Chris, we really want this, it's a celebration. Sixteen years; back in 2003 the number one movie of the year was actually Finding Nemo. Of course we waited a long time for there. It goes without saying that all of us were a little bit younger. And boy, in those days, I started working with VMR in 2002, so that journey of virtualization was real early. There was no cloud talking we had kind of the XSP's and some of the earlier things. But so much has changed, and what I have loved is this journey that the users that are attending here. We're actually here in the Expo hall, and if you look, why are there no people in here right now? Because they are all in the break out sessions understanding what are the skill sets that they need today and tomorrow to help them in their journey; virtualization, cloud, DevOps, all of these changes there. Chris, you started this as a user to help share with your peers, so, we've had you on the program many times, bring us back. >> Yeah, so think back to 2003. There was no way to share information. There's no Google, no YouTube, no Facebook groups, Meetups, no Game of Thrones. >> We had to go to books and stuff like that. >> Exactly. >> Read the paper. >> So white papers, those were the big deal. You had the Microsoft books that were two inches thick and glossy. >> Yeah, I wonder how many of our younger audience would know the acronym RTFM? Read The Fine Manual please, is what we're doing. Dawn, this event, as I said, we've been at the winter event at Gillette Stadium, you brought in some of the Patriot players we've had the pleasure of interviewing. This Summer event is epic. I know people that come from very long distances to swim in the community, get the information. There's a little bit of lobster at the end of the day. >> There's a lot of lobster at the end of the day. >> So give us the community that you look to help build and foster, and what this event has meant to you over the years. >> For me it's really a place for everybody in the community to come together and share their knowledge with their peers. Something may work for me maybe it will work for you. Let's get together and talk about it. The best way to learn something is from somebody that may have done it, or done it, messed it up, learned something, like to share it with you. So, it really is about working with your peers, learning something from your sponsors and all these companies that you work with everyday. What's new, what's going on. So this is the place to go to get all that. >> Wait, Dawn, I thought you weren't a tech person. >> I'm not a tech person. >> That answer was spot on because one of the things I loved about the virtualization community, is we were all learning in the early days. And it required a little bit of work. There's this theory known as the IKEA effect. Sometimes if you actually help build it a little bit, you actually like it a little bit more. And this community really epitomizes that in the virtualization community and cloud. We've been talking about cloud now for a decade but it's still relatively early days on how this multi-hybrid cloud fits together, how operations are changing, so, Chris, bring us through a little bit of that arc. >> Well, I'll think about it, back in 2003, there was only VMwire. There was only one virtualization platform, if you didn't use VMwire, you were doing bare metal Windows install or Unix install on physical servers. Well, back when we changed, there was Hyper-V, that was coming out, AWS was just coming out, so that's when we kind of made the jump from just being a VMwire user to a virtual technology. So we could talk about the cloud, we could share those experiences and have that same journey together, and hopefully learn and lead, get smarter together as a group, you can learn faster as a group than you can by yourself. >> Yeah, and as we know, Chris, and we've talked about this, the IT industry is never "Hey, give me a clean "sheet of paper and we'll start everything." We know it is additive and all of these things go together, so cloud did not obviate the need for virtualization, so all of these things go together, and how do I make sure as my job doesn't get completely eliminated or, I was talking to somebody who said "If I've been doing the same thing for 10 years, "will I be out of a job?" They said, "Well hopefully you really really like "what you're doing cause if you think "you can keep doing what you're doing, "that is all you will ever be able to do." And I thought that was a very poignant comment. >> Yeah, Matt Broberg's talk this morning about what's your next job going to be, what skillsets do you need to be relevant in 10 years, and it's the same thing, I mean we said the same thing 10, 15 years ago. You can't be a Windows admin anymore, you can't be a VMwire admin anymore, you can't be a cloud admin anymore in five years. >> Yeah, so Dawn, give our audience a little bit of the scope of this event, as I said, I know people that have flown in from the Carolinas, from Colorado, from all over, from California and the like, 16 years of this event, this community is not just New England, it really has had a broad impact. >> Right, and it's huge, people plan their vacations around this, I've had people come from Europe, they fly over here, stay in the state of Maine, they go to L.L. Bean, they do all those things because they plan their vacation, they know they need to be here for the VTUG event, so it's meant a lot, because you do get so many different variety of people, you have the sponsors, you have the end users, you have media, you have bloggers, you have pretty much just everybody comes together to really be that community, so it's meant a lot to me, it's been a long 16 years but it's meant a lot. >> All right, so the question people are asking, this is the final VTUG, so no more winter event at Gillette, this is the final event tonight at Gritty's, so explain to us how that happened. >> It is the final event, 16 years, we're all getting older, it's bittersweet, but we've just realized that it takes a lot of time to put these together, it takes a lot of sponsors, it takes a lot of users, the users continue to come, but unfortunately the sponsors pay for it, and really don't have that following with the sponsors that we used to have, unfortunately. >> There are a lot more events, there are a lot more ways to find customers, so they're going to the meetups and they're doing their own events. >> Yeah, to your opening point Chris, 16 years ago it was much tougher to find sources. Now the challenge we have is there's too many options out there, there are too many events, trust me, I go to too many events, but this one has always been one that we've always looked forward, so please from the community, want to say thank you so much, it has always been one of our favorite things to kick off the year with when we do the winter one, and the summer one, I've made this trip a couple of times, it is a little warm in here, I think brings back to the roots of this event, remember it was four or five years ago it was 110 degrees out, and then you switched to this facility, so of course the air conditioning decides to go out, because we know in IT, sometimes things break. >> Start in the heat, end in the heat. >> So Chris, want to give you the final word for the final VTUG. >> You know, I'm just very proud and happy with this community, it truly is a community, it wasn't us, it wasn't theCUBE, it wasn't the vendors, it was everyone working together to make a community that helped each other out, so thanks to everyone. >> Chris and Dawn, thank you so much, we're happy to be a small piece of this community, and look forward to staying in touch with you in your future endeavors. Thanks so much, I'm Stu Miniman, we have a full day of coverage here, keynote speaker, some of the users that have traveled around, really focusing on the community here at the VTUG Summer Slam, as always, thank you for watching theCUBE.

