>>Hello, and welcome to this special presentation. This is the cube on cloud startups, our special event of Amazon web services, startup showcase. I'm John furrier, host of the cube, and excited to be here to talk about the hottest startups around cloud cloud computing data and the future of the enterprise. We've got Rob Harris, vice president of solutions consulting for star dog. Great company, Rob. Great to see you. Thanks for coming on. So this is a showcase presentation with AWS showcase startup showcase. You guys are a fast growing startup knowledge graph. We did a video explaining kind of what we did in the cube conversation. Um, really interesting category this, uh, eight hubs cloud startups with you guys. Talk about what you got. Take a minute to explain star dog and what you got. >>Sure. Yeah, here at startup, we are really a knowledge graph platform company. So we help build a knowledge graph for our customers tying together the data inside the organization and with data on the cloud in order for them to be able to find search and understand the context and relationship of all that data within their own organization. So that's really what we try to facilitate and make successful for our customers. >>Awesome. What market are you guys targeting? What's the market opportunity. Can you explain the market space that you're building product value in and what's your focus? >>Sure. Yeah, it's, it's pretty exciting. We do a lot from an industry perspective, we target a lot, uh, life sciences or financial the services, and it just tends to be, those are the ones that are most excited and getting started with this, but we certainly have a much broader set of customers in government or in manufacturing. What we really look for is the horizontal type solution, where you have a lot of systems that you want to tie together, or you want to have that understanding of your data all within context throughout your organization. So anybody struggling with that kind of tying of your data together, whether it's on the cloud or on prem, that's what we really go after >>Disruption. Who are you disrupting as you come into the marketplace? I love Amazon so hot startups because they got an eye clean take on something, but someone usually is being impacted. Who is, who are you guys disrupting as you come into? >>Yeah, a lot of times we find we're disrupting traditional ETL, right? So centralizing of all your data into one big platform, a lot of people have gone down this path of trying to create these large repositories data lakes, data warehouses. Yeah. We try to provide the additional value on top of them by not forcing you to continue to invest in moving and centralizing all your data together, but connecting it and providing context, um, while leaving and leveraging the mid worries. >>Awesome. Cause there's a big market opportunity as data warehouses becomes modernized and horizontal control planes and cloud computing is data is the key competitive advantage. Uh, great disruption. Great opportunity. So let's talk about the business star dog. What do you guys, uh, talk about the company, uh, where the headquarters is? The, how many employees what's the business model? How do you guys make money? Yeah, >>Well, a headquarters is always a little bit tricky nowadays is we were also distributed, but officially it is in Arlington Virginia. Uh, although we are all over the globe, uh, mostly in the United States and Europe, certainly as we look at, uh, how, how do we go to market and what do we do related to that? We have a subscription-based model where we help our customers get started usually small, um, by leveraging a package that they can run either on prem or in the cloud or directly from the AWS marketplace and letting them connect to the data and then growing out as they grow within their organization, larger, more interplay enterprise wide type of installations. So that's how we kind of go after it, uh, from, from our company perspective. >>So your go to market then for the company, is it bottoms up organic growth, kind of a freemium get in there? Or is it kind of a mid, mid tier or how do you guys look at that, that entry? >>It's a great question. That's exactly right. A lot of times we do start with a freemium type of model. We do have free trials and use usability to get started very quickly without having to talk to a salesperson or without having to pay up front in order to see the value, because we want you to be able to understand the value you're going to get out of our platform right off the bat and get started. Then after you've really tried it out and you see where it could apply within your organization, we help make it enterprise. >>I have to ask you how the business model of SAS, obviously clouds. Great. Are you guys leveraging Amazon web services marketplace at all? >>We are we're on the marketplace today, um, with the, both the free trial, as well as the ability through, you know, private offers to do whole production instances. So we're really excited about being a part of the marketplace. What we found is that sometimes customers want to run on the cloud. Sometimes they want to run on prem, wherever they want to run. We want to be sure that we're there. >>Yeah. Alex, let's pull up that slide on the hybrid, uh, architecture for these guys. So I want to bring this up since you brought up the business model and you talk about hybrid. This is interesting. This gets into the business model and this is kind of transitions into kind of the technology architecture. Could you walk me through this slide, the knowledge graph and the hybrid cloud. Why is this important for you guys and why is it important for customers? >>This is great. Thank you for, uh, for pulling this up. What this is really showing is as we look toward the future, as we really look at how people are deploying knowledge, graphs, and managing their data, we see that one of the big problems they're trying to address is what about cloud, uh, data that's on the cloud would a bit dated it's on prem. Maybe it's in multiple VPCs that you have within the Amazon environment. How do you tie all this together? And we all know that moving data around between all of these zones can be expensive and time consuming and difficult. And so we've come up with an architecture that allows you to run the knowledge, graph an agent of the knowledge graph in each of these zones. And they can all talk to each