>> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The pandemic precipitated what is shaping up to be a permanent shift in cybersecurity spending patterns. As a direct result of hybrid work, CSOs have vested heavily in endpoint security, identity access management, cloud security, and further hardening the network beyond the headquarters. We've reported on this extensively in this Breaking Analysis series. Moreover, the need to build security into applications from the start rather than bolting protection on as an afterthought has led to vastly high heightened awareness around DevSecOps. Finally, attacking security as a data problem with automation and AI is fueling new innovations in cyber products and services and startups. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we present our quarterly findings in the security industry, and share the latest ETR survey data on the spending momentum and market movers. Let's start with the most recent news in cybersecurity. Nary a week goes by without more concerning news. The latest focus in the headlines is, of course, Russia's relentless cyber attacks on critical infrastructure in the Ukraine, including banking, government websites, weaponizing information. The hacker group, BlackByte, put a double whammy on the San Francisco 49ers, meaning they exfiltrated data and they encrypted the organization's files as part of its ransomware attack. Then there's the best Super Bowl ad last Sunday, the Coinbase floating QR code. Did you catch that? As people rushed to scan the code and participate in the Coinbase Bitcoin giveaway, it highlights yet another exposure, meaning we're always told not to click on links that we don't trust or we've never seen, but so many people activated this random QR code on their smartphones that it crashed Coinbase's website. What does that tell you? In other news, Securonix raised a billion dollars. They did this raise on top of Lacework's massive $1.3 billion raise last November. Both of these companies are attacking security with data automation and APIs that can engage machine intelligence. Securonix, specifically in the announcement, mentioned the uptake from MSSPs, managed security service providers, something we've talked about in this series. And that's a trend that we see as increasingly gaining traction as customers are just drawing in and drowning in security incidents. Peter McKay's company, Snyk, acquired Fugue, a company focused on making sure security policies are consistent throughout the software development life cycle. It's a really an example of a developer-defined security approach where policy can be checked at the dev, deployment, and production phases to ensure the same policies are in place at all stages, including monitoring at runtime. Fugue, according to Crunchbase, had raised $85 million to date. In some other company news, Cisco was rumored to be acquiring Splunk for not much more than Splunk is worth today. And the talks reportedly broke down. This would be a major move in security by Cisco and underscores the pressure to consolidate. Cisco would get an extremely strong customer base and through efficiencies could improve Splunk's profitability, but it seems like the premium Cisco was willing to pay was not enough to entice board to act. Splunk board, that is. Datadog blew away its earnings, and the stock was up 12%. It's pulled back now, thanks to Putin, but it's one of those companies that is disrupting Splunk. Datadog is less than half the size of Splunk, revenue-wise, but its valuation is more than 2 1/2 times greater. Finally, Elastic, another Splunk disruptor, settled its trademark dispute with AWS, and now AWS will now stop using the name Elasticsearch. All right, let's take a high level look at how cyber companies have performed in the stock market over time. Here's a graph of the Cyber ETF, and you can see the March 1st crosshairs of 2020 signifying the start of the lockdown. The trajectory of cybersecurity stocks is shown by the orange and blue lines, and it surely has steepened post March of 2020. And, of course, it's been down with the market lately, but the run up, as you can see, was substantial and eclipsed the trajectory of the previous cycles over the last couple of years, owing much of the momentum to the spending dynamics that we talked about at our open. Let's now drill into some of the names that we've been following over the last few years and take a look at the firm level. This chart shows some data that we've been tracking since before the pandemic. The top rows show the S&P 500 and the NASDAQ prices, and the bottom rows show specific stocks. The first column is the index price or the market cap of the company just before the pandemic, then the same data one year later. Then the next column shows the peak value during the pandemic, and then the current value. Then it shows in the next column where it is today, in percentage terms, i.e., how far has it pulled back from the peak, then the delta from pre-pandemic, in other words, how much did the issue earn or lose during the pandemic for investors? We then compare the pre-pandemic revenue multiple using a trailing 12-month revenue metric. Sorry, that's what we used. It's easy to get. (laughs) And that's the revenue multiple compared to the August in 2020, when multiples were really high, and where they are today, and then a recent quarterly growth rate guide based on the last earnings report. That's the last column. Okay, so I'm throwing a lot of data at you here, but what does it tell us? First, the S&P and the NAS are well up from pre-pandemic levels, yet they're off 9% and 15%, respectively, from their peaks today. That was earlier on Friday morning. Now let's look at the names more closely. Splunk has been struggling. It definitely had a tailwind from the pandemic as all boats seem to rise, but its execution has been lacking. It's now 30% off from its pre-pandemic levels. (groans) And it's multiple is compressing, and perhaps Cisco thought it could pick up the company for a discount. Now let's talk about Palo Alto Networks. We had reported on some of the challenges the company faced moving into a cloud-friendly model. that was before the pandemic. And we talked about the divergence between Palo Alto's stock price and the valuations relative to Fortinet, and we said at the time, we fully expected Palo Alto to rebound, and that's exactly what happened. It rode the tailwinds of the last two years. It's up over 100% from its pre-COVID levels, and its revenue multiple is expanding, owing to the nice growth rates. Now Fortinet had been doing well coming into the pandemic. In fact, we said it was executing on a cloud strategy better than Palo Alto Networks, hence that divergence in valuations at the time. So it didn't get as much of a boost from the pandemic. Didn't get that momentum at first, but the company's been executing very well. And as you can see, with 155% increase in valuation since just before the pandemic, it's going more than okay for Fortinet. Now, Okta is a name that we've really followed closely, the identity access management specialist that rocketed. But since it's Auth0 acquisition, it's pulled back. Investors are concerned about its guidance and its profitability. And several analyst have downgraded their price targets on Okta. We still really like the company. The Auth0 acquisition gives Okta a developer vector, and we think the company is going hard after market presence and is willing to sacrifice short-term profitability. We actually like that posture. It's very Frank Slupin-like. This company spends a lot of money on R&D and go-to-market. The question is, does Okta have inherent profitability? The company, as they say, spends a ton in some really key areas but it looks to us like it's going to establish a footprint. It's guiding revenue CAGR in the mid-30s over the mid to long-term and near term should beat that benchmark handily. But you can see the red highlights on Okta. And even though Okta is up 59% from its pre-pandemic levels, it's far behind its peers shown in the chart, especially CrowdStrike and Zscaler, the latter being somewhat less impacted by the pullback in stocks recently, of course, due to the fears of inflation and interest rates, and, of course, Russian invasion escalation. But these high flyers, they were bound to pull back. The question is can they maintain their category leadership? And for the most part, we think they can. All right, let's get into some of the ETR data. Here's our favorite XY view with net score, or spending momentum on the Y-axis, and market share or pervasiveness in the data center on the horizontal axis. That red 40% line, that indicates a highly elevated spending level. And the chart inserts to the right, that shows how the data is plotted with net score and shared N in each of the columns by each company. Okay, so this is an eye chart, but there really are three main takeaways. One is that it's a crowded market. And this shows only the companies ETR captures in its survey. We filtered on those that had more than 50 mentions. So there's others in the ETR survey that we're not showing here, and there are many more out there which don't get reported in the spending data in the ETR survey. Secondly, there are a lot of companies above the 40% mark, and plenty with respectable net scores just below. Third, check out SentinelOne, Elastic, Tanium, Datadog, Netskope, and Darktrace. Each has under 100 N's but we're watching these companies closely. They're popping up in the survey, and they're catching our attention, especially SentinelOne, post-IPO. So we wanted to pare this back a bit and filter the data some more. So let's look at companies with more than 100 mentions in the same chart. It gets a little cleaner this picture, but it's still crowded. Auth0 leads everyone in net score. Okta is also up there, so that's very positive sign since they had just acquired Auth0. CrowdStrike SalePoint, Cyberark, CloudFlare, and Zscaler are all right up there as well. And then there's the bigger security companies. Palo Alto Network, very impressive because it's well above the 40% mark, and it has a big presence in the survey, and, of course, in the market. And Microsoft as well. They're such a big whale. They skew the data for everybody else to kind of mess up these charts. And the position of Cisco and Splunk make for an interesting combination. They get both decent net scores, not above the 40% line but they got a good presence in the survey as well. Thinking about the acquisition, Al Shugart was the CEO of of Seagate, and founder. Brilliant Silicon valley icon and engineer. Great business person. I was asking him one time, hey, you thinking about buying this company or that company? And of course, he's not going to tell me who he's thinking about buying or acquiring. He said, let me just tell you this. If you want to know what I'm thinking, ask yourself if it were free, would you take it? And he said the answer's not always obviously yes, because acquisitions can be messy and disruptive. In the case of Cisco and Splunk, I think the answer would be a definitive yes It would expand Cisco's portfolio and make it the leader in security, with an opportunity to bring greater operating leverage to Splunk. Cisco's just got to pay more if it wants that asset. It's got to pay more than the supposed $20 billion offer that it made. It's going to have to get kind of probably north of 23 billion. I pinged my ETR colleague, Erik Bradley, on this, and he generally agreed. He's very close to the security space. He said, Splunk isn't growing the customer base but the customers are sticky. I totally agree. Cisco could roll Splunk into its security suite. Splunk is the leader in that space, security information and event management, and Cisco really is missing that piece of the pie. All right, let's filter the data even more and look at some of the companies that have moved in the survey over the past year and a half. We'll go back here to July 2020. Same two-dimensional chart. And we're isolating here Auth0, Okta, SalePoint CrowdStrike, Zscaler, Cyberark, Fortinet, and Cisco. No Microsoft. That cleans up the chart. Okay, why these firms? Because they've made some major moves to the right, and some even up since last July. And that's what this next chart shows. Here's the data from the January 2022 survey. The arrow start points show the position that we just showed you earlier in July 2020, and all these players have made major moves to the right. How come? Well, it's likely a combination of strong execution, and the fact that security is on the radar of every CEO, CIO, of course, CSOs, business heads, boards of directors. Everyone is thinking about security. The market momentum is there, especially for the leaders. And it's quite tremendous. All right, let's now look at what's become a bit of a tradition with Breaking Analysis, and look at the firms that have earned four stars. Four-star firms are leaders in the ETR survey that demonstrate both a large presence, that's that X-axis that we showed you, and elevated spending momentum. Now in this chart, we filter the N's. Has to be greater than 100. And we isolate on those companies. So more than 100 responses in the survey. On the left-hand side of the chart, we sort by net score or spending velocity. On the right-hand side, we sort by shared N's or presence in the dataset. We show the top 20 for each of the categories. And the red line shows the top 10 cutoffs. Companies that show up in the top 10 for both spending momentum and presence in the data set earn four stars. If they show up in one, and make the top 10 in