(digital music) >> Welcome back to "theCUBE's" live coverage of AWS re:Invent 2022. Lisa Martin here in Las Vegas with Dave Vellante. Dave, we've been here.. This is our third day, we started Monday night. We've done well over 70 interviews so far. I've lost count. >> Yeah, I don't count anymore. (Lisa laughing) >> Just go with the flow. >> We've been talking all things Cloud with AWS it's ecosystem of partners and customers. We're excited to welcome a couple of folks from Pluralsight to the program. Talking about the state of Cloud. Faye Ellis joins us, Principle Training Architect at A Cloud Guru, Pluralsight. Mattias Andersson is also here, Principle Developer Advocate at Pluralsight. Guys welcome to the queue. >> Thank you >> Thank you so much for having us. >> Great to have you. >> Mattias: Glad to be here. >> Just in case our audience isn't familiar with A Cloud Guru and Pluralsight, why don't you give us just that high level elevator pitch? >> Yeah, well we basically help organizations transform their people so that they can deliver Cloud transformations within their own organizations. So it's all about upskilling and getting people Cloud fluent and ready to rock Cloud in their own organizations. >> Love that, Cloud fluent. But what are you hearing from the developer community? Your a developer advocate. We've seen so much pivot towards the developers really influencing business decisions, business direction. What's the voice of the developer like these days? >> Well, I think that a lot of developers are recognizing that the Cloud does offer a lot of value for the things that they're wanting to get done. Developers generally want to do things, they want to build things, they want stuff that they can look at and say, "Hey I made that and it's really good and it solves problems." And so I'm hearing a lot of people talking about how they value things like serverless, to be able to build those sorts of systems without a whole lot of other people necessarily needing to support them. They can get so much built on their own even. And then as teams, they can accomplish a lot of, again, the same sorts of projects. They can build those forward much more efficiently as a smaller team than they could have in the past without that technology. So I'm hearing a lot about that. Especially because I'm working with Cloud so much is what I mean, right? >> So it's kind of putting the power back into their hands as developers. Instead of having to wait for the infrastructure people or the support people to create a server so that they can deploy applications, there's a lot more tools to allow them to actually do that for themselves, isn't there? >> Absolutely, absolutely. It opens up so many doors. >> So pre-Ukraine, we were writing about the skills shortage. I call it the slingshot economy. All right. Oh wow it's like this talent war. And then all of a sudden, Twitter layoffs and there's this talent on the street. Now it might not be a perfect match, but what are you seeing in terms of new talent coming on that you can train and coach. How are you seeing the match and the alignment with what the demand for talent? Now I know your philosophy is you should be producers of talent, not consumers of talent. I get that. >> Faye: Yeah. >> But to produce talent you've got to coach, train, assist people. So what are you seeing today? What's the state of that sort of market? >> That's a really good question. I mean our State of Cloud report, it says that 75% of tech leaders are building all their new products and features in the Cloud. But what was the other stat, Mattias? >> Only 8% of the actual individuals that are working with the technology say that they have extensive skills with the Cloud. So that's a huge gap between the people who are wanting to build that forward as the leadership of the organization and the people that they have available, whether it's internal to their organization or external. So they do have a lot of people who are working in technology already in their organizations in general. But they do need to invest in that. Those technologists are learning things all the time. But are they maybe not learning the right things? Are they not learning them effectively? Are they not moving the organization forward? >> Dave: So go ahead, please. >> Yeah, so we think it's all about like nurturing the talent that you have already in your own organization. And those are the people who really know your business. And you know, it takes time to kind of upskill and really, really develop those Cloud skills and develop that experience. But it's not always the right thing to take on new teams. Like bring in new people and then you've got to get them up to speed with your own business. And actually isn't it much more wonderful to be able to nurture the talent within your own organization and and create that long-term relationship with your own employees. >> So where do you start? Like to get to work for Amazon you got to prove that you're reasonably professional. I mean everybody, the whole company has to like spin up an EC2 instance and do something with it. Is that where you start? Is it sort of education and what's available? What's the Cloud? Or is it more advanced than that? You're looking for maybe people with a technical mind that you're.. or do you have.. obviously have different levels, but take us through sort of the anatomy of experience. >> When you say, "Where do you start?" Who are you meaning? Are you meaning an organization, an individual, a team? >> You guys, when you bring on.. begin to expose an individual to the Cloud, >> Mattias: Right. >> Their objective is to become proficient at something. >> Right. >> Right. And so is it something that you have 100, 101, 201, basically? >> Well, you know what, if you want to learn how to swim you got to jump in the water. That's what I always think. And we focus on practical skills, the ability to do something, to get something done. Get something configured within the Cloud. A lot of the time our customers are asking us for skills that kind of go beyond certification. And for a really long time we were.. A Cloud Guru has been famous for getting engineers certified. But that's just one piece of the puzzle, isn't it? Certification is wonderful, but it's that chicken and egg scenario that I think that you were alluding to which is that you need experience to get the experience. So how are you going to get that experience? And we've got loads of different ideas to help people to actually do that. On our platform we've got lots of practical exercises that you can do. Building out serverless websites, configuring a web application firewall, building a VPC. We've got troubleshooting labs, we've got challenge labs, that kind of thing. And we've also got some free resources, haven't we as well, Mattias. >> Yes. >> Things like our Cloud Portfolio Challenges, which are like little projects that you can complete all by yourself. Creating serverless websites, playing around with SageMaker. You get some requirements and you have to design and actually build that. But it's all about getting that hands-on practice and that's kind of what we focus on. And we start off with easy things, and then we kind of layer it up and layer it up. And we kind of build on the easy foundations until, before you know it, you're Cloud fluent. >> Yeah, I think that there is a lot of value.. You were mentioning to, just to circle back on certifications, that is a really valuable way for a lot of people to start to take a look at the certifications that AWS offers, for example, and say, "How can I use those to guide my learning?" Because I know that sometimes people look at certifications as like a replacement for some sort of an assessment or whatever. And it's not really that most of the time. Most of the time the key value is that it guides people to learn a scope of material that is really valuable to them. And in particular it uncovers blind spots for them. So to answer your question of "Where do you start as an individual?".. People often ask me, "Okay, so I know all these things, which certifications should I get?" And I say, the Cloud Practitioner is the place to start. And they're like, "Oh, but maybe that's too easy." And I say, maybe it is, but then it's going to be really quick for you. If it's not really quick for you, then it was really valuable. You learned those key things. And if it was really quick but you didn't spend a lot of time on it and now you're just that much further along on the next certification that sort of guides you to the next larger scope. So it's a really valuable system that I often guide people to. To say that you can jump into that, anyone actually can jump into the Cloud Practitioner and learn that. And we often recommend that across an entire organization, you could potentially have everyone that gets that Cloud Practitioner. Whether you're finance or sales or leadership executive, the individual teams in technology departments of course. But everyone can get that Cloud fluency and then they can communicate far more effectively with each other. So it's not just the technologists that are needing to do that. >> Absolutely. And I think also it's about leading by example. If you're in leadership and you are asking your engineers to upskill themselves so that you can deliver your transformation goals, well actually, it's leadership responsibility to lead by example as well. And I heard a wonderful story from a customer. Just yesterday, a female CFO in her seventies just got her Cloud Practitioner certification. >> Lisa: Right on. >> I mean, that's wonderful. As I said before, a career in Cloud is a commitment to learning. It's lifelong learning. So yeah, that's wonderful. And long may it continue. I'd love to be in my seventies still learning new things and still rocking it. Maybe not the CFO, maybe something different. But yeah, that would be wonderful. >> How do you define Cloud fluency? There's so many opportunities that you both talked about and you walked through really kind of the step-by-step process. But how would someone define themselves as Cloud fluent? And how.. it's almost like what you were talking about, Mattias, is sort of the democratization of Cloud fluency across an organization, but what does it actually look like? >> Wow, good question. For me, I think it means everybody speaking the same language and having a common understanding. And I think that does kind of hark back to what you were saying before, Mattias, about the foundational certifications. The Cloud Practitioner type certification. What do you think? >> Yeah, I think a part of it is a mindset shift that people need to understand a different way of thinking about technology. That Cloud isn't just another tool just like all the others. It's a different way, a higher level of abstraction in technology that makes us more effective and efficient because of that. But because of that, also, we need to think about it in not the same way as we were before. So if you take it to the language analogy, instead of memorizing a few phrases like "Where is the bathroom?" or "How much does that cost?" or whatever, you have an understanding of the flow of the language. You understand that okay, there are verbs and nouns and I can put them together in this way. Oh, adjectives, those are kind of interesting. I can add those to things. And you have this model, mental model for how you can interact with the technology just like you would interact with the language or whatever other things. So the mental model actually, I think, is really the key thing that I keep coming back to a lot when people are learning that the mental model that you have for something is really what.. this sort of helps you understand the mastery of that. It's whether your mental model is mature and it's not changing a lot as you're learning new information, that's a really valuable milestone for someone to get to. Because as you're learning new things.. otherwise you would make assumptions, and then you learn new things that challenge those assumptions and you have to change the mental model to move forward. So the fluency is when that mental model, you have the understanding and you can then communicate. >> Yep. Love that. Last question for you guys is, we have about a minute left. If you had a billboard that you could put anywhere about A Cloud Guru at Pluralsight and what you're enabling with respect to Cloud fluency. I want you to each kind of take about 30 seconds to.. from your perspective, what would it say? >> Oh my goodness. I think it would say something like, Cloud is for everybody. It's no longer this elitist, difficult to understand, abstract thing. And I think it's something that is inclusive to everybody and that we should all be embracing it. And if you don't do it, you are going to be left behind because your competitors are going to be getting the advantages from Cloud. You're going to miss that competitive advantage and you're going to lose out. So yeah, that's probably quite a lot to put on a billboard. >> I love it. And Mattias, what would your billboard say? >> Ah, let me think. Okay. I might say something like, "The future of technology is accessible and important if you're in a technology career." I don't know, now it's getting more wordy. That's not quite right. But the point is that the Cloud really is the future of technology. It's not just some other little tool that's a fad or whatever. It's a different way of approaching technology. I'm realizing you're asking about the billboard as a short thing. The Cloud is the future. You can do it. You should do it. (everyone laughing) >> Drop the mic. Nailed it! Faye, Mattias, thank you so much joining us.. >> Thank you so much, we really appreciate it. >> Lisa: This was a great session. >> Thank you. >> Lisa: Great to have A Cloud Guru by Pluralsight on the program. We appreciate you stopping by. >> Oh, thank you so much. >> Thank you both so very much. >> We appreciate it. >> Lisa: Our pleasure. >> Thank you. For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE", the leader in live enterprise and emerging tech coverage. (digital music)

(upbeat music) >> Hello and welcome to theCUBE's presentation of the AWS Startup Showcase. This is season two, episode four of the ongoing series covering the exciting hot startups from the AWS ecosystem. Here we're talking about cybersecurity in this episode. I'm your host, John Furrier here we're excited to have CUBE alumni who's back Snehal Antani who's the CEO and co-founder of Horizon3.ai talking about exploitable weaknesses and vulnerabilities with autonomous pen testing. Snehal, it's great to see you. Thanks for coming back. >> Likewise, John. I think it's been about five years since you and I were on the stage together. And I've missed it, but I'm glad to see you again. >> Well, before we get into the showcase about your new startup, that's extremely successful, amazing margins, great product. You have a unique journey. We talked about this prior to you doing the journey, but you have a great story. You left the startup world to go into the startup, like world of self defense, public defense, NSA. What group did you go to in the public sector became a private partner. >> My background, I'm a software engineer by education and trade. I started my career at IBM. I was a CIO at GE Capital, and I think we met once when I was there and I became the CTO of Splunk. And we spent a lot of time together when I was at Splunk. And at the end of 2017, I decided to take a break from industry and really kind of solve problems that I cared deeply about and solve problems that mattered. So I left industry and joined the US Special Operations Community and spent about four years in US Special Operations, where I grew more personally and professionally than in anything I'd ever done in my career. And exited that time, met my co-founder in special ops. And then as he retired from the air force, we started Horizon3. >> So there's really, I want to bring that up one, 'cause it's fascinating that not a lot of people in Silicon Valley and tech would do that. So thanks for the service. And I know everyone who's out there in the public sector knows that this is a really important time for the tactical edge in our military, a lot of things going on around the world. So thanks for the service and a great journey. But there's a storyline with the company you're running now that you started. I know you get the jacket on there. I noticed get a little military vibe to it. Cybersecurity, I mean, every company's on their own now. They have to build their own militia. There is no government supporting companies anymore. There's no militia. No one's on the shores of our country defending the citizens and the companies, they got to offend for themselves. So every company has to have their own military. >> In many ways, you don't see anti-aircraft rocket launchers on top of the JP Morgan building in New York City because they rely on the government for air defense. But in cyber it's very different. Every company is on their own to defend for themselves. And what's interesting is this blend. If you look at the Ukraine, Russia war, as an example, a thousand companies have decided to withdraw from the Russian economy and those thousand companies we should expect to be in the ire of the Russian government and their proxies at some point. And so it's not just those companies, but their suppliers, their distributors. And it's no longer about cyber attack for extortion through ransomware, but rather cyber attack for punishment and retaliation for leaving. Those companies are on their own to defend themselves. There's no government that is dedicated to supporting them. So yeah, the reality is that cybersecurity, it's the burden of the organization. And also your attack surface has expanded to not just be your footprint, but if an adversary wants to punish you for leaving their economy, they can get, if you're in agriculture, they could disrupt your ability to farm or they could get all your fruit to spoil at the border 'cause they disrupted your distributors and so on. So I think the entire world is going to change over the next 18 to 24 months. And I think this idea of cybersecurity is going to become truly a national problem and a problem that breaks down any corporate barriers that we see in previously. >> What are some of the things that inspired you to start this company? And I loved your approach of thinking about the customer, your customer, as defending themselves in context to threats, really leaning into it, being ready and able to defend. Horizon3 has a lot of that kind of military thinking for the good of the company. What's the motivation? Why this company? Why now? What's the value proposition? >> So there's two parts to why the company and why now. The first part was what my observation, when I left industry realm or my military background is watching "Jack Ryan" and "Tropic Thunder" and I didn't come from the military world. And so when I entered the special operations community, step one was to keep my mouth shut, learn, listen, and really observe and understand what made that community so impressive. And obviously the people and it's not about them being fast runners or great shooters or awesome swimmers, but rather there are learn-it-alls that can solve any problem as a team under pressure, which is the exact culture you want to have in any startup, early stage companies are learn-it-alls that can solve any problem under pressure as a team. So I had this immediate advantage when we started Horizon3, where a third of Horizon3 employees came from that special operations community. So one is this awesome talent. But the second part that, I remember this quote from a special operations commander that said we use live rounds in training because if we used fake rounds or rubber bullets, everyone would act like metal of honor winners. And the whole idea there is you train like you fight, you build that muscle memory for crisis and response and so on upfront. So when you're in the thick of it, you already know how to react. And this aligns to a pain I had in industry. I had no idea I was secure until the bad guy showed up. I had no idea if I was fixing the right vulnerabilities, logging the right data in Splunk, or if my CrowdStrike EDR platform was configured correctly, I had to wait for the bad guys to show up. I didn't know if my people knew how to respond to an incident. So what I wanted to do was proactively verify my security posture, proactively harden my systems. I needed to do that by continuously pen testing myself or continuously testing my security posture. And there just wasn't any way to do that where an IT admin or a network engineer could in three clicks have the power of a 20 year pen testing expert. And that was really what we set out to do, not build a autonomous pen testing platform for security people, build it so that anybody can quickly test their security posture and then use the output to fix problems that truly matter. >> So the value preposition, if I get this right is, there's a lot of companies out there doing pen tests. And I know I hate pen tests. They're like, cause you do DevOps, it changes you got to do another pen test. So it makes sense to do autonomous pen testing. So congratulations on seeing that that's obvious to that, but a lot of other have consulting tied to it. Which seems like you need to train someone and you guys taking a different approach. >> Yeah, we actually, as a company have zero consulting, zero professional services. And the whole idea is that build a true software as a service offering where an intern, in fact, we've got a video of a nine year old that in three clicks can run pen tests against themselves. And because of that, you can wire pen tests into your DevOps tool chain. You can run multiple pen tests today. In fact, I've got customers running 40, 50 pen tests a month against their organization. And that what that does is completely lowers the barrier of entry for being able to verify your posture. If you have consulting on average, when I was a CIO, it was at least a three month lead time to schedule consultants to show up and then they'd show up, they'd embarrass the security team, they'd make everyone look bad, 'cause they're going to get in, leave behind a report. And that report was almost identical to what they found last year because the older that report, the one the date itself gets stale, the context changes and so on. And then eventually you just don't even bother fixing it. Or if you fix a problem, you don't have the skills to verify that has been fixed. So I think that consulting led model was acceptable when you viewed security as a compliance checkbox, where once a year was sufficient to meet your like PCI requirements. But if you're really operating with a wartime mindset and you actually need to harden and secure your environment, you've got to be running pen test regularly against your organization from different perspectives, inside, outside, from the cloud, from work, from home environments and everything in between. >> So for the CISOs out there, for the CSOs and the CXOs, what's the pitch to them because I see your jacket that says Horizon3 AI, trust but verify. But this trust is, but is canceled out, just as verify. What's the product that you guys are offering the service. Describe what it is and why they should look at it. >> Yeah, sure. So one, when I back when I was the CIO, don't tell me we're secure in PowerPoint. Show me we're secure right now. Show me we're secure again tomorrow. And then show me we're secure again next week because my environment is constantly changing and the adversary always has a vote and they're always evolving. And this whole idea of show me we're secure. Don't trust that your security tools are working, verify that they can detect and respond and stifle an attack and then verify tomorrow, verify next week. That's the big mind shift. Now what we do is-- >> John: How do they respond to that by the way? Like they don't believe you at first or what's the story. >> I think, there's actually a very bifurcated response. There are still a decent chunk of CIOs and CSOs that have a security is a compliance checkbox mindset. So my attitude with them is I'm not going to convince you. You believe it's a checkbox. I'll just wait for you to get breached and sell to your replacement, 'cause you'll get fired. And in the meantime, I spend all my energy with those that actually care about proactively securing and hardening their environments. >> That's true. People do get fired. Can you give an example of what you're saying about this environment being ready, proving that you're secure today, tomorrow and a few weeks out. Give me an example. >> Of, yeah, I'll give you actually a customer example. There was a healthcare organization and they had about 5,000 hosts in their environment and they did everything right. They had Fortinet as their EDR platform. They had user behavior analytics in place that they had purchased and tuned. And when they ran a pen test self-service, our product node zero immediately started to discover every host on the network. It then fingerprinted all those hosts and found it was able to get code execution on three machines. So it got code execution, dumped credentials, laterally maneuvered, and became a domain administrator, which in IT, if an attacker becomes a domain admin, they've got keys to the kingdom. So at first the question was, how did the node zero pen test become domain admin? How'd they get code execution, Fortinet should have detected and stopped it. Well, it turned out Fortinet was misconfigured on three boxes out of 5,000. And these guys had no idea and it's just automation that went wrong and so on. And now they would've only known they had misconfigured their EDR platform on three hosts if the attacker had showed up. The second question though was, why didn't they catch the lateral movement? Which all their marketing brochures say they're supposed to catch. And it turned out that that customer purchased the wrong Fortinet modules. One again, they had no idea. They thought they were doing the right thing. So don't trust just installing your tools is good enough. You've got to exercise and verify them. We've got tons of stories from patches that didn't actually apply to being able to find the AWS admin credentials on a local file system. And then using that to log in and take over the cloud. In fact, I gave this talk at Black Hat on war stories from running 10,000 pen tests. And that's just the reality is, you don't know that these tools and processes are working for you until the bad guys have shown. >> The velocities there. You can accelerate through logs, you know from the days you've been there. This is now the threat. Being, I won't say lazy, but just not careful or just not thinking. >> Well, I'll do an example. We have a lot of customers that are Horizon3 customers and Splunk customers. And what you'll see their behavior is, is they'll have Horizon3 