other and coordinate with each other. So they can see data that exists within that zone and pass it on to the other pieces as required or as needed to minimize your kind of in and out fees. And to leverage that all that data in one, in one place >>I asked you because this comes up a lot in our coverage, um, data mobility, uh, moving data is expensive. Um, how does that impact you guys in customers? A lot of people have been looking at, Hey, you know, the economics of the cloud are phenomenal, but at some point, if you've got a lot of data, you move compute to the data or you kind of think differently, how do you guys look at that? That trend? >>Yeah, that's, that's really our key value prop is people struggle with this. As people try to figure out how do I handle this large amount of data without having to generate all this additional costs about moving it around. We really look about how do I push that compute down to the storage layers, where the data already exists. And so if you think about our product architecture and you know, we, I know we have a slide on how our product is really built and how it's pulled together. When you look at our core core architecture, we have the graph that represents that connected data, but the exciting part of our architecture, what we do differently than everyone else is by allowing you to keep the data in its existing data silos, whether it's applications or repositories documents that you already have out there, we allow you to connect to that data where it is cross zone, whether it's on prem or on the cloud. >>And by leveraging the power of start on the virtualization engine, you can connect that data and be able to represent it from one source without having to move it around. But because we also have a persistence layer that's built into our product, you can really determine where's the best home. Is it data that you're going to use a lot and thereby should be really close to where the query engine is? Or is it something where you want to federate it out and leverage that compute at that storage layer itself? That flexibility is really why our customers come to us and are excited to use, start off. >>That's awesome. Great, great stuff. Love, love. The slides. Love to look at some pictures that describe the architecture both as well as the product. I love how you got the enterprise high-grade applications and then you're integrating with other partners. I think that's a really key, uh, value. And I think if you're not integrating well in this modern era, you probably won't be surviving much longer. It's pretty much a game changer at this point when knows that a question on the technology and product. Now keeping it on this theme. What's your secret sauce. Every company's got a secret sauce. What is star dog's secret sauce? >>Our secret sauce is really how do we coordinate across all of those applications? So if you can imagine you have, you know, Oracle database or Redshift repository, and you're trying to be able to unify that data in real time across those applications. There's a lot of thought and needs to go about how to do that efficiently. You don't want to take all the database from both repositories, move them, all that data into one place and then figure it out. And so our query planner, how do we coordinate across the multiple applications is really what makes us different and special >>On the Symantec modeling that you're doing? Because I see there's a lot of data there. You got to kind of get an understanding context. Um, how do you guys look at reusability metadata on data? This has become a very key point on not just data warehouse, but it's becoming much more about addressability and discoverability in as fast as possible, low latency, uh, with intelligence, this has been a big discussion. How do you guys look at that aspect of the reusability of the data? >>Yeah, it's, it's one of the exciting parts about starting with a semantic graph and then extending into these capabilities around virtualization and reasoning and inference by starting with the semantic graph, we allow you to, you know, incrementally invest in building out your model and then being able to reuse that model as you, as you go through your implementations. Yeah. That's been a, a big failing as people have looked at the analytical movements recently is so many times people spin up a repository, they answer a particular question and they do an absolutely fine job, but then we have your next question. You have to spin up another repository, build more views, re ETL the data. And then the semantic technology is what allows you to create that common understanding and reuse it over and over and over again. And I think it's time for that to hit mainstream. You know, it's been around a while. It's something that has taken some time to get some adoption around, but now that we really have build up awareness around it and we've shelled, the technology can scale the large volumes. Uh, I think it's time to be able to leverage the value that reasonability brings. Yeah. >>One final question on the product and the technology and kind of the architecture is how do you guys connect the dots going forward as more and more edge nodes become available in the network as that architecture of hybrid that we talked earlier about becomes so complex and so connected. I mean, you could have more connectedness than ever before. Um, it's very complex networks graph theory, right? You're talking about a lot of edges and a lot of traversal it's billions and billions of edges. I mean, this is it's complicated. How do you guys create, how do you guys see that unfolding and how and why the star dog remained relevant in that configuration? >>Yeah. And the simple fact is that people need help, right? It can't be that you're going to define all those edges and connections by hand yourself through some systems or keys. It's a great way to get started, but it's not sufficient in order to really get the value out of that graph that you expect. And the ways we do that is twofold. The first bit is really an influencing or reasoning capability. Being able to look at this structure of the data, how it's composed and create connections between that data based on, you know, logical, logical rules. The second is machine learning, right? Machine learning is high. We use things like linear regression algorithms or other types of community detection algorithms in order to