one, and make the top 20 in the other, they get two stars. And we've added a one-star category as honorable mention for those companies that make the top 20 in both categories. Microsoft, Palo Alto Networks, CrowdStrike, and Okta make the four-star grade. Okta makes it even without Auth0, which has the number one net score in this data set with 115 shared N to boot. So you can add that to Okta. The weighted average would pull Okta's net score to just above Cyberark's into fourth place. And its shared N would bump Okta up to third place on the right-hand side of the chart Cisco, Splunk, Proofpoint, KnowBe4, Zscaler, and Cyberark get two stars. And then you can see the honorable mentions with one star. Now thinking about a Cisco, Splunk combination. You'd get an entity with a net score in the mid-20s. Yeah, not too bad, definitely respectable. But they'd be number one on the right-hand side of this chart, with the largest market presence in the survey by far. Okay, let's wrap. The trends around hybrid work, cloud migration and the attacker escalation that continue to drive cybersecurity momentum and they're going to do so indefinitely. And we've got some bullet points here that you're seeing private companies, (laughs) they're picking up gobs of money, which really speaks to the fact that there's no silver bullet in this market. It's complex, chaotic, and cash-rich. This idea of MSSPs on the rise is going to continue, we think. About half the mid-size and large organization in the US don't have a SecOps, a security operation center, and outsourcing to one that can be tapped on a consumption basis, cloud-like, as a service just makes sense to us. We see the momentum that companies that we've highlighted over the many quarters of Breaking Analysis are forming. They're forming a strong base in the market. They're going for market share and footprint, and they're focusing on growth, at bringing in new talent. They have good balance sheets and strong management teams and we think they'll be leading companies in the future, Zscaler, CrowdStrike, Okta, SentinelOne, Cyberark, SalePoint, over time, joining the ranks of billion dollar cyber firms, when I say billion dollar, billion dollar revenue like Palo Alto Networks, Fortinet, and Splunk, if it doesn't get acquired. These independent firms that really focus on security. Which underscores the pressure and consolidation and M&A in the whole space. It's almost assured with the fragmentation of companies and so many new entrants fighting for escape velocity that this market is going to continue with robust M&A and consolidation. Okay, that's it for today. Thanks to my colleague, Stephanie Chan, who helped research this week's topics, and Alex Myerson on the production team. He also manages the Breaking Analysis podcast. Kristen Martin and Cheryl Knight, who get the word out. Thank you to all. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcast. Check out ETR's website at etr.ai. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me at david.vellante@siliconangle.com. @dvellante is my DM. Comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week. Be safe, be well, and we'll see you next time. (upbeat music)

>>Machine intelligence is everywhere. AI is being embedded into our everyday lives, through applications, process automation, social media, ad tech, and it's permeating virtually every industry and touching everyone. Now, a major issue with machine learning and deep learning is trust in the outcome. That is the black box problem. What is that? Well, the black box issue arises when we can see the input and the output of the data, but we don't know what happens in the middle. Take a simple example of a picture of a cat or a hotdog for you. Silicon valley fans, the machine analyzes the picture and determines it's a cat, but we really don't know exactly how the machine determined that. Why is it a problem? Well, if it's a cat on social media, maybe it isn't so onerous, but what if it's a medical diagnosis facilitated by a machine? And what if that diagnosis is wrong? >>Or what if the machine is using deep learning to qualify an individual for a home loan and that person applying for the loan gets rejected. Was that decision based on bias? If the technology to produce that result is opaque. Well, you get the point. There are serious implications of not understanding how decisions are made with AI. So we're going to dig into the issue and the topic of how to make AI explainable and operationalize AI. And with me are two guests today, Shea speaky, who's the co-founder and COO of cognitive scale and long time friend of the cube and newly minted CEO of cognitive scale. Bob pitchy, Yano, gents. Welcome to the cube, Bob. Good to see you again. Welcome back on. >>Thanks for having us >>Say, let me start with you. Why did you start the company? I think you started the company in 2013. Give us a little history and the why behind cognitive scale. >>Sure. David. So, um, look, I spent some time, um, you know, through multiple startups, but I ended up at IBM, which is where I met Bob. And one of the things that we did was the commercialization of IBM Watson initially. And that led to, uh, uh, thinking about how do you operationalize this because of the, a lot of people thinking about data science and machine learning in isolation, building models, you know, trying to come up with better ways to deliver some kind of a prediction, but if you truly want to operationalize it, you need to think about scale that enterprises need. So, you know, we were in the early days, enamored by ways, I'm still in landed by ways. The application that takes me from point a to point B and our view is look as you go from point a to point B, but if you happen to be, um, let's say a patient or a financial services customer, imagine if you could have a raise like application giving you all the insights that you needed telling you at the right moment, you know, what was needed, the right explanation so that it could guide you through the journey. >>So that was really the sort of the thesis behind cognitive scale is how do you apply AI, uh, to solve problems like that in regulated industries like health care management services, but do it in a way that it's done at scale where you can get, bring the output of the data scientists, application developers, and then those insights that can be powered into those end applications like CRM systems, mobile applications, web applications, applications that consumers like us, whether it be in a healthcare setting or a financial services setting can get the benefit of those insights, but have the appropriate sort of evidence and transparency behind it. So that was the, that was the thesis for. >>Got it. Thank you for that. Now, Bob, I got to ask you, I knew you couldn't stay in the sidelines, my friend. So, uh, so what was it that you saw in the marketplace that Lord you back in to, to take on the CEO role? >>Yeah, so David is an exciting space and, uh, you're right. I couldn't stay on the sideline stuff. So look, I always felt that, uh, enterprise AI had a promise to keep. Um, and I don't think that many enterprises would say, you know, with their experience that yeah, we're getting the value that we wanted out of it. We're getting the scale that we wanted out of it. Um, and we're really satisfied with what it's delivered to us so far. So I felt there was a gap in keeping that promise and I saw cognitive scale as an important company and being able to fill that gap. And the reason that that gap exists is that, you know, enterprise AI, unlike AI, that relates to one particular conversational service or one particular small narrow domain application is really a team sport. You know, it involves all sorts of roles, um, and all sorts of aspects of a working enterprise. >>That's already scaled with systems of engagement, um, and, and systems of record. And we show up in the, with the ability to actually help put all of that together. It's a brown field, so to speak, not a Greenfield, um, and where Shea and Matt and Minosh and the team really focused was on what are the important last mile problems, uh, that an enterprise needs to address that aren't necessarily addressed with any one tool that might serve some members of that team? Because there are a lot of great tools out there in the space of AI or machine learning or deep learning, but they don't necessarily help come together to, to deliver the outcomes that an enterprise wants. So what are those important aspects? And then also, where do we apply AI inside of our platform and our capabilities to kind of take that operationalization to the next level, uh, with, you know, very specific insights and to take that journey and make it highly personalized while also making it more transparent and explainable. >>So what's the ICP, the ideal customer profile, is it, is it highly regulated industries? Is it, is it developers? Uh, maybe you could parse that a little bit. >>Yeah. So we do focus in healthcare and in financial services. And part of the reason for that is the problem is very difficult for them. You know, you're, you're working in a space where, you know, you have rules and regulations about when and how you need to engage with that client. So the bar for trust is very, very high and everything that we do is around trusted AI, which means, you know, thinking about using the data platforms and the model platforms in a way to create marketplaces, where being able to utilize that data is something that's provisioned in permission before we go out and do that assembly so that the target customer really is somebody who's driving digital transformation in those regulated industries. It might be a chief digital officer. It might be a chief client officer, customer officer, somebody who's really trying to understand. I have a very fragmented view of my member or of my patient or my client. And I want to be able to utilize AI to help that client get better outcomes or to make sure that they're not lost in the system by understanding and more holistically understanding them in a more personalized way, but while always maintaining, you know, that that chain of trust >>Got it. So can we get into the product like a little bit more about what the product is and maybe share, you can give us a census to kind of where you started and the evolution of the portfolio >>Look where we started there is, um, the application of AI, right? So look, the product and the platform was all being developed, but our biggest sort of view from the start had been, how do you get into the trenches and apply this to solve problems? And as well, pointed out, one of the areas we picked was healthcare because it is a tough industry. There's a lot of data, but there's a lot of regulation. And it's truly where you need the notion of being able to explain your decision at a really granular level, because those decisions have some serious consequences. So, you know, he started building a platform out and, um, a core product is called cortex. It's the, it's a software platform on top of this. These applications are built, but to our engagements over the last six, seven years, working with customers in healthcare, in financial services, some of the largest banks, the largest healthcare organizations, we have developed a software product to essentially help you scale enterprise AI, but it starts with how do you build these systems? >>Building the systems requires us to provide tooling that can help developers take models, data that exists within the enterprise, bring it together, rapidly, assemble this, orchestrate these different components, stand up. These systems, deploy these systems again in a very complex environment that includes, you know, on-prem systems as well as on the cloud, and then be able to done on APIs that can plug into an application. So we had to essentially think of this entire problem end to end, and that's poor cortex does, but extremely important part of cortex that didn't start off. Initially. We certainly had all the, you know, the, the makings of a trusted AI would be founded the industry wasn't quite ready over time. We've developed capabilities around explainability being able to detect bias. So not only are you building these end to end systems, assembling them and deploying them, you have as a first-class citizen built into this product, the notion of being able to understand bias, being able to detect whether there's the appropriate level of explainability to make a decision and all of that's embedded within the cortex platform. So that's what the platform does. And it's now in its sixth generation as we >>Speak. Yeah. So Dave, if you think about the platform, it really has three primary components. One is this, uh, uh, application development or assembly platform that fits between existing AI tools and models and data and systems of engagement. And that allows for those AI developers to rapidly visualize and orchestrate those aspects. And in that regard were tremendous partners with people like IBM, Microsoft H2O people that provide aspects that are helping develop the data platform, the data fabric, things like the, uh, data science tools to be able to then feed this platform. And then on the front end, really helping transform those systems of engagement into things that are more personalized with better recommendations in a more targeted space with explainable decisions. So that's one element that's called cortex fabric. There's another component called cortex certify. And that capability is largely around the model intelligence model introspection. >>It works, uh, across things that are