>> Live from Las Vegas. It's the queue covering Adobe Summit twenty nineteen brought to you by Adobe. >> Hey, welcome back, everyone. Cube live coverage here in Las Vegas for Adobe sum of twenty nineteen. I'm John for which have Frick. Where he with a cube alumni that had job for three years. And you'LL Bhambri, Vice president of Platform Engineering at Adobe. Great to see you. Thanks for coming by. >> Thank you. >> Let's talk. Engineering. That was your line on the keynote. Great Kino today, by the way, super impressed with content. I'm washing that slides you're presenting, like were to cloud company. I'm failing my Amazon reinvent here. You guys built a really cool platform. Take us through. This was your mission. That's true. So take us through your journey. So how'd we get here? How did you get this beautiful platform? >> So, you know, we've been at it for a few years, and as you know, we've seen CEOs and see emos late. That their focus is to really deliver, you know, delightful experiences to their customers. And not just once, but throughout the journey off the customer. Right? Delight your customer. Every step of the way is what you'LL hear from Adobe from our customers. And we are really helping them to do that. And obviously, in order to do that, there is on, as you well know, that data is behind everything to do with experiences as well. There is a lot ofthe interaction of data and bringing it all together to really understand that holistic view of the customer is super important. And, you know, as you've been this realist, you know, the holistic view of the customer. It's not that you just ended once, and you forget about it, right? You have to build this in real time because the interactions that customers are having with brands are to wear through mobile devices to the apse that they're using off the those brands. And the businesses have to understand that whole journey off the customers and understand what their preferences are. Write what? You know what they like, what they don't like and be able to keeping like that context really during the journey. Whether they're coming to their Web site for the first time are they are repeat, customers be able to give them the right experience at every touch point. And that's where you need all of this data, which is a lot of data. So so you know, We've been on this big data journey on me personally, even, you know, for a long time. But the scale that I've seen here I had not seen before >> our IBM conscious when you weren't IBM prior from Hadoop World, you had your eye on this big data trend. Now, at Adobe, when you have really data coming in with apple cases out in the market place to put a platform together. Hard task. But I want to ask you specific question around that. Looking at the architecture slide you have and analytics cloud and add Cloud a marketing cloud in the commerce cloud. They all have Marcus that they have to address and be highly effective as almost appear placed in alone. But now, integrating across each other now with the journey that you guys were put together is difficult. I know that from a computer science background. How does how did you guys look at that? Architecturally, what were some of the guiding principles around building that? Because you don't want to compromise the capabilities of those functional elements. So you decompose and I get that. How did you put it all together? What was the key guiding principle around. >> Yeah, so that's a really good question, because I mean, Adobe has bean delivering applications, right? Like you said, whether it's around analytics, our marketing cloud or advertising. And now we obviously just acquired the commerce cloud on DH. When you look at the common stuff around all of this, it's data, right? Data being captured, two different channels, data that needs to be curated, you know, having a common data dictionary so that, you know, things mean the same on DH, even though they're captured two different channels. So gathering this data curating this data, organizing it for that holistic view of the customer organizing it so that you can do B I, and reporting on that data is all something that we pull together in the platform there. Now it becomes that whether it is you're doing analytics on this right, which could be a B I and the putting all your doing I and Melander is to do your next best action. All your targeting these customers with personalized content. You're doing it on that single version of the truth, which is the real time customer profile that powers all of these different clouds. So that it's not like when you do reporting you have one view ofthe a customer. But when you're trying to show them personalized content, half the view is lost because the data was siloed. So we've gone past all of that. There's no data silos now, right? >> Real time customer profile is literally being updated all the time. That's the key in great, exciting part about it is a curious >> kind of philosophically. And execution is like you've been in this space for a long time, and one of the jokes I left shares, you know, we used to make decisions based on a sampling of something that happened in the past. Now you know, we can make decisions based on all of the data that's happening now, but at the same time, your challenges, that source's heir changing all the time. The speed of the input is changing all the time, and the expected return on your reaction is shortening all the time. So from from just a date, a professional and I'm sure it's super exciting and super scary to move that paradigm shift to you got to deliver the right thing right now >> and you know, one of the key things field is that as all of this data was being gathered, right, obviously this data has to be gathered with these events are occurring. So if you look at glands, their customers are global. They are transacting browsing, whether it's on where mobile devices with that land globally around the world. That means data has to be collected from these globally distributed edges. And it has to be brought in processed in real time pending that profile. And as the data keeps coming, the profile is updated right? And and you can't have stained a dying, they're right, because otherwise, you know you are action ing based on something that happened five minutes ago. You know how we've seen that you buy something and you're still getting ads off that same product that you buy even a day or two days late? >> Already bought ten anymore. Ten. >> So that's because that bland has a stale profile off you, right? But if they had the real time customer profile, then there's no way that they would be delivering our action ing