build more connections in the data so that you can get really unlock that value that you're looking for. When you're leveraging graph technology, >>A lot of secret sauce here, a lot of technology graph, super exciting. Let's get into the final segment around customer traction and what you guys have seen with customers. Um, what are some of the use cases that are popular and what happens if customers aren't going down this road? What are they missing out on? Um, I mean, it's the classic fear of missing out and fear of getting screwed over right. Are going out of business. I mean, that's, that's motivational at some level, but you know, there are the, do I wait and people who waited on cloud computing by the way were left behind and some never survived. So we're almost in this same dynamic with customers. At some point you got to put the toe in the water, so to speak or get going to take us through some customer examples and use cases where, >>Or this is working. Yeah. I think both of those areas are, are, uh, great ones to hit on. So when you think about what are we missing out on one of our largest customer bases really in pharmaceuticals. Yeah. And they're using this technology in order to find more connections in the data so that they can really decrease the amount of time for getting a drug to market on the research and development. They can look more at leveraging the data they've already connected using related items to be able to accelerate their investments and waiting costs them hundreds of millions, if not billions of dollars. So there are certainly ones where being able to adopt this technology early and get value out of early, really pays off in. And they're not the only ones. That's the only, that's the only the life sciences space. But there's also the idea to use it, as you said, really about what else am I missing out on? >>And the data fabric movement, this movement around, how do I lower the cost in my organization about moving data around creating more ETL jobs, leveraging all these data assets already have that the data fabric movement is the idea of how do we really automate that? How do we accelerate that? How do we make that an easier process so that it just doesn't cost as much to manage all this data in an organization. And I've observed that more and more. We have customers coming to us, really interested in this type of use cases that relates to our technology and they are getting ahead of their competitors by really lowering their, it costs in line to focus on these higher value activities. >>Life of the customers is what for you with, with startup? Why, how do they win? What's the reason why they buy and take the freemium. And when do they convert over? Well, take me through the progression of value. When do they see something and why do they increase their sure. >>Assumption? Yeah. That, I mean, the bottom line is you want to try to get more value out of your data at a lower cost and make it easier and faster to do. And so getting started in a single use case, trying out our free version, representing your data and taking a look at what it could look like under a common model, connecting it up with our virtualization services is a great way to try out the technology and really, you know, put your toe in the water to see is this something that would be a value to organization as you see that value unlock is you really understand that you can leverage these days assets with this lower time to value, you know, days in order to unlock a whole repository and connected to another repository. That's where we love to engage with you and help show you how you can make that successful in a more production environment. >>I like about some of the things you're talking about star dog has kind of that aspirin aspect, but also a growth, um, uh, vitamin E as well, in terms of the value proposition, a lot of companies are overwhelmed with the data, but yet you have this path towards more creation of value through the knowledge graph and reasoning and other other value. When does a customer, and this is kind of comes back to the customers who are out there potentially watching prospects or future customers. When do they know they need to call you guys up? Is it because they have too many sources? Could you take me through what it, what it looks like in a prospect's environment where they would really win with start a what's it look like? What are some of the signs that they need to engage, start out? >>Yeah. The two big things that we've seen repeated in our customer base over and over again, is if you have a large number of systems out there that aren't connected, that you don't see how all the data it can be pulled together between those systems, because the different data formats or different languages or different ways that the data is created in those systems start off, can certainly help. The second is if you have a large data warehouse or a data Lake, and you don't see the value being generated out of that, because people don't understand where the data is or what context it has with other data within those repositories, both of those situations are one where we think you'd get a lot of value out of start off. And we'd love to talk to you. >>So would, so just secondly, understand this. So if you have a lot of systems that either are not connected or connected, whatever, that's great, a lot of sources sitting around, you know, whether it's spreadsheets or Oracle or >>Red shift, whatever it is, we've loved it that's right. >>Ingest as much as possible from sources >>That's right. Ingest or connect. I mean, that's really the value that we bring is you don't have to pull it all in. You can just map and leverage the data where it lives. We have customers that have petabyte repositories that just mapped that data in to start off, and we can really facilitate pulling out the value of those systems without you having to move it around again, to another request, >>Ingest, connect, and visually see value. That's right. It sounds, it sounds like a tagline, um, great stuff. So just give some examples of who's using it. What big names? Um, obviously you guys, aren't hot startup coming out of the Amazon cloud showcase. Uh, congratulations. What are some names that have worked with you guys that can give an indicator of the company that you're keeping right now in terms of, >>Yeah, I mean our largest customer by far right now, our longest customer