of cost model driven, but other things that are based on deterministic algorithms, as well as rule-based algorithms to provide that explainability of decisions that are made upstream before they get to the black box model, because organizations are discovering that many times the data has, you know, aspects of dimensions to it and, and, and biases to it before it gets to the model. So they want to understand that entire chain of, of, uh, of decisioning before it gets there. And then there's the notion of some pew, preacher rated applications and blueprints to rapidly deliver outcomes in some key repeating areas like customer experience or like lead generation. Um, those elements where almost every customer we engage with, who is thinking about digital transformation wants to start by providing better client experience. They want to reduce costs. They want to have operational savings while driving up things like NPS and improving the outcomes for the people they're serving. So we have those sets of applications that we built over time that imagine that being that first use application, that starter set, that also trains the customer on how to you utilize this operational platform. And then they're off to the races building out those next use cases. So what we see as one typical insertion place play that returns value, and then they're scaling rapidly. Now I want to cover some secret sauce inside of the platform. >>Yeah. So before you do, I think, I just want to clarify, so the cortex fabric, cause that's really where I wanted to go next, but the cortex fabric, it seems like that's the way in which you're helping people operationalize inject use familiar tooling. It sounds like, am I correct? That the cortex certify is where you're kind of peeling the onion of that complicated, whether it's deep learning or neural networks, which is that's where the black box exists. Maybe you could tell us, you know, is that where the secret sauce lives, if not, where is it? And if >>It actually is in all places right though. So there's some really important, uh, introductions of capabilities, because like I mentioned, many times these, uh, regulated industries have been developed and highly fragmented pillars. Just think about the insurance companies between property casualty and personal lines. Um, many times they have grown through acquisition. So they have these systems of record that are, that are really delivering the operational aspects of the company's products, but the customers are sometimes lost in the scenes. And so they've built master data management capabilities and data warehouse capabilities to try to serve that. But they find that when they then go to apply AI across some of those curated data environments, it's still not sufficient. So we developed an element of being able to rapidly assemble what we call a profile of one. It's a very, very intimate profile around declared data sources, uh, that relate to a key business entity. >>In most cases, it's a person, it's a member, it's a patient, it's a client, but it can be a product for some of our clients. It's real estate. Uh, it's a listing. Um, you know, it can be someone who's enjoying a theme park. It can be someone who's a shopper in a grocery store. Um, it can be a region. So it's any key business entity. And one of the places where we applied our AI knowledge is by being able to extract key information out of these declared systems and then start to make longitudinal observations about those systems and to learn about them. And then line those up with prediction engines that both we supply as well as third parties and the customers themselves supply them. So in this theme of operationalization, they're constantly coming up with new innovations or a new model that they might want to interject into that engagement application. Our platform with this profile of one allows them to align that model directly into that profile, get the benefits of what we've already done, but then also continue to enhance, differentiate and provide even greater, uh, greater value to that client. IBM is providing aspects of those models that we can plug in. And many of our clients are that's really >>Well. That's interesting. So that profile of one is kind of the instantiation of that secret sauce, but you mentioned like master data management data warehouse, and, you know, as well as I do Bob we've we've we've decades of failures trying to get a 360 degree view for example of the customer. Uh, it's just, just not real time. It's not as current as we would want it to be. The quality is not necessarily there. It's a very asynchronous process. Things have changed the processing power. You and I have talked about this a lot. We have much more data now. So it's that, that, that profile one. So, but also you mentioned curated apps, customer experience, and lead gen. You mentioned those two, uh, and you've also talked about digital transformation. So it sounds like you're supporting, and maybe this is not necessarily the case, but I'm curious as to what's going on here, maybe supporting more revenue generation in the early phases than say privacy or compliance, or is it actually, do you have use cases for both? >>It's all, it's all of it. Um, and, and shake and, you know, really talk passionately about some of the things we've helped clients do, like for instance, uh, J money. Why don't you talk about the, the hospital, um, uh, uh, you know, discharge processes. >>Absolutely. So, so, you know, just to make this a bit more real, they, you know, when you talk about a profile on one, it's about understanding of patient, as I said earlier, but it's trying to bring this notion of not just the things that you know about the patient you call that declared information. You can find the system in, you can find this information in traditional EMR systems, right? But imagine bringing in, uh, observed information, things that you observed an interaction with the patient, uh, and then bring in inferences that you can then start drawing on top of that. So to bring this to a live example, imagine at the point of care, knowing when all the conditions are right for the patient to be discharged after surgery. And oftentimes as you know, those, if all the different evidence of the different elements that don't come together, you can make some really serious mistakes in terms of patient discharge, bad things can happen. >>Patient could be readmitted or even worse. That could be a serious outcome. Now, how do you bring that information at the point of care for the person making a decision, but not just looking at the information, you know, but also understanding not just the clinical information, but the social, the socioeconomic information, and then making sure that that decision has the appropriate evidence behind it. So then when you do make that decision, you have the appropriate sort of, uh, you know, the guidance behind it for audit reasons, but also for ensuring that you don't have a bad outcome. So that's the example Bob's talking about, where we have a flight this in real settings, in, in healthcare, but also in financial services and other industries where you can make these decisions based on the machine, telling you with a lot of detail behind it, whether this is the right decision to be made, we call this explainability and the evidence that's needed. >>You know, that's interesting. I, I, I'm imagining a use case in my mind where after a patient leaves, so often there's just a complete disconnect with the patient, unless that patient has problems and goes back, but that patient might have some problems, but they forget it's too much of a pain in the neck to go back, but, but the system can now track this and we could get much more accurate information and that could help in future diagnoses and, and also decision-making for a patient in terms of, of outcomes and probability of success. Um, question, what do you actually sell? So it's a middleware product. It's a, how do I license it? >>It's a, it's a, uh, it's a software platform. So we sell software, um, and it is deployed in the customer's cloud environment of choice. Uh, of course we support complete hybrid cloud capabilities. Um, we support native cloud deployments on top of Microsoft and Amazon and Google. And we support IBM's hybrid cloud initiative with red hat OpenShift as well, which also puts us in a position to both support those public cloud environments, as well as the customer's private cloud environments. So constructed with Kubernetes in that environment, um, which helps the customer also re you know, realize the value of that operational appar operationalization, because they can modify those applications and then redeploy them directly into their cloud environment and start to see those as struck to see those spaces. Now, I want to cover a couple of the other components of the secret sauce, if I could date to make sure that you've got a couple other elements where some real breakthroughs are occurring, uh, in these spaces. >>Um, so Dave, you and I, you know, we're passionate about the semiconductor industry, uh, and you know, we know what is, you know, happening with regard to innovation and broadening the people who are now siliconized their intellectual property and a lot of that's happening because those companies who have been able to figure out how to manufacture or how to design those semiconductors are operationalizing those platforms with our customers. So you have people like apple who are able to really break out of the scene and do things by utilizing utilities and macros their own knowledge about how things need to work. And it's just, it's very similar to what we're talking about doing here for enterprise AI, they're operationalizing that construction, but none of those companies would actually start creating the actual devices until they go through simulation and design. Correct. Well, when you think about most enterprises and how they develop software, they just immediately start to develop the code and they're going through AB testing, but they're all writing code. >>They're developing those assets. They're creating many, many models. You know, some organizations say 90% of the models they create. They never use some say 50, and they think that's good. But when you think about that in terms of, you know, the capital that's being deployed, both on the resources, as well as the infrastructure, that's potentially a lot of waste as well. So one of the breakthroughs is, uh, the creation of what we call synthetic data and simulations inside of our, of our operational platform. So cortex fabric allows someone to actually say, look, this is my data pattern. And because it's sensitive data, it might be, you know, PII. Um, we can help them by saying, okay, what is the pattern of that data? And then we can create synthetic data off of that pattern for someone to experiment with how a model might function or how that might work in the application context. >>And then to run that through a set of simulations, if they want to bring a new model into an application and say, what will the outcomes of this model be before I deployed into production, we allow them to drive simulations across millions or billions of interactions to understand what is that model going to be effective. Was it going to make a difference for that individual or for this application or for the cost savings goal and outcomes that I'm trying to drive? So just think about what that means in terms of that digital transformation officers, having the great idea, being in the C-suite and saying, I want to do this with my business. Oftentimes they have to turn around to the CIO or the chief data officer and say, when can you get me that data? And we all know the answer to that question. They go like this, like the, yeah, I've got a couple other things on the plate and I'll get to that as soon as I can. >>Now we're able to liberate that. Now we're able to say, look, you know, what's the concept that you're trying to develop. Let's create the synthetic data off of that environment. We have a Corpus of data that we have collected through various client directions that many times gets that bootstrapped and then drive that through simulation. So we're able to drive from imagination of what could be the outcome to really getting high confidence that this initiative is going to have a meaningful value for the enterprise. And then that stimulates the right kind of following and the right kind of endorsement, uh, throughout really driving that change to the enterprise and that aspect of the simulations, the ability to plan out what that looks like and develop those synthetic aspects is another important element that the secret sauce inside of cortex fabric, >>Back to the semiconductor innovation, I can do that very cheaply. I think, I think I I'm thinking AWS cloud, I could experiment using graviton or maybe do a little bit of training with some, you know, new processors and, and then containerize it, bring it back to my on-premise state and apply it. Uh, and so, uh, just a as you say, a much more agile environment, um, yeah, >>Speed efficiency, um, and the ability to validate the hypothesis that, that started the process. >>Guys, think about the Tam, the total available market. Can we have that discussion? How big is that? >>I mean, if you think about the spend across, uh, the healthcare space and financial services, we're talking about hundreds of billions, uh, in that, in terms of what the enterprise AI opportunity, as in just those spaces. And remember financial services is a broad spectrum. So one of the things that we're actually