based on that stale information. So just like the data was being gathered from edges even when we have to deliver the experiences right. This is where edge computing comes into the picture, right? So we are also taking. So when you look at the whole architecture of the platform, yes, it's based on the cloud and you know it's a big data stack. It's completely assassin offering. But there is also a big edge computing part of the platform, which is where all the hard data is collected. Process and action and to your point, trade, like as we build, say, predictive models on Ex Best action on the data that's on the cloud. The scoring off the models has to happen on the edges where the events are crying. So this is a complicated engineering problem. But that's why I guess we love it. >> Big smile. So the data is critical. So about how adobes changed over the past few years because you guys did clown. I heard the nuance. I heard that keynote, you know, reading through the names of the lines. Is that it? It's hard to get data right at the beginning. Yeah, get cloud right now. You got data rights. Take us through that point because this is where I think the key to success is how to make that data work. Because if you're gonna have open AP eyes and open data integrity, that data right database, it's a time Siri's aircraft dated. A lot of different applications might choose certain technology. Yes, you have to deal with that. How, how important is the texture on that? >> So So that's why that's a great question that, you know, from a platform standpoint, our goal is that we have to be able to answer the questions with the right laden see or speed as well as relevancy, right? So when we talk really time, it's about it's Leighton sees. You know, when you talk to engineers, they only talk agency. But it's not that right. It's needn't see and relevancy. So in order to depending on. Like if it's more like B I r. Reporting kind off questions or queries, you need to organize the data certainly for, you know, single lookups off customers, right? You have to organize the data differently, and that's where our I'd be comes into the picture that how do we partition and organize this data to meet the needs ofthe both operational as well as the more, you know, like analytical kind ofthe workloads. So we support both and to your point, also that, you know, then we need a sequel database where there's no sequel database are a graft database. I mean, those are choices we make, but on top, they're providing FBI's. So we're abstracting all of that from the user. And you know how where we direct question, that's all R ight, but their applications are not going to break because they're writing to the FBI's. So as technologies advance underneath, we make those choices, but again so that they're getting the right agency and relevancy. >> So in the cloud game, we used to talk about this when you when you're on the Cuban way, an IBM the devil's movement was full tilt and they use the term infrastructure is code. Uh, so you're kind of getting out. I want to get your reaction to this Is that if applications and workloads are the use, cases are gonna determine the date of structures, data architecture and Leighton see relevance equation isn't. Then there's a new kind of infrastructures code emerging. Is that data as code? So, or maybe it's this should that workloads dictate what type of data diversity and Leighton see relevance is needed Or is that come from the network again? The question is, workloads are kind of in charge, I guess. What? I'm trying to get out. So >> I Yeah, I would say that, you know, as a platform, you have to support all of these workloads, right? So which means that from an architecture standpoint, we have to make sure that whether it's analytical, kindof a question or workload like B. I reporting whether it is, you know, more like an operational kind ofthe question around, You know that you want to just do a quick question around. You know, what did this customer by or what John's action happened? The underneath data structures and databases we have to pick the right ones so that way are able to support both >> the expectations, the expected yes, the expectations of the workload. >> It is. >> You're running commerce. Leighton Seon Relevance. Low latent. She's going to be in the milliseconds or >> gut ache >> and relevance. Gus, have a high bar there, too. Analytics query for a B. I tool might be, if every second so again, this is a huge Delta in terms of capabilities, and I think that will happen on the flies hard. Yes. How do you guys do that was sauce. >> Yeah, so that's That's the, you know, underlying technology that you know the way we are bending, that is, so that you can support both of those and wait with the customers were sticking to that. They wants equal access to the data they're getting. That's equal access now, depending on the kind ofthe queries, whether they, Paula's B I and reporting are more like transactional kind of things in nature. That's the that. Those are the right technical choices that we're making behind the scenes so that the user, those on our lab print right, because they can really focus on the insights that they're getting and really making decisions based on that inside and not get caught into how to bend all of these different pieces so that they can support both of these work clothes. The other thing is that you know a lot off the time that has Bean spent an I T. Has Bean to figure out all of this so that the CEO can support the line of business like the CMO now by, you know, Adobe taking. Get off this all this. It's heavy lifting. That idea had to do. I think that, you know it will be able to meet the requirements of the line of business much faster. And there's going to be, you know, the agility that is needed to support the business. I think that's really our goal in how we support the CEOs so that they don't worry about all this technology, all the data management, how to collect all this data from globally distributed edges. I mean, that's the partnership that we are, you know, bending with the CEOs so that we help them in their journey off, really helping their line of business deliver the best experiences >> on Jewel. Great to see you having so much fun, Toby. Thank you. What's it like there? Tell us, what's it like working in a job? You got a platform? Certainly. There's a lot of hard problems to solve. So you got that on the engineering side, tell us what the cultures like they're >> doing is a fantastic company. I mean, I just love every bit every every minute that I spend here is fantastic. It's, you know, great people open culture open to new ideas on DH. You know, I guess, uh, >> all the >> creative cloud you know has got the straight of it. Eve itches in fused in people. So it's just it's it's just being a blast and and, you know, people recognize them. Barton's off how data is so critical to delivering those delightful experiences, and it's very rewarding to just see how focused everybody is in the company to really help businesses delight their customers. So it's zygo >> system is great, but the developer ecosystem What's your reaction to that of the >> I mean Adobe Io is I don't know. I feel, you know, Yeah, So that's so if you think of all the creators that work with Adobe products and build their applications, I mean, the ecosystem is very rich. So combined creatives on the data and I t I mean >> so we should call the marketing native like cloud native accomplice of developers, developers. It's coming together >> on DH because >> cats living together I mean, this is >> called wait. Call them that experience maker's late. So we are really bringing experience makers, developers, data, scientists all together >> It's a whole new level for a >> whole new level. It's thanks >> for coming on. Sharing the insights. Cube coverage live here, and it will be some in Las Vegas. I'm John for your jefe. Rick, Stay with us. We're here for two days. We're in day one of wall to wall coverage at Adobe Summit. We write back.