has been NASA. Um, so they've been a really exciting user of the platform we've been really to see them leverage the platform. Schneider electric has been a long time user, uh, Bayer FINRA in the U S which is a financial services watchdog organization. These are customers that are getting a lot of value out of our platform today, and we're excited to work with them. >>Awesome, Rob, great to see you. Congratulations. Uh, take a minute to just give the plug for the commercial. How do we engage? What's the culture like, um, you guys hiring, what's the, what's the state of that? What's the state of the company. >>Yeah, no, it's a, it's a great thank you for, uh, for bringing that up where, you know, we're an exciting growing company. Um, as we really reach out more and more to connect more people's data, we find that we're always looking at more resources on building out more conductivity between the individual data sources. So more understanding on that front, as well as more, a professional services type folks to help people through the process. We've really been trying to minimize the amount of effort that you have to have in order to get started, but we know that people like a helping hands. So we're always looking for people we're always growing and we're excited to have the chance to, you know, bring this technology out beyond just the semantic group that is historically been here. >>You know, you've got a great job. Vice-president solutions consulting, essentially you're in a product role, but more like a solution architect meets products, uh, customer facing, and also product century. You're kind of the center of all the action. So what's the coolest thing you've seen, um, from a customer standpoint or an architecture or, um, a deployment or an engagement that you've been involved with. That's been kind of like, Oh, wow, that's cool. That's game. That's something new that we've been, we wouldn't have seen a few years ago. Take us through just an example, anecdotal, you don't have to share the company name or you. >>That's a great question. Um, there is a company that is working on self-driving cars and being able to leverage the knowledge graph to pull together all of the videos and material they get from the vehicles themselves, as well as static information about the sensors. Uh, that's been pretty exciting to see. I, I, I just recently purchased the festival myself. So I'm excited about the whole self-driving car world and to be able to help them participate with these companies is, is pretty exciting. Um, we, we just help one of the large drug manufacturers come to market with one of their drugs earlier than expected. You know, that's a, that's a pretty exciting feeling to know that you can really help people, um, by just connecting the data they already have and letting them leverage those resources, uh, that that really is something that we're going to be very calm >>And the bridge to the future that the customers have to cross with you is also pretty compelling. You got industrial IOT and more and more data to take a quick minute to describe what that future looks like. >>Yeah. You know, as we see more and more automation in this process, we see a couple of different really, you know, exploding areas. The first off, you know, you hit the nail on the head is data being able to bring in more edge devices, being able to really process that data on the fly and be able to help answer questions as these changes in data are occur within these sources. Um, that's certainly part of the future. And the other thing that we're really excited about is this more automatic data discovery with an organization. How can we have an agent that goes out and kind of can infer really even what your data is about in the structure of your data without a lot of input for you. And so we've been working a lot with building up these models automatically and letting you have the foundation for integrating your data, um, and just the push of a button. So we're excited about walking, Alexa, our customers in this journey as well. >>It's, it's a fun area. You talk about reasoning. That's one of the key value propositions that you guys have. You talk about AI, you talk about bots and soon it's going to be thinking machines for us. They're going to be doing all the work. >>I hope they're not too soon, but I am excited about that idea as well. I can go. I do think that, uh, you know, if you look at organizations today, it's fascinating how it's not, that the problems are different, but we're trying to automate as much of it as possible so that we can work on that, the real value clumps of our organizations. And it's not that kind of drudgery work. I started as a DBA back in my career, um, just trying to keep the database up and running, you know, nowadays, you know, all these autonomous databases and self indexing, and self-correcting, it's just not a passive lead as much anymore. You know, we hope we can bring that to the data infrastructure automation. >>It's a double-edged sword gun, right. It's amazing, done wrong. It could cause some damage and flipped some, some pain and hurt. And so you got to figure it out, got to have the right data sets, gotta have the right software, um, and a great future. Rob Harris, congratulations for being a cannabis startup showcase here on the cube on cloud startups, uh, with AWS, uh, led partnership. Thank you for coming on and being part of this event. Thank you again. Okay. Rob Harris, vice president solutions consulting at star dog here for the coupon cloud. I'm John furrier. Thanks for watching. >>Yeah.

>>hello. >>Welcome to the special key conversation. I'm John ferry, host of the queue here in Palo Alto, California, featuring star dog is a great hot start-up. We've got a great guest, Rob Harris, vice president of solutions consulting for star dog here talking about some of the cloud growth, um, knowledge graphs, the role of data. Obviously there's a huge sea change. You're seeing real value coming out of this COVID as companies coming out of the pandemic, new opportunities, new use cases, new expectations, highly accelerated shift happening, and we're here to break it down. Rob, thanks for joining us on the cube conversation. Great to be here. So got, I'm excited to talk to you guys about your company and specifically the value proposition I've been talking for almost since 2007 around graph databases with Neo four J came out