starting to roll out today in fact, is a SAS service that we developed. That's based on top of our offerings called trust star trust star.ai, and trust star is a set of personalized insights that get delivered directly to the loan officer inside of, uh, an institution who's trying to, uh, really match, uh, lending to someone who wants to buy a property. Um, and when you think about many of those organizations, they have very, very high demand. They've got a lot of information, they've got a lot of regulation they need to adhere to. >>But many times they're very analytically challenged in terms of the tools they have to be able to serve those needs. So what's happening with new listings, what's happening with my competitors, what's happening. As people move from high tax states, where they want to potentially leave into new, more attractive toxin and opportunity-based environments where they're not known to those lending institutions that maybe, you know, they're, they're trying to be married up with. So we've developed a set of insights that are, is, this is a subscription service trust r.ai, um, which goes directly to the loan officer. And then we use our platform behind the scenes to use things like the home disclosure act, data, MLS data, other data that is typically Isagenix to those sources and providing very customized insights to help that buyer journey. And of course, along the way, we can identify things like are some of the decisions more difficult to explain, are there potential biases that might be involved in that environment as people are applying for mortgages, and we can really drive growth through inclusion for those lending institutions, because they might just not understand that potential client well enough, that we can identify the kind of things that they can do to know them better. >>And the benefit is really to hold there, right? And shale, I'll let you jump in, but to me, it's twofold. There. One is, you know, you want to have accurate decisions. You want to have low risk decisions. And if you want to be able to explain that to an individual that may get rejected, here's why, um, and, and it wasn't because of bias. It was because of XYZ and you need to work on these things, but go ahead shape. >>Now, this is going to add that point here, Dave, which is a double-faced point on the dam. One of the things that, and the reason why, you know, industries like healthcare, financial services spending billions, it's not because they look at AI in isolation, they actually looking at the existing processes. So, you know, established disciplines like CRM or supply chain procurement, whether it is contact center and so on. And the examples that we gave you earlier, it's about infusing AI into those existing applications, existing systems. And that's, what's creating the left because what's been missing so far is the silos of data and you traditional traditional transaction systems, but this notion of intelligence that can be infused into the systems and that's, what's creating this massive market opportunity for us. >>Yeah. And I think, um, I think a lot of people just misunderstood in the, or in the early, early days of the AI, you know, new AI when we came out of the AI winter, if you will, people thought, okay, the incumbents are in big trouble now because they are not, they're not AI developers, but really what you guys are showing is it's not about building your own AI. It's about applying AI and having the tools to do so. The incumbents actually have a huge advantage because they've got the systems in place. They can, if they, if they're smart, they can infuse AI and then extract value out of that for their customers. >>And that's why, you know, companies like, uh, like IBM are an investor in a great partner in this space. Anthem is an investor, uh, you know, of the company, but also, you know, someone who can utilize the capabilities, Microsoft, uh, Intel, um, you know, we've been, we've been, uh, you know, really blessed with a great backing Norwest venture partners, um, obviously is, uh, an investor in us as well. So, you know, we've seen the ability to really help those organizations think about, um, you know, where that future lies. But one of the things that is also, you know, one of the gaps in the promises when a C-suite executive like a digital transformation officer, chief digital chief customer officer, they're having their idea, they want to be accountable to that idea. They're having that idea in the boardroom. And they're saying, look, I think I can improve my customer satisfaction and, uh, by 20 points and decrease the cost of my call center by 20 or 30 or 50 points. >>Um, but they need to be able to measure that. So one of the other things that, uh, we've done a cognitive scale is help them understand the progress that they're making across those business goals. Um, now when you think about this people like Andrew Nang, or just really talking about this aspect of goal oriented AI, don't start with the problem, start with what your business goal is, start with, what outcome you're trying to drive, and then think about how AI helps you along that goal. We're delivering this now in our product, our version six product. So while some people are saying, yeah, this is really the right way to potentially do it. We have those capabilities in the product. And what we do is we identify this notion of the campaign, an AI campaign. So when the case that I just gave you where the chief digital officer is saying, I want to drive customer satisfaction up. >>I want to have more explainable decisions, and I want to drive cost down. Maybe I want to drive, call avoidance. Um, you know, and I want to be able to reduce a handling time, um, to drive those costs down, that is a campaign. And then underneath that campaign, there's all sorts of missions that support that campaign. Some of them are very long running. Some of them are very ephemeral. Some of them are cyclical, and we have this notion of the campaign and then admission planner that supports the goals of that campaign, showing that a leader, how they're doing against that goal by measuring the outcomes of every interaction against that mission and all the missions against the campaign. So, you know, we think accountability is an important part of that process as well. And we've never engaged an executive that says, I want to do this, but I don't want to be accountable to the result, but they're having a hard time identifying I'm spending this money. >>How do I ensure that I'm getting the return? And so we've put our, you know, our secret sauce into that space as well. And that includes, you know, the information around the trustworthiness of those, uh, capabilities. Um, and I should mention as well, you know, when we think about that aspect of the responsible AI capabilities, it's really important. The partnerships that we're driving across that space, no one company is going to have the perfect model intelligence tool to be able to address an enterprise's needs. It's much like cybersecurity, right? People thought initially, well, I'll do it myself. I'll just turn up my firewall. You know, I'll make my applications, you know, uh, you know, roll access much more granular. I'll turn down the permissions on the database and I'll be safe from cybersecurity. And then they realized, no, that's not how it was going to work. >>And by the way, the threats already inside and there's, long-term persistent code running, and you have to be able to scan it, have intelligence around it. And there are different capabilities that are specialized for different components of that problem. The same is going to be turnaround responsible and trustworthy AI. So we're partnered with people like IBM, people like Microsoft and others to really understand how we take the best of what it is that they're doing partner with the best, uh, that they're doing and make those outcomes better for clients. And then there's also leaders like the responsible AI Institute, which is a non-profit independent organization who were thinking about a new rating systems for, um, the space of responsible and trusted AI, thinking about things like certifications for professionals that really drive that notion of education, which is an important component of addressing the problem. And we're providing the integration of our tools directly with those assessments and those certifications. So if someone gets started with our platform, they're already using an ecosystem that includes independent thinkers from across the entire industry, um, including public sector, as well as the private sector, to be able to be on the cutting edge of what it's going to take to really step up to the challenge in that space. >>Yeah. You guys got a lot going on. I mean, you're eight years in now and you've got now an executive to really drive the next scale. You mentioned Bob, some of your investors, uh, Anthem, IBM Norwest, uh, I it's Crunchbase, right? It says you've raised 40 million. Is that the right number? Where are you in fundraising? What can you tell? >>Um, they're a little behind where we are, but, uh, you know, we're staged B and, uh, you know, we're looking forward to now really driving that growth. We're past that startup phase, and now we're into the growth phase. Um, and we're seeing, you know, the focus that we've applied in the industries, um, really starting to pay off, you know, initially it would be a couple of months as a customer was starting to understand what to be able to do with our capabilities to address their challenges. Now we're seeing that happen in weeks. So now is the right time to be able to drive that scalability. So we'll be, you know, looking in the market of how we assemble that, uh, you know, necessary capability to grow. Um, Shay and I have worked, uh, in the past year of, uh, with the board support of building out our go to market around that space. >>Um, and in the first hundred days, it's all about alignment because when you're going to go through that growth phase growth phase, you really have to make sure that things were pointed in the right direction and pointed together in the right direction, simplifying what it is that we're doing for the market. So people could really understand, you know, how unique we are in this space, um, and what they can expect out of an engagement with us. Um, and then, you know, really driving that aspect of designing to go to market. Um, and then scaling that. >>Yeah, I think I, it sounds like you've got, you got, if you're, if you're in down to days or weeks in terms of the ROI, it sounds like you've got product market fit nailed. Now it's about sort of the next phase is you really driving your go to market and the science behind how your dimension and your, your sales productivity, and you can now codify what you've learned in that first phase. I like the approach. A lot of, a lot of times you see companies, of course, this comes out of the west coast, east coast guy, but you see the double, double, triple, triple grow, grow, grow, grow, grow, and then, and then churn becomes that silent killer of the S the software company. I think you guys, it sounds you've, you've taken a much, much more adult-like approach, and now you're ready to really drive that scale. I think it's the new formula really for success for hitting escape velocity. Guys, we got to go, but thanks so much. Uh, uh, Bob, I'll give you the last word, w w w what you mentioned some of your a hundred day priorities. Maybe you can summarize that and what should we be looking for as Martin? >>I mean, I, I think, I think the, you know, the, our measures of success are our clients measure success and the same for our partners. So we're not doing this alone, we're doing it with system integrator partners, and we're doing it with a great technology partners in the market as well. So this is a part about keeping that promise for enterprise AI. And one of the things that I'll say just in the last couple of minutes is, you know, this is not just a company with a great vision and great engineers to develop out this great portfolio, but it's a company with great values, great commitments to its employees and the marketplace and the communities we serve. So I was attracted to the culture of this company, as well as I was, uh, to the, uh, innovation and what they mean to the, to the space of a, >>And I said, I said, I'll give you last word. Actually, I got a question for Shea you Austin based, is that correct? >>But we have a global presence, obviously I'm operating out of Austin, other parts of the U S but, uh, offices in, in, uh, in the UK, as well as in India, >>You're not moving to tax-free Texas. Like everybody else. >>I've got to, I've got an important home, uh, and life in Connecticut cell. I'll be traveling back and forth between Connecticut and Austin, but keeping my home there. >>Thanks for coming on and best of luck, we want to follow your progress and really appreciate your time today. Good luck. >>Thank you, Dave. All right. >>Thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