up on one screen. And every single attacker command executed with its timestamp is up on that screen. And then look at Splunk and say, hey, we were able to dump vCenter credentials from VMware products at this time on this host, what did Splunk see or what didn't they see? Why were no logs generated? And it turns out that they had some logging blind spots. So what they'll actually do is run us to almost like stimulate the defensive tools and then see what did the tools catch? What did they miss? What are those blind spots and how do they fix it. >> So your price called node zero. You mentioned that. Is that specifically a suite, a tool, a platform. How do people consume and engage with you guys? >> So the way that we work, the whole product is designed to be self-service. So once again, while we have a sales team, the whole intent is you don't need to have to talk to a sales rep to start using the product, you can log in right now, go to Horizon3.ai, you can run a trial log in with your Google ID, your LinkedIn ID, start running pen test against your home or against your network against this organization right now, without talking to anybody. The whole idea is self-service, run a pen test in three clicks and give you the power of that 20 year pen testing expert. And then what'll happen is node zero will execute and then it'll provide to you a full report of here are all of the different paths or attack paths or sequences where we are able to become an admin in your environment. And then for every attack path, here is the path or the kill chain, the proof of exploitation for every step along the way. Here's exactly what you've got to do to fix it. And then once you've fixed it, here's how you verify that you've truly fixed the problem. And this whole aha moment is run us to find problems. You fix them, rerun us to verify that the problem has been fixed. >> Talk about the company, how many people do you have and get some stats? >> Yeah, so we started writing code in January of 2020, right before the pandemic hit. And then about 10 months later at the end of 2020, we launched the first version of the product. We've been in the market for now about two and a half years total from start of the company till present. We've got 130 employees. We've got more customers than we do employees, which is really cool. And instead our customers shift from running one pen test a year to 40, 50 pen test. >> John: And it's full SaaS. >> The whole product is full SaaS. So no consulting, no pro serve. You run as often as you-- >> Who's downloading, who's buying the product. >> What's amazing is, we have customers in almost every section or sector now. So we're not overly rotated towards like healthcare or financial services. We've got state and local education or K through 12 education, state and local government, a number of healthcare companies, financial services, manufacturing. We've got organizations that large enterprises. >> John: Security's diverse. >> It's very diverse. >> I mean, ransomware must be a big driver. I mean, is that something that you're seeing a lot. >> It is. And the thing about ransomware is, if you peel back the outcome of ransomware, which is extortion, at the end of the day, what ransomware organizations or criminals or APTs will do is they'll find out who all your employees are online. They will then figure out if you've got 7,000 employees, all it takes is one of them to have a bad password. And then attackers are going to credential spray to find that one person with a bad password or whose Netflix password that's on the dark web is also their same password to log in here, 'cause most people reuse. And then from there they're going to most likely in your organization, the domain user, when you log in, like you probably have local admin on your laptop. If you're a windows machine and I've got local admin on your laptop, I'm going to be able to dump credentials, get the admin credentials and then start to laterally maneuver. Attackers don't have to hack in using zero days like you see in the movies, often they're logging in with valid user IDs and passwords that they've found and collected from somewhere else. And then they make that, they maneuver by making a low plus a low equal a high. And the other thing in financial services, we spend all of our time fixing critical vulnerabilities, attackers know that. So they've adapted to finding ways to chain together, low priority vulnerabilities and misconfigurations and dangerous defaults to become admin. So while we've over rotated towards just fixing the highs and the criticals attackers have adapted. And once again they have a vote, they're always evolving their tactics. >> And how do you prevent that from happening? >> So we actually apply those same tactics. Rarely do we actually need a CVE to compromise your environment. We will harvest credentials, just like an attacker. We will find misconfigurations and dangerous defaults, just like an attacker. We will combine those together. We'll make use of exploitable vulnerabilities as appropriate and use that to compromise your environment. So the tactics that, in many ways we've built a digital weapon and the tactics we apply are the exact same tactics that are applied by the adversary. >> So you guys basically simulate hacking. >> We actually do the hacking. Simulate means there's a fakeness to it. >> So you guys do hack. >> We actually compromise. >> Like sneakers the movie, those sneakers movie for the old folks like me. >> And in fact that was my inspiration. I've had this idea for over a decade now, which is I want to be able to look at anything that laptop, this Wi-Fi network, gear in hospital or a truck driving by and know, I can figure out how to gain initial access, rip that environment apart and be able to opponent. >> Okay, Chuck, he's not allowed in the studio anymore. (laughs) No, seriously. Some people are exposed. I mean, some companies don't have anything. But there's always passwords or so most people have that argument. Well, there's nothing to protect here. Not a lot of sensitive data. How do you respond to that? Do you see that being kind of putting the head in the sand or? >> Yeah, it's actually, it's less, there's not sensitive data, but more we've installed or applied multifactor authentication, attackers can't get in now. Well MFA only applies or does not apply to lower level protocols. So I can find a user ID password, log in through SMB, which isn't protected by multifactor authentication and still upon your environment. So unfortunately I think as a security industry, we've become very good at giving a false sense of security to organizations. >> John: Compliance drives that behavior. >> Compliance drives that. And what we need. Back to don't tell me we're secure, show me, we've got to, I think, change that to a trust but verify, but get rid of the trust piece of it, just to verify. >> Okay, we got a lot of CISOs and CSOs watching this showcase, looking at the hot startups, what's the message to the executives there. Do they want to become more leaning in more hawkish if you will, to use the military term on security? I mean, I heard one CISO say, security first then compliance 'cause compliance can make you complacent and then you're unsecure at that point. >> I actually say that. I agree. One definitely security is different and more important than being compliant. I think there's another emerging concept, which is I'd rather be defensible than secure. What I mean by that is security is a point in time state. I am secure right now. I may not be secure tomorrow 'cause something's changed. But if I'm defensible, then what I have is that muscle memory to detect, respondent and stifle an attack. And that's what's more important. Can I detect you? How long did it take me to detect you? Can I stifle you from achieving your objective? How long did it take me to stifle you? What did you use to get in to gain access? How long did that sit in my environment? How long did it take me to fix it? So on and so forth. But I think it's being defensible and being able to rapidly adapt to changing tactics by the adversary is more important. >> This is the evolution of how the red line never moved. You got the adversaries in our networks and our banks. Now they hang out and they wait. So everyone thinks they're secure. But when they start getting hacked, they're not really in a position to defend, the alarms go off. Where's the playbook. Team springs into action. I mean, you kind of get the visual there, but this is really the issue being defensible means having your own essentially military for your company. >> Being defensible, I think has two pieces. One is you've got to have this culture and process in place of training like you fight because you want to build that incident response muscle memory ahead of time. You don't want to have to learn how to respond to an incident in the middle of the incident. So that is that proactively verifying your posture and continuous pen testing is critical there. The second part is the actual fundamentals in place so you can detect and stifle as appropriate. And also being able to do that. When you are continuously verifying your posture, you need to verify your entire posture, not just your test systems, which is what most people do. But you have to be able to safely pen test your production systems, your cloud environments, your perimeter. You've got to assume that the bad guys are going to get in, once they're in, what can they do? So don't just say that my perimeter's secure and I'm good to go. It's the soft squishy center that attackers are going to get into. And from there, can you detect them and can you stop them? >> Snehal, take me through the use. You got to be sold on this, I love this topic. Alright, pen test. Is it, what am I buying? Just pen test as a service. You mentioned dark web. Are you actually buying credentials online on behalf of the customer? What is the product? What am I buying if I'm the CISO from Horizon3? What's the service? What's the product, be specific. >> So very specifically and one just principles. The first principle is when I was a buyer, I hated being nickled and dimed buyer vendors, which was, I had to buy 15 different modules in order to achieve an objective. Just give me one line item, make it super easy to buy and don't nickel and dime me. Because I've spent time as a buyer that very much has permeated throughout the company. So there is a single skew from Horizon3. It is an annual subscription based on how big your environment is. And it is inclusive of on-prem internal pen tests, external pen tests, cloud attacks, work from home attacks, our ability to harvest credentials from the dark web and from open source sources. Being able to crack those credentials, compromise. All of that is included as a singles skew. All you get as a CISO is a singles skew, annual subscription, and you can run as many pen tests as you want. Some customers still stick to, maybe one pen test a quarter, but most customers shift when they realize there's no limit, we don't nickel and dime. They can run 10, 20, 30, 40 a month. >> Well, it's not nickel and dime in the sense that, it's more like dollars and hundreds because they know what to expect if it's classic cloud consumption. They kind of know what their environment, can people try it. Let's just say I have a huge environment, I have a cloud, I have an on-premise private cloud. Can I dabble and set parameters around pricing? >> Yes you can. So one is you can dabble and set perimeter around scope, which is like manufacturing does this, do not touch the production line that's on at the moment. We've got a hospital that says every time they run a pen test, any machine that's actually connected to a patient must be excluded. So you can actually set the parameters for what's in scope and what's out of scope up front, most again we're designed to be safe to run against production so you can set the parameters for scope. You can set the parameters for cost if you want. But our recommendation is I'd rather figure out what you can afford and let you test everything in your environment than try to squeeze every penny from you by only making you buy what can afford as a smaller-- >> So the variable ratio, if you will is, how much they spend is the size of their environment and usage. >> Just size of the environment. >> So it could be a big ticket item for a CISO then. >> It could, if you're really large, but for the most part-- >> What's large? >> I mean, if you were Walmart, well, let me back up. What I heard is global 10 companies spend anywhere from 50 to a hundred million dollars a year on security testing. So they're already spending a ton of money, but they're spending it on consultants that show up maybe a couple of times a year. They don't have, humans can't scale to test a million hosts in your environment. And so you're already spending that money, spend a fraction of that and use us and run as much as you want. And that's really what it comes down to. >> John: All right. So what's the response from customers? >> What's really interesting is there are three use cases. The first is that SOC manager that is using us to verify that their security tools are actually working. So their Splunk environment is logging the right data. It's integrating properly with CrowdStrike, it's integrating properly with their active directory services and their password policies. So the SOC manager is using us to verify the effectiveness of their security controls. The second use case is the IT director that is using us to proactively harden their systems. Did they install VMware correctly? Did they install their Cisco gear correctly? Are they patching right? And then the third are for the companies that are lucky to have their own internal pen test and red teams where they use us like a force multiplier. So if you've got 10 people on your red team and you still have a million IPs or hosts in your environment, you still don't have enough people for that coverage. So they'll use us to do recon at scale and attack at scale and let the humans focus on the really juicy hard stuff that humans are successful at. >> Love the product. Again, I'm trying to think about how I engage on the test. Is there pilots? Is there a demo version? >> There's a free trials. So we do 30 day free trials. The output can actually be used to meet your SOC 2 requirements. So in many ways you can just use us to get a free SOC 2 pen test report right now, if you want. Go to the website, log in for a free trial, you can log into your Google ID or your LinkedIn ID, run a pen test against your organization and use that to answer your PCI segmentation test requirements, your SOC 2 requirements, but you will be hooked. You will want to run us more often. And you'll get a Horizon3 tattoo. >> The first hits free as they say in the drug business. >> Yeah. >> I mean, so you're seeing that kind of response then, trial converts. >> It's exactly. In fact, we have a very well defined aha moment, which is you run us to find, you fix, you run us to verify, we have 100% technical win rate when our customers hit a find, fix, verify cycle, then it's about budget and urgency. But 100% technical win rate because of that aha moment, 'cause people realize, holy crap, I don't have to wait six months to verify that my problems have actually been fixed. I can just come in, click, verify, rerun the entire pen test or rerun a very specific part of it on what I just patched my environment. >> Congratulations, great stuff. You're here part of the AWS Startup Showcase. So I have to ask, what's the relationship with AWS, you're on their cloud. What kind of actions going on there? Is there secret sauce on there? What's going on? >> So one is we are AWS customers ourselves, our brains command and control infrastructure. All of our analytics are all running on AWS. It's amazing, when we run a pen test, we are able to use AWS and we'll spin up a virtual private cloud just for that pen test. It's completely ephemeral, it's all Lambda functions and graph analytics and other techniques. When the pen test ends, you can delete, there's a single use Docker container that gets deleted from your environment so you have nothing on-prem to deal with and the entire virtual private cloud tears itself down. So at any given moment, if we're running 50 pen tests or a hundred pen tests, self-service, there's a hundred virtual private clouds being managed in AWS that are spinning up, running and tearing down. It's an absolutely amazing underlying platform for us to make use of. Two is that many customers that have hybrid environments. So they've got a cloud infrastructure, an Office 365 infrastructure and an on-prem infrastructure. We are a single attack platform that can test all of that together. No one else can do it. And so the AWS customers that are especially AWS hybrid customers are the ones that we do really well targeting. >> Got it. And that's awesome. And that's the benefit of cloud? >> Absolutely. And the AWS marketplace. What's absolutely amazing is the competitive advantage being part of the marketplace has for us, because the simple thing is my customers, if they already have dedicated cloud spend, they can use their approved cloud spend to pay for Horizon3 through the marketplace. So you don't have to, if you already have that budget dedicated, you can use that through the marketplace. The other is you've already got the vendor processes in place, you can purchase through your existing AWS account. So what I love about the AWS company is one, the infrastructure we use for our own pen test, two, the marketplace, and then three, the customers that span that hybrid cloud environment. That's right in our strike zone. >> Awesome. Well, congratulations. And thanks for being part of the showcase and I'm sure your product is going to do very, very well. It's very built for what people want. Self-service get in, get the value quickly. >> No agents to install, no consultants to hire. safe to run against production. It's what I wanted. >> Great to see you and congratulations and what a great story. And we're going to keep following you. Thanks for coming on. >> Snehal: Phenomenal. Thank you, John. >> This is the AWS Startup Showcase. I'm John John Furrier, your host. This is season two, episode four on cybersecurity. Thanks for watching. (upbeat music)

(upbeat music) >> Hi, everybody. We're wrapping up day two of AWS Re:Inforce 2022. This is theCUBE, my name is Dave Vellante. And one of the folks that we featured, one of the companies that we featured in the AWS startup showcase season two, episode four, was Illumio. And of course their here at the security theme event. PJ Kerner is CTO and Co-Founder of Illumio. Great to see you, welcome back to theCUBE. >> Thanks for having me. >> I always like to ask co-founders, people with co-founder in their titles, like go back to why you started the company. Let's go back to 2013. Why'd you start the company? >> Absolutely. Because back in 2013, one of the things that we sort of saw as technology trends, and it was mostly AWS was, there were really three things. One was dynamic workloads. People were putting workloads into production faster and faster. You talk about auto scale groups and now you talk about containers. Like things were getting faster and faster in terms of compute. Second thing was applications were getting more connected, right? The Netflix architecture is one define that kind of extreme example of hyper connectivity, but applications were, we'd call it the API economy or whatever, they were getting more connected. And the third problem back in 2013 was the problems around lateral movement. And at that point it was more around nation state actors and APTs that were in those environments for a lot of those customers. So those three trends were kind of, what do we need to do in security differently? And that's how Illumio started. >> So, okay, you say nation state that's obviously changed in the ROI of for hackers has become pretty good. And I guess your job is to reduce the ROI, but so what's the relationship PJ between the API economy, you talked about in that lateral movement? Are they kind of go hand in hand? >> They do. I think one thing that we have as a mission is, and I think it's really important to understand is to prevent breaches from becoming cyber disasters, right? And I use this metaphor around kind the submarine. And if you think about how submarines are built, submarines are built with water tight compartments inside the submarine. So when there is a physical breach, right, what happens? Like you get a torpedo or whatever, and it comes through the hall, you close off that compartment, there are redundant systems in place, but you close off that compartment, that one small thing you've lost, but the whole ship hasn't gone down and you sort of have survived. That's physical kind of resiliency and those same kind of techniques in terms of segmentation, compartmentalization inside your environments, is what makes good cyber resiliency. So prevent it from becoming a disaster. >> So you bring that micro segmentation analogy, the submarine analogy with micro segmentation to logical security, correct? >> Absolutely, yes. >> So that was your idea in 2013. Now we fast forward to 2022. It's no longer just nation states, things like ransomware are top of mind. I mean, everybody's like worried about what happened with solar winds and Log4j and on and on and on. So what's the mindset of the CISO today? >> I think you said it right. So ransomware, because if you think about the CIA triangle, confidentiality, integrity, availability, what does ransomware really does? It really attacks the availability problem, right? If you lock up all your laptops and can't actually do business anymore, you have an availability problem, right. They might not have stole your data, but they locked it up, but you can't do business, maybe you restore from backups. So that availability problem has made it more visible to CEOs and board level, like people. And so they've been talking about ransomware as a problem. And so that has given the CISO either more dollars, more authority to sort of attack that problem. And lateral movement is the primary way that ransomware gets around and becomes a disaster, as opposed to just locking up one machine when you lock up your entire environment, and thus some of the fear around colonial pipeline came in, that's when the disaster comes into play and you want to be avoiding that. >> Describe in more detail what you mean by lateral movement. I think it's implied, but you enter into a point and then instead of going, you're saying necessarily directly for the asset that you're going after, you're traversing the network, you're traversing other assets. Maybe you could describe that. >> Yeah, I mean, so often what happens is there's an initial point of breach. Like someone has a password or somebody clicked on a phishing link or something, and you have compromise into that environment, right? And then you might be compromised into a low level place that doesn't have a lot of data or is not worthwhile. Then you have to get from that place to data that is actually valuable, and that's where lateral movement comes into place. But also, I mean, you bring up a good point is like lateral movement prevention tools. Like, one way we've done some research around if you like, segmentation is, imagine putting up a maze inside your data center or cloud, right. So that, like how the attacker has to get from that initial breach to the crown jewels takes a lot longer when you have, a segmented environment, as opposed to, if you have a very flat network, it is just go from there to go find that asset. >> Hence, you just increase the denominator in the ROI equation and that just lowers the value for the hacker. They go elsewhere. >> It is an economic, you're right, it's all about economics. It's a time to target is what some our research like. So if you're a quick time to target, you're much easier to sort of get that value for the hacker. If it's a long time, they're going to get frustrated, they're going to stop and might not be economically viable. It's like the, you only have to run faster than the-- >> The two people with the bear chasing you, right. (laughs) Let's talk about zero trust. So it's a topic that prior to the pandemic, I think a lot of people thought it was a buzzword. I have said actually, it's become a mandate. Having said that others, I mean, AWS in particular kind of rolled their eyes and said, ah, we've always been zero trust. They were sort of forced into the discussion. What's your point of view on zero trust? Is it a buzzword? Does it have meaning, what is that meaning to Illumio? >> Well, for me there's actually two, there's two really important concepts. I mean, zero trust is a security philosophy. And so one is the idea of least privilege. And that's not a new idea. So when AWS says they've done it, they have embraced these privileges, a lot of good systems that have been built from scratch do, but not everybody has least privilege kind of controls everywhere. Secondly, least privilege is not about a one time thing. It is about a continuously monitoring. If you sort of take, people leave the company, applications get shut down. Like you need to shut down that access to actually continuously achieve that kind of least privilege stance. The other part that I think is really important that has come more recently is the assume breach mentality, right? And assume breach is something where you assume the attacker is, they've already clicked on, like stop trying to prevent. Well, I mean, you always still should probably prevent the people from clicking on the bad links, but from a security practitioner point of view, assume this has already happened, right. They're already inside. And then what do you have to do? Like back to what I was saying about setting up that maze ahead of time, right. To increase that time to target, that's something you have to do if you kind of assume breach and don't think, oh, a harder shell on my submarine is going to be the way I'm going to survive, right. So that mentality is, I will say is new and really important part of a zero trust philosophy. >> Yeah, so this is interesting because I mean, you kind of the old days, I don't know, decade plus ago, failure meant you get fired, breach meant you get fired. So we want to talk about it. And then of course that mentality had to change 'cause everybody's getting breached and this idea of least privilege. So in other words, if someone's not explicitly or a machine is not explicitly authorized to access an asset, they are not allowed, it's denied. So it's like Frank Slootman would say, if there's doubt, there's no doubt. And so is that right? >> It is. I mean, and if you think about it back to the disaster versus the breach, imagine they did get into an application. I mean, lamps stacks will have vulnerabilities from now to the end of time and people will get in. But what if you