>> Live from Stanford University. It's the Cube covering Global Women and Data Science Conference brought to you by Silicon Angle media. >> Welcome back to the Cubes Coverage of the fourth annual Women in Data Science Conference. This global winds event is the fourth annual our fourth year here, covering it for the Cuban Lisa Martin, joined by Gianluca Pecorino, the director on the Stanford Institute for Computational and Mathematical Engineering. Gianluca, it's a pleasure to have you on the program. Thank you. So the Institute for Computational and Mathematical Engineering. I see M e. Tell us a little bit about that and its involvement in wins. >> Yes, so the status has. Bean was funded fifteen years ago at Stanford as a really hard before computation of mathematics at Stanford. The intention was to connect computations and in general, the disciplines around campus towards using computing for evolution, for starting new ideas for pursuing new endeavors. And I think it's being extremely successful over the years in creating a number of different opportunities. Now weeds started four years ago. As you mentioned, it's part of an idea that the prior director advising me, Margo Garretson, had with few others, and I think the position of I see me at the center of campus really helped bring these events sort of across different fields and this different disciplines. And I think, has Bean extremely successful in expanding and creating a new, a completely new movement, a completely new way of off off engaging with with a large, very large community. And I think I seem, has Bean very happy to play this role? And I'm continuing to be excited about the opportunities >> you mentioned expansion and movement to things that jump out. Expansion way mentioned fourth annual on Lee started This Is three and a half years ago knew that twenty fifteen and we were had the pleasure of having Margo Garrett send one of the co founders of Woods on the Cube last year at wigs. And I loved how she actually said. Very cheeky winds really started sort of as a revenge conference for her and the co founders, looking at all of the technology, events and industry events and single a lack of diversity. But in terms of expansion, this there are one hundred fifty plus regional winds events this year in fifty plus countries. They're expecting over one hundred thousand people to engage this expansion. In this movement that you mentioned, it's palpable. Tell us about your Where's the impetus for you to be involved in the woods movement. >> Well, I think my interest in in data science and which particular is because of the role that I seem years in the education at Stanford. We obviously have a very important opportunity toe renew and remodel our curriculum and provide new opportunities for for education off the new generations and clearly starting with with the opportunity off being such an audience and reaching so many different discipline. It's a very different fields. Helps us understand exactly how to put that curriculum together. And so my focus of my interest has been mostly on making sure that I see me alliance with these new directions. And when we establish the institute, computational mathematics didn't really not have data is a very, very critical component, but we are adjusting to that clearly is becoming more and more important. We want to make sure we are ready for it, and we make sure that the students through our curriculum are ready for the world out there. >> So let's talk about this. The students and the curriculum. You've been a professor at Stanford for a very long time before we get into the specifics of today's curriculum. Tell me a little bit about how you have seen that evolve over time as we know that. You know, we're sort of in terms of where the involvement and women and technology and stump field words in the eighties and how that's dropped off. Tell me a little bit about the evolution in that curriculum that you've seen and where the ice Amy is today with that adaptation. >> Yes, certainly. The evolution has bean very quick. In the last few years, we have seen, um in a number of opportunity emerging because of the technology that is out there. The fact that certainly for data science, both the software and the artwork and the technology, the methodology, the algorithms are all in the open so that there is no real barrier into sort of getting started. And I think that helps everybody sort of getting excited about the idea and the opportunity very, very quickly. So we don't really need to goto an extensive curriculum to be ableto ready, solve problems and have an impact. And I think that, perhaps is one one other reason why we are sort of in a level playing field right. Everything is is available to everybody with relatively minor investment at the beginning. And so I think that certainly a difference with respect what the disciplines, where instead, it was much more laborious process to go through before you can actually start having an impact. Suffering every o opportunity, toe change world to toe come, you know, sort of your your vision's sort of impact in the world. So I think that's That's definitely something that the data science and the recent development into the science have created. And so I think, in terms of our role, sort of continuing role in this is tow Pet Shop six. You know, expand the view ofthe data. Science is not just the algorithm, the technology, the statistical learning that you need to accomplish. A student is a new comet into the field, but also is other other elements. And I would say certainly the challenges that we are that are opposed to data. Since they are challenges that have to do with the attics with privacy on DSO, these are clear, clearly difficult to handle because they require knowledge across disciplines the typical air not related to stem in In a traditional sense. But then, on the other hand, I think is the opportunity to be really creative. Data is not analyzing on its own right. He needs the input are sort of help in creating a story. And I think that's that's another element that he makes data science a little bit different. Another stem disciplines intend to be much more ascetic, much more sort of a cold if you like. I think >> that's where the things to you that I find really interesting is if you look at all the statistical and computational skills as you mentioned, that a good data scientist needs to have as we look at some of the challenges with the amount of data being created. So you mentioned privacy, ethics, cybersecurity issues. The create creative element is key