>>It's my pleasure to be one of the hosts of the cube on cloud and the startup showcase brought to you by AWS. This is Dave Vellante and for years, the cube has been following the trail of data. And with the relentless March of data growth, this idea of a single version of the truth has become more and more elusive. Moreover data has become the lifeblood of a digital business. And if there's one thing that we've learned throughout the pandemic, if you're not digital, you're in trouble. So we've seen firsthand the critical importance of reliable and trusted data. And with me to talk about his company and the trends in the market is many sued as the CTO and co-founder of Reltio Maneesh. Welcome to the program. >>Thank you, Dave. It's a pleasure to be here. >>Okay. Let's start with, let's go back to you and your co-founders when you started Reltio it was back in the early days of the big data movement cloud was kind of just starting to take off, but what problems did you see then and what are enterprises struggling with today, especially with, with data as a source of digital innovation. >>They, if you look at the changes that have taken place in the landscape over the course of the last 10 years, when we started Reltio in 2011, there were a few secular trends that were coming to life. One was a cloud compute type of capabilities being provided by vendors like AWS. It was starting to pick up steam where making, uh, compute capabilities available at scale to solve large data problems was becoming real impossible. The second thing that we saw was, uh, this big trend of, uh, you know, you can not have a wall to wall, one single application that solves your entire business problem. Those visions have come and gone. And, uh, we are seeing more of the best of breed application type of a landscape where even if you look within a specific function, let's say sales or marketing, you have more than a dozen applications that any company is using today. >>And that trend was starting to emerge where we knew very well, that the number of systems that we would have to work with would continue to increase. And, uh, that created a problem of where would you get the single source of truth or the single best version of a customer, a supplier, a product that you're trying to sell those types of critical pieces of information that are core to any business that's out there today. And, um, you know, that created the opportunity for us at Reltio to think about the problem at scale for every company out there, every business who needed this kind of a capability and for us to provide this capability in the cloud as a software, as a service, uh, uh, offering. So that's where, uh, you know, the foundation of Reltio started. And the core problem that we wanted to solve was to bridge the gap that was created by all these data silos and create a unified view of the core critical information that these companies run on. >>Yeah. I mean, the cloud is this giant, you know, hyper distributed system data by its very nature is distributed. It's interesting what you were sort of implying about, you know, the days of the monolithic app are gone by my business partner years ago, John furrier and the cube said data is going to become the new development kit. And we've certainly seen that with the, the pandemic, but tell us more about Reltio and how you help customers deal with that notion of data, silo, data silos, data fragmentation, how do you solve that problem? >>So, data fragmentation is what exists today. And, um, you know, with the Reltio, uh, software as a service offering that we provide, we allow customers to stitch together and unify the data coming from these different fragmented, siloed, uh, applications or data sources that they have within their enterprise at the same time. Um, there's a lot of dependence on the third party data. You know, when you think about, uh, different problems that you're trying to solve, you have, uh, for B2B type of information that in Bradstreet type of data providers in life sciences, you have IQ via type of data providers. Um, you know, as you look at other verticals, there is a specialized third-party data provider for any, and every kind of information that most of the enterprise businesses want to combine with their in-house data or first party data to get the best view of who they're dealing with, who are they working with, you know, who are the customers that they're serving and use that information also as a starting point for the digital transformation that they want to get to. >>Um, and that's where Reltio fits in as the only platform that can help stitch together, this kind of, uh, information and create a 360 degree view that spans all the data silos and provides that for real-time use for BI and analytics to benefit from, for data science to benefit from. And then this emerging notion of, uh, data in itself is a, um, you know, key starting point that is used by us, uh, in order to make any decisions, just like, uh, we go, you know, if I, they wanted to look at information about you, I would go to places like LinkedIn, look up the information. And then our, my next set of decisions with that information, if somebody wanted to look up information on Reltio, they would go to, let's say Crunchbase as an example, and look up, uh, who are the investors? How much money have we raised all those details that are available? It's not a CRM system by itself, but it is an information application that can aid and assist in the decision-making process as a starting point. And that user experience on top of the data becomes an important vehicle for us to provide, uh, as a part of the Reltio platform capabilities. >>Awesome. Thank you. And I want to get into the, to the tech, but before we do, maybe we just cut to the chase and maybe you can talk about some of the examples of, of Reltio and action. Some of the customers that you can talk about, maybe the industries that are, that are really adopting this. W what can you tell us there, Maneesh, >>Um, we work across a few different verticals, some of the key verticals that we work in our life sciences, um, and travel and hospitality and financial services, insurance, um, S uh, retail, as an example, those are some of the key verticals for us, but, uh, to give you some examples of, uh, the type of problems that customers are solving with Reltio as the data unification platform, um, let's take CarMax as an example, CarMax is a customer who's in the business of, uh, buying used cars, selling used cars, servicing those used cars. And then, um, you know, you as a customer, don't just transact with them. Once you, you know, you've had a car for three years, you go back and look at what can you trade in that car for, but in order for CarMax to provide a service to you that, uh, goes across all the different touch points, whether you are visiting them at their store location, uh, trying to test drive a car or viewing, uh, information about the various vehicles on their website, or just, uh, you know, punching in the registration number of your car, just to see what is the appraisal from them in terms of how much will they pay for your car? >>This requires a lot of data behind the scenes for them to provide a seamless journey across all touch points and the type of information that they use, uh Reltio for aggregating, unifying, and then making available across all these touch points is all of the information about the customers, all of the information about, uh, the, uh, household, uh, you know, the understanding that they're trying to achieve because, uh, life events can, uh, be buying signals, uh, for, uh, consumers like uni, as well as, uh, who was the, um, associate who helped you either in the selling of a car buying of a car, because business is all about building relationships for the longer term lifetime value that they want to capture. And in that process, um, making sure that they're providing continuity of relationship, they need to keep track of that data. And then the vehicle itself, the vehicle that you buy yourself, uh, there is a lot of information in order to price it, right, that needs to be gathered, uh, from multiple sources. So the continuum of data all the way from consumer to the vehicle is aggregated from multiple sources, unified inside Reltio, and then made available, uh, through API APIs or through other methods, and means to the various applications can be either built on top of that information, or can consume that information in order to better aid and assist the processes, business processes that those applications have to run end to end. Well, it sounds like >>That's come along. Sorry. >>I was just going to say it that's one example and, uh, you know, across other verticals that are other similar examples of how companies are leveraging, Reltio >>Just say, can come a long way from simple linear clickstream analysis of a website. I mean, you're talking about really rich information and, and, you know, happy to dig into some other examples, but, but I wonder how does it work? I mean, what's the magic behind it? What's the, the tech look like, I mean, obviously you leveraging AWS, maybe you could talk about how so, and maybe some of the services there and some of your unique IP. >>Yeah. Um, you know, so the unique opportunity for us when we started in 2011 was really to leverage the power of the cloud. We started building out this capability on top of AWS back in 2011. And, uh, you know, if you think about, uh, the problem itself, uh, the problem has been around as long as you have had more than one system to run your business, but the magnitude of the problem has expanded several fold. Um, you know, for example, I have been in this area was, uh, responsible for creating some of the previous generation capabilities and, uh, most of the friction in those previous generation MDM or master data management type of solutions, um, as the, you know, the technical term that is used to refer to this area, uh, was that those systems could not keep pace with the increasing number of sources or the depth and breadth of the information that, uh, customers want to capture, whether it is, uh, you know, about a patient or a product, or let's say a supplier that you're working well. >>Uh, there is always additional information that you can capture and, uh, you know, use to better inform the decisions for the next engagement and, uh, that kind of model where the number of sources we're always going to increase the depth and breadth of information was always going to increase. The previous generation systems were not geared to handle that. So we decided that not only would we use at scale compute capabilities in the cloud, um, with the products like AWS as the backbone, but also solve some of the core problems around how more sources of information can be unified at scale. And then the last mile, which is the ability to consume such rich information, just locking it in a data warehouse has been sort of the problem in the past. And you talked about the clickstream analysis, uh, analytics has a place, but most of the analytics is a rear view mirror picture of the, uh, you know, work that you have to do, versus everybody that we talked to, uh, as a potential customer, wanted to solve the problem of what can we do at the point of engagement, how can we influence decisions? >>So, you know, I'll give you an example. I think, uh, everybody's familiar with Quicken loans, um, as the mortgage lender and, uh, in the mortgage lending business, uh, Quicken loans is the customer who's using Reltio as the customer data, um, unification platform behind the scenes. But every interaction that takes place, their goal is that they have a very narrow time window, um, you know, anywhere from 10 minutes to about an hour, where if somebody expresses an interest in refinancing or getting a mortgage, they have to close that, uh, business within that, uh, Hart window, the conversion ratios are exponentially better in that hot window versus waiting for 48 hours to come back with the answer of what will you be able to refinance your mortgage, uh, at. And, uh, they've been able to use this notion of real time data, where as soon as you come in through the website, or if you come in through the rocket mortgage app, or you're talking to a broker by calling the one 800 number, they are able to triangulate that it's the same person coming from any of these different channels and respond to that person, whether an offer, uh, ASAP so that, uh, there is no opportunity for the competition to get in and present you with a better offer. >>So those are the types of things where the time to, uh, conversion or the time to action is being looked at. And everybody's trying to shrink that time down, uh, that ability to respond in real time with the capabilities was sort of the last mile missing out of this equation, which didn't exist with previous generation capabilities. And now customers are able to benefit from that. >>That is an awesome example. I know at firsthand, I'm a customer of Quicken and rocket, and when you experience that environment, it's totally different than anything you've ever seen before. So it's helpful to hear you explain, like what's behind that because it's, it's truly disruptive. And I, and I'll tell you, the other thing that, that sort of triggered a thought was that we use the word realtime a lot, and we try to develop years ago. We said, what does real-time really mean? And the, the answer we CA we landed on was before you lose the customer, and that's kind of what you just described. Uh, and that is what gives as an example, a quick and a real advantage again, having experienced it firsthand. It's, it's pretty, pretty tremendous. So that's a nice, that's a, that's a nice reference. Um, so, and the other thing that struck me is that what I wanted to ask you, how it's different from sort of legacy master data management solutions, and you sort of described that they've seized to me, they got to take their, their traditional on-prem stack, rip it out, stick it in the cloud is okay, we got our stack in the cloud. >>Now your technical approach is dramatically different. You had the advantage of