got in through a low value asset, 'cause these are some of the stories, you got in through a low value asset and you were sort of contained and you had access to that low value data. Let's say you even locked it up or you stole it all. Like it's not that important to the customer. That's different than when you pivot from that low value asset now into high value assets where it becomes much more catastrophic for those customers. So that kind of prevention, it is important. >> What do you make of this... Couple things, we've heard a lot about encrypt everything. It seems like these days again, in the old days, you'd love to encrypt everything, but there was always a performance hit, but we're hearing encrypt everything, John asked me the day John Furrier is like, okay, we're hearing about encrypting data at rest. What about data in motion? Now you hear about confidential computing and nitro and they're actually encrypting data in the flow. What do you make of that whole confidential computing down at the semiconductor level that they're actually doing things like enclaves and the arm architecture, how much of the problem does that address? How much does it still leave open? >> That's a hard question to answer-- >> But you're a CTO. So that's why I can ask you these questions. >> But I think it's the age old adage of defense in depth. I mean, I do think equivalent to what we're kind of doing from the networking point of view to do network segmentation. This is another layer of that compartmentalization and we'll sort of provide similar containment of breach. And that's really what we're looking for now, rather than prevention of the breach and rather than just detection of the breach, containment of that breach. >> Well, so it's actually similar philosophy brought to the wider network. >> Absolutely. And it needs to be brought at all levels. I think that's the, no one level is going to solve the problem. It's across all those levels is where you have to. >> What are the organizational implications of, it feels like the cloud is now becoming... I don't want to say the first layer of defense because it is if you're all in the cloud, but it's not, if you're a hybrid, but it's still, it's becoming increasingly a more important layer of defense. And then I feel like the CISO and the development team is like the next layer maybe audit is the third layer of defense. How are you seeing organizations sort of respond to that? The organizational roles changing, the CISO role changing. >> Well there's two good questions in there. So one is, there's one interesting thing that we are seeing about people. Like a lot of our customers are hybrid in their environment. They have a cloud, they have an on-prem environment and these two things need to work together. And in that case, I mean, the massive compute that you can be doing in the AWS actually increases the attack surface on that hybrid environment. So there's some challenges there and yes, you're absolutely right. The cloud brings some new tools to play, to sort of decrease that. But it's an interesting place we see where there's a attack surface that occurs between different infrastructure types, between AWS and on-prem of our environment. Now, the second part of your question was really around how the developers play into this. And I'm a big proponent of, I mean, security is kind of a team sport. And one of the things that we've done in some of our products is help people... So we all know the developers, like they know they're part of the security story, right? But they're not security professionals. They don't have all of the tools and all of the experience. And all of the red teaming time to sort of know where some of their mistakes might be made. So I am optimistic. They do their best, right. But what the security team needs is a way to not just tell them, like slap on the knuckles, like developer you're doing the wrong thing, but they really need a way to sort of say, okay, yes, you could do better. And here's some concrete ways that you can do better. So a lot of our systems kind of look at data, understand the data, analyze the data, and provide concrete recommendations. And there's a virtual cycle there. As long as you play the team sport, right. It's not a us versus them. It's like, how can we both win there? >> So this is a really interesting conversation because the developer all of a sudden is increasingly responsible for security. They got to worry about they're using containers. Now they got to worry about containers security. They got to worry about the run time. They got to worry about the platform. And to your point, it's like, okay, this burden is now on them. Not only do they have to be productive and produce awesome code, they got to make sure it's secure. So that role is changing. So are they up for the task? I mean, I got to believe that a lot of developers are like, oh, something else I have to worry about. So how are your customers resolving that? >> So I think they're up for the task. I think what is needed though, is a CISO and a security team again, who knows it's a team sport. Like some technologies adopted from the top down, like the CIO can say, here's what we're doing and then everybody has to do it. Some technologies adopted from the bottom up, right. It's where this individual team says, oh, we're using this thing and we're using these tools. Oh yeah, we're using containers and we're using this flavor of containers. And this other group uses Lambda services and so on. And the security team has to react because they can't mandate. They have to sort of work with those teams. So I see the best groups of people is where you have security teams who know they have to enable the developers and the developers who actually want to work with the security team. So it's the right kind of person, the right kind of CISO, right kind of security teams. It doesn't treat it as adversarial. And it works when they both work together. And that's where, your question is, how ingrained is that in the industry, that I can't say, but I know that does work. And I know that's the direction people are going. >> And I understand it's a spectrum, but I hear what you're saying. That is the best practice, the right organizational model, I guess it's cultural. I mean, it's not like there's some magic tool to make it all, the security team and the dev team collaboration tool, maybe there is, I don't know, but I think the mindset and the culture has to really be the starting point. >> Well, there is. I just talk about this idea. So however you sort of feel about DevOps and DevSecOps and so on, one core principle I see is really kind of empathy between like the developers and the operations folks, so the developers and the security team. And one way I actually, and we act like this at Illumio but one thing we do is like, you have to truly have empathy. You kind have to do somebody else's job, right. Not just like, think about it or talk about it, like do it. So there are places where the security team gets embedded deep in the organization where some of the developers get embedded in the operations work and that empathy. I know whether they go back to do what they were doing, what they learned about how the other side has to work. Some of the challenges, what they see is really valuable in sort of building that collaboration. >> So it's not job swapping, but it's embedding, is maybe how they gain that empathy. >> Exactly. And they're not experts in all those things, but do them take on those summer responsibilities, be accountable for some of those things. Now, not just do it on the side and go over somebody's shoulder, but like be accountable for something. >> That's interesting, not just observational, but actually say, okay, this is on you for some period of time. >> That is where you actually feel the pain of the other person, which is what is valuable. And so that's how you can build one of those cultures. I mean, you do need support all the way from the top, right. To be able to do that. >> For sure. And of course there are lightweight versions of that. Maybe if you don't have the stomach for... Lena Smart was on this morning, CISO of Mongo. And she was saying, she pairs like the security pros that can walk on water with the regular employees and they get to ask all these Colombo questions of the experts and the experts get to hear it and say, oh, I have to now explain this like I'm explaining it to a 10 year old, or maybe not a 10 year old, but a teenager, actually teenager's probably well ahead of us, but you know what I'm saying? And so that kind of cross correlation, and then essentially the folks that aren't security experts, they absorb enough and they can pass it on throughout the organization. And that's how she was saying she emphasizes culture building. >> And I will say, I think, Steve Smith, the CISO of AWS, like I've heard him talk a number of times and like, they do that here at like, they have some of the spirit and they've built it in and it's all the way from the top, right. And that's where if you have security over and a little silo off to the side, you're never going to do that. When the CEO supports the security professionals as a part of the business, that's when you can do the right thing. >> So you remember around the time that you and you guys started Illumio, the conversation was, security must be a board level topic. Yes, it should be, is it really, it was becoming that way. It wasn't there yet. It clearly is now, there's no question about it. >> No, ransomware. >> Right, of course. >> Let's thank ransomware. >> Right. Thank you. Maybe that's a silver lining. Now, the conversation is around, is it a organizational wide issue? And it needs to be, it needs to be, but it really isn't fully. I mean, how many organizations actually do that type of training, certainly large organizations do. It's part of the onboarding process, but even small companies are starting to do that now saying, okay, as part of the onboarding process, you got to watch this training video and sure that you've done it. And maybe that's not enough, but it's a start. >> Well, and I do think that's where, if we get back to zero trust, I mean, zero trust being a philosophy that you can adopt. I mean, we apply that kind of least privilege model to everything. And when people know that people know that this is something we do, right. That you only get access to things 'cause least privileges, you get access to absolutely to the things you need to do your job, but nothing more. And that applies to everybody in the organization. And when people sort of know this is the culture and they sort of work by that, like zero trust being that philosophy sort of helps infuse it into the organization. >> I agree with that, but I think the hard part of that in terms of implementing it for organizations is, companies like AWS, they have the tools, the people, the practitioners that can bring that to bear, many organizations don't. So it becomes an important prioritization exercise. So they have to say, okay, where do we want to apply that least privilege and apply that technology? 'Cause we don't have the resources to do it across the entire portfolio. >> And I'll give you a simple example of where it'll fail. So let's say, oh, we're least privilege, right. And so you asked for something to do your job and it takes four weeks for you to get that access. Guess what? Zero trust out the door at that organization. If you don't have again, the tools, right. To be able to walk that walk. And so it is something where you can't just say it, right. You do have to do it. >> So I feel like it's pyramid. It's got to start. I think it's got to be top down. Maybe not, I mean certainly bottom up from the developer mindset. No question about that. But in terms of where you start. Whether it's financial data or other confidential data, great. We're going to apply that here and we're not going to necessarily, it's a balance, where's the risk? Go hard on those places where there's the biggest risk. Maybe not create organizational friction where there's less risk and then over time, bring that in. >> And I think, I'll say one of the failure modes that we sort of seen around zero trust, if you go too big, too early, right. You actually have to find small wins in your organization and you pointed out some good ones. So focus on like, if you know where critical assets are, that's a good place to sort of start. Building it into the business as usual. So for example, one thing we recommend is people start in the developing zero trust segmentation policy during the development, or at least the test phase of rolling out a new application as you sort of work your way into production, as opposed to having to retro segment everything. So get it into the culture, either high value assets or work like that, or just pick something small. We've actually seen customers use our software to sort of like lock down RDP like back to ransomware, loves RDP lateral movement. So why can we go everywhere to everywhere with RDP? Well, you need it to sort of solve some problems, but just focus on that one little slice of your environment, one application and lock that down. That's a way to get started and that sort of attacks the ransomware problem. So there's lots of ways, but you got to make some demonstrable first steps and build that momentum over time to sort of get to that ultimate end goal. >> PJ Illumio has always been a thought leader in security generally in this topic specifically. So thanks for coming back on theCUBE. It's always great to have you guys. >> All right. Thanks, been great. >> All right. And thank you for watching. Keep it right there. This is Dave Vellante for theCUBE's coverage of AWS re:Inforce 2022 from Boston. We'll be right back. (upbeat music)

>>the Cube presents H P E discovered 2022. Brought to you by H P E. >>Hi, buddy Dave Balon and Jon Ferrier Wrapping up the cubes. Coverage of day two, hp Discover 2022. We're live from Las Vegas. Vishal Lall is here. He's the senior vice president and general manager for HP ES Green Lake Cloud Services Solutions. Michelle, good to see you again. >>Likewise. David, good to see you. It was about a year ago that we met here. Or maybe nine months >>ago. That's right. Uh, September of last year. A new role >>for you. Is that right? I was starting that new role when I last met you. Yeah, but it's been nine months. Three quarters? What have you learned so far? I mean, it's been quite a right, right? I mean, when I was starting off, I had, you know, about three priorities we've executed on on all of them. So, I mean, if you remember back then they we talked about, you know, improving a cloud experience. We talked about data and analytics being a focus area and then building on the marketplace. I think you heard a lot of that over the last couple of days here. Right? So we've enhanced our cloud experience. We added a private cloud, which was the big announcement yesterday or day before yesterday that Antonio made so that's been I mean, we've been testing that with customers. Great feedback so far. Right? And we're super excited about that. And, uh, you know, uh, down there, the test drive section people are testing that. So we're getting really, really good feedback. Really good acceptance from customers on the data and Analytics side. We you know, we launched the S three connector. We also had the analytics platform. And then we launched data fabric as a service a couple of days ago, right, which is kind of like back into that hybrid world. And then on the marketplace side, we've added a tonne of partners going deep with them about 80 plus partners now different SVS. So again, I think, uh, great. I think we've accomplished a lot over the last three quarters or so lot more to be done. Though >>the marketplace is really interesting to us because it's a hallmark of cloud. You've got to have a market price. Talk about how that's evolving and what your vision is for market. Yes, >>you're exactly right. I mean, having a broad marketplace provides a full for the platform, right? It's a chicken and egg. You need both. You need a good platform on which a good marketplace can set, but the vice versa as well. And what we're doing two things there, Right? One Is we expanding coverage of the marketplace. So we're adding more SVS into the marketplace. But at the same time, we're adding more capabilities into the marketplace. So, for example, we just demoed earlier today quickly deploy capabilities, right? So we have an I S p in the marketplace, they're tested. They are, uh, the work with the solution. But now you can you can collect to deploy directly on our infrastructure over time, the lad, commerce capabilities, licencing capabilities, etcetera. But again, we are super excited about that capability because I think it's important from a customer perspective. >>I want to ask you about that, because that's again the marketplace will be the ultimate arbiter of value creation, ecosystem and marketplace. Go hand in hand. What's your vision for what a successful ecosystem looks like? What's your expectation now that Green Lake is up and running. I stay up and running, but like we've been following the announcement, it just gets better. It's up to the right. So we're anticipating an ecosystem surge. Yeah. What are you expecting? And what's your vision for? How the ecosystem is going to develop out? Yeah. I >>mean, I've been meeting with a lot of our partners over the last couple of days, and you're right, right? I mean, I think of them in three or four buckets right there. I s V s and the I S P is coming to two forms right there. Bigger solutions, right? I think of being Nutanix, right, Home wall, big, bigger solutions. And then they are smaller software packages. I think Mom would think about open source, right? So again, one of them is targeted to developers, the other to the I t. Tops. But that's kind of one bucket, right? I s P s, uh, the second is around the channel partners who take this to market and they're asking us, Hey, this is fantastic. Help us understand how we can help you take this to market. And I think the other bucket system indicators right. I met with a few today and they're all excited about. They're like, Hey, we have some tooling. We have the manage services capabilities. How can we take your cloud? Because they build great practise around extent around. Sorry. Aws around? Uh, sure. So they're like, how can we build a similar practise around Green Lake? So again, those are the big buckets. I would say. Yeah, >>that's a great answer. Great commentary. I want to just follow up on that real quick. You don't mind? So a couple things we're seeing observing I want to get your reaction to is with a i machine learning. And the promise of that vertical specialisation is creating unique opportunities on with these platforms. And the other one is the rise of the managed service provider because expertise are hard to come by. You want kubernetes? Good luck finding talent. So managed services seem to be exploding. How does that fit into the buckets? Or is it all three buckets or you guys enable that? How do you see that coming? And then the vertical piece? >>A really good question. What we're doing is through our software, we're trying to abstract a lot of the complexity of take communities, right? So we are actually off. We have actually automated a whole bunch of communities functionality in our software, and then we provide managed services around it with very little. I would say human labour associated with it is is software manage? But at the same time we are. What we are trying to do is make sure that we enable that same functionality to our partners. So a lot of it is software automation, but then they can wrap their services around it, and that way we can scale the business right. So again, our first principle is automated as much as we can to software right abstract complexity and then as needed, uh, at the Manus Services. >>So you get some functionality for HP to have it and then encourage the ecosystem to fill it in or replicated >>or replicated, right? I mean, I don't think it's either or it should be both right. We can provide many services or we should have our our partners provide manage services. That's how we scale the business. We are the end of the day. We are product and product company, right, and it can manifest itself and services. That discussion was consumed, but it's still I p based. So >>let's quantify, you know, some of that momentum. I think the last time you call your over $800 million now in a are are you gotta You're growing at triple digits. Uh, you got a big backlog. Forget the exact number. Uh, give us a I >>mean, the momentum is fantastic Day. Right. So we have about $7 billion in total contract value, Right? Significant. We have 1600 customers now. Unique customers are running Green Lake. We have, um, your triple dip growth year over year. So the last quarter, we had 100% growth year over year. So again, fantastic momentum. I mean, the other couple, like one other metric I would like to talk about is the, um the stickiness factor associated tension in our retention, right? As renewal's is running in, like, high nineties, right? So if you think about it, that's a reflection of the value proposition of, like, >>that's that's kind of on a unit basis, if you will. That's the number >>on the revenue basis on >>revenue basis. Okay? >>And the 1600 customers. He's talking about the size and actually big numbers. Must be large companies that are. They're >>both right. So I'll give you some examples, right? So I mean, there are large companies. They come from different industries. Different geography is we're seeing, like, the momentum across every single geo, every single industry. I mean, just to take some examples. BMW, for example. Uh, I mean, they're running the entire electrical electric car fleet data collection on data fabric on Green Lake, right? Texas Children's Health on the on the healthcare side. Right On the public sector side, I was with with Carl Hunt yesterday. He's the CEO of County of Essex, New Jersey. So they are running the entire operations on Green Lake. So just if you look at it, Barclays the financial sector, right? I mean, they're running 100,000 workloads of three legs. So if you just look at the scale large companies, small companies, public sector in India, we have Steel Authority of India, which is the largest steel producer there. So, you know, we're seeing it across multiple industries. Multiple geography is great. Great uptake. >>Yeah. We were talking yesterday on our wrap up kind of dissecting through the news. I want to ask you the question that we were riffing on and see if we can get some clarity on it. If I'm a customer, CI or C so or buyer HP have been working with you or your team for for years. What's the value proposition? Finish this sentence. I work with HPV because blank because green like, brings new value proposition. What is that? Fill in that blank for >>me. So I mean, as we, uh, talked with us speaking with customers, customers are looking at alternatives at all times, right? Sometimes there's other providers on premises, sometimes as public cloud. And, uh, as we look at it, uh, I mean, we have value propositions across both. Right. So from a public cloud perspective, some of the challenges that our customers cr around latency around, uh, post predictability, right? That variability cost is really kind of like a challenge. It's around compliance, right? Uh, things of that nature is not open systems, right? I mean, sometimes, you know, they feel locked into a cloud provider, especially when they're using proprietary services. So those are some of the things that we have solved for them as compared to kind of like, you know, the other on premises vendors. I would say the marketplace that we spoke about earlier is huge differentiator. We have this huge marketplace. Now that's developing. Uh, we have high levels of automation that we have built, right, which is, uh, you know, which tells you about the TCO that we can drive for the customers. What? The other thing that is really cool that be introduced in the public in the private cloud is fungible itty across infrastructure. Right? So basically on the same infrastructure you can run. Um, virtual machines, containers, bare metals, any application he wants, you can decommission and commission the infrastructure on the fly. So what it does, is it no matter where it is? Uh, on premises, right? Yeah, earlier. I mean, if you think about it, the infrastructure was dedicated for a certain application. Now we're basically we have basically made it compose herbal, right? And that way, what? Really? Uh, that doesnt increases utilisation so you can get increased utilisation. High automation. What drives lower tco. So you've got a >>horizontal basically platform now that handle a variety of work and >>and these were close. Can sit anywhere to your point, right? I mean, we could have a four node workload out in a manufacturing setting multiple racks in a data centre, and it's all run by the same cloud prints, same software train. So it's really extensive. >>And you can call on the resources that you need for that particular workload. >>Exactly what you need them exactly. Right. >>Excellent. Give you the last word kind of takeaways from Discover. And where when we talk, when we sit down and talk next year, it's about where do you want to be? >>I mean, you know, I think, as you probably saw from discovered, this is, like, very different. Antonio did a live demo of our product, right? Uh, visual school, right? I mean, we haven't done that in a while, so I mean, you started. It >>didn't die like Bill Gates and demos. No, >>no, no, no. I think, uh, so I think you'll see more of that from us. I mean, I'm focused on three things, right? I'm focused on the cloud experience we spoke about. So what we are doing now is making sure that we increase the time for that, uh, make it very, you know, um, attractive to different industries to certifications like HIPAA, etcetera. So that's kind of one focus. So I just drive harder at that adoption of that of the private out, right across different industries and different customer segments. The second is more on the data and analytics I spoke about. You will have more and more analytic capabilities that you'll see, um, building upon data fabric as a service. And this is a marketplace. So that's like it's very specific is the three focus areas were driving hard. All right, we'll be watching >>number two. Instrumentation is really keen >>in the marketplace to I mean, you mentioned Mongo. Some other data platforms that we're going to see here. That's going to be, I think. Critical for Monetisation on the on on Green Lake. Absolutely. Uh, Michelle, thanks so much for coming back in the Cube. >>Thank you. Thanks for coming. All >>right, keep it right. There will be John, and I'll be back up to wrap up the day with a couple of heavies from I d. C. You're watching the cube. Mhm. Mm mm. Mhm.