for the analysis. Other things, too. That interest me, and I'd love to get your thoughts on how you see this being developed on the curriculum. Helping is is empathy, collaboration, communication skills. Where is that in the curriculum and how important you are? Those other skills to the hard skills >> that that's That's a great question. And I think where is in the curriculum? I think we're lagging behind that. This is one of the opportunities that we have to actually connect to our other places on campus, where instead the education is built much more closely around some of these topics is that you mentioned. So I think you know, again, I the real advantage in the real opportunity we have is that the data science in general reaches out to all these different disciplines in a very, very new way if you like. I think it's it's probably one of the reasons why so attractive toe younger generation is the fact that it's not just the art skills. You do need to have a lot off understanding of the technology, the foundational statistics and mathematics and so on. But it's much more than that. Communication is very important. Teamwork is extremely important. Transparency is very important. There are there are really all these elements that do not really make that they really didn't have a place in some of the more traditional dissidents. And I think that that's definitely a great way off. Sort of refreshing are way off, even considering education and curriculum. >> When you talk to some like the next to the younger generations. Is that one of the things that they find are they pleasantly surprised, knowing that I need to actually be pretty well rounded to me? A successful data scientists? It's how I analyzed the data. How I tell a story, is that something that you still find that excites but surprises this younger generation of well, that >> certainly is a component, very important component of the excitement of the sea. Are there the fact that you can really build the story, tell a story, communicated story and oven, in fact, immediately, quickly, I think is a is something that the newer generation really see it assess a great opportunity and, you know, and it tried to me. So I mean, it has been very difficult for more traditional disciplines to have the same level of impact, partly because the communities tend to be very close, very limited with with a lot of scrutiny. I think what we have in India, the scientists, that is really a lot off you no can do attitude the lot off, Really. You know, creative force that is >> behind, you know, >> basically this movement, but in general data science, I think that >> you write. The impacts is so potent and we've seen it and we're seeing it in every industry across the globe. But it's such an exciting time with Gianluca. We thank you so much for sharing some of your time on the program this morning and look forward to hearing more great things that the ice Amy is helping with prospective women in Stem over the next year. >> Absolutely. Thank you very much. >> My pleasure. We want to thank you. You're watching the Cube live from the fourth annual Women and Data Science Conference here at Stanford University. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.

(upbeat music) >> Live from Las Vegas. It's the Cube. Covering, Boomi World, 2018. Brought to you by Dell Boomi. >> Hello everyone, welcome to the live Cube coverage here in Las Vegas, the Wynn Hotel for Dell Boomi World 18. So, exclusive coverage. We're here all day. Wall to wall coverage covering the impact of cloud native to application developers and owners and for businesses. I'm John Furrier with Lisa Martin here. We're here with Michael Dell. 13th time on the Cube. He's the founder and CEO of Dell Technologies. Continuing to defy logic. Growing leaps and bounds. Continuing to do more in the new era of IT and computing. Mike, great to see you. Thanks for coming. >> Great to be with you. Lisa, John, always fun. And here at Boomi World it's really exciting to see the ecosystem continue to grow. As people try to connect everything together Boomi is right there. Incredible business last quarter. Booking growth, 80%, 7500 customers. I still can't find a customer that doesn't need Boomi. The team continues to evolve what the capabilities. We've just had a great show here. 1000 customers showed up. Lot's of great customer stories about how they're integrating all their apps and data together. With the tsunami of data that is coming, it just gets more and more important and interesting and fun. >> You know, you mentioned on the key note stage with CEO Boomi, talking about some performance numbers that you always throw out, server growth. Continuing to grow, okay. The pundants were saying oh servers, that's cloud server-less. You still need compute, networking and storage but they do change with the cloud and SaaS has proven that business model of as a service is key. Boomi's got this little secret weapon around the unified platform that integrates a lot of these traditional components that is still going to be foundational but yet set up the next wave around AI, Edge, data tsunami that you mentioned. This is a key variable in the architectural shift. Can you talk about how you see that playing out? Because you got a couple big pieces on the chess board. VMWare, the continuous Dell Technologies portfolio kind of as the table stakes. This is kind of interesting new architecture. Explain how you see that. >> Pivotal, Dell EMC, VMWare. >> So a lot of pieces. >> Right. >> How does Boomi play into that? Because if it does be a glue layer if you will for lack of a better word, it can be very powerful. >> Yeah, so the challenge is when you go to Software as a Service, how do you connect the things together? Now, connecting 1 or 2 together is pretty straight forward. But when you start having 50 or 100 of these things, and then you've got on premise systems and now you want to have actions like an employee does something and based on their roll then something else happens, you have work flow. And then you get this, you go from a couple billion PCs to 5 billion smart phones to 100s of billions of connected things out there with this explosion in the edge. How you integrate and connect everything together with work flow and do it securely is super, super important. So we're seeing just an explosion of use cases. There was some great examples from a city digitizing and being able to detect leaks and when traffic lights aren't working. The used cases are pretty unlimited and Boomi and Pivitol play sort of at the top layer for us so the applications and integrating all the data and allowing customers to express their competitive advantage with software and data and AI and machine learning. And then of course we've got VM Ware to virtualize everything from the data center to the network and beyond. With NSX, what we're doing with NFE and software to fine win. And then of course we're the initial infrastructure company. Absolute number 1 in all aspects of the data center. And growing much faster than any of the competitors. >> And I want to also get your thoughts on VM Ware announced up to this morning, actually Barcelona time for VM Ware Europe, the acquisition of Heptio. >> Absolutely. >> Okay, Pat Kelson said in VM World, we're going in, we're going to