having a clean sheet of paper, right? I mean, from a, from an CTO's perspective, what's your, >>Yeah. The clean sheet of paper is the luxury that we have, you know, having seen this movie before having, um, you know, looked at solving this problem with previous generation technologies, it was really the opportunity to start with a clean sheet of paper and define a cloud native architecture for solving the problem at scale. So just to give you an example, um, you know, across all of our customers, we are today managing, um, uh, about 6.5 billion consolidated profiles of people, organizations, product locations, um, you know, assets, uh, those kinds of details. And these are, these are the types of, uh, crown jewels of the business that every business runs on. You know, for example, if you wanted to, um, let's say you're a large company, like, uh, you know, Ford and you wanted to figure out how much business are you doing, where the, uh, you know, another large company, because the other large company could be a global organization, could be spread across multiple geographies, could have multiple subsidiaries associated with it. >>It's been a very difficult answer to understand what is the total book of business that they have with that other, um, big, uh, customer and, uh, you know, being able to have the right, uh, unified, uh, relevant, rich clean as the starting point that gives you visibility to that data, and then allows you to run precise analytics on top of that data, or, uh, you know, drive, uh, any kind of, uh, conclusions out of the data science type of algorithms or MLAI algorithms that you're trying to run. Um, you have to have that foundation of clean data to work with in order to get to those answers. >>Nice. Uh, and then I had questions on just the model is this, it's a SAS model. I presume, how, how is it priced? Do you have a, do you have a freemium? How do I get started? Maybe you could give us some color. >>Yeah, we are a SAS provider. We do everything in the cloud, uh, offer it as a SAS offering, um, for customers to leverage and benefit from our pricing is based on the volume of, uh, uh, consolidated profiles. And the, I use the word profiles because this is not the traditional, uh, data model where you have rows columns, foreign keys. This is a, you know, a profile of a customer, regardless of attribution or any other details that you want to capture. And, um, you know, that just as an example is what we consider as a profile. So number of consolidated profiles under management is the key vector of pricing. Uh, customers can start small and they can grow from there. We have customers who manage anywhere from a few hundred thousand profiles, uh, you know, off these different types of data domains, customer, patient provider, uh, product, uh, asset, those types of details. But, uh, then they grow and some of the customers, uh, HP Inc, as a customer is managing close to 1.5 billion profiles of B2B businesses at a global scale of B2C consumers at global scale. And they continue to expand that footprint as they look at other opportunities to use the single source of truth capabilities provided by Reltio. >>And your relationship with AWS you're, you're obviously building on top of AWS, you're taking advantage of the cloud native capabilities. Are you in the AWS marketplace? Maybe you could talk about AWS relationship a bit. >>Yeah. AWS has been a key partner for us, uh, since the very beginning, uh, we are now on the marketplace. Uh, customers can start with the free version of the product, um, and start to play with the product, understand it better, uh, and then move into the paid tier, um, you know, as they bring in more data, uh, into Reltio. And, uh, you know, we also, uh, have, uh, the partnership with AWS where, uh, you know, customers can benefit from the relationship where they are able to, um, uh, use the, the spend against Reltio to offset the commitment credits that they have for AWS, um, you know, as a cloud provider. So, uh, you know, we are working closely with AWS on key verticals, like life sciences, travel and hospitality as a starting point. >>Nice that love, love, those credits, um, company update, uh, you know, head count funding, revenue trajectory, what kind of metrics are you comfortable sharing? >>So, uh, we are currently, uh, at about, um, you know, slightly North of 300 people, uh, overall at rail queue, we will, uh, grow from 300 to about 400 people this year, uh, itself. Uh, we are, uh, uh, you know, we just put out a press release, uh, where we mentioned some of the subscription ARR we finished last year at about $74 million in ARR. And we are, uh, looking at, uh, crossing the a hundred million dollar ARR, um, uh, threshold, uh, later this year. So we're on a great growth trajectory and, uh, the businesses, uh, performing really well. And we are, uh, looking at working with more customers and helping them solve this, uh, uh, you know, data silo, fragmentation of data problem by having them leverage the Reltio capability at scale across their enterprise. >>That's some impressive growth. Congratulations, w w we're, I'm sure adding a hundred people you're hiring all over the place, but where we get some of your priorities. >>So, um, you know, the, as the business is growing, we are spending equally both on the R and D side of the house, uh, investing more there, but at the same time, also on our go to market, uh, so that we can extend our reach, make sure that, uh, more people know about, uh, Reltio and can start leveraging the benefit of, uh, the technology that we have built on top of, uh, AWS. >>Yeah. I mean, it sounds like you've obviously nailed product market fit, and now you're, you know, scaling and scaling the go to market. You moved from CEO into the CTO role. Maybe you could talk about that a little bit. Why, why, what was prompted that move >>Problems of luxury, uh, you know, as I like to call them, uh, once you know, that you're on a great growth trajectory and, uh, the business is performing well, it's all about, uh, figuring out ways of, uh, you know, making sure that you can drive harder and faster towards that growth, uh, milestones, uh, that you want to achieve. And, uh, you know, for us, uh, the story is no different. Uh, the team has done a wonderful job of, uh, making sure that we can build the right platform, um, you know, work towards this opportunity, that PC, which by the way, um, they just to share with you, uh, MDM or master data management has always been underestimated as a, uh, you know, yes, there is a problem that needs to be solved, but the market sizing was, uh, in a, not as clear, but some of the most recent, uh, estimates from analysts like Gartner, but the, uh, you know, sort of the new incarnation of, uh, data unification and master data management at about a $30 billion, uh, you know, uh, Tam or this market. >>So with that comes the responsibility that we have to really make sure that we are able to bring this capability to a wide array of customers. And with that, uh, I looked at, uh, you know, how could we scale the business faster and have the right team to work, uh, help us maximize the opportunity. And that's why, uh, you know, we decided, uh, that it was the right point in time for me to bring in somebody who's, uh, worked, uh, at, uh, the stretch of, you know, taking a company from just a a hundred million dollars in ARR to, uh, you know, half a billion dollars in ARR and doing it at a global scale. So Chris Highland, uh, you know, has had that experience and having him take on the CEO role, uh, really puts us on a tremendous, uh, our path to tremendous growth and achieving that, uh, with the right team. >>Yeah. And I think I appreciate your comments on the Tam. I love to look at the Tam and to do a lot of Tam analysis. And I think a lot of times when you define the future Tam based on sort of historical categories, you sometimes under count them. I mean, to me, you guys are in the, the, the digital business business. I mean, the data transformation, the company transformation business, I mean, that could be order of magnitude even bigger. So I think the future is bright for your company. Reltio Maneesh. And thank you so much for coming on the program really appreciate. >>Well, thanks for having me, uh, really enjoyed it. Thank you. >>Okay. Thank you for watching. You're watching the cubes startup showcase. We'll be right back.

>> Narrator: From theCUBE's studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, this is Dave Vellante. Welcome to this CUBE conversation. We got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. >> Hey Dave, good to see you again, appreciate it. >> Yeah cheers, so tell us a little bit about HVR. Give us the background of the company, we'll get into a little bit of the history. >> Yeah sure, so at HVR we are changing the way companies routes and access their data. And as we know, data really is the lifeblood of organizations today, and if that stops moving, or stop circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations, the most predominant place today is to the cloud, into platforms such as Snowflake, where we've seen massive traction. >> Yeah boy, have we ever. I mean, of course, last week, we saw the Snowflake IPO. The industry is abuzz with that, but so tell us a little bit more about the history of the company. What's the background of you guys? Where did you all come from? >> Sure, the company originated out of the Netherlands, at Amsterdam, founded in 2012, helping solve the issue that customer's was having moving data efficiently at scale across all across a wide area network. And obviously, the cloud is one of those endpoint. And therefore a company, such as the Dutch Postal Service personnel, where today we now move the data to Azure and AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I founded a company that did auditing recovery, or SQL Server databases. And we did that through reading the logs. And so then sold that company to Golden Gate, and had that sort of foundation there, in those early days. So, I mean again, Azure haven't been moving data efficiently as we can across these organizations with it, with the key aim of allowing customers to make decisions on the freshest data. Which today's really, table stakes. >> Yeah, so, okay, so we should think about you, as I want to invoke Einstein here, move as much data as you need to, but no more, right? 'Cause it's hard to move data. So your high speeds kind of data mover, efficiency at scale. Is that how we should think about you? >> Absolutely, I mean, at our core, we are CDC trades that capture moving incremental workloads of data, moving the updates across the network, you mean, combined with the distributed architecture that's highly flexible and extensible. And these days, just that one point, customers want to make decisions on us as much as they can get. We have companies that we're doing this for, a large apparel company that's taking some of their not only their core sales data, but some of that IoT data that they get, and sort of blending that together. And given the ability to have a full view of the organization, so they can make better decisions. So it's moving as much data as they can, but also, you need to do that in a very efficient way. >> Yeah, I mean, you mentioned Snowflake, so what I'd like to do is take my old data warehouse, and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake, or whatever modern cloud database I'm using, and then apply whatever machine intelligence, and really analyze it. So really that is kind of the problem that you're solving, is getting all that data to a place where it actually can be acted on, and turned into insights, is that right? >> Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud, and that's obviously where Snowflake fit in as well. But from our point of views of supporting over 30 different platforms. I mean data is generated, data is created in a number of different source systems. And so our ability to support each of those in this very efficient way, using these techniques such as CDCs, is going to capture the data at source, and then weaving it together into some consolidated platform where they can do the type of analysis they need to do on that. And obviously, the cloud is the predominant target system of choice with something like a Snowflake there in either these clouds. I mean, we support a number of different technologies in there. But yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean we've heard luminaries like Andy Jassie stand up at last year at Reinvent, he talked about Nitro, and the big pipes, and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? >> Absolutely, I mean, it starts with  how you going to acquire data? And you want to do that in the least obtrusive way to the database. So we'll actually go in, and we read the transaction logs of each of these databases. They all generate logs. And we go read the logs systems, all these different source systems, and then put it through our webs and secret sauce, and how we how we move the data, and how we compress that data as well. So, I mean, if you want to move data across a wide area network, I mean, the technique that a few companies use, such as ourselves, is change data capture. And you're moving incremental updates, incremental workloads, the change data across a network. But then combine that with the ability that we have around some of the compression techniques that we use, and, and then just into very distributed architecture, that was one of the things that made me join HVR after my previous experiences, and seeing that how