>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please 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, and we'll see you next time. (upbeat music)

(upbeat music) >> Lisa Martin: Hi, everyone. Welcome to The Cube's coverage of women in tech International Women's Day 2022. I'm your host, Lisa Martin. Nicole Parafita joins me next: head of communications, people and culture at AWS Latin America. Nicole, it's great to have you on the program. >> Nicole: Thank you for having me. It's a pleasure. >> Tell me a little bit about your role as head of communications, people and culture. >> Super. So my role is very, very new. I've started in this role like two months ago, so really, really new. And as you said I lead the communications, people and culture team, which is dedicated to understanding people's needs, fostering leadership development, increasing diversity and inclusion, enabling employee recognition, and of course mitigating burnout, which is something we're seeing all across the world due to working from home and all of that. So it's a huge, huge task. And of course it is aligned to Amazon's 15 leadership principle which is striving to be Earth's best employer. So huge challenge. >> Lisa: So tell me a little- so this is a brand new role as you said, just a couple of months. Was the pandemic a factor? And you mentioned burnout. I mean, that's one of the things that I think we've all been struggling with. Was that an influence in creating the role that you're in? >> So there are many many things that led to creating this organization. I think that the first one is this new leadership principle which is striving to be Earth's best employer. There's - people is our top priority and we want to work with them and for them so that we generate engaging content, training materials and we work on enabling them, right? So the first one is striving to be Earth's best employer and that alignment. The second is the priority that our VP in Latin America gives its people. It's the key differentiator that we have at AWS: our culture and it's people and how our people live the culture. And the third thing would be the fact that we're growing, we're growing so fast. We're hiring so many people in the last year so, and we need to make sure we keep this day one culture alive and strong. So yes, we need to make sure that all these people that were hired since March 2020 and never set foot in a physical office, in an AWS physical office live the leadership principles, understand them deeply and can apply all these mechanisms from our culture in their day to day basis. Those are the key three things that led to the creation of this org. >> So you mentioned the leadership principles striving to be Earth's best employer. How does that, how is that connected to International Women's Day and what you're doing in terms of really bringing diversity and equality and inclusion into AWS LATAM? >> I love this question. I think, as I said before, culture and people is our top priority. We're learning a lot. We, this new leadership principle which is striving to be Earth's best employer acknowledges that we're not the best, but that we're working very hard to become Earth's best employer. And all the efforts that we're doing are related to feedback, right? We're listening a lot to our, what our employees are saying and what the market is saying to build the best employee experience we can for everybody. And first of all, I'd say that our culture and our mission is to become, or to be, the most customer-centric company in the world. And for that, we need to be super diverse and inclusive. We need to get as many backgrounds and life experiences we can so that we can invent in the name of our customers. So building this diverse team really helps our business but also, as Jeff Bezos says, "it's the right thing to do." It's what we need to do. So what do we mean when we talk about inclusion, diversity and equity? I think it's good to define these three things, these key pillars of our culture. The first one is inclusion, which about belonging, right? It's about giving the physical- the psychological, sorry, safety to people so that they feel represented. This is super important for us. How do we make people feel comfortable where they work at? And some examples of this that I wanted to share with you. First of all, there's a mechanism that we use internally at AWS, that it's called Connections. Connections is a daily live feedback tool. So at AWS, we don't believe in having an annual survey for listening to employees, to what employees have to say. We believe in having real time feedback and this tool is that, exactly that. So every day I would turn on my computer and I would see a question from this Connection system. And one of the things that we're tracking is, the team I'm on helps me feel included at work. So we would say yes, no, or different options that we give the employees. And we would track how they feel. And according to that data we would implement different initiatives. So we're working on real time feedback from the team so that we can act fast and help the team feel better, right? The other thing that I would would say about belonging is that in AWS we have 13 affinity groups. We have 90,000 Amazonians across hundreds of chapters around the world who work towards different initiatives. One of them, for example, if it's Women at Amazon, Women at Amazon is a huge organization within Amazon with more than 80 chapters worldwide. And the objective of this affinity group is attracting, developing, and retaining women in both tech and non-tech roles across all Amazon business. As an example of the kind of initiatives that they drive, we can talk about Break the Bias. I'm not sure if you heard about this, but it's a huge initiative. It's a webinar that we will be hosting in Latin America on International Women Day on the 8th of March and we will have women sharing amazing stories. We will have, for example, Marta Ferero. Marta Ferero is the founder of a startup, a Colombian startup, called Ubits, which is like the Netflix of corporate training in her own words, among others. And we will also have recruiting specialists that will give advice on how to give and accept in our careers. So those are the kind of initiatives that we're trying to do to attract and retain and develop talent. This is more like an attracting talent thing because it's an open webinar that we have that. Yeah. >> Go ahead. >> So that's about inclusion, which is belonging and how do we make people belong to certain groups within Amazon? The second thing is about diversity which is feeling, it's about feeling represented, right? And it's not about only gender. It can be about race. It can be about ethnicity, sexual orientation, age. We want everyone to feel represented. But now, if we're talking about International Women's Day let me talk a little bit about female representation. And I am very proud to share that we finished 2021 with 18% of female representation in the leadership team in the LATAM leadership team, which means people reporting to the LATAM VP, the vice president, Jaime. And we started 2022 with 35% female representation which is a huge improvement from one year to the other. So that are the numbers, right? But it's not just about numbers. It's the fact that these women that are now part of the leadership team have been given very important tasks. And as my boss always says, "don't tell me about your strategy. Tell me about where you're putting your resources and I'll tell you what your strategy is." And I love the fact that he picked very amazing women to lead very important missions within LATAM. For example, let me just give you an example, Carolina Pina, who joined us from the public sector team is leading this massive training organization. And like the name implies, this organization focuses on generating talent at a huge scale. And this is, I don't know, one of the most long term oriented tasks that we have, and it has a huge impact on Latin America, not only AWS business, but on Latin America. It's focused on really transforming our region into something different so that people can have a better quality of life. So those are the things that really amaze me. We've been given very important tasks, like this one, to really move forward in terms of cloud transformation and the transformation of the countries we operate in, which is amazing, I think. >> It is amazing. >> The last - >> Go ahead. >> The last topic, I'm sorry, I'm speaking too much, but just to close. The last thing that I want to say is equity, which is one of the key things that we have in our culture and equity is about fairness. It's about generating or giving the same amount of opportunities to everybody. The fact that we're massively training people in Latin America is about fairness about generating the skills. And the other thing that we're doing that is super important is that we're changing our interview process so that we make sure we have diverse, a diverse set of interviewers participating in the processes, right? So that people feel represented from the moment they start their journey with AWS with the first phone screen, right? So those things for me are really transformative and talk about what we're trying to do. And of course it has an impact on gender, but it also has an impact on a broader scale from a diversity, equity and inclusion perspective which I think talks about the humanity of AWS. It's not just about the technology it's about transforming people's lives and helping Latin America, or the countries we operate in, to be better, right? For the good. >> Right. That's a great focus. Is that kind of a shift in AWS' culture in terms of really focusing on diversity? Or is that something that's really kind of been there from the beginning? >> So I think it's been here from the beginning, but now, for example, in Latin America, we're growing a lot. So we have more resources that we can allocate to really focus on this initiative. So aligning to these new leadership principal that was launched in July, or published in July, we always were very committed to diversity, equity and inclusion, but now we have more resources so that we can double down on this huge bet. And I feel very proud about that. >> Lisa: Tell me a little bit about, in the few remaining minutes that we have, I'm curious about your background. Were you always interested in tech or STEM? Was that something that you gravitated towards from when you were young, or was it something that you got into a little bit later? >> So my background is communications. I studied advertising, so no. I'm not a science or engineer-focused person, but from at early age I started working in tech companies, so I learned a lot. I had the chance to live in different countries like Mexico or the UK or the US where I always had the chance to interact with many amazing men and women that were focused on technology. So, no, I'm not a technology expert but I've always been related to people who know a lot about this. And I learned a lot in that process. And, you know, I've always seen like this, I don't how to explain, but this initiative or this will to make everyone feel comfortable where they work. I've seen this at AWS. And as I said before, we started the interview I'm eight months pregnant at this point. I'm about to take a five month leave which is a lot more than what the law gives me in Argentina, for example, where I'm located. So those are the kind of things that really make me feel comfortable where I work with and really proud of where I work with. And I want everybody to have the chance to get this type of job so that they can feel the way I feel, right? And I'm talking about men, women, people with disabilities, and many other type of affinity people, right? >> Right. It's so important to be able to have that comfort because your productivity is better, your performance is better, and ultimately the company benefits as those employees feel comfortable in the environment in which they're working and that they have the freedoms to be curious. Talk to me a little bit about some of the things, you mentioned the stat of 2020 to - 2021, excuse me, to 2022, almost doubling the number of women. >> Yep. >> Talk to me about some of the things that you're looking forward to as 2022 progresses. >> Wow, I'm the, you know, every time we have a performance review at AWS you get asked this question, what are you most excited about? Right. And this year I was excited about so many things that the list, I mean I didn't have enough characters to write about that. I think we are always trying to just confirm our beliefs at AWS. And this is the, what I like the most about working here. AWS or Amazon really values people who are curious, are always learning, and always trying to listen to other opinions. And this is key for our culture. I'm very excited about the fact that we're putting, we're turning on mechanisms to have even more feedback than we used to have, not just from customers and partners, but also from our employees. So the fact that we're having real time feedback will really make us better as an organization and always with this day-one culture in mind, which is very fast, right? We're making decisions very fast. We're very dynamic, we're learning on the go. We fail, sometimes. We fail, but we learn very fast. We fail fast. We used to say that we learn, we fail fast. And failure is part of our culture of innovation. So we're learning, we're failing, at some point we're implementing changes. And it's like a very interesting flywheel, right. Of growth. And it's very fast. So my job is very dynamic and I'm very excited about this. I'm hiring a team. I have a team of four people. I already hired two people and I need one more. So I'm very excited about that. I'm very excited to see what our employees are capable of. I mean, they're always inventing on behalf of our customers and partners. And it's always amazing to see the results from the year end, right. You get to tell stories from customers and partners that you never imagined you were going to tell. So I'm very excited about all those things. >> Lisa: Excellent. Well, good luck with the baby. Thank you so much for sharing. What your role is doing and how it's really helping to drive that diversity and inclusion and equity within Amazon. It's such an important cultural element and it's exciting to hear this strategic focus that AWS has. Nicole, we appreciate your time. >> Thank you very much, Lisa, for having me. >> My pleasure. For Nicole Parafita, I'm Lisa Martin. You're watching women in tech International Women's Day, 2022. (upbeat music)

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

(upbeat music) >> Hi, everyone, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're wrapping up four days of coverage, two sets. Two remote sets, one in Boston, one in Palo Alto. And really, it's a pleasure to introduce Benoit Dageville. He's the Press Co-founder of Snowflake and President of Products. Benoit, thanks for taking some time out and coming to theCUBE. >> Yeah, thank you for having me, Dave. >> You know, it's really a pleasure. We've been watching Snowflake since, maybe not 2012, but mid last decade you hit our radar. We said, "Wow, this company is going to go places." And yeah, we made that call correctly. But it's been a pleasure to sort of follow you. We've talked a little bit remotely. I kind of want to go back to some of the fundamentals. First of all, I wanted mention your earnings last night. If you guys didn't see it, again, triple digit growth, $1.8 billion RPO, cashflow actually looking pretty good. So, pretty amazing. Oh, and 173% NRR, you know, wow. And Mike Scarpelli is kind of bummed that you did so well. And I know why, right? Because it's going to be at some point, and he dials it down for the expectations and Wall Street says, "Oh, he's sandbagging." And then at some point you're actually going to meet expectations and people are going to go, "Oh, they met expectations." But anyway, he's a smart guy, he know what he's doing. (Benoit laughing) I loved it, it was so funny listening to him last night. But anyway, I want to go back to, when I talked to practitioners about data warehousing pre-cloud, they would say sound bites like, it's like a snake swallowing a basketball, they would tell me. And the other thing they said, "We just chased the chips. Every time a new Intel chip comes out, we have to bring in new servers, and we're struggling." The cloud changed all that. Your vision and Terry's vision changed all that. Maybe go back to the fundamentals of what you saw. >> Yeah, we really wanted to address what we call the data challenges. And if you remember at that time, data challenge was first of the volume of data, machine-generated data. So it was way more than just structured data, right? Machine-generated data is weblogs, and it's at petabyte scale. And there was no good solution for that type of data. Big data was not a great solution, Hadoop was really bad. And there was no good solution for that. So we thought we should do something for big data. The other aspect was concurrency, right? Everyone wants to use these data analytic platform in an enterprise, right? And you have more and more workload running against the same data, and the systems that were built were not scaling for these workloads. So you had to silo data, right? That's the only way big enterprise could deal with that, is to create many different silos, Oracle, Teradata, data mass, you would hear data mass. All of it was to afloat, right, this data? And then there was the, what do we call, data sharing. How to get access to data which is not born inside the enterprise, right? So with Terry, we wanted to solve all these challenges and we thought the only way to solve it was the cloud. And the cloud has really two free aspects. One is the elasticity, for all of a sudden, you can run every workload that you want concurrently, in parallel, on different computer resources, and you can run them against the same data. So this is kind of the data lake model, if you want. At the same time, you can, in the cloud, create a service. So you can remove complexity from users and make it really easy for new workloads to be added to the system, because you can manage, you can create a managed service, where all the sudden our customers, they don't need to manage infrastructure, they don't need to patch, they don't need to tune. Everything is done by Snowflake, the service, and they can just load in and run their query. And the third aspect is really collaboration. Is how to connect data sets together. And that's almost a new product for Snowflake, this data sharing. So we really at Snowflake was all about combining big data and data warehouse in one system in the cloud, and have only one single system where you can put all your data and all your workload. >> So you weren't necessarily trying to solve the data warehouse problem, you were trying to solve a data problem. And then it just so happened data warehouse was a logical entry point for you. >> It's really not that. Yes, we wanted to solve the data problem. And for us big data was a really important problem to solve. So from day one, Snowflake was all about machine generated data, petabyte scale, but we wanted to do it right. And for us, right was not compromising on data warehouse principle, which is a CDT of transaction, which is really fast response time, and which is also simplicity. So as I said, we wanted to solve kind of all the problems at the time of volume of data, concurrency, and these sharing aspects. >> This was 2012. You knew at that time that Hadoop wasn't going to be the answer. >> No, I mean, we were really, I mean, everyone knew that. Everyone knew Hadoop was really bad. You know, complex to manage, really slow. It had good aspects, right? This was the only system that could manage petabyte scale data sets. That's the only thing- >> Cheaply. >> Yeah, and cheaply which was good. And we wanted really to do that, plus have all the good attributes of data warehouse system. And at the same time, we wanted to build a system where if you are data warehouse customer, if you are coming from Teradata, you can migrate to Snowflake and you will get to a system which is faster than what you had on-premise, right. That's why it's pretty cool. So we wanted to do big data without compromising on data warehouse. >> So several years ago we looked at the hyperscalers and said, "Wow, last year they spent $100 billion in CapEx." And so, we started to think about this abstraction layer. And then we saw what you guys announced with the data cloud. We call it super clouds. And we see that as exactly what you're building. So that's clearly not just a data warehouse or database, it's technology that really hides the underlying complexity of all those clouds, and it allows you to have federated governance and data sharing, all those things. Can you talk about sort of how you think about that architecture? >> So for me, what I say is that really Snowflake is the worldwide web of data. And we are indeed a super cloud, or we are super-posed to the infrastructure cloud, which is our friends at Amazon, and of course, Azure, I mean, Microsoft and Google. And as any cloud, we have regions, Snowflake regions all over the world, and located on different cloud providers. At the same time, our platform is global in the sense that every region interconnects with all the other regions, this is our snow grid and data mesh, if you want. So that as an organization you can have your presence on several Snowflake region. It doesn't matter which cloud provider, so you can mix AWS with Azure. You can use our cloud like that. And indeed you can, this is a cloud where you can store your data, that's the thing that really matters, and data is structured, but it's machine structure, as I say, machine generated, petabyte scale, but there's also unstructured, right? We have added support for images, text, videos, where you can process this data in our system, and that's the workload spout. And workload, what is very important is that you can run this workload, any number of workloads. So the number of workloads is effectively unlimited with Snowflake because each workload can have its dedicated set of compute resources all operating on the same data set. And the type of workloads is also very important. It's not only about dashboards and data warehouse, it's data engineering, it's data science, it's building application. We have many of our customers who are building full-scale cloud applications on top of Snowflake. >> Yeah so the other thing, if you're not familiar with Snowflake, I don't know, maybe your head has been in the sand for a while, but separating compute and storage, I don't know if you were the first, but you were certainly the first to popularize it. And that allowed you to solve that chasing the chips problem and the swallowing the basketball, right? Because you have virtually infinite resources now at your disposal. >> Yeah, this is really the concurrency challenge that I was mentioning. Everyone wants to access the data. And of course, if everyone runs on the same set of compute resources, you have a bottleneck. So Snowflake was really about this multi-workload. We call it Multi-Cluster Shared Data Architecture. But it's not difficult to run multiple cluster if you don't have consistency of data. So how to do that while maintaining transactional property of data as CDT, right? You cannot modify data from different clusters. And when you commit, every other cluster will immediately see the change, right, as if everyone was running on the same cluster. So that was the challenge that we solve when we started Snowflake. >> Used the term data mesh. What is data mesh to Snowflake? Is it a concept, is it fabric? >> No, it's a very interesting point. As much as we like to centralize data, this becomes a bottleneck, right? When you are a large organization with different independent units, everyone wants to manage their own data and they have domain-specific expertise about that data. So having it centralized in IT is not practical. At the same time, you really want to be able to connect these different data sets together and join different data together, right? So that's the data mesh architecture. Each data set is managed independently by business owners, and then there is a contract which is exposed to others, and you can combine. And Snowflake architectures with data sharing, right. Data sharing that can happen within an organization, or across organization, allows you to connect any data with any other data on our platform. >> Yeah, so when I first heard that term, you guys using the term data mesh, I got very excited because it was kind of the data mesh is, my view, anyway, is going to be the fundamental architecture of this decade and beyond. And the principles, if I understand it correctly, you're applying the principles of Jim Octagon's data mesh within Snowflake. So decentralized data doesn't have to be physically in one place. Logically it's in the data cloud. >> It's logically decentralized, right? It's independently managed, and the reason, right, is the data that you need to use is not produced by your, even if in your company you want to centralize the data and having only one organization, let's say IT managing that, let's say, pretend. Yet you need to connect with other datasets, which is managed by other organizations. So by nature, the data that you use cannot be centralized, right? So now that you have this principle, if you have a platform where you can store all the data, wherever it is, and you can connect these data very seamlessly, then we can use that platform for your enterprise, right? To have different business units independently manage their data sets, connects these together so that as a company you have a 360 view of your customers, for example. But you can expand that outside of your enterprise and connect with data sets, which are from your vertical, for example, financial data set that you don't have in your company, or any public data set. >> And the other key principles, I think, that you've touched on really is the line of business now. Increasingly they're building data products that are creating value, and then also there's a self-service component. Assuming there's the fourth principle, governance. You got to have federated governance. And it seems like you've kind of ticked the boxes, more than tick the boxes, but engineered a solution to solve for those. >> No, it's very true. So Snowflake was really built to be really simple to use. And you're right. Our vision was, it would be more than IT, right? Who is going to use Snowflake is going now to be business unit, because you do not have to manage infrastructure. You do not have to patch. You do not have to do these things that business cannot do. You just have to load your data and run your queries, and run your applications. So now business can directly use Snowflake and create value from that. And yes, you're right, then connect that data with other data sets and to get maximum insights. >> Can you please talk about some of the things you do with AWS here at the event. I'm interested in what you're doing with your machine learning initiatives that you've recently announced, the AI piece. >> Yes, so one key aspects is data is not only about SQL, right? We started with SQL, but we expanded our platform to what we call data programmability, which is really about running program at scale across a large volume of data. And this was made popular with a programming model which was introduced by Pendal, DataFrames. Later taken by Spark, and now we have DataFrames in Snowflake, Where we are different than other systems, is that these DataFrame programs, which are in Python, or Java, or Scala, you program with data. These DataFrames are compiled to our single execution platforms. So we have one single execution platform, which is a data flow execution platform, which can run both SQL very efficiently, as I said, data warehouse speed, and also these very complex programs running Python and Java against this data. And this is a single platform. You don't need to use two different systems. >> Now so, you kind of really attack the traditional analytics base. People said, "Wow, Snowflake's really easy." Now you're injecting AI and machine intelligence. I see Databricks coming at it from the other angle. They started with machine learning, now they're sort of going after the analytics. Does there need to be a semantic layer to connect, 'cause it's the same raw data. Does there need to be a semantic layer to connect those two worlds? >> Yes, and that's what we are doing in our platform. And that's very novel to Snowflake. As I said, you interact with data in different program. You pick your program. You are a SQL programmer, use SQL. You are a Python programmer, use DataFrames with Python. It doesn't really matter. And then the semantic layer is our compiler and our processing engine, is going to translate both your program and my program in Python, your program in SQL, to the same execution platform and to the same programming language that Snowflake internally, we don't expose our programming language, but it's a data flow programming language that our execution platform executes. So at the end, we might execute exactly the same program, potentially. And that's very important because we spent all our IP and all our time, engineering time to optimize this platform, to make it the fastest platform. And we want to use that platform for any type of workloads, whether it's data programs or SQL. >> Now, you and Terry were at Oracle, so you know a lot about bench marketing. As Larry would stand up and say, "We killed the competition." You guys are probably behind it, right. So you know all about that. >> We are very behind it. >> So you know a lot about that. I've had some experience, I'm not a technologist, but I'm an observer and analyst. You have to take benchmarking with a very big grain of salt. So you guys have generally stayed away from that. Databricks came out and they came up with all these benchmarks. So you had to respond, because otherwise it's out there. Now you reran the benchmarks, you took out the materialized views and all the expensive stuff that they included in your cost, your price performance, but then you wrote, I thought, a very cogent blog. Maybe you could talk about sort of why you did that and your general philosophy around bench marketing. >> Yeah, from day one, with Terry we say never again we will participate in this really stupid benchmark war, because it's really not in the interest of customers. And we have been really at the frontline of that war with Terry, both of us, really doing special tricks, right? And optimizing this query to death, this query that no one runs apart from the synthetic benchmark. We optimize them to death to have the best number when we were at Oracle. And we decided that this is really not helping customers in the end. So we said, with Snowflake, we'll not do that. And actually, we are not the only one not to do that. If you look at who has published TPC-DS, you will see no one, none of the big vendors. It's not because they cannot run TPC-DS, Oracle can run it, I know that. And all the other big data warehouse vendor can, but it's something of a little bit of past. And TPC was really important at some point, and is not really relevant now. So we are not going to compete. And that's what we said is basically now our blog. We are not interesting in participating in this war. We want to invest our engineering effort and our IP in solving real world issues and performance issues that we have. And we want to improve our engine for these real world customers. And the nice thing with Snowflake, because it's a service, we see exactly all the queries that our customers are executing. So we know where we are struggling as a system, and that's where we want to invest and we want to improve. And if you look at many announcements that we made, it's all about under-the-cover improving Snowflake and getting the benefit of this improvement to our customer. So that was the message of that blog. And yes, the message was okay. Mr. Databricks, it's nice, and it's perfect that, I mean, everyone makes a decision, right? We made the decision not to participate. Databricks made another decision, which is very fine, and that's fine that they publish their number on their system. Where it is not fine is that they published number using Snowflake and misrepresenting our performance. And that's what we wanted also to correct. >> Yeah, well, thank you for going into that. I know it's, look, leaders don't necessarily have to get involved in that mudslide. (crosstalk) Enough said about that, so that's cool. I want to ask you, I interviewed Frank last spring, right after the lockdown, he was kind enough to come on virtually, and I asked him about on-prem. And he was, you know Frank, he doesn't mix words, He said, "We're not getting into a halfway house. That's not going to happen." And of course, you really can't do what you do on-prem. You can't separate compute, some have tried, but it's not the same. But at the same time that you see like Andreessen comes out with this blog that says a huge portion of your cost of goods sold is going to be the cloud, so you're going to have to repatriate. Help me square that circle. Is it cloud forever? Is it will you never say never? What can you share of that? >> I will never say never, it's not my style. I always say you can always change your mind, and maybe different factors can change your mind. What was true at some point might not be true at a later point. But as of now, I don't see any reason for us to go on-premise. As you mentioned at the beginning, right, Snowflake is growing like crazy. The world is moving to the cloud. I think maybe it goes both ways, but I would say 90% or 99% of the world is moving to the cloud. Maybe 1% is coming back for some very specific reasons. I don't think that the world is going to move back on-premise. So in the end we might miss a small percentage of the workload that will stay on-premise and that's okay. >> And as well, if you dig into some of the financial statements you'll see, read the notes where you've renegotiated, right? We're talking big numbers. Hundreds and hundreds of millions of dollars of cost reduction, actually more, over a 10 year period. Billions of your cloud bills. So the cloud suppliers, they don't want to lose you as a customer, right? You're one of their biggest customer. So it's awesome. Last question is kind of, your work now is to really drive the data cloud, get adoption up, build that supercloud, we call it. Maybe you could talk a little bit about how you see the future. >> The future is really broadened, the scope of Snowflake, and really, I would say the marketplace, and data sharing, and services, which are directly built natively on Snowflake and are shared through our platform, and can operate, it can mix data on provider-side with data on consumer-side, and creating this collaboration within the Snowflake data cloud, I think is really the future. And we are really only scratching the surface of that. And you can see the enthusiasm of Snowflake data cloud and vertical industry We have nuanced the final show data cloud. Industry, complete vertical industry, latching on that concept and collaborating via Snowflake, which was not possible before. And I think you talked about machine learning, for example. Machine learning, collaboration through machine learning, the ones who are building this advanced model might not be the same as the one who are consuming this model, right? It might be this collaboration between expertise and consumer of that expertise. So we are really at the beginning of this interconnected world. And to me the world wide web of data that we are creating is really going to be amazing. And it's all about connecting. >> And I'm glad you mentioned the ecosystem. I didn't give enough attention to that. Because as a cloud provider, which essentially you are, you've got to have a strong ecosystem. That's a hallmark of cloud. And then the other, vertical, that we didn't touch on, is media and entertainment. A lot of direct-to-consumer. I think healthcare is going to be a huge vertical for you guys. All right we got to go, Terry. Thanks so much for coming on "theCUBE." I really appreciate you. >> Thanks, Dave. >> And thank you for watching. This a wrap from AWS re:Invent 2021. "theCUBE," the leader in global tech coverage. We'll see you next time. (upbeat music)