make Kubernetes the dial tone. This is a key architectural component around orchestration. Containers certainly everyone knows, that's been standardized. People love containers. They're using them. As applications need to be more efficiently built out, out of the Boomi's value proposition, Kubernetes and these cloud native things are super important. What's your view on that? Great acquisitions, very young company? Not 34 billion dollars for a Red Hat like IBM bought but a small tuck in. How important is that trend for you? >> Well, think about what we've done with Pivitol and VM Ware together with the Pivitol container service and now adding Heptio with 2 of the 3 founders of the whole Kubernetes movement. We're going to be making Kubernetes just part of the dial tone of vSpheres. So for virtually all the customers out there, 600000 of them that use vSphere, it'll just be super easy to now have Kubernetes containers built into their vSphere environment. That's the vision. We've got a great team working on it across VM Ware and Pivitol and now the Heptio team. Adding to it. We're super pumped about all this. >> If your friend asked you at a party this weekend, hey Michael, why is Kubernetes important? What do you say to that? >> I guess it would depend on how much they know about this. >> They're a business owner responsible for application development. >> Yeah. >> They are owning to transform their organization. They realize clouds going to be a part of it. They here Kubernetes really popular, it's trending. But it's a technology. A lot of people are now getting this for the first time and seeing it as the early dopples have shown it. They try to want to know the impact and why it's important. Why is Kubernetes important as you start to get into this orchestration of apps and work loads across clouds. Why is it important? >> I think people don't want to get locked in to a particular place when it comes to their infrastructure. Kubernetes has clearly won the battle in terms of being able to be that abstraction layer. That's the simple thing that is super exciting. When it sort of went from cloud to hybrid cloud to multi cloud, people realized they wanted a 2 way street where they could move things back and forth. And now with the edge, they want to move it to the edge. With the distributed core. This explosion in data, this dat tsunami really requires a whole new set of tools in terms of the software infrastructure to be able to make it all work. >> So transformation is ... You're talking about Dell Technologies now. 34 years later you have 7 corporations under that. Done a lot to keep those brands, as they're very valuable. Dell Boomi as a business unit. Transformation is essential and Dell Boomi wants to be the transformation partner. It's also incredibly difficult. IT transformation. Digital, security, workforce. Dell Boomi works and Dell Technologies with a lot of large enterprise organizations that are still probably fairly not as well connected as they should be to find new value, new business dreams. How do you talk with customers, large enterprises that need to transform to stay competitive? Where do they start? And how dose the Dell transformation story in and of itself help those customers feel confident in what Dell Technologies can deliver? >> Right, well first thing I'd say is we actually work with customers of all sizes. We have an enormous business with small and medium and large customers. We're number 1 across the whole spectrum. We serve 99% of the Fortune 500. Since your question is about those types. They're looking at the digital transformation and figuring out this is really not an IT project. It's about technology becoming pervasive in everything that they're doing. From sells to marketing, to product creation to their whole fundamental strategy. So then it shows up in the office of the CEO and business line executives and they're having to reimagine. And so they look for a partner and Dell Technologies is very unique. 2 years and 2 months ago we put together all these companies and it's been fabulous. We've been growing double digits consistently and the response has been great because we can deliver a complete set of capabilities. Now you're right, change management, and how do I do it in my company, that's a big deal. So they're pulling on us to bring them more of a ... The don't want us to show up with a bunch of parts and drop em off. They want us to actually build them a solution that is specific to their needs. Help them implement it. In many cases, run it for them. So we do much of that ourselves with our own services organization. 60000 plus people in our services organization. And of course we have the best, all the great SIs out there that are helping customers implement and run and manage like I said, 99% of the Fortune 500. We're right there with them in this digital transformation. Of course we do the IT, the workforce, the PCs and of course security. Unbelievably important. Your whole brand trust is all based on that so we wrap the whole thing with security and no company has the breath that we have. I think we've kind of won the hearts and minds of the decision makers because of the capabilities that we have. Not that we take it for granted. We have to go earn that trust every single day. We have unbelievably talented people in our company. Over 20000 engineers. Scientists, PHDs. About 90% of them are software engineers. This is a very different company than it was 5 or 10 years ago. We're having a blast. It's a rocket ship, so. >> I had a chance to interview an IT leader and his name is Allen Bean. He's the global CTO and head of IT innovation at Proctor and Gamble. He brought the cloud to Coca-Cola. Has had a career all in IT going back to DHL in the 90s and 80s. So we were talking and I asked him, does IT matter. And Dave Alampi always brings up the book by Nick Carr. And we always talk about it. >> Love it. Such a fun topper, yeah. >> And so he says, quote, at that time some people thought it didn't matter, everyone was kind of complaining, but he says it does matter. It's a competitive advantage. And over the decades IT was outsourced. And now people are trying to bring that back in and make it a competitive advantage. This is now ... It's a mandate basically. So as people who have been kind of anemic with IT, they've got people running stuff but eventually outsource all the value. They got to bring that value in. Cloud is that opportunity. How do you respond to the leaders out there trying to figure this out. What are the keys to success around bringing back the competitive advantage and using the cloud for things that aren't core to the core competency but getting that core competency nailed down. What's your vision. >> Yeah, well, look, I mean, it's all about understanding what is your competitive differentiation and advantage as a business. And if you give that away to somebody else, you're going to be out of business in not too much time. Packers applications are great for things that aren't differentiated. But if you actually do something that's unique and valuable and special and you can't express that in software with your own data, you're going to have a problem, right? This is what companies are figuring out. This is what we're doing with Pivitol and Boomi allowing companies