that really fits in today's world of real time and cloud. I mean, those are table stakes things. >> Okay, so it's that change data capture? >> Yeah. >> Now, of course, you've got to initially seed the target. And so you do that, if I understand you use data reduction techniques, so that you're minimizing the amount of data. And then what? Do you use asynchronous methodologies, dial it down, dial it up, off hours, how does that work? >> Absolutely, exactly what you've said they mean. So we're going to we're, initially, there's an initial association, or an initial concept, where you take a copy of all of that data that sits in that source system, and replicating that over to the target system, you turn on that CDC mechanism, which is then weaving that change data. At the same time, you're compressing it, you're encrypting it, you're making sure it's highly secure, and loading that in the most efficient way into their target systems. And so we either do a lot of that, or we also work with, if there's a ETL vendor involved, that's doing some level of transformations, and they take over the transformation capabilities, or loading. We obviously do a fair amount of that ourselves as well. But it depends on what is the architecture that's in there for the customer as well. The key thing is that what we also have is, we have this compare and repair ability that's built into the product. So we will move data across, and we make sure that data that gets moved from A to B is absolutely accurate. I mean people want to know that their data can move faster, they want it to be efficient, but they also want it to be secure. They want to know that they have a peace of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all the different platforms as well. So something else that just sets us apart in that as well. >> So I want to understand the business case, if you will. I mean, is it as simple as, "Hey, we can move way more data faster. "We can do it at a lower cost." What's the business case for you guys, and the business impact? >> Absolutely, so I mean, the key thing is the business case is moving that data as efficiently as we can across this, so they can make these decisions. So our biggest online retailer in the US uses us, on the biggest busiest system. They have some standard vendors in there, but they use us, because of the scalability that we can achieve there, of making decisions on their financial data, and all the transactions that happen between the main E-commerce site, and all the third party vendors. That's us moving that data across there as efficiently as they can. And first we look at it as pretty much it's subscription based, and it's all connection based type pricing as well. >> Okay, I want to ask you about pricing. >> Yeah. >> Pricing transparency is a big topic in the industry today, but how do you how do you price? Let's start there. >> Yeah, we charge a simple per connection price. So what are the number of source systems, a connection is a source system or a target system. And we try to very simply, we try and keep it as simple as possible, and charge them on the connections. So they will buy a packet of five connections, they have source systems, two target systems. And it's pretty much as simple as that. >> You mentioned security before. So you're encrypting the data. So your data in motion's encrypted. What else do we need to know about security? >> Yeah, you mean, that we have this concept and how we handle, and we have this wallet concept, and how we integrate with the standard security systems that those customers have already, in the in this architecture. So it's something that we're constantly doing. I mean, there's there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that the data that is moving is highly secure. >> Let's talk a little bit about cloud, and maybe the architecture. Are you running in the cloud, are you running on prem, both, across clouds. How does that work? >> Yeah, all of the above. So I mean, what we see today is majority of the data is still generated on prem. And then the majority of the talks we see are in the cloud, and this is not a one time thing, this is continuous. I mean, they've moved their analytical workload into the cloud. You mean they have these large events a few times a year, and they want the ability to scale up and scale down. So we typically see you mean, right now, you need analytics, data warehouses, that type of workload is sitting in the cloud, because of the elasticity, and the scalability, and the reasons the cloud was brought on. So absolutely, we can support the cloud to cloud, we can support on prem to cloud, I think you mean, a lot of companies adopting this hybrid strategy that we've seen certainly for the foreseeable next five years. But yeah, absolutely. The source of target systems considered on prem or in the cloud. >> And where's the point of control? Is it wherever I want it to be? >> Absolutely. >> Is it in one of the clouds on prem? >> Yeah absolutely, you can put that point of control where you want it to be. We have a concept of agents, these agents search on the source and target systems. And then we have the, it's at the edge of your brain, the hub that is controlling what is happening. This data movement that can be sitting with a source system, separately, or on target system. So it's highly extensible and flexible architecture there as well. >> So if something goes wrong, it's the HVR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit, what happens when something goes wrong? >> Yeah absolutely, I mean, we have things that are built into the product that help us highlight what has gone wrong, and how we can correct those. And then there's alerts that get sent back to us to the to the end customer. And there's been a whole bunch of training, and stuff that's taken place for then what actions they can take, but there's a lot of it is controlled through HVR core system that handles that. So we are working next step. So as we move as a service into more of an autonomous data integration model ourselves, whichever, a bunch of exciting things coming up, that just takes that off to the next levels. >> Right, well Golden Gate Heritage just sold that to Oracle, they're pretty hardcore about things like recovery. Anthony, how do you think about the market? The total available market? Can you take us through your opportunity broadly? >> Yeah absolutely, you mean, there's the core opportunity in the space that we play, as where customers want to move data, they don't want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing a lot of traction around this particular data that resides in these critical business systems such as SAP, that is something you're asking earlier about, what are some core things on our product. We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go, and then decode this data that sits between these cluster pool tables, combine that with our CDC techniques, and move their data across a network. And so particularly, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why? It's these big, large enterprises that are now ready to consume more. We've seen that tail wind from our perspective, as well as taking these workloads such as SAP, and moving that into something like these cloud platforms, such as a Snowflake. And so that's where we see the immediate opportunity for us. And then and then branching out from there further, but I mean, that is the core immediate area of focus right now. >> Okay, so we've talked about Snowflake a couple of times, and other platforms, they're not the only one, but they're the hot one right now. When you think about what organizations are doing, they're trying to really streamline their data pipeline to get to turn raw data into insights. So you're seeing that emerging organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake, obviously, is one piece of that. Where's your value in that pipeline? Is it all about getting the data into that stream? >> Yeah, you just mentioned something there that we have an issue internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we building value on that data as well, particularly around some of our SAP type initiatives, and solutions related to that, that we're bringing out as well. So one it's absolutely going in acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale, the biggest companies in the world use us to do that, across there and giving them that ability to make decisions on the freshest data. Therein lies the value of them being able to make decisions on data that is a few seconds, few minutes old, versus some other technology they may be using that takes hours days. You mean that is it, keeping large companies that we work with today. I mean keeping toner paper on shelves, I mean one thing that happened after COVID. I mean one of our big customers was making them out their former process, and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital, and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them reinvent their businesses, drive down that digital transformation strategy, is the key areas there. No data, they can't make those type of decisions. >> Yeah, so I mean, your vision really, I mean, you're betting on data. I always say don't bet against the data. But really, that's kind of the premise here. Is the data is going to continue to grow. And data, I often say data is plentiful insights aren't. And we use the Broma you said before. So really, maybe, good to summarize the vision for us, where you want to take this thing? Yeah, absolutely so we're going to continue building on what we have, making it easier to use. Certainly, as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering, and some of the other core areas. But ultimately, it's getting closer to the business user. In today's world, there is many IT tech-savvy people sitting in the business side of organization, as they are in IT, if not more. And so as we go down that flow with our product, it's getting closer to those end users, because they're at the forefront of wanting this data. As we said that the data is the lifeblood of an organization. And so given an ability to drive the actual power that they need to run the data, is a core part of that vision. So we have some some strategic initiatives around some acquisitions, as well, but also continue to build on the product. I mean, there's, as I say, I mean sources and targets come and go, there's new ones that are created each week, and new adoptions, and so we've got to support those. That's our table stakes, and then continue to make it easier to use, scale even quicker, more autonomous, those type of things. >> And you're working with a lot of big companies, the company's well funded if Crunchbase is up to date, over $50 million in funding. Give us up right there. >> Yeah absolutely, I mean a company is well funded, we're on a good footing. Obviously, it's a very hot space to be in. With COVID this year, like everybody, we sat down and looked in sort of everyone said, "Okay well, let's have a look how "this whole thing's going to shake out, "and get good plan A, B and C in action." And we've sort of ended up with Plan A plus, we've done an annual budget for the year. We had our best quarter ever, and Q2, 193% year over year growth. And it's just, the momentum is just there, I think at large. I mean obviously, it sounds cliche, a lot of people say it around digital transformation and COVID. Absolutely, we've been building this engine for a few years now. And it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, they're sort of taking a month or two now. It's been getting driven down from the top. So all of that's come together for us very fortunately, the timing has been ideal. And then tie in something like a Snowflake traction, as you said, we support many other platforms. But all of that together, it just set up really nicely for us, fortunately. >> That's amazing, I mean, with all the turmoil that's going on in the world right now. And all the pain in many businesses. I tell you, I interview people all day every day, and the technology business is really humming. So that's awesome to hear that you guys. I mean, especially if you're in the right place, and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts, and give us the update on HVR, really interesting. >> Absolutely, I appreciate the time and opportunity. >> Alright, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hi, I'm Stu Miniman coming to you from our Boston area studio and this is a special CUBE Conversation, we always love talking to startups around the industry, understanding how they're creating innovation, doing new things out there, and oftentimes one of the exits for those companies is they do get acquired, and happy to welcome back to the program one of our CUBE alumni, Sheng Liang, he is the cofounder and CEO of Rancher, today there was an announcement for a definitive acquisition of SUSE, who our audience will know well, we were at SUSECON, so Sheng, first of all, thank you for joining us, and congratulations to you and the team on joining SUSE here in the near future. >> Thank you, Stu, I'm glad to be here. >> All right, so Sheng, why don't you give our audience a little bit of context, so I've known Rancher since the very early days, I knew Rancher before most people had heard the word Kubernetes, it was about containerization, it was about helping customers, there was that cattles versus pets, so that Rancher analogy was, hey, we're going to be your rancher and help you deal with that sprawl and all of those pieces out there, where you don't want to know them by name