(upbeat music) >> Hey everyone. Welcome back to Las Vegas. The Cube is live. I can't say that enough. We are alive at AWS re:Invent 2021. Lisa Martin with Dave Nicholson. Hey Dave. >> Hey Lisa. >> Having a good day so far. >> So far, so good. >> We have an alumni back with us. We have about a hundred segments on the cube at AWS remit. We've got one of our original alumni back with us. Zaki Bajwa joins us the global head of partner solution engineers at Stripe. Zaki welcome back. >> Thank you, Lisa, thank you, Dave. Pleasure to be here. >> Lisa: Isn't it great to be back in person? >> Love it. Love it. Can't do a whiteboard virtually, you can, it's not the same. >> It's not the same and all those conversations I'm sure that you've had with partners and with customers the last couple of days that you just can't replicate that over zoom. >> Zaki: Exactly. >> So just for anyone who doesn't understand, AWS has a massive ecosystem of partners. So we'll get to talk about Stripe and AWS, but for anyone that doesn't know what Stripe is, give us the lowdown. You guys started 10 years ago. Talk to us about Stripe, the business strategy, what it's like today. >> Yeah, sure. So you guys know Stripe started 10 years ago by two brothers, John and Patrick Collison. And they've really focused on the developer and helping the developers accelerate digital commerce. Why? Cause the status quo at the time was one where a developer needed to, you know, build banking relationships with issuing banks, merchant banks, card networks, payment networks, tax liabilities, data compliance, and all of these manual processes that they had to deal with. So what Stripe aspires to do is build a complete commerce platform. Leveraging our integrated suite of products that is really allowing us to build what we call the global payments and treasury network. So if you think about the global payment and treasury network or what we call the G P T N it's meant to not only help abstract all of that complexity from a global payment infrastructure point of view, but also help move money in a simple and borderless and a programmable way just like we do in the internet. So that's the core essence of Stripe is to build this global payment treasury network to allow for money movement to happen in a simple and borderless manner. >> Simple and borderless two key things there. How has the business strategy evolved in the last 10 years and specifically in the last 20, 22 months? >> Yeah. Great question. So as you can imagine with COVID, you know, David you can order a cup of coffee or a brand new car, and that whole direct to consumer model has accelerated in COVID right. We've accelerated ourselves going to upwards of 6,000 employees. We've been able to answer or manage upwards of 170 billion API requests in the last 12 months alone. Right we deliver upwards of five nines from a availability performance point of view. That means 13 seconds of downtime or less a month. And we're doing this originally starting off for the developer David as you talked about allowing developers to deliver, you know, what I call process payments, accept payments and reconcile payments. But the evolution that you're talking about Lisa has really led to three key areas of focus that our users are requesting from us. And Stripe's first operating principle is really that user first mentality similar to the Amazons where we listen to our users and they're really asking for three key areas of focus. Number one is all around modernizing their digital commerce. So this is big enterprises coming to us and saying, whether I'm a uni lever or a Ford, how do you help me with a direct to consumer a e-commerce type platform? Number one. Secondly, is companies like Deliveroo and Lyft creating what we call marketplaces. Also think about Twitter and clubhouse, more solopreneurs entrepreneurs kind of marketplaces. Third is all around SaaS business models. So think about slack and Atlassian. That are customer vivers and accelerating the journey with us around digitizing digital commerce. So that's the first area of evolution. The second area is all around what we call embedded FinTech. So we know just like Amazon helped accelerate infrastructure as a service, platform as a service and function as a service. We're helping accelerate FinTech as a service. So we believe every company in every industry aspires to add more and more FinTech capabilities in their core services that they offer to their customers. So think about a Shopify or a Lyft they're adding more FinTech capabilities, leveraging Stripe APIs that they offer to their consumers. Likewise, when you think about a Monzo bank or a and 26, what we call Neo banks. They're creating more banking as a service component so a second area of evolution is all around FinTech as a service or embedded FinTech. And the third area of focus again, listen to our users is all around users are saying. Hey, Stripe, you have our financial data. How do you help us more with business operations and automating and optimizing our business operations? So this is revenue management, revenue reconciliation, financial reporting, all of the business processes, you and I know, code to cash, order to cash, pay to procure. Help us automate, optimize, and not just optimize, but help us create net new business models. So these are the three key areas of evolution that we've seen modernizing digital commerce, embedded FinTech, and then certainly last but not least business operations and automating that. >> And your target audience is the developers. Or are you having conversations now that are more, I mean, this is like transformative to industries and disruptive. Are you having conversations higher up in the chain? >> Great, great question. And this is the parallel with Amazon, just like Amazon started with developers, AWS. And then what up to the C-suite, if you will, we're seeing the same exact thing. Obviously our DNA is developer first making it intuitive, natural easy for developers to build on Stripe. But we're seeing more and more C-suite leaders come to us and saying, help us evolve our business model, help us modernize and digitize net new business models to get new revenue streams. So those parallel work streams are both developer mindset and C-suite led is certainly a big evolution for us. And we're looking to learn from our Amazon friends as to the success that they've had there. >> Do you have any examples of projects that developers have proposed that were at first glance, completely outlandish? Something that, you know, is there any sort of corner of the chart use case where Stripe didn't think of it, some developer came up with the idea, maybe it can't be done yet. If you have an example of that, that would be very interesting. >> Yeah, I'll give you two examples. So as I said, we're definitely a user first entity. That's our operating principle. We always think about the user. So let me go to developers and say, what are you struggling with? What are you thinking about? What are the next set of things you need from us? And a simple comment around tax started to come up and do you know in the U S there's 11,000 tax jurisdictions that you and you're selling something online have to abide to these different jurisdictions. So one of the things that we then evolved into is created a Stripe tax product, which initially users or developers were really struggling with and working on. So we created a Stripe tax product. We've done an acquisition called tax jar that helps us accelerate that journey for tax. The other one is this notion of low code that we see in the marketplace right now, where developers saying. Hey, give me more embeddables on top of the primitives that you've created on top of the APIs. So we went leveraging what our customers have already done, created things like a checkout capability, which is a simple redirect highly customized for conversion, which you can just integrate to one API. You have a full checkout capability. You can embed that into your platform, which didn't exist before and needed you to really integrate into different APIs. So all of these capabilities are what developers have really focused on and built that we've done leverage and Excel on. >> Yeah, I think between Lisa and myself, we've paid taxes in about 7,000 of those >> Lisa: Yeah, probably. >> Not 11,000 jurisdictions, but all the various sales taxes and everything else. So we're sort of familiar with it. >> I think so, so here we are, you know, on the floor at re-invent. Great, as we said to be back in person, the 10th annual, but with, as each year goes by AWS has a ecosystem of partners gets bigger and bigger. The flywheel gets, I don't know, I think faster and faster, the number of announcements that came out yesterday and today talk to us about some of the common traits that Stripe and AWS share. >> Yeah. So I've mentioned a few of them. One is certainly the user first mentality where we're listening to users. That tax example is a perfect one of how do we decide new features, new capability based on user first, Amazon does that better than anyone else. Second is that developer mindset focus on the developer. Those will be the core persona we target give you an example, Lyft, we all know Lyft. They wanted to create instant payouts for their drivers. So their developers came to us and say, our developers don't want to get paid. I'm sorry. Our drivers don't want to get paid in a week or two weeks. So we work with their developers who create a instant payout mechanism. Now in six months, over 40% of their drivers are using Stripe instant payout powered by Stripe. And that's a developer first mindset again, back to AWS. And then the third is really around the go to market. And the market opportunity is very similar. You talked about the developer persona and the C-suite very similar to Amazon. But also we're not just catering to enterprise and strategic big customers. We are just so much focused on startups, SMB, mid-market, digital native, just like Amazon is. And I would say the last parallel, which is probably the most important one is innovation. I come from enterprise software where we looked at monthly, quarterly, biannual, annual release cycles. Well, as Stripe, all of that goes out the door just like Amazon. We may have a hundred to a thousand APIs in motion at any time in alpha beta production. And just like Amazon we're iterating and releasing new innovations consistently. So I would say that's probably the most important one that we have with Amazon. >> So a lot of synergies there like deep integrated trusted partner synergies it sounds like. >> Agreed, definitely and then we're seeing this. I was going more as we are going more up market. We're seeing a demand for end to end solutions that require integrations with a CRM vendor for customer 360 with our accounting vendor for pivotal procure order to cash, billing accounting with a e-commerce company like Adobe Magento to do better econ. So more end to end solutions with these tech partners, we're working with our GSI to help deliver those end to end solutions. And certainly, but not least the dev agencies who are still sort of our core constituents that help us keep relevant with those developers. >> You mentioned this at the outset, but some things bear repeating. Can you go into a little more detail on the difference between me wanting to start up a business and take credit cards as payment 10 years ago? Let's say versus today, how much of the friction have you removed from that system? >> It is literally an hour to two hour process versus weeks and months before. >> But what are those steps? Like who would I, you mentioned this, again you mentioned this already, but the go through that, go through that again who would I have to reach out to, to make this happen? And we were talking, you know, relationships with banks, et cetera, et cetera. >> Yeah. So it starts at initiating and registering that company. So imagine you going and having to register a company today, you can do that with a Stripe Atlas product in a matter of hours, get your EIN number, get your tax jurisdictions on your registration as a Delaware entity within the U S you can be anywhere at globally and go do that within a matter of one hour. That's number one, you start there. From there, then it's a matter of embedding payment embeddables within your e-commerce platform, marketplace platform, et cetera. As you've heard us talk about seven lines of code to get payments going, you can quickly onboard accept payments, process payments, reconcile payments all within an hour. And that's just the start. But now you get into more complex use cases around marketplaces and multi-party connection. Multi-party payouts, different commission rates, different subscription models. Think about a flat tier model, a metered tier model, all of these different things that we've abstracted and allow you to just use one to three different integrations to help accelerate and use that in your digital commerce platform. So all of these different workflows have is what we've automated through our APIs. >> Dave: That's unbelievable. >> Yeah. >> It really is. >> It is unbelievable, the amount of automation and innovation that's gone on in such a short time period. What are some of the things as we kind of wrap up here that we can look forward to from stripe from a roadmap perspective, technology wise, partner wise? >> Yes. I mean, we have a slew of data you can imagine billions of billions of transactional data. And you guys know what we do with data is we're looking at fraud prevention. We're looking at, we have a product called radar that looks at fraud, we're doing acceptance, adaptive acceptance to do more AIML learned data and authorization. We're also looking at how do we feed a lot of this financial data into the right mechanisms to allow you to then create new business models on top of this, whether it's cross sell upsell to new user business capture. As well as you know, one of the things I did not talk about, which coming from a farming background is this notion of Stripe climate. Where we have upwards of 2000 companies across 37 countries that are leveraging our Stripe climate product to give back to tech advanced companies that are helping in carbon offset. And super exciting times there from an ESG environmental social governance point of view. So all of those combined is what excites us about the future at Stripe. >> Wow. The future seems unlimited. Lots going on. >> Super excited. Zaki, thank you so much for joining Dave and me talking about what's going on with Stripe. All the innovation that's going on. The synergies with AWS and what's coming down the pipe. We appreciate your insights and your time. >> Thank you, Lisa, thank you, David. Appreciated All right. For Dave Nicholson, I'm Lisa Martin. You're watching the Cube. The global leader in live tech coverage. (lighthearted piano music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante data mesh is a new way of thinking about how to use data to create organizational value leading edge practitioners are beginning to implement data mesh in earnest and importantly data mesh is not a single tool or a rigid reference architecture if you will rather it's an architectural and organizational model that's really designed to address the shortcomings of decades of data challenges and failures many of which we've talked about on the cube as important by the way it's a new way to think about how to leverage data at scale across an organization and across ecosystems data mesh in our view will become the defining paradigm for the next generation of data excellence hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome the founder and creator of data mesh author thought leader technologist jamaak dagani shamak thank you for joining us today good to see you hi dave it's great to be here all right real quick let's talk about what we're going to cover i'll introduce or reintroduce you to jamaac she joined us earlier this year in our cube on cloud program she's the director of emerging tech at dot works north america and a thought leader practitioner software engineer architect and a passionate advocate for decentralized technology solutions and and data architectures and jamaa since we last had you on as a guest which was less than a year ago i think you've written two books in your spare time one on data mesh and another called software architecture the hard parts both published by o'reilly so how are you you've been busy i've been busy yes um good it's been a great year it's been a busy year i'm looking forward to the end of the year and the end of these two books but it's great to be back and um speaking with you well you got to be pleased with the the momentum that data mesh has and let's just jump back to the agenda for a bit and get that out of the way we're going to set the stage by sharing some etr data our partner our data partner on the spending profile and some of the key data sectors and then we're going to review the four key principles of data mesh just it's always worthwhile to sort of set that framework we'll talk a little bit about some of the dependencies and the data flows and we're really going to dig today into principle number three and a bit around the self-service data platforms and to that end we're going to talk about some of the learnings that shamak has captured since she embarked on the datamess journey with her colleagues and her clients and we specifically want to talk about some of the successful models for building the data mesh experience and then we're going to hit on some practical advice and we'll wrap with some thought exercises maybe a little tongue-in-cheek some of the community questions that we get so the first thing i want to do we'll just get this out of the way is introduce the spending climate we use this xy chart to do this we do this all the time it shows the spending profiles and the etr data set for some of the more data related sectors of the ecr etr taxonomy they they dropped their october data last friday so i'm using the july survey here we'll get into the october survey in future weeks but about 1500 respondents i don't see a dramatic change coming in the october survey but the the y-axis is net score or spending momentum the horizontal axis is market share or presence in the data set and that red line that 40 percent anything over that we consider elevated so for the past eight quarters or so we've seen machine learning slash ai rpa containers and cloud is the four areas where cios and technology buyers have shown the highest net scores and as we've said what's so impressive for cloud is it's both pervasive and it shows high velocity from a spending standpoint and we plotted the three other data related areas database edw analytics bi and big data and storage the first two well under the red line are still elevated the storage market continues to kind of plot along and we've we've plotted the outsourced it just to balance it out for context that's an area that's not so hot right now so i just want to point out that these areas ai automation containers and cloud they're all relevant to data and they're fundamental building blocks of data architectures as are the two that are directly related to data database and analytics and of course storage so it just gives you a picture of the spending sector so i wanted to share this slide jamark uh that that we presented in that you presented in your webinar i love this it's a taxonomy put together by matt turk who's a vc and he called this the the mad landscape machine learning and ai and data and jamock the key point here is there's no lack of tooling you've you've made the the data mesh concept sort of tools agnostic it's not like we need more tools to succeed in data mesh right absolutely great i think we have plenty of tools i think what's missing is a meta architecture that defines the landscape in a way that it's in step with organizational growth and then defines that meta architecture in a way that these tools can actually interoperable and to operate and integrate really well like the the clients right now have a lot of challenges in terms of picking the right tool regardless of the technology they go down the path either they have to go in and big you know bite into a big data solution and then try to fit the other integrated solutions around it or as you see go to that menu of large list of applications and spend a lot of time trying to kind of integrate and stitch this tooling together so i'm hoping that data mesh creates that kind of meta architecture for tools to interoperate and plug in and i think our conversation today around self-subjective platform um hopefully eliminate that yeah we'll definitely circle back because that's one of the questions we get all the time from the community okay let's review the four main principles of data mesh for those who might not be familiar with it and those who are it's worth reviewing jamar allow me to introduce them and then we can discuss a bit so a big frustration i hear constantly from practitioners is that the data teams don't have domain context the data team is separated from the lines of business and as a result they have to constantly context switch and as such there's a lack of alignment so principle number one is focused on putting end-to-end data ownership in the hands of the domain or what i would call the business lines the second principle is data as a product which does cause people's brains to hurt sometimes but it's a key component and if you start sort of thinking about it you'll and talking to people who have done it it actually makes a lot of sense and this leads to principle number three which is a self-serve data infrastructure which we're going to drill into quite a bit today and then the question we always get is when we introduce data meshes how to enforce governance in a federated model so let me bring up a more detailed slide jamar with the dependencies and ask you to comment please sure but as you said the the really the root cause we're trying to address is the siloing of the data external to where the action happens where the data gets produced where the data needs to be shared when the data gets used right in the context of the business so it's about the the really the root cause of the centralization gets addressed by distribution of the accountability end to end back to the domains and these domains this distribution of accountability technical accountability to the domains have already happened in the last you know decade or so we saw the transition from you know one general i.t addressing all of the needs of the organization to technology groups within the itu or even outside of the iit aligning themselves to build applications and services that the different business units need so what data mesh does it just extends that model and say okay we're aligning business with the tech and data now right so both application of the data in ml or inside generation in the domains related to the domain's needs as well as sharing the data that the domains are generating with the rest of the organization but the moment you do that then you have to solve other problems that may arise and that you know gives birth to the second principle which is about um data as a product as a way of preventing data siloing happening within the domain so changing the focus of the domains that are now producing data from i'm just going to create that data i collect for myself and that satisfy my needs to in fact the responsibility of domain is to share the data as a product with all of the you know wonderful characteristics that a product has and i think that leads to really interesting architectural and technical implications of what actually constitutes state has a product and we can have a separate conversation but once you do that then that's the point in the conversation that cio says well how do i even manage the cost of operation if i decentralize you know building and sharing data to my technical teams to my application teams do i need to go and hire another hundred data engineers and i think that's the role of a self-serve data platform in a way that it enables and empowers generalist technologies that we already have in the technical domains the the majority population of our developers these days right so the data platform attempts to mobilize the generalist technologies to become data producers to become data consumers and really rethink what tools these people need um and the last last principle so data platform is really to giving autonomy to domain teams and empowering them and reducing the cost of ownership of the data products and finally as you mentioned the question around how do i still assure that these different data products are interoperable are secure you know respecting privacy now in a decentralized fashion right when we are respecting the sovereignty or the domain ownership of um each domain and that leads to uh this idea of both from operational model um you know applying some sort of a federation where the domain owners are accountable for interoperability of their data product they have incentives that are aligned with global harmony of the data mesh as well as from the technology perspective thinking about this data is a product with a new lens with a lens that all of those policies that need to be respected by these data products such as privacy such as confidentiality can we encode these policies as computational executable units and encode them in every data product so that um we get automation we get governance through automation so that's uh those that's the relationship the complex relationship between the four principles yeah thank you for that i mean it's just a couple of points there's so many important points in there but the idea of the silos and the data as a product sort of breaking down those cells because if you have a product and you want to sell more of it you make it discoverable and you know as a p l manager you put it out there you want to share it as opposed to hide it and then you know this idea of managing the cost you know number three where people say well centralize and and you can be more efficient but that but that essentially was the the failure in your other point related point is generalist versus specialist that's kind of one of the failures of hadoop was you had these hyper specialist roles emerge and and so you couldn't scale and so let's talk about the goals of data mesh for a moment you've said that the objective is to extend exchange you call it a new unit of value between data producers and data consumers and that unit of value is a data product and you've stated that a goal is to lower the cognitive load on our brains i love this and simplify the way in which data are presented to both producers and consumers and doing so in a self-serve manner that eliminates the tapping on the shoulders or emails or raising tickets so how you know i'm trying to understand how data should be used etc so please explain why this is so important and how you've seen organizations reduce the friction across the data flows and the interconnectedness of things like data products across the company yeah i mean this is important um as you mentioned you know initially when this whole idea of a data-driven innovation came to exist and we needed all sorts of you know technology stacks we we centralized um creation of the data and usage of the data and that's okay when you first get started with where the expertise and knowledge is not yet diffused and it's only you know the privilege of a very few people in the organization but as we move to a data driven um you know innovation cycle in the organization as we learn how data can unlock new new programs new models of experience new products then it's really really important as you mentioned to get the consumers and producers talk to each other directly without a broker in the middle because even though that having that centralized broker could be a cost-effective model but if you if we include uh the cost of missed opportunity for something that we could have innovated well we missed that opportunity because of months of looking for the right data then that cost parented the cost benefit parameters and formula changes so um so to to have that innovation um really embedded data-driven innovation embedded into every domain every team we need to enable a model where the producer can directly peer-to-peer discover the data uh use it understand it and use it so the litmus test for that would be going from you know a hypothesis that you know as a data scientist i think there is a pattern and there is an insight in um you know in in the customer behavior that if i have access to all of the different informations about the customer all of the different touch points i might be able to discover that pattern and personalize the experience of my customer the liquid stuff is going from that hypothesis to finding all of the different sources be able to understanding and be able to connect them um and then turn them them into you know training of my machine learning and and the rest is i guess known as an intelligent product got it thank you so i i you know a lot of what we do here in breaking it house is we try to curate and then point people to new resources so we will have some additional resources because this this is not superficial uh what you and your colleagues in the community are creating but but so i do want to you know curate some of the other material that you had so if i bring up this next chart the left-hand side is a curated description both sides of your observations of most of the monolithic data platforms they're optimized for control they serve a centralized team that has hyper-specialized roles as we talked about the operational stacks are running running enterprise software they're on kubernetes and the microservices are isolated from let's say the spark clusters you know which are managing the analytical data etc whereas the data mesh proposes much greater autonomy and the management of code and data pipelines and policy as independent entities versus a single unit and you've made this the point that we have to enable generalists to borrow from so many other examples in the in the industry so it's an architecture based on decentralized thinking that can really be applied to any domain really domain agnostic in a way yes and i think if i pick one key point from that diagram is really um or that comparison is the um the the the data platforms or the the platform capabilities need to present a continuous experience from an application developer building an application that generates some data let's say i have an e-commerce application that generates some data to the data product that now presents and shares that data as as temporal immutable facts that can be used for analytics to the data scientist that uses that data to personalize the experience to the deployment of that ml model now back to that e-commerce application so if we really look at this continuous journey um the walls between these separate platforms that we have built needs to come down the platforms underneath that generate you know that support the operational systems versus supported data platforms versus supporting the ml models they need to kind of play really nicely together because as a user i'll probably fall off the cliff every time i go through these stages of this value stream um so then the interoperability of our data solutions and operational solutions need to increase drastically because so far we've got away with running operational systems an application on one end of the organization running and data analytics in another and build a spaghetti pipeline to you know connect them together neither of the ends are happy i hear from data scientists you know data analyst pointing finger at the application developer saying you're not developing your database the right way and application point dipping you're saying my database is for running my application it wasn't designed for sharing analytical data so so we've got to really what data mesh as a mesh tries to do is bring these two world together closer because and then the platform itself has to come closer and turn into a continuous set of you know services and capabilities as opposed to this disjointed big you know isolated stacks very powerful observations there so we want to dig a little bit deeper into the platform uh jamar can have you explain your thinking here because it's everybody always goes to the platform what do i do with the infrastructure what do i do so you've stressed the importance of interfaces the entries to and the exits from the platform and you've said you use a particular parlance to describe it and and this chart kind of shows what you call the planes not layers the planes of the platform it's complicated with a lot of connection points so please explain these planes and how they fit together sure i mean there was a really good point that you started with that um when we think about capabilities or that enables build of application builds of our data products build their analytical solutions usually we jump too quickly to the deep end of the actual implementation of these technologies right do i need to go buy a data catalog or do i need you know some sort of a warehouse storage and what i'm trying to kind of elevate us up and out is to to to force us to think about interfaces and apis the experiences that the platform needs to provide to run this secure safe trustworthy you know performance mesh of data products and if you focus on then the interfaces the implementation underneath can swap out right you can you can swap one for the other over time so that's the purpose of like having those lollipops and focusing and emphasizing okay what is the interface that provides a certain capability like the storage like the data product life cycle management and so on the purpose of the planes the mesh experience playing data product expense utility plan is really giving us a language to classify different set of interfaces and capabilities that play nicely together to provide that cohesive journey of a data product developer data consumer so then the three planes are really around okay at the bottom layer we have a lot of utilities we have that mad mac turks you know kind of mad data tooling chart so we have a lot of utilities right now they they manage workflow management you know they they do um data processing you've got your spark link you've got your storage you've got your lake storage you've got your um time series of storage you've got a lot of tooling at that level but the layer that we kind of need to imagine and build today we don't buy yet as as long as i know is this linger that allows us to uh exchange that um unit of value right to build and manage these data products so so the language and the apis and interface of this product data product experience plan is not oh i need this storage or i need that you know workflow processing is that i have a data product it needs to deliver certain types of data so i need to be able to model my data it needs to as part of this data product i need to write some processing code that keeps this data constantly alive because it's receiving you know upstream let's say user interactions with a website and generating the profile of my user so i need to be able to to write that i need to serve the data i need to keep the data alive and i need to provide a set of slos and guarantees for my data so that good documentation so that some you know someone who comes to data product knows but what's the cadence of refresh what's the retention of the data and a lot of other slos that i need to provide and finally i need to be able to enforce and guarantee certain policies in terms of access control privacy encryption and so on so as a data product developer i just work with this unit a complete autonomous self-contained unit um and the platform should give me ways of provisioning this unit and testing this unit and so on that's why kind of i emphasize on the experience and of course we're not dealing with one or two data product we're dealing with a mesh of data products so at the kind of mesh level experience we need a set of capabilities and interfaces to be able to search the mesh for the right data to be able to explore the knowledge graph that emerges from this interconnection of data products need to be able to observe the mesh for any anomalies did we create one of these giant master data products that all the data goes into and all the data comes out of how we found ourselves the bottlenecks to be able to kind of do those level machine level capabilities we need to have a certain level of apis and interfaces and once we decide and decide what constitutes that to satisfy this mesh experience then we can step back and say okay now what sort of a tool do i need to build or buy to satisfy them and that's that is not what the data community or data part of our organizations used to i think traditionally we're very comfortable with buying a tool and then changing the way we work to serve to serve the tool and this is slightly inverse to that model that we might be comfortable with right and pragmatists will will to tell you people who've implemented data match they'll tell you they spent a lot of time on figuring out data as a product and the definitions there the organizational the getting getting domain experts to actually own the data and and that's and and they will tell you look the technology will come and go and so to your point if you have those lollipops and those interfaces you'll be able to evolve because we know one thing's for sure in this business technology is going to change um so you you had some practical advice um and i wanted to discuss that for those that are thinking about data mesh i scraped this slide from your presentation that you made and and by the way we'll put links in there your colleague emily who i believe is a data scientist had some really great points there as well that that practitioners should dig into but you made a couple of points that i'd like you to summarize and to me that you know the big takeaway was it's not a one and done this is not a 60-day project it's a it's a journey and i know that's kind of cliche but it's so very true here yes um this was a few starting points for um people who are embarking on building or buying the platform that enables the people enables the mesh creation so it was it was a bit of a focus on kind of the platform angle and i think the first one is what we just discussed you know instead of thinking about mechanisms that you're building think about the experiences that you're enabling uh identify who are the people like what are the what is the persona of data scientists i mean data scientist has a wide range of personas or did a product developer the same what is the persona i need to develop today or enable empower today what skill sets do they have and and so think about experience as mechanisms i think we are at this really magical point i mean how many times in our lifetime we come across a complete blanks you know kind of white space to a degree to innovate so so let's take that opportunity and use a bit of a creativity while being pragmatic of course we need solutions today or yesterday but but still think about the experiences not not mechanisms that you need to buy so that was kind of the first step and and the nice thing about that is that there is an evolutionary there is an iterative path to maturity of your data mesh i mean if you start with thinking about okay which are the initial use cases i need to enable what are the data products that those use cases depend on that we need to unlock and what is the persona of my or general skill set of my data product developer what are the interfaces i need to enable you can start with the simplest possible platform for your first two use cases and then think about okay the next set of data you know data developers they have a different set of needs maybe today i just enable the sql-like querying of the data tomorrow i enable the data scientists file based access of the data the day after i enable the streaming aspect so so have this evolutionary kind of path ahead of you and don't think that you have to start with building out everything i mean one of the things we've done is taking this harvesting approach that we work collaboratively with those technical cross-functional domains that are building the data products and see how they are using those utilities and harvesting what they are building as the solutions for themselves back into the back into the platform but at the end of the day we have to think about mobilization of the large you know largest population of technologies we have we'd have to think about diffusing the technology and making it available and accessible by the generous technologies that you know and we've come a long way like we've we've gone through these sort of paradigm shifts in terms of mobile development in terms of functional programming in terms of cloud operation it's not that we are we're struggling with learning something new but we have to learn something that works nicely with the rest of the tooling that we have in our you know toolbox right now so so again put that generalist as the uh as one of your center personas not the only person of course we will have specialists of course we will always have data scientists specialists but any problem that can be solved as a general kind of engineering problem and i think there's a lot of aspects of data michigan that can be just a simple engineering problem um let's just approach it that way and then create the tooling um to empower those journalists great thank you so listen i've i've been around a long time and so as an analyst i've seen many waves and we we often say language matters um and so i mean i've seen it with the mainframe language it was different than the pc language it's different than internet different than cloud different than big data et cetera et cetera and so we have to evolve our language and so i was going to throw a couple things out here i often say data is not the new oil because because data doesn't live by the laws of scarcity we're not running out of data but i get the analogy it's powerful it powered the industrial economy but it's it's it's bigger than that what do you what do you feel what do you think when you hear the data is the new oil yeah i don't respond to those data as the gold or oil or whatever scarce resource because as you said it evokes a very different emotion it doesn't evoke the emotion of i want to use this i want to utilize it feels like i need to kind of hide it and collect it and keep it to myself and not share it with anyone it doesn't evoke that emotion of sharing i really do think that data and i with it with a little asterisk and i think the definition of data changes and that's why i keep using the language of data product or data quantum data becomes the um the most important essential element of existence of uh computation what do i mean by that i mean that you know a lot of applications that we have written so far are based on logic imperative logic if this happens do that and else do the other and we're moving to a world where those applications generating data that we then look at and and the data that's generated becomes the source the patterns that we can exploit to build our applications as in you know um curate the weekly playlist for dave every monday based on what he has listened to and the you know other people has listened to based on his you know profile so so we're moving to the world that is not so much about applications using the data necessarily to run their businesses that data is really truly is the foundational building block for the applications of the future and then i think in that we need to rethink the definition of the data and maybe that's for a different conversation but that's that's i really think we have to converge the the processing that the data together the substance substance and the processing together to have a unit that is uh composable reusable trustworthy and that's that's the idea behind the kind of data product as an atomic unit of um what we build from future solutions got it now something else that that i heard you say or read that really struck me because it's another sort of often stated phrase which is data is you know our most valuable asset and and you push back a little bit on that um when you hear people call data and asset people people said often have said they think data should be or will eventually be listed as an asset on the balance sheet and i i in hearing what you said i thought about that i said well you know maybe data as a product that's an income statement thing that's generating revenue or it's cutting costs it's not necessarily because i don't share my my assets with people i don't make them discoverable add some color to this discussion i think so i think it's it's actually interesting you mentioned that because i read the new policy in china that cfos actually have a line item around the data that they capture we don't have to go to the political conversation around authoritarian of um collecting data and the power that that creates and the society that leads to but that aside that big conversation little conversation aside i think you're right i mean the data as an asset generates a different behavior it's um it creates different performance metrics that we would measure i mean before conversation around data mesh came to you know kind of exist we were measuring the success of our data teams by the terabytes of data they were collecting by the thousands of tables that they had you know stamped as golden data none of that leads to necessarily there's no direct line i can see between that and actually the value that data generated but if we invert that so that's why i think it's rather harmful because it leads to the wrong measures metrics to measure for success so if you invert that to a bit of a product thinking or something that you share to delight the experience of users your measures are very different your measures are the the happiness of the user they decrease lead time for them to actually use and get value out of it they're um you know the growth of the population of the users so it evokes a very different uh kind of behavior and success metrics i do say if if i may that i probably come back and regret the choice of word around product one day because of the monetization aspect of it but maybe there is a better word to use but but that's the best i think we can use at this point in time why do you say that jamar because it's too directly related to monetization that has a negative connotation or it might might not apply in things like healthcare or you know i think because if we want to take your shortcuts and i remember this conversation years back that people think that the reason to you know kind of collect data or have data so that we can sell it you know it's just the monetization of the data and we have this idea of the data market places and so on and i think that is actually the least valuable um you know outcome that we can get from thinking about data as a product that direct cell an exchange of data as a monetary you know exchange of value so so i think that might redirect our attention to something that really matters which is um enabling using data for generating ultimately value for people for the customers for the organizations for the partners as opposed to thinking about it as a unit of exchange for for money i love data as a product i think you were your instinct was was right on and i think i'm glad you brought that up because because i think people misunderstood you know in the last decade data as selling data directly but you really what you're talking about is using data as a you know ingredient to actually build a product that has value and value either generate revenue cut costs or help with a mission like it could be saving lives but in some way for a commercial company it's about the bottom line and that's just the way it is so i i love data as a product i think it's going to stick so one of the other things that struck me in one of your webinars was one of the q a one of the questions was can i finally get rid of my data warehouse so i want to talk about the data warehouse the data lake jpmc used that term the data lake which some people don't like i know john furrier my business partner doesn't like that term but the data hub and one of the things i've learned from sort of observing your work is that whether it's a data lake a data warehouse data hub data whatever it's it should be a discoverable node on the mesh it really doesn't matter the the technology what are your your thoughts on that yeah i think the the really shift is from a centralized data warehouse to data warehouse where it fits so i think if you just cross that centralized piece uh we are all in agreement that data warehousing provides you know interesting and capable interesting capabilities that are still required perhaps as a edge node of the mesh that is optimizing for certain queries let's say financial reporting and we still want to direct a fair bit of data into a node that is just for those financial reportings and it requires the precision and the um you know the speed of um operation that the warehouse technology provides so i think um definitely that technology has a place where it falls apart is when you want to have a warehouse to rule you know all of your data and model canonically model your data because um it you have to put so much energy into you know kind of try to harness this model and create this very complex the complex and fragile snowflake schemas and so on that that's all you do you spend energy against the entropy of your organization to try to get your arms around this model and the model is constantly out of step with what's happening in reality because reality the model the reality of the business is moving faster than our ability to model everything into into uh into one you know canonical representation i think that's the one we need to you know challenge not necessarily application of data warehousing on a node i want to close by coming back to the issues of standards um you've specifically envisioned data mesh to be technology agnostic as i said before and of course everyone myself included we're going to run a vendor's technology platform through a data mesh filter the reality is per the matt turc chart we showed earlier there are lots of technologies that that can be nodes within the data mesh or facilitate data sharing or governance etc but there's clearly a lack of standardization i'm sometimes skeptical that the vendor community will drive this but maybe like you know kubernetes you know google or some other internet giant is going to contribute something to open source that addresses this problem but talk a little bit more about your thoughts on standardization what kinds of standards are needed and where do you think they'll come from sure i mean the you write that the vendors are not today incentivized to create those open standards because majority of the vet not all of them but some vendors operational model is about bring your data to my platform and then bring your computation to me uh and all will be great and and that will be great for a portion of the clients and portion of environments where that complexity we're talking about doesn't exist so so we need yes other players perhaps maybe um some of the cloud providers or people that are more incentivized to open um open their platform in a way for data sharing so as a starting point i think standardization around data sharing so if you look at the spectrum right now we have um a de facto sound it's not even a standard for something like sql i mean everybody's bastardized to call and extended it with so many things that i don't even know what this standard sql is anymore but we have that for some form of a querying but beyond that i know for example folks at databricks to start to create some standards around delta sharing and sharing the data in different models so i think data sharing as a concept the same way that apis were about capability sharing so we need to have the data apis or analytical data apis and data sharing extended to go beyond simply sql or languages like that i think we need standards around computational prior policies so this is again something that is formulating in the operational world we have a few standards around how do you articulate access control how do you identify the agents who are trying to access with different authentication mechanism we need to bring some of those our ad our own you know our data specific um articulation of policies uh some something as simple as uh identity management across different technologies it's non-existent so if you want to secure your data across three different technologies there is no common way of saying who's the agent that is acting uh to act to to access the data can i authenticate and authorize them so so those are some of the very basic building blocks and then the gravy on top would be new standards around enriched kind of semantic modeling of the data so we have a common language to describe the semantic of the data in different nodes and then relationship between them we have prior work with rdf and folks that were focused on i guess linking data across the web with the um kind of the data web i guess work that we had in the past we need to revisit those and see their practicality in the enterprise con context so so data modeling a rich language for data semantic modeling and data connectivity most importantly i think those are some of the items on my wish list that's good well we'll do our part to try to keep the standards you know push that push that uh uh movement jamaica we're going to leave it there i'm so grateful to have you uh come on to the cube really appreciate your time it's just always a pleasure you're such a clear thinker so thanks again thank you dave that's it's wonderful to be here now we're going to post a number of links to some of the great work that jamark and her team and her books and so you check that out because we remember we publish each week on siliconangle.com and wikibon.com and these episodes are all available as podcasts wherever you listen listen to just search breaking analysis podcast don't forget to check out etr.plus for all the survey data do keep in touch i'm at d vallante follow jamac d z h a m a k d or you can email me at david.velante at siliconangle.com comment on the linkedin post this is dave vellante for the cube insights powered by etrbwell and we'll see you next time you

>> Okay, we continue now with the theme of turning ideas into insights so ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only. And a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real-time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal we heard, or at least semi normal as we begin to better understand and forecast demand and supply imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz, who's a Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike who is the vice president of industry solutions and value management at Cloudera. Welcome to both of you. >> Welcome, thank you. >> Thanks Dave. >> All right Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >> It's really about democratization. If you can't make your data accessible, it's not usable. Nobody's able to understand what's happening in the business and they don't understand what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships with their customers due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and peck around within your ecosystem to find what it is that's important. >> Great thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. Just getting better insights into what customers need and when do they need it? >> Cindy, platform is another core principle. How should we be thinking about data platforms in this day and age? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing being the most successful. >> Great, thank you. Michelle let's move on a little bit and talk about practices and processes, the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >> Yeah, it's a really great question 'cause it's pretty complex when you have to start to connect your data to your business. The first thing to really gravitate towards is what are you trying to do. And what Cindy was describing with those customer examples is that they're all based off of business goals, off of very specific use cases. That helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in near time or real time, or later on, as you're doing your strategic planning. What that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, "Well, can I also measure the outcomes from those processes and using data and using insight? Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my analytic capabilities that are allowing me to be effective in those environments?" But everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it? But coming in more from that business perspective, to then start to be data driven, recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions. And ultimately getting to the point of being insight driven, where you're able to both describe what you want your business to be with your data, using analytics to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize and you can innovate. Because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >> I like how you said near time or real time, because it is a spectrum. And at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo take a look at the business processes and value stream itself. And if you can understand how people, and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process? Or are you collecting information, asking for information that is going to help satisfy a downstream process step or a downstream decision? So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize do you need that real time, near real time, or do you want to start creating greater consistency by bringing all of those signals together in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process, and the decision points, and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >> Got it. Let's, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? >> Yeah. And that's kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. Do they bring those same principles of value, of being insight driven? So they have to have those core values themselves in order to help you as a business person enable those capabilities. >> So Cindy I'm cool with fuel, but it's like super fuel when you talk about data. 'Cause it's not scarce, right? You're never going to run out. (Dave chuckling) So Michelle, let's talk about leadership. Who leads? What does so-called leadership look like in an organization that's insight driven? >> So I think the really interesting thing that is starting to evolve as late is that organizations, enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be. Data driving into the insight or the raw data itself has the ability to set in motion what's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, your CRO coming back and saying, I need better data. I need information that's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come. Not just one month, two months, three months, or a year from now, but in a week or tomorrow. And so that is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity. You have your chief data officer that is shaping the experiences with data and with insight and reconciling what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities. And either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data-driven, but ultimately to be insight driven, you're seeing way more participation and leadership at that C-suite level and just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >> Great, thank you. Let's wrap, and I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. Companies can also have data, and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're leveraging data to actually help you make and drive those decisions. So when we use the term you're data-driven, you can't just use the term tongue-in-cheek. It actually means that I'm using the recent, the relevant, and the accuracy of data to actually make the decisions for me, because we're all advancing upon, we're talking about artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could add to what Cindy just said and maybe add as well any advice that you have around creating and defining a data strategy. >> So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset, it has value. But you may not necessarily know what that value is yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action, for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away the gap between when you see it, know it, and then get the most value and really exploit what that is at the time when it's right, so in the moment. We talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. So are we just introducing it as data-driven organizations where we could see spreadsheets and PowerPoint presentations and lots of mapping to help make longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder if I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight, there is none, it's all coming together for the best outcomes. >> Right, it's like people are acting on perfect or near perfect information. Or machines are doing so with a high degree of confidence. Great advice and insights, and thank you both for sharing your thoughts with our audience today, it was great to have you. >> Thank you. >> Thank you. >> Okay, now we're going to go into our industry deep dives. There are six industry breakouts. Financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments. Now each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session of choice. Or for more information, click on the agenda page and take a look to see which session is the best fit for you and then dive in. Join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community, and enjoy the rest of the day. (upbeat music)

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

(upbeat intro music) >> More than a decade ago, the team at Wikibon coined the term Server SAN. We saw the opportunity to dramatically change the storage infrastructure layer and predicted a major change in technologies that would hit the market. Server SAN had three fundamental attributes. First of all, it was software led. So all the traditionally expensive controller functions like snapshots and clones and de-dupe and replication, compression, encryption, et cetera, they were done in software directly challenging a two to three decade long storage controller paradigm. The second principle was it leveraged and shared storage inside of servers. And the third it enabled any-to-any typology between servers and storage. Now, at the time we defined this coming trend in a relatively narrow sense inside of a data center location, for example, but in the past decade, two additional major trends have emerged. First the software defined data center became the dominant model, thanks to VMware and others. And while this eliminated a lot of overhead, it also exposed another problem. Specifically data centers today allocate probably we estimate around 35% of CPU cores and cycles to managing things like storage and network and security, offloading those functions. This is wasted cores and doing this with traditional general purpose x86 processors is expensive and it's not efficient. This is why we've been reporting so aggressively on ARM's ascendancy into the enterprise. It's not only coming it's here and we're going to talk about that today. The second mega trend is cloud computing. Hyperscale infrastructure has allowed technology companies to put a management and control plane into the cloud and expand beyond our narrow server SAN scope within a single data center and support the management of distributed data at massive scale. And today we're on the cusp of a new era of infrastructure. And one of the startups in this space is Nebulon. Hello everybody, this is Dave Vellante, and welcome to this Cube Conversation where we welcome in two great guests, Craig Nunes, Cube alum, co-founder and COO at Nebulon and Tobias Flitsch who's director of product management at Nebulon. Guys, welcome. Great to see you. >> So good to be here Dave. Feels awesome. >> Soon, face to face. Craig, I'm heading your way. >> I can't wait. >> Craig, you heard my narrative upfront and I'm wondering are those the trends that you guys saw when you, when you started the company, what are the major shifts in the world today that, that caused you and your co-founders to launch Nebulon? >> Yeah, I'll give you sort of the way we think about the world, which I think aligns super right with, with what you're talking about, you know, over the last several years, organizations have had a great deal of experience with public cloud data centers. And I think like any platform or technology that is, you know, gets its use in a variety of ways, you know, a bit of savvy is being developed by organizations on, you know, what do I put where, how do I manage things in the most efficient way possible? And there are, in terms of the types of folks we're focused on in Nebulon's business, we see now kind of three groups of people emerging, and, and we sort of simply coined three terms, the returners, the removers, and the remainers. I'll explain what I mean by each of those, the returners are folks who maybe early on, you know, hit the gas on cloud, moved, you know, everything in, a lot in, and realize that while it's awesome for some things, for other things, it was less optimal. Maybe cost became a factor or visibility into what was going on with their data was a factor, security, service levels, whatever. And they've decided to move some of those workloads back. Returners. There are what I call the removers that are taking workloads from, you know, born in the cloud. On-prem, you know, and this was talked a lot about in Martine's blog that, you know, talked about a lot of the growth companies that built up such a large footprint in the public cloud, that economics were kind of working against them. You can, depending on the knobs you turn, you know, you're probably spending two and a half X, two X, what you might spend if you own your own factory. And you can argue about, you know, where your leverage is in negotiating your pricing with the cloud vendors, but there's a big gap. The last one is, and I think probably the most significant in terms of who we've engaged with is the remainers. And the remainers are, you know, hybrid IT organizations. They've got assets in the cloud and on-prem, they aspire to an operational model that is consistent across everything and, you know, leveraging all the best stuff that they observed in their cloud-based assets. And it's kind of our view that frankly we take from, from this constituency that, when people talk about cloud or cloud first, they're moving to something that is really more an operating model versus a destination or data center choice. And so, we get people on the phone every day, talking about cloud first. And when you kind of dig into what they're after, it's operating model characteristics, not which data center do I put it in, and those, those decisions are separating. And so that, you know, it's really that focus for us is where, we believe we're doing something unique for that group of customers. >> Yeah. Cloud first doesn't doesn't mean cloud only. And of course followers of this program know, we talk a lot about this, this definition of cloud is changing, it's evolving, It's moving to the edge, it's moving to data centers, data centers are moving to the cloud. Cross-cloud, it's that big layer that's expanding. And so I think the definition of cloud, even particularly in customer's minds is evolving. There's no question about it. People, they'll look at what VMware is doing in AWS and say, okay, that's cloud, but they'll also look at things like VMware cloud foundation and say oh yeah, that's cloud too. So to me, the beauty of cloud is in the eye of the customer beholder. So I buy that. Tobias. I wonder if you could talk about how this all translates into product, because you guys start up, you got to sell stuff, you use this term smart infrastructure, what is that? How does this all turn into stuff you can sell? >> Right. Yeah. So let me back up a little bit and talk a little bit about, you know, what we at Nebulon do. So we are a cloud based software company, and we're delivering sort of a new category of smart infrastructure. And if you think about things that you would know from your everyday surroundings, smart infrastructure is really all around us. Think smart home technology like Google Nest as an example. And what this all has in common is that there's a cloud control plane that is managing some IOT end points and smart devices in various locations. And by doing that, customers gain benefits like easy remote management, right? You can manage your thermostat, your temperature from anywhere in the world basically. You don't have to worry about automated software updates anymore, and you can easily automate your home, your infrastructure, through this cloud control plane and translating this idea to the data center, right? This idea is not necessarily new, right? If you look into the networking space with Meraki networks, now Cisco or Mist Systems now Juniper, they've really pioneered efforts in cloud management. So smart network infrastructure, and the key problem that they solved there is, you know, managing these vast amount of access points and switches that are scattered across the data centers across campuses, and, you know, the data center. Now, if you translate that to what Nebulon does, it's really applying this smart infrastructure idea, this methodology to application infrastructure, specifically to compute and storage infrastructure. And that's essentially what we're doing. So with smart infrastructure, basically our offering it at Nebulon, the product, that comes with the benefits of this cloud experience, public cloud operating model, as we've talked about, some of our customers look at the cloud as an operating model rather than a destination, a physical location. And with that, we bring to us this model, this, this experience as operating a model to on-premises application infrastructure, and really what you get with this broad offering from Nebulon and the benefits are really circling it out, you know, four areas, first of all, rapid time to value, right? So application owners think people that are not specialists or experts when it comes to IT infrastructure, but more generalists, they can provision on-premise application infrastructure in less than 10 minutes, right? It can go from, from just bare metal physical racks to the full application stack in less than 10 minutes, so they're up and running a lot quicker. And they can immediately deliver services to their end customers, cloud-like operations, this, this notion of zero touch remote management, which now with the last couple of months with this strange time that we're with COVID is, you know, turnout is becoming more and more relevant really as in remotely administrating and management of infrastructure that scales from just hundreds of nodes to thousands of nodes. It doesn't really matter with behind the scenes software updates, with global AI analytics and insights, and basically overall combined reduce the operational overhead when it comes to on-premises infrastructure by up to 75%, right? The other thing is support for any application, whether it's containerized, virtualized, or even bare metal applications. And the idea here is really consistent leveraging server-based storage that doesn't require any Nebulon-specific software on