to build all this together. And look I think as it relates to cloud, customers have figured out it's multi cloud, right? It's a workload dependent discussion. Some workloads are great in the public cloud but in many cases, not so much, right? As we've modernized and automated the infrastructure we have customers that tell us hey our private cloud for our predictable workload, which is 90%, is 5, 6 times less expensive than AWS. We're building these converge, hyper converge, like the fast track to the automated modernized infrastructure. And look, you can decide. But we're seeing customers that want to move things back and forth and we're seeing a bit of a boomerang. Where customers have said oh everything you upload to the cloud, and no, not everything. >> And the digital transformation really is making IT a competitive advantage. So I had a long ranging interview. It's up on YouTube. I asked him a final question. I always said, okay, so you know, he's transforming Proctor and Gamble. I said okay, as you look ads and all those things what's the next mountain that you're going to climb? You're an IT pro, you said in the agenda. And I'll read you the quote. I want to get your reaction. He said, "I think we're looking forward. Latency is still an issue. We have to find ways to defeat latency and we're not going to do it through basic physics, we're going to have to change out business models, change our technology, distribution, change everything that we're doing. Consumers and customers are demanding instant access to enhanced information through AI and machine learning right at the point when they want it." So this is his next mountain. This is kind of what you were talking about on the stage here at the Dell Boomi event around the impact of AI and data. What's your reaction to that quote? >> Well to me this is all about the edge and 5G coming around the corner. And you look at all the big telcos. They're all piling in on 5G because it's 1000 times faster and 1000 times less latency. That's going to be a big turbo charge. The rocket ship. And it will just create an explosion in data and compute on the edge. And a lot of it's going to stay on the edge. Because you'll have these edge devices talking to each other. A whole new class of applications and capabilities because of that. That's super exciting. We're already seeing it with this build out of distributed core. And that's why we see so much growth in the data center business. >> So Michael, Dell Boomi, if you look at Boomi for a second, was named by the Gartner Magic Quadrant of 2018 as a leader in Ipads. Today they talked about ... >> Again, I think 6th or 7th year in a row. It's been there for quite some time. >> An established leader in an established market. But today they were talking about, hey we want to change the, we want to redefine the I in Ipads to intelligence. How is Dell Technologies and Boomi particularly starting to leverage terra bites and terra bites of customer meta data to make your systems smarter? To enable businesses to truly connect. Prim, edge devices as things continue to get more distributed and data becomes more critical? >> Yeah, so, the key to AI and all of its variance of machine learning, deep learning neural network is the data. The data is the fuel for the rocket ship of AI. And the challenge is, if you have your data spread out in 100 softwares of service providers and 3 public clouds and here and there and where's all your data? We don't really know. How do you fuel the rocket? It becomes a very difficult problem. This is the problem that we're beginning to address for our customers. We're going to have an event all about AI coming up I think next week. Where we're going to be talking much more about this. We got a number of offerings that we're rolling out. We've been helping customers for years build their data lakes and curate the data. And of course Pivitol and Boomi are essential to how you bring all of this together and make sense of it. Because if you just have all the data but you can't actually use it. If you're not already using AI and it's variance to improve your products and services, you're doing it wrong. We've identified over 450 projects just within Dell Technologies internally. As I mentioned on stage, we've sold about 700 million computers since I started in my dorm room. We have enormous telemetry data. Imagine, if you will, that something doesn't work exactly the way it's supposed to. Okay? What's the chance that has never happened before? >> Zero. >> The answers almost zero, right? Our job is to take all this data that we have, use all this intelligence and actually prevent it from happening. So we're building all kinds of intelligence and AI and preventative technology into all of our solutions from the data center to the desk top to the edge, to the multi cloud so that all these systems are just self healing and auto magically way more reliable. >> Auto magically, I like that. It just sounds like what you're saying is Dell Technologies articulating it's value and it's differentiation because you're using that data. >> You have to. >> To identify insight, to take action immediately. >> And to your point about the big companies, they have an advantage but it's a bit of a time value expiring advantage. They have the data that the new entrance don't have. >> Right. >> But they have to activate it quickly with this new computer science or else they'll be dinosaurs, right? Nobody wants to be a dinosaur. >> Michael, what's the business drivers, and you talk to customers all the time, that they're seeing and that matter most to them. Is it agility, is it transform the customer employee experience, compliant security? How would you view the pattern around the most important business driver for your customers that are trying to put the business transformation together with digital. Could you comment just anecdotally what you see? >> I think every customer is a little bit different in their journey. Some customers, security is number 1. Because of the kind of business that they're in and it just has to be that way. For other customers it's how do I increase my speed to the solution. It used to be we need a new feature. We'll get it in a year or 2. How about never. Does never work for you? That's kind of the old IT. Now with agile development you've got, what we're doing with Pivotol cloud foundry, you've got companies implementing, these are giant companies. Biggest companies in the world. They're implementing new things like in 2 or 3 weeks. It's amazing how fast. Speed and as a chief executive, that's what you crave. How can I take this new requirement that I heard from the customer and turn it into a feature that I can go offer very, very quickly? That's what you want to be able to do. It's what we used to be able to do when we were little tiny cubs. How do you do it with 200000 people? >> I want to get your thoughts on a trend that you popularized early on in your career, the direct business model, you also had the just in time manufacturing kind of ethos of build it, build to order, really streamline efficiency. So I want to kind of take the leap to now a new generation with cloud native where you have workflows and efficiencies. You have integration. So in a way the customers are now going direct to their customers and