and the like, so help us understand how what was announced today is meeting along the journey that you set out for with Rancher. >> Absolutely, so SUSE is the largest independent opensource software company in the world, and they're a leader in enterprise Linux. Today they announced they have signed a definitive agreement to acquire Rancher, so we started Rancher about six years ago, as Stu said, to really build the next generation enterprise compute platform. And in the beginning, we thought we're going to just base our technology based on Docker containers, but pretty soon Kubernetes was just clearly becoming an industry standard, so Rancher actually became the most widely used enterprise Kubernetes platform, so really with the combination of Rancher and SUSE going forward, we're going to be able to supply the enterprise container platform of choice for lots and lots of customers out there. >> Yeah, just for our audience that might not be as familiar with Rancher, why don't you give us your position in where we are with the Kubernetes landscape, I've talked about many times on theCUBE, a few years ago it was all about "Hey, are we going to have some distribution war?" Rancher has an option in that space, but today it's multicloud, Rancher works with all of the cloud Kubernetes versions, so what is it that Rancher does uniquely, and of course as you mentioned, opensource is a key piece of what you're doing. >> Exactly, Stu, thanks for the question. So this is really a good lead-up into describing what Rancher does, and some of the industry dynamics, and the great opportunity we see with SUSE. So many of you, I'm sure, have heard about Kubernetes, Kubernetes is this container orchestration platform that basically works everywhere, and you can deploy all kinds of applications, and run these applications through Kubernetes, it doesn't really matter, fundamentally, what infrastructure you use anymore, so the great thing about Kubernetes is whether you deploy your apps on AWS or on Azure, or on on-premise bare metal, or vSphere clusters, or out there in IoT gateways and 5G base stations and surveillance cameras, literally everywhere, Kubernetes will run, so it's, in our world I like to think about Kubernetes as the standard for compute. If you kind of make the analogy, what's the standard of networking, that's TCPIP, so networking used to be very different, decades ago, there used to be different kinds of networking and at best you had a local area network for a small number of computers to talk to each other, but today with TCPIP as a standard, we have internet, we have Cisco, we have Google, we have Amazon, so I really think as successful as cloud computing has been, and how much impact it has had to actually push digital transformation and app modernization forward, a lot of organizations are kind of stuck between their desire to take advantage of a cloud provider, one specific cloud provider, all the bells and whistles, versus any cloud provider, not a single cloud provider can actually supply infrastructure for everything that a large enterprise would need. You may be in a country, you may be in some remote locations, you may be in your own private data center, so the market really really demands a standard form of compute infrastructure, and that turned out to be Kubernetes, that is the true, Kubernetes started as a way Google internally ran their containers, but what it really hit the stride was a couple years ago, people started realizing for once, compute could be standardized, and that's where Rancher came in, Rancher is a Kubernetes management platform. We help organizations tie together all of their Kubernetes clusters, regardless where they are, and you can see this is a very natural evolution of organizations who embark on this Kubernetes journey, and by definition Rancher has to be open, because who, this is such a strategic piece of software, who would want their single point of control for all compute to be actually closed and proprietary? Rancher is 100% opensource, and not only that, Rancher works with everyone, it really doesn't matter who implements Kubernetes for you, I mean Rancher could implement Kubernetes for you, we have a Kubernetes distro as well, we actually have, we're particularly well-known for Kubernetes distro design for resource constrained deployments on the edge, called K3S, some of you might have heard about it, but really, we don't care, I mean we work with upstream Kubernetes distro, any CNCF-compliant Kubernetes distro, or one of many many other popular cloud hosted Kubernetes services like EKS, GKE, AKS, and with Rancher, enterprise can start to treat all of these Kubernetes clusters as fungible resources, as catalysts, so that is basically our vision, and they can focus on modernizing their application, running their application reliably, and that's really what Rancher's about. >> Okay, so Sheng, being acquired by SUSE, I'd love to hear a little bit, what does this mean for the product, what does it mean for your customers, what does it mean for you personally? According to Crunchbase, you'd raised 95 million dollars, as you said, over the six years. It's reported by CNBC, that the acquisition's in the ballpark of 600 to 700 million, so that would be about a 6X increment over what was invested, not sure if you can comment on the finances, and would love to hear what this means going forward for Rancher and its ecosystem. >> Yeah, actually, I know there's tons of rumors going around, but the acquisition price, SUSE's decided not to disclose the acquisition price, so I'm not going to comment on that. Rancher's been a very cash-efficient business, there's been no shortage of funding, but even amounts to 95 million dollars that we raised, we really haven't spent majority of it, we probably spent just about a third of the money we raised, in fact our last run to fundraise was just three, four month ago, it was a 40 million dollar series D, and we didn't even need that, I mean we could've just continued with the series C money that we raised a couple years ago, which we barely started spending either. So the great thing about Rancher's business is because we're such a product-driven company, with opensource software, you develop a unique product that actually solves a real problem, and then there's just no barrier to adoption, so this stuff just spreads organically, people download and install, and then they put it in mission-critical production. Then they seek us out for commercial subscription, and the main value they're getting out of commercial subscription is really the confidence that they can actually rely on the software to power their mission-critical workload, so once they really start using Rancher, they recognize that Rancher as an organization provide, so this business model's worked out really well for us. Vast majority of our deals are based on inbound leads, and that's why we've been so efficient, and that's I think one of the things that really attracted SUSE as well. It's just, these days you don't just want a business that you have to do heavy weight, heavy duty, old fashioned enterprise (indistinct), because that's really expensive, and when so much of that value is building through some kind of a bundling or locking, sooner or later customers know better, right? They want to get away. So we really wanted to provide a opensource, and open, more important than opensource is actually open, lot of people don't realize there are actually lots of opensource software even in the market that are not really quite open, that might seem like a contradiction, but you can have opensource software which you eventually package it in a way, you don't even make the source code available easily, you don't make it easy to rebuild the stuff, so Rancher is truly open and opensource, people just download opensource software, run it in the day they need it, our Enterprise subscription we will support, the day they don't need it, they will actually continue to run the same piece of software, and we'd be happy to continue to provide them with patches and security fixes, so as an organization we really have to provide that continuous value, and it worked out really well, because, this is such a important piece of software. SUSE has this model that I saw on their website, and it really appeals to us, it's called the power of many, so SUSE, turns out they not only completely understand and buy into our commitment to open and opensource, but they're completely open in terms of supporting the whole ecosystem, the software stack, that not only they produce, but their partners produce, in many cases even their competitors produce, so that kind of mentality really resonated with us. >> Yeah, so Sheng, you wrote in the article announcing the acquisition that when the deal closes, you'll be running engineering and innovation inside of SUSE, if I remember right, Thomas Di Giacomo has a similar title to that right now in SUSE, course Melissa Di Donato is the CEO of SUSE. Of course the comparison that everyone will have is you are now the OpenShift to SUSE. You're no stranger to OpenShift, Rancher competes against RedHat OpenShift out on the market. I wonder if you could share a little bit, what do you see in your customer base for people out there that says "Hey, how should I think of Rancher "compared to what RedHat's been doing with OpenShift?" >> Yeah, I mean I think RedHat did a lot of good things for opensource, for Linux, for Kubernetes, and for the community, OpenShift being primarily a Kubernetes distro and on top of that, RedHat built a number of enhanced capabilities, but at the end of the day, we don't believe OpenShift by itself actually solves the kind of problem we're seeing with customers today, and that's why as much investment has gone into OpenShift, we just see no slowdown, in fact an acceleration of demand of Rancher, so we don't, Rancher always thrived by being different, and the nice thing about SUSE being a independent company, as opposed to a part of a much larger organization like RedHat, is where we're going to be as an organization 100% focused on bringing the best experience to customers, and solve customers' business problems, as they transform their legacy application suite into cloud-native infrastructure. So I think the opportunity is so large, and there's going to be enough market there for multiple players, but we measure our success by how many people, how much adoption we're actually getting out of our software, and I said in the beginning, Rancher is the most widely used enterprise Kubernetes platform, and out of that, what real value we're delivering to our customers, and I think we solve those problems, we'll be able to build a fantastic business with SUSE. >> Excellent. Sheng, I'm wondering if we could just look back a little bit, you're no stranger to acquisitions, remember back when Cloud.com was acquired by Citrix, back when we had the stack wars between CloudStack and OpenStack and the like, I'm curious what lessons you learned having gone through that, that you took away, and prepared you for what you're doing here, and how you might do things a little bit differently, with the SUSE acquisition. >> Yeah, my experience with Cloud.com acquired by Citrix was very good, in fact, and a lot of times, you really got to figure out a way to adapt to actually make sure that Rancher as a standalone business, or back then, Cloud.com was a standalone business, how are they actually fitting to the acquirer's business as a whole? So when Cloud.com was acquired, it was pretty clear, as attractive as the CloudStack business was, really the bigger prize for Citrix was to actually modernize and cloudify their desktop business, which absolutely was like a two billion dollar business, growing to three billion dollars back then, I think it's even bigger now, with now everyone working remote. So we at Citrix, we not only continued to grow the CloudStack business, but more importantly, one of the things I'm the most proud of is we really played up a crucial role in modernizing and cloudifying the Citrix mainline business. So this time around, I think the alignment between what Rancher does and what SUSE does is even more apparent, obviously, until the deal actually closes, we're not really allowed to actually plan or execute on some of the integration synergies, but at a higher level, I don't see any difficulty for SUSE to be able to effectively market, and service their global base of customers, using the Rancher technology, so it's just the synergy between Kubernetes and Linux is just so much stronger, and in some sense, I think I've used this term before, Kubernetes is almost like the new Linux, so it just seems like a very natural place for SUSE to evolve into anyway, so I'm very very bullish about the potential synergy with the acquisition, I just can't wait to roll up my hands and get going as soon as the deal closes. >> All right, well Sheng, thank you so much for joining us, absolutely from our standpoint, we look at it, it's a natural fit of what Rancher does into SUSE, as you stated. The opensource vision, the community, and customer-focused absolutely align, so best of luck with the integration, looking forward to seeing you when you have your new role and hearing more about Rancher's journey, now part of SUSE. Thanks for joining us. >> Thank you Stu, it's always great talking to you. >> All right, and be sure, we'll definitely catch up with Rancher's team at the KubeCon + CloudNativeCon European show, which is of course virtual, as well as many other events down the road. I'm Stu Miniman, and thank you for watching theCUBE.