the server. So you get the full power of your application servers for your applications. Again, as the servers intended. And then the fourth benefit when it comes to smart infrastructure is, is of course doing this all at a lower cost and with better data center density. And that is really comparing it to three-tier architectures where you have your server, your SAN fabric, and then you have an external storage, but also when you compare it with hyper-converged infrastructure software, right, that is consuming resources of the application servers, think CPU, think memory and networking. So basically you get a lot more density with that approach compared to those architectures. >> Okay, I want to dig into some of that differentiation too, but what exactly do I buy from you? Do I buy a software subscription? Is that right? Can you explain that a little bit? >> Right. So basically the way we do this is it's really leveraging two key new innovations, right? So, and you see why I made the bridge to smart home technology, because the approach is civil, right? The one is, you know, the introduction of a cloud control plane that basically manage this on-premise application infrastructure, of course, that is delivered to customers as a service. The second one is, you know, a new infrastructure model that uses IOT endpoint technology, and that is embedded into standard application servers and the storage within this application servers. Let me add a couple of words to that to explain a little bit more, so really at the heart of smart infrastructure, in order to deliver this public cloud experience for any on-prem application is this cloud-based control plane, right? So we've built this, how we recommend our customers to use a public cloud, and that is built, you know, building your software on modern technologies that are vendor-agnostic. So it could essentially run anywhere, whether it is, you know, any public cloud vendor, or if we want to run in our own data centers, when regulatory requirements change, it's massively scalable and responsive, no matter how large the managed infrastructure is. But really the interesting part here, Dave, is that the customer doesn't really have to worry about any of that, it's delivered as a service. So what a customer gets from this cloud control plane is a single API end point, how they get it with a public cloud. They get a web user interface, from which they can manage all of their infrastructure, no matter how many devices, no matter where it is, can be in the data center, can be in an edge location anywhere in the world, they get template-based provisioning much like a marketplace in a public cloud. They get analytics, predictive support services, and super easy automation capabilities. Now the second thing that I mentioned is this server embedded software, the server embedded infrastructure software, and that is running on a PCIE based offload engine. And that is really acting as this managed IOT endpoint within the application server that I managed that I mentioned earlier. And that approach really further converges modern application infrastructure. And it really replaces the software defined storage approach that you'll find in hyper-converged infrastructure software. And that is really by embedding the data services, the storage data service into silicon within the server. Now this offload engine, we call that a services processing unit or SPU in short. And that is really what differentiates us from hyper-converged infrastructure. And it's quite different than a regular accelerator card that you get with some of the hyper-converged infrastructure offerings. And it's different in the sense that the SPU runs basically all of the shared and local data services, and it's not just accelerating individual algorithms, individual functions. And it basically provides all of these services aside the CPU with the boot drive, with data drives. And in essence provides you with this a separate fall domain from the service, so for example, if you reboot your server, the data plan remains intact. You know, it's not impacted for that. >> Okay. So I want to stay on that for just a second, Craig, if I could, I get very clear how you're different from, as Tobias said, the three-tier server SAN fabric, external array, the HCI thing's interesting because in some respects, the HCI has, you know, guys take Nutanix, they talk about cloud and becoming more friendly with developers and API piece, but what's your point of view Craig on how you position relative to say HCI? >> Yeah, absolutely. So everyone gets what three-tier architecture is and was, and HCI software, you know, emerged as an alternative to the three-tier architectures. Everyone I think today understands that data services are, you know, SDS is software hosted in the operating system of each HCI device and consume some amount of CPU, memory, network, whatever. And it's typically constrained to a hypervisor environment, kind of where we're most of that stuff is done. And over time, these platforms have added some monitoring capabilities, predictive analytics, typically provided by the vendor's cloud, right? And as Tobias mentioned, some HCIS vendors have augmented this approach by adding an accelerator to make things like compression and dedupe go faster, right? Think SimpliVity or something like that. The difference that we're talking about here is, the infrastructure software that we deliver as a service is embedded right into server silicon. So it's not sitting in the operating system of choice. And what that means is you get the full power of the server you bought for your workloads. It's not constrained to a hypervisor-only environment, it's OS agnostic. And, you know, it's entirely controlled and administered by the cloud versus with, you know, most HCIS is an on-prem console that manages a cluster or two on-prem. And, you know, think of it from a automation perspective. When you automate something, you've got to set up your playbook kind of cluster by cluster. And depending what versions they're on, APIs are changing, behaviors are changing. So a very different approach at scale. And so again, for us, we're talking about something that gives you a much more efficient infrastructure that is then managed by the cloud and gives you this full kind of operational model you would expect for any kind of cloud-based deployment. >> You know, I got to go back, you guys obviously have some three-part DNA hanging around and you know, of course you remember well, the three-part ASIC, it was kind of famous at the time and it was unique. And I bring that up only because you've mentioned a couple of times the silicon and a lot of people yeah, whatever, but we have been on this, especially, particularly with ARM. And I want to share with the audience, if you follow my breaking analysis, you know this. If you look at the historical curve of Moore's law with x86, it's the doubling of performance every two years, roughly, that comes out to about 40% a year. That's moderated down to about 30% a year now, if you look at the ARM ecosystem and take for instance, apple A15, and the previous series, for example, over the last five years, the performance, when you combine the CPU, GPU, NPU, the accelerators, the DSPs, which by the way, are all customizable. That's growing at 110% a year, and the SOC costs 50 bucks. So my point is that you guys are riding perfect example of doing offloads with a way more efficient architecture. You're now on that curve, that's growing at 100% plus per year. Whereas a lot of the legacy storage is still on that 30% a year curve, and so cheaper, lower power. That's why I love to buy, as you were bringing in the IOT and the smart infrastructure, this is the future of storage and infrastructure. >> Absolutely. And the thing I would emphasize is it's not limited to storage, storage is a big issue, but we're talking about your application infrastructure and you brought up something interesting on the GPU, the SmartNIC of things, and just to kind of level set with everybody there, there's the HCI world, and then there's this SmartNIC DPU world, whatever you want to call it, where it's effectively a network card, it's got that specialized processing onboard and firmware to provide some network security storage services, and think of it as a PCIE card in your server. It connects to an external storage system, so think Nvidia Bluefield 2 connecting to an external NVME storage device. And the interesting thing about that is, you know, storage processing is offloaded from the server. So as we said earlier, good, right, you get the server back to your application, but storage moves out of the server. And it starts to look a little bit like an external storage approach versus a server based approach. And infrastructure management is done by, you know, the server SmartNIC with some monitoring and analytics coming from, you know, your supplier's cloud support service. So complexity creeps back in, if you start to lose that, you know, heavily converged approach. Again, we are taking advantage of storage within the server and, you know, keeping this a real server based approach, but distinguishing ourselves from the HCI approach. Cause there's a real ROI there. And when we talked to folks who are looking at new and different ways, we talk a lot about the cloud and I think we've done a bit of that already, but then at the end of the day, folks are trying to figure out well, okay, but then what do I buy to enable this? And what you buy is your standard server recipe. So think your favorite HPE, Lenovo, Supermicro, whatever, whatever your brand, and it's going to come enabled with this IOT end point within it, so it's really a smart server, if you will, that can then be controlled by our cloud. And so you're effectively buying, you know, from your favorite server vendor, a server option that is this endpoint and a subscription. You don't buy any of this from us, by the way, it's all coming from them. And that's the way we deliver this. >> You know, sorry to get into the plumbing, but this is something we've been on and a facet of it. Is that silicon custom designed or is it pretty much off the shelf, do you guys add any value to it? >> No, there are off the shelf options that can deliver tremendous horsepower on that form factor. And so we take advantage of that to, you know, do what we do in terms of, you know, creating these sort of smart servers with our end point. And so that's where we're at. >> Yeah. Awesome. So guys, what's your sweet spot, you know, why are customers, you know, what are you seeing customers adopting? Maybe some examples you guys can share? >> Yeah, absolutely. So I think Tobias mentioned that because of the architectural approach, there's a lot of flexibility there, you can run virtualized, containerized, bare metal applications. The question is where are folks choosing to get started? And those use cases with our existing customers revolved heavily around virtualization modernization. So they're going back in to their virtualized environment, whether their existing infrastructure is array-based or HCI-based. And they're looking to streamline that, save money, automate more, the usual things. The second area is the distributed edge. You know, the edge is going through tremendous transformation with IOT devices, 5g, and trying to get processing closer to where customers are doing work. And so that distributed edge is a real opportunity because again, it's a more cost-effective, more dense infrastructure. The cloud effectively can manage across all of these sites through a single API. And then the third area is cloud service provider transformation. We do a fair bit of business with, you know, cloud service providers, CTOs, who are looking at trying to build top line growth, trying to create new services and, and drive better bottom line. And so this is really, you know, as much as a revenue opportunity for them as cost saving opportunity. And then the last one is this notion of, you know, bringing the cloud on-prem, we've done a cloud repatriation deal. And I know you've seen a little of that, but maybe not a lot of it. And, you know, I can tell you in our first deals, we've already seen it, so it's out there. Those are the places where people are getting started with us today. >> It's just interesting, you're right. I don't see a ton of it, but if I'm going to repatriate, I don't want to go backwards. I don't want to repatriate to legacy. So it actually does kind of make sense that I repatriate to essentially a component of on-prem cloud that's managed in the cloud, that makes sense to me to buy. But today you're managing from the cloud, you're managing on-prem infrastructure. Maybe you could show us a little leg, share a little roadmap, I mean, where are you guys headed from a product standpoint? >> Right, so I'm not going to go too far on the limb there, but obviously, right. So one of the key benefits of a cloud managed platform is this notion of a single API, right. We talked about the distributed edge where, you know, think of retailer that has, you know, thousands of stores, each store having local infrastructure. And, you know, if you think about the challenges that come with, you know, just administrating those systems, rolling out firmware updates, rolling out updates in general, monitoring those systems, et cetera. So having a single console, a cloud console to administrate all of that infrastructure, obviously, you know, the benefits are easy now. If you think about, if you're thinking about that and spin it further, right? So from the use cases and the types of users that we've see, and Craig talked about them at the very beginning, you can think about this as this is a hybrid world, right. Customers will have data that they'll have in the public cloud. They will have data and applications in their data centers and at the edge, obviously it is our objective to deliver the same experience that they gained from the public cloud on-prem, and eventually, you know, those two things can come closer together. Apart from that, we're constantly improving the data services. And as you mentioned, ARM is, is on a path that is becoming stronger and faster. So obviously we're going to leverage on that and build out our data storage services and become faster. But really the key thing that I'd like to, to mention all the time, and this is related to roadmap, but rather feature delivery, right? So the majority of what we do is in the cloud, our business logic in the cloud, the capabilities, the things that make infrastructure work are delivered in the cloud. And, you know, it's provided as a service. So compared with your Gmail, you know, your cloud services, one day, you don't have a feature, the next day you have a feature, so we're continuously rolling out new capabilities through our cloud. >> And that's about feature acceleration as opposed to technical debt, which is what you get with legacy features, feature creep. >> Absolutely. The other thing I would say too, is a big focus for us now is to help our customers more easily consume this new concept. And we've already got, you know, SDKs for things like Python and PowerShell and some of those things, but we've got, I think, nearly ready, an Ansible SDK. We're trying to help folks better kind of use case by use case, spin this stuff up within their organization, their infrastructure. Because again, part of our objective, we know that IT professionals have, you know, a lot of inertia when they're, you know, moving stuff around in their own data center. And we're aiming to make this, you know, a much simpler, more agile experience to deploy and grow over time. >> We've got to go, but Craig, quick company stats. Am I correct, you've raised just under 20 million. Where are you on funding? What's your head count today? >> I am going to plead the fifth on all of that. >> Oh, okay. Keep it stealth. Staying a little stealthy, I love it. Really excited for you. I love what you're doing. It's really starting to come into focus. And so congratulations. You know, you got a ways to go, but Tobias and Craig, appreciate you coming on The Cube today. And thank you for watching this Cube Conversation. This is Dave Vellante. We'll see you next time. (upbeat outro music)

>>Hi, Felix. Great to be here. >>Likewise. Um, so when I started reading about data mesh, I think about a year ago, I found myself the more I read about it, the more I find myself agreeing with other principles behind data mesh, it actually took me back to almost the starting of Colibra 13 years ago, based on the research we were doing on semantic technologies, even personally my own master thesis, which was about domain driven ontologies. And we'll talk about domain-driven as it's a key principle behind data mesh, but before we get into that, let's not assume that everybody knows what data measures about. Although we've seen a lot of traction and momentum, which is fantastic to see, but maybe if you could start by talking about some of the key principles and, and a brief overview of what data mesh, uh, Isabella of >>Course, well, they're happy to, uh, so Dana mesh is an approach is a new approach. It's a decentralized, decentralized approach to managing and accessing data and particularly analytical data at scale. So we can break that down a little bit. What is analytical data? Well, analytical data is the data that fuels our reporting as a business intelligence. Most importantly, the machine learning training, right? So it's the data, that's, it's an aggregate view of historical events that happens across organizations, many domains within organizations, or even beyond one organization, right? Um, and today we manage, uh, this analytical data through very centralized solutions. So whether it's a data lake or data warehouse or combinations of the two, and, uh, to be honest, we have kind of outsource the accountability for it, to the data team, right? It doesn't happen within the domains. Uh, what we have found ourselves with is, uh, central button next. >>So as we see the growth in the scale of organizations, in terms of the origins of the data and in terms of the great expectations for the data, all of these wonderful use cases that are, that requires access to that, unless we're data, uh, we find ourselves kind of constraints and limited in agility to respond, you know, because we have a centralized bottleneck from team to technology, to architecture. So there's a mesh kind of is that looks at the past what we've done, accidental complexity that we've kind of created and tries to reimagine a different way of, uh, managing and accessing data that can truly scale as this origins of the data grows. As they become available within one organization, we didn't want a cloud or another, and it links down really the approach based on four principles. Uh, so I so far, I haven't tried to be prescriptive as exactly how you implement it. >>I leave that to Elizabeth, to the imaginations of the users. Um, of course I have my opinions, but, but without being prescriptive, I think there are full shifts that needs to happen. One is, uh, we need to start breaking down the, kind of this complex problem of accessing to data around boundaries that can allow this to scale out a solution. So boundaries that are, that naturally fits into that model or domains, right. Our business domain. So, so there's a first principle is the domain ownership of the data. So analytical data will be shared and served and accountable, uh, by the domains where they come from. And then the second dimension of that is, okay. So once we break down this, the ownership of the database on domains, how can we prevent this data siloing? So the second principle is really treating data as a product. >>So considering the success of that data based on the access and usability and the lifelong experience of data analysts, data scientists. So we talk about data as a product and that the third principle is to really make it possible feasible. We need to really rethink our data platforms, our infrastructure capabilities, and create a new set ourselves of capabilities that allows domain in fact, to own their data in fact, to manage the life cycle of their analytical data. So then self-serve daytime frustration and platform is the fourth principle. And the last principle is really around governance because we have to think about governance. In fact, when I first wrote it down, this was like a little kind of concern in, in embedded in what some of my texts and I thought about, okay, now to make this real, we need to think about securing and quality of the data accessibility of the data at scale, in a fashion that embraces this autonomous domain ownership. So we have to think about how can we make this real with competition of governance? How can we make those domains be part of the governance, federated governance, federally, the competition of governance is the fourth principle. So at insurance it's a organizational shift, it's an architectural change. And of course technology needs to change to get us to decentralize access and management of Emily's school data. >>Yeah, I think that makes a ton of sense. If you want to scale, typically you have to think much more distributed versus centralized at we've seen it in other practices as well, that domain-driven thinking as well. I think, especially around engineering, right? We've seen a lot of the same principles and best practices in order to scale engineering teams and not make the same mistakes again, but maybe we can start there with kind of the core principles around that domain driven thinking. Can you elaborate a little bit on that? Why that is so important than the kind of data organizations, data functions as well? >>Absolutely. I mean, if you look at your organizations, organizations are complex systems, right? There are eight made of parts, which are basically domains functions of the business, your automation and your customer management, yourselves marketing. And then the behavior of the organization is the result of an intuitive, you know, network of dependencies and interactions with these domains. So if we just overlay data on this complex system, it does make sense to really, to scale, to bring the ownership and, um, really access to data right at the domain where it originates, right. But to the people who know that data best and most capable of providing that data. So to optimize response, to change, to optimize creating new features, new services, new machine learning models, we've got to kind of think about your call optimization, but not that the cost of global good. Right. Uh, so the domain ownership really talks about giving autonomy to the domains and accountability to provide their data and model the data, um, in a responsible way, be accountable for its quality. >>So no collect some of the empower them and localize some of those responsibilities, but at the same time, you know, thinking about the global goods, so what are they, how that domain needs to be accountable against the other domains on the mission? That's the governance piece covers that. And that leads to some interesting kind of architectural shifts, because when you think about not submission of the data, then you think about, okay, if I have a machine learning model that needs, you know, three pieces of the data from the different domains, I ended up actually distributing the computer also back to those domains. So it actually starts shifting kind of architectural as well. We start with ownership. Yeah, >>No, I think that makes a ton of sense, but I can imagine people thinking, well, if you're organizing, according to these domains, aren't gonna be going to grades different silos, even more silos. And I think that's where it second principle that's, um, think of data as a product and it comes in, I think that's incredibly powerful in my mind. It's powerful because it helps us think about usability. It helps us think about the consumer of that data and really packaging it in the right way. And as one sentence that I've heard you use that I think is incredibly powerful, it's less collecting, more connecting. Um, and can you elaborate on that a little bit? >>Absolutely. I mean the power and the value of the data is not enhanced, which we have got and stored on this, right. It's really about connecting that data to other data sets to aluminate new insights. The higher order information is connecting that data to the users, right. Then they want to use it. So that's why I think, uh, if we shift that thinking from just collecting more in one place, like whatever, and ability to connect datasets, then, then arrive at a different solution. So, uh, I think data as a product, as you said, exactly, was a kind of a response to the challenges that domain-driven siloing could create. And the idea is that the data that now these domains own needs to be shared with some accountability and incentive structure as a product. So if you bring product thinking to data, what does that mean? >>That means delighting the experience that there are users who are they, they're the data analysts, data scientists. So, you know, how can we delight their experience of their journey starts with a hypothesis. I have a question. Do I have right data to answer this question with a particular model? Let me discover it, let me find it if it's useful. Do I trust it? So really fascinated in that journey? I think we have two choices in that we have the choice of source of that data. The people who are really shouldn't be accountable for it, shrug off the responsibility and say, you know, I dumped this data on some event streaming and somebody downstream, the governance or data team will take care of a terror again. So it usable piece of information. And that's what we have done for, you know, half century almost. And, or let's say let's bring intention of providing quality data back to the source and make the folks both empower them and make them accountable for providing that data right at the source as a product. And I think by being intentional about that, um, w we're going to remove a lot of accidental complexity that we have created with, you know, labyrinth pipelines of moving data from one place to another, and try to build quality back into it. Um, and that requires, you know, architectural shifts, organizational shifts, incentive models, and the whole package, >>The hope is absolutely. And we'll talk about that. Federated computational governance is going to be a really an important aspect, but the other part of kind of data as a product next to usability is whole trust. Right? If you, if you want to use it, why is also trusts so important if you think about data as a product? >>Well, uh, I mean, maybe we turn this question back to you. Would you buy the shiniest product if you don't trust it, if you, if you don't trust where it comes from, can I use it? Is it, does it have integrity? I wouldn't. I think, I think it's almost irresponsible to use the data that you can trust, right. And the, really the meaning of the trust is that, do I know enough about this data to, to, for it, to be useful for the purpose that I'm using it for? So, um, I think trust is absolutely fundamental to, as a fundamental characteristics of a data as a product. And again, it comes back to breaching the gap between what the data user knows needs to know to really trust them, use that data, to find it, whether it's suitable and what they know today. So we can bridge that gap with, uh, you know, adding documentation, adding SLRs, adding lineage, like all of these additional information, but not only that, but also having people that are accountable for providing that integrity and those silos and guaranteeing. So it's really those product owners. So I think, um, it's just, for me, it's a non trust is a non-negotiable characteristic of the data as a product, like any other consumer product. >>Exactly. Like you said, if you think about consumer product, consumer marketplace is almost Uber of Amazon, of Airbnb. You have the simple rating as a very simple way of showing trust and those two and those different stakeholders and that almost. And we also say, okay, how do we actually get there? And I think data measure also talks a little bit about the roles responsibilities. And I think the importance overall of a, of a data product owner probably is aligned with that, that importance and trust. Yeah, >>Absolutely. I think we can't just wish for these good things happens without putting the accountability and the right roles in place. And the data product owner is just the starting point for us to stop playing hot potato. When it comes to, you know, who owns the data will be accountable for not so much. Who's the actual owner of that data because the owner of the data is you and me where the data comes really from, but it's the data product owner who's going to be responsible for the life cycle of this. They know when the data gets changed with consumers, meaning you feel as a new information, make sure that that gets carried out and maybe one day retire that data. So that long term ownership with intimate understanding of the needs of the user for that data, as well as the data itself and the domain itself and managing the life cycle of that, uh, I think that's a, that's a necessary role. >>Um, and then we have to think about why would anybody want to be a data product owner, right? What are the incentives we have to set up in the infrastructure, you know, in the organization. Um, and it really comes down to, I think, adopting prior art that exists in the product ownership landscape and bring it really to the data and assume the data users as the, as the customers, right. To make them happy. So our incentives on KPIs for these people before they get product on it needs to be aligned with the happiness of their data users. >>Yep. I love that. The alignment again, to the consumer using things like we know from product management, product owner of these roles and reusing that for data, I think that makes it makes a ton of sense. And it's a good leeway to talk a little about governance, right? We mentioned already federated governance, computational governance at we seeing that challenge often with our customers centralizing versus decentralizing. How do we find the right balance? Can you talk a little bit about that in the context of data mesh? How do we, how do we do this? >>Yeah, absolutely. I think the, I was hoping to pack three concepts in the title of the governance, but I thought that would be quite mouthful. So, uh, as you mentioned, uh, the kind of that federated aspects, the competition aspects, and I think embedded governance, I would, if I could add another kind of phrasing there and really it's about, um, as we talked about to how to make it happen. So I think the Federation matters because the people who are really in a position listed this, their product owners in a position to provide data in a trustworthy, with integrity and secure way, they have to have a stake in doing that, right. They have to be accountable, not just for their little domain or a big domain, but also they have to have an accountability for the mesh. So some of the concerns that are applied to all of the data front, I've seen fluid, how we secure them are consistently really secure them. >>How do we model the data or the schema language or the SLO metrics, or that allows this, uh, data to be interoperable so we can join multiple data products. So we have to have, I think, a set of policies that are really minimum set of policies that we have to apply globally to all the data products and then in a federated fashion, incentivize the data product owners. So have a stake in that and make that happen because there's always going to be a challenge in prioritizing. Would I add another few attributes? So my data sets to make my customers happy, or would I adopt that this standardized modeling language, right? They have to make that kind of continuous, um, kind of prioritization. Um, and they have to be incentivized to do both. Right. Uh, and then the other piece of it is okay, if we want to apply these consistent policies, across many data products and the mesh, how would it be physically possible? >>And the only way I can see, and I have seen it done in service mesh would be possible is by embedding those policies as competition, as code into every single data product. And how do we do that again, platform has a big part of it. So be able to have this embedded policy engines and whatever those things are into the data products, uh, and to, to be able to competition. So by default, when you become a data product, as part of the scaffolding of that data product, you get all of these, um, kind of computational capabilities to configure your, your policies according to the global policies. >>No, that makes sense. That makes, that makes it on a sense. That makes sense. >>I'm just curious. Really. So you've been at this for a while. You've built this system for the 13 years came from kind of academic background. So, uh, to be honest, we run into your products, lots of our clients, and there's always like a chat conversation within ThoughtWorks that, uh, do you guys know about this product then? So and so, oh, I should have curious, well, how do you think data governance tehcnology then skip and you need to shift with data mesh, right. And, and if, if I would ask, how would your roadmap changes with database? >>Yeah, I think it's a really good question. Um, what I don't want to do is to make, make the mistake that Venice often make and think of data mesh as a product. I think it's a much more holistic mindset change, right? That that's organization. Yes. It needs to be a kind of a platform enablement component there. And we've actually, I think authentically what, how we think about governance, that's very aligned with some of the principles and data measures that federate their thinking or customers know about going to communities domains or operating model. We really support that flexibility. I think from a roadmap perspective, I think making that even easier, uh, as always kind of a, a focus focus area for us, um, specifically around data measures are a few things that come to mind. Uh, one, I think is connectivity, right? If you, if you give different teams more ownership and accountability, we're not going to live in a world where all of the data is going to be stored on one location, right? >>You want to give people themes the opportunity and the accountability to make their own technology decisions so that they are fit for purpose. So I think whatever platform being able to really provide out of the box connectivity to a very wide, um, area or a range of technologies, I think is absolutely critical, um, on the, on the product as a or data as a product, thinking that usability, I think that's top of mind, uh, that's part of our roadmap. You're going to hear us, uh, stock about that tomorrow as well. Um, that data consumer, how do we make it as easy as possible for people to discover data that they can trust that they can access? Um, and in that thinking is a big part of our roadmap. So again, making that as easy as possible, uh, is a, is a big part of it. >>And, and also on the, I think the computation aspect that you mentioned, I think we believe in as well, if, if it's just documentation is going to be really hard to keep that alive, right? And so you have to make an active, we have to get close to the actual data. So if you think about a policy enforcement, for example, some things we're talking about, it's not just definition is the enforcement data quality. That's why we are so excited about our or data quality, um, acquisition as well. Um, so these are a couple of the things that we're thinking of, again, your, your, um, your, your, uh, message around from collecting to connecting. We talk about unity. I think that that works really, really well with our mission and vision as well. So mark, thank you so much. I wish we had more time to continue the conversation, uh, but it's been great to have a conversation here. Thank you so much for being here today and, uh, let's continue to work on that on data. Hello. I'm excited >>To see it. Just come to like.

>>Mhm Yes. Hello and welcome back to the cubes coverage of red hat summit 2021 virtual. I'm john for your host of the cube we're here with Hillary Hunter, the VP and CTO and IBM fellow of IBM cloud at IBM. Hillary, Great to see you welcome back, You're no stranger to us in the cube your dentist few times. Thanks for coming on. >>Thanks so much for having me back. Great to talk more today >>I believe I B M is the premier sponsor for red hat summit this year. No, I mean I think they're somewhat interested in what's happening. >>Yeah, you know, somebody is such a great event for us because it brings together clients that, you know, we work together with red head on and gives us a chance to really talk about that overall journey to cloud and everything that we offer around cloud and cloud adoption um, and around redheads capabilities as well. So we look forward to the summit every year for sure. >>You know, the new IBM red hat relationship obviously pretty tight and successful seeing the early formations and customer attraction and just kind of the momentum, I'll never forget that Red hat something was in SAN Francisco. I sat down with Arvin at that time, uh, Red hat was not part of IBM and it was interesting. He was so tied into cloud native. It was almost as if he was dry running the acquisition, which he announced just moments later after that. But you can see the balance. The Ceo at IBM really totally sees the cloud. He sees that experience. He sees the customer impact. This has been an interesting year, especially with Covid and with the combination of red hat and IBM, this cloud priority for IT leaders is more important than ever before. What's your, what's your take on this? Because clearly you guys are all in on cloud, but not what people think, what's your, what's your view on this? >>Yeah. You know, from, from the perspective of those that are kind of data oriented IBM Institute for Business Value, did lots of studies over the last year, you know, saying that over 60% of leaders feel, you know, increased urgency to get to the cloud, um they're intending to accelerate their program to the cloud, but I think, you know, just even as consumers where each very conscious that our digital behaviors have changed a lot in the last year and we see that in our enterprise client base where um everything from, you know, a bank, we work that that that had to stand up their countries equivalent of the payroll protection program in a matter of weeks, which is just kind of unheard of to do something that robust that quickly or um, you know, retail obviously dealing with major changes, manufacturing, dealing with major changes and all consumers wanting to consume things on an app basis and such, not going into brick and mortar stores and such. And so everything has changed and months, I would say have sort of timeframes of months have been the norm instead of years for um, taking applications forward and modernizing them. And so this journey to cloud has compressed, It's accelerated. And as one client I spoke with said, uh, in the midst of last year, you know, it is existential that I get to cloud with urgency and I think That's been that has been the theme of 2020 and now also 2021. And so it is, it is the core technology for moving faster and dealing with all the change that we're all experiencing. >>That's just so right on point. But I got I want to ask you because this is the key trend enterprises are now realizing that cloud native architecture is based on open source specifically is a key architectural first principle now. >>Yeah. >>What's your, what, what would you say to the folks out there who were listening to this and watching this video, Who were out in the enterprise going, hey, that's a good call. I'm glad I did it. So I don't have any cognitive dissidence or I better get there faster. >>Yeah. You know, open source is such an important part of this conversation because I always say that open source moves at the rate and pays a global innovation, which is kind of a cute phrase that I really don't mean it in anyways, cute. It really is the case that the purpose of open sources for people globally to be contributing. And there's been innovation on everything from climate change to you know, musical applications to um things that are the fundamentals of major enterprise mission critical workloads that have happened is everyone is adopting cloud and open source faster. And so I think that, you know this choice to be on open source is a choice really, you know, to move at the pace of global innovation. It's a choice too um leverage capabilities that are portable and it's a choice to have flexibility in deployment because where everyone's I. T is deployed has also changed. And the balance of sort of where people need the cloud to kind of come to life and be has also changed as everyone's going through this period of significant change. >>That's awesome. IBM like Red has been a long supporter and has a history of supporting open source projects from Lenox to kubernetes. You guys, I think put a billion dollars in Lenox way back when it first started. Really power that movement. That's going back into the history books there. So how are you guys all collaborating today to advance the open source solutions for clients? >>Yeah, we remain very heavily invested in open source communities and invested in work jointly with Red Hat. Um you know, we enabled the technology known as um uh Rackham the short name for the Red Hat advanced cluster management software, um you know, in this last year, um and so, you know, provided that capability um to to become the basis of that that product. So we continue to, you know, move major projects into open source and we continue to encourage external innovators as well to create new capabilities. And open source are called for code initiatives for developers as an example, um have had specific programs around um uh social justice and racial issues. Um we have a new call for code out encouraging open source projects around climate change and sustainable agriculture and all those kind of topics and so everything from you know, topics with developers to core product portfolio for us. Um We have a very uh very firm commitment in an ongoing sustained contribution on an open source basis. >>I think that's important. Just to call out just to kind of take a little sidebar here. Um you guys really have a strong mission driven culture at IBM want to give you props for that. Just take a minute to say, Congratulations call for code incredible initiative. You guys do a great job. So congratulations on that. Appreciate. >>Thank you. Thank you. >>Um as a sponsor of Red Hat Summit this year, I am sponsoring the zone Read at um you have you have two sessions that you're hosting, Could you talk about what's going on? >>Yeah, the the two sessions, so one that I'm hosting is around um getting what we call 2.5 x value out of your cloud journey. Um and really looking at kind of how we're working with clients from the start of the journey of considering cloud through to actually deploying and managing environments and operating model on the cloud um and where we can extract greater value and then another session um that I'm doing with Roger Primo, our senior vice President for strategy at IBM We're talking about lessons and clouded option from the Fortune 500, so we're talking there about coca cola european bottling partners, about lumen technologies um and um also about wonderman Thompson, um and what they're doing with us with clouds, so kind of two sessions, kind of one talking a sort of a chalkboard style um A little bit of an informal conversation about what is value meaning cloud or what are we trying to get out of it together? Um And then a session with roger really kind of focused on enterprise use cases and real stories of cloud adoption. >>Alright so bottom line what's going to be in the sessions, why should I attend? What's the yeah >>so you know honest honestly I think that there's kind of this um there's this great hunger I would say in the industry right now to ascertain value um and in all I. T. Decision making, that's the key question right? Um not just go to the cloud because everyone's going to the cloud or not just adopt you know open source technologies because it's you know something that someone said to do, but what value are we going to get out of it? And then how do we have an intentional conversation about cloud architecture? How do we think about managing across environments in a consistent way? Um how do we think about extracting value in that journey of application, modernization, um and how do we structure and plan that in a way? Um that results in value to the business at the end of the day, because this notion of digital transformation is really what's underlying it. You want a different business outcome at the end of the day and the decisions that you take in your cloud journey picking. Um and open hybrid, multi cloud architecture leveraging technologies like IBM cloud satellite to have a consistent control plan across your environments, um leveraging particular programs that we have around security and compliance to accelerate the journey for regulated industries etcetera. Taking intentional decisions that are relevant to your industry that enable future flexibility and then enable a broad ecosystem of content, for example, through red hat marketplace, all the capabilities and content that deploy onto open shift, et cetera. Those are core foundational decisions that then unlock that value in the cloud journey and really result in a successful cloud experience and not just I kind of tried it and I did or didn't get out of it what I was expecting. So that's really what, you know, we talk about in these in these two sessions, um and walk through um in the second session than, you know, some client use cases of, of different levels and stages in that cloud journey, some really core enterprise capabilities and then Greenfield whitespace completely new capabilities and cloud can address that full spectrum. >>That's exciting not to get all nerdy for a second here, But you know, you bring up cloud architecture, hybrid cloud architecture and correct me if I'm wrong if you're going to address it because I think this is what I'm reporting and hearing in the industry against the killer problem everyone's trying to solve is you mentioned, um, data, you mentioned control playing for data, you mentioned security. These are like horizontally scalable operating model concepts. So if you think about an operating system, this is this is the architecture that becomes the cloud model hybrid model because it's not just public cloud cloud native or being born in the cloud. Like a startup. The integration of operating at scale is a distributed computing model. So you have an operating system concept with some systems engineering. Yeah, it sounds like a computer to me, right. It sounds like a mainframe. Sounds like something like that where you're thinking about not just software but operating model is, am I getting that right? Because this is like fundamental. >>Yeah, it's so fundamental. And I think it's a great analogy, right? I think it's um you know, everyone has kind of, their different description of what cloud is, what constitutes cloud and all that kind of thing, but I think it's great to think of it as a system, it's a system for computing and what we're trying to do with cloud, what we're trying to do with kubernetes is to orchestrate a bunch of, you know, computing in a consistent way, as, you know, other functions within a single server do. Um What we're trying to do with open shift is, you know, to enable um clients to consume things in a consistent way across many different environments. Again, that's the same sort of function um conceptually as, you know, an operating system or something like that is supposed to provide is to have a platform fundamentally, I think the word platform is important, right? Have a platform that's consistent across many environments and enables people to be productive in all those environments where they need to be doing their computing. >>We were talking before we came on camera about cloud history and we were kind of riffing back and forth around, oh yeah, five years ago or six years ago was all the conversations go to the cloud now, it's like serious conscience around the maturity of cloud and how to operate that scale in the cloud, which is complex, it's complex system and you have complexity around system complexity and novelty complexity, so you have kind of all these new things happening. So I want to ask you because you're an IBM fellow and you're on the cloud side at IBM with all this red hat goodness you've got going on, Can you give us a preview of the maturity model that you see the IBM season, that red hats doing so that these architectures can be consistent across the platforms, because you've got def sec ops, you've got all these new things, you've got security and data at scale, it's not that obviously it's not easy, but it has to be easier. What's what's the preview of the maturity model? >>Yeah, you know, it really is about kind of a one plus one equals three conversation because red hats approach to provide a consistent platform across different environments in terms of Lennox and Kubernetes and the open shift platform um enables that first conversation about consistency and maturity um in many cases comes from consistency, being able to have standards and consistency and deployment across different environments leads to efficiency. Um But then IBM odds on that, you know, a set of conversations also around data governance, um consistency of data, cataloguing data management across environments, machine learning and ai right bringing in A. I. For I. T. Operations, helping you be more efficient to diagnose problems in the IT environment, other things like that. And then, you know, in addition, you know, automation ultimately right when we're talking about F. R. I. T. Ops, but also automation which begins down at the open shift level, you know with use of answerable and other things like that and extends them up into automation and monitoring of the environment and the workloads and other things like that. And so it really is a set of unlocking value through increasing amounts of insight, consistency across environments, layering that up into the data layer. Um And then overall being able to do that, you know efficiently um and and in a consistent way across the different environments, you know, where cloud needs to be deployed in order to be most effective, >>You know, David Hunt and I always talk about IBM and all the years we've been covering with the Cube, I mean we've pretty much been to every IBM events since the Cube was founded and we're on our 11th year now watching the progression, you guys have so much expertise in so many different verticals, just a history and the expertise and the knowledge and the people. They're so smart. Um I have to ask you how you evolved your portfolio with the cloud now um as it's gone through, as we are in the 2021 having these mature conversations around, you know, full integration, large scale enterprise deployments, Critical Mission Mission Critical Applications, critical infrastructure, data, cybersecurity, global scale. How are you evolve your portfolio to better support your clients in this new environment? >>Yeah, there's a lot in there and you hit a lot of the keywords already. Thank you. But but I think that you know um we have oriented our portfolio is such that all of our systems support Red hat um and open shift, um our cloud, we have redhead open shift as a managed service and kubernetes is at the core of what we're doing as a cloud provider and achieving our own operational efficiencies um from the perspective of our software portfolio, our core products are delivered in the form of what we refer to as cloud packs on open shift and therefore deploy across all these different environments where open shift is supported, um products available through Red hat marketplace, you know, which facilitates the billing and purchasing an acquisition and installation of anything within the red hat ecosystem. And I think, you know, for us this is also then become also a journey about operational efficiency. We're working with many of our clients is we're kind of chatting about before about their cloud operating model, about their transformation um and ultimately in many cases about consumption of cloud as a service. Um and so um as we, you know, extend our own cloud capabilities, you know, out into other environment through distributed cloud program, what we refer to as as IBM cloud satellite, you know, that enables consistent and secure deployment of cloud um into any environment um where someone needs, you know, cloud to be operated. Um And that operating model conversation with our clients, you know, has to do with their own open shift environments that has to do with their software from IBM, it has to do their cloud services. And we're really ultimately looking to partner with clients to find efficiency in each stage of that journey and application modernization in deployment and then in getting consistency across all their environments, leveraging everything from uh the red hat, you know, ACM capabilities for cluster management up through a i for beauty shops and automation and use of a common console across services. And so it's an exciting time because we've been able to align our portfolio, get consistency and delivery of the red half capabilities across our full portfolio and then enable clients to progress to really efficient consumption of cloud. >>That's awesome. Great stuff there. I got to ask you the question that's on probably your customers minds. They say, okay, Hillary, you got me sold me on this. I get what's going on, I just gotta go faster. How do I advance my hybrid cloud model faster? What are you gonna do for me? What do you have within the red hat world and IBM world? How are you gonna make me go faster? That's in high quality way? >>Yeah. You know, we often like to start with an assessment of the application landscape because you move faster by moving strategically, right? So assessing applications and the opportunity to move most quickly into a cloud model, um, what to containerized first, what to invest in lift and shift perspective, etcetera. So we we help people look at um what is strategic to move and where the return on investment will be the greatest. We help them also with migrations, Right? So we can help jump in with additional skills and establish a cloud center of competency and other things like that. That can help them move faster as well as move faster with us. And I think ultimately choosing the right portfolio for what is defined as cloud is so important, having uh, an open based architecture and cloud deployment choice is so important so that you don't get stuck in where you made some of your initial decisions. And so I think those are kind of the three core components to how we're helping our clients move as quickly as possible and at the rate and pace that the current climate frankly demands of everyone. >>You know, I was joking with a friend the other night about databases and how generations you have an argument about what is it database, what's it used for. And then when you kind of get to that argument, all agree. Then a new database comes along and then it's for different functions. Just the growth in the internet and computing. Same with cloud, you kind of see a parallel thing where it's like debate, what is cloud? Why does he even exist? People have different definitions. That was, you know, I mean a decade or so ago. And then now we're at almost another point where it's again another read definition of, okay, what's next for cloud? It's almost like an inflection point here again. So with that I got to ask you as a fellow and IBM VP and Cto, what is the IBM cloud because if I'm going to have a discussion with IBM at the center of it, what does it mean to me? That's what people would like to know. How do you respond to that? >>Yeah. You know, I think two things I think number one to the, to the question of accelerating people's journeys to the cloud, we are very focused within the IBM cloud business um on our industry specific programs on our work with our traditional enterprise client base and regulated industries, things like what we're doing in cloud for financial services, where we're taking cloud, um and not just doing some sort of marketing but doing technology, which contextualize is cloud to tackle the difficult problems of those industries. So financial services, telco uh et cetera. And so I think that's really about next generation cloud, right? Not cloud, just for oh, I'm consuming some sauce, and so it's going to be in the cloud. Um but SAS and I SV capabilities and an organization's own capabilities delivered in a way appropriate to their industry in in a way that enables them to consume cloud faster. And I think along those lines then kind of second thing of, you know, whereas cloud headed the conversation in the industry around confidential computing, I think is increasingly important. Um It's an area that we've invested now for several generations of technology capability, confidential computing means being able to operate even in a cloud environment where there are others around um but still have complete privacy and authority over what you're doing. And that extra degree of protection is so important right now. It's such a critical conversation um with all of our clients. Obviously those in things like, you know, digital assets, custody or healthcare records or other things like that are very concerned and focused about data privacy and protection. And these technologies are obvious to them in many cases that yes, they should take that extra step and leverage confidential computing and additional data protection. But really confidential computing we're seeing growing as a topic zero trust other models like that because everyone wants to know that not only are they moving faster because they're moving to cloud, but they're doing so in a way that is without any compromise in their total security, um and their data protection on behalf of their clients. So it's exciting times. >>So it's so exciting just to think about the possibilities because trust more than ever now, we're on a global society, whether it's cyber security or personal interactions to data signing off on code, what's the mutability of it? I mean, it's a complete interplay of all the fun things of uh of the technology kind of coming together. >>Absolutely, yeah. There is so much coming together and confidential computing and realizing it has been a decade long journey for us. Right? We brought our first products actually into cloud in 2019, but its hardware, it's software, it services. It's a lot of different things coming together. Um but we've been able to bring them together, bring them together at enterprise scale able to run entire databases and large workloads and you know um pharmaceutical record system for Germany and customer records for daimler and um you know what we're doing with banks globally etcetera and so you know it's it's wonderful to see all of that work from our research division and our developers and our cloud teams kind of come together and come to fruition and and really be real and be product sizable. So it's it's very exciting times and it's it's a conversation that I think I encourage everyone to learn a little bit more about confidential computing. >>Hillary hunter. Thank you for coming on the cube. Vice President CTO and IBM fellow which is a big distinction at IBM. Congratulations and thanks for coming on the Cuban sharing your insight. Always a pleasure to have you on an expert always. Great conversation. Thanks for coming on. >>Thanks so much for having me. It was a pleasure. >>Okay, so cubes coverage of red Hat Summit 21 of course, IBM think is right around the corner as well. So that's gonna be another great event as well. I'm john Feehery, a host of the cube bringing all the action. Thanks for watching. Yeah.