wanting to compose and build solutions. As you said on stage, these are going to be new problems that not yet have been identified. New solutions. So that customers have to be what you did. They got to build their own. So they got to build their own, they got to have the suppliers, they got to have the code. How do you see customers being successful if they want to take that efficiency approach? Kind of be 5 nines if you will in this new modern era. Because this is the challenge that they have. They have to build their own. They need suppliers. They need you guys. How do you see the customers being successful in that scenario? >> Yeah, I think what they're trying to do is shrink the time from when at that point of customer interaction, they can use the data to make the service and the product better and if it's like this lengthy value chain with all these different intermediaries and it takes weeks or months or never, that's just way too slow. They want it to be like instantaneous. How do they create that direct relationship with their customers? I only had 1000 dollars when I started so we couldn't really afford much so each dollar you invest very carefully. We just kind of out of necessity came up with some ideas that ... >> You were efficient because you had to be. >> We didn't have any choice, right? >> So when we talk about integration, we talk about it's the foundation of digital transformation, we've talked about IT, security, workforce. One of the things that you mentioned earlier that I'd like to get your perspective on, a different view of transformation is cultural. An enterprise organization as you mentioned has a huge advantage of a tremendous wealth of data. With that amount of data and the need for speed as you just talked about, where, in your opinion, and your experience, is cultural transformation as an enabler of an enterprise to really be able to react that quickly to develop new products, new revenue strengths? >> Yeah, I think it's a big challenge. And a lot of customers struggle with change management. You never want a good crisis go to waste. We sort of grew up in the business where it was change or die, quick or dead. If you don't do it you're gone, right? This was just the way our business, this was just how we had to compete. It's what we grew up in. And I think what's happened is more and more businesses are that way now. It requires the business leaders to say hey friends, we've got a real challenge here and we've got to move faster. It is change or die, it's quick or dead, I think for all businesses because this is the fastest time ever but it's the slowest time relative to the future. It's just going to get faster and faster. If companies ... The only way you get good at change is to do it more frequently. And so if you've never changed anything for 80 years in your company and all the sudden you start trying to change, it's really hard. You just have to start. >> How do you inspire say employees at Dell Technologies who've been with you for a very long time to be able to be open and agile themselves to help facilitate this transformation? >> I believe we built it into our culture that they understand that change is good as opposed to change is bad. If you fear something well then it's bad, right? We precondition people to say okay we're going to change something. Not to say every time we change something it works perfectly. We make mistakes, we learn, we trial and error. That's all fine. Fail fast. But you need a culture where you can embrace change. No question about it. I think a lot of companies that didn't really have that are figuring that out and either by crisis or by leadership or by some combination they're then forced into it. For me, it's what we grew up in. Because hey it's a tough world out there. >> Mike, I want to ask you a final question. Thanks for coming on and spending the time with us. Great interview here. Good length. Recently in the news with a lot of commentary from us as well as the industry around IBM buying Red Hat. I made a comment around the innovation piece of this and I want to get your thoughts on that because when you bought EMC, it was a merger of equals. You integrated that and the growth that you've been successful since then, I want to get your perspective. I want you to take a minute to explain to folks watching, when you did the merger equal with EMC, what happened? You've been successful integrating the organization. What innovative things have you done since the EMC merger of equals? Take a minute to explain, again, there's a lot of moving pieces on the table. You got VM Wares, you got Pivitol, you got Boomi. A lot of moving parts in your plan. You've been successful with the numbers. Financial performance shows it. Take a minute to explain what happened, where's the innovation coming out of Dell Technologies? >> So in hind sight, it looks pretty obvious, right? You take the leader and servers and the leader in storage and you say hey infrastructure hardware goes together. And by the way, if you have the leader of infrastructure software, VM Wares, you put that all together. Wow, that'd be really great. And turns out it was. It was actually much better than we thought. And so customers have really bought into that and then with Pivitol and Boomi and Rsave, Virtustream, Secureworks etc., we have such a complete set of capabilities that customers have said, hey, why do I want to buy from 20 smaller less capable companies and integrate it myself versus you guys will just do all this for me. If they were buying from 2 or 3 or 4 parts of Dell Technologies they'll say, well, why don't we just take the others, right? We been picking up huge amounts of share across the whole business. I'm talking about like 10s of billions of dollars of growth here. There's clearly a consolidation going on in the kind of existing parts of the industry but we've also got massive investments in the new cloud native parts and software defined, and security. It's been a real blessing to be able to pull all of these teams together. We had this relationship with EMC going back from 2001. We were very early supporters of VM Ware. We had a theory of victory and it's played out very well. The teams have really gelled enormously well and the customers have continued to give us their trust. >> I think, first of all servers, storage, networking is never going away. It's the holy trinity of anything in computing. Just looks different and consumes differently. But I think people underestimate the execution innovation that you guys have done. You didn't skip a beat. VM Ware didn't skip a beat. So things have happened, so that was a challenge of the integration. >> Not everybody predicted that it was going to go that way. It's actually gone much better than even we had planned. The revenue synergies have been much larger. >> Well congratulations and thanks for taking the time on the Cube. Michael Dell is here inside the Cube here at Boomi World 18. Dell Boomi World. It's the part of Dell Technologies. We think of them being the power engine for data processing, data growth, powering AI, integrating all the application workloads. I'm John Furrier with Lisa Martin. Stay tuned for more coverage after this short break. (upbeat music) >> Since the dawn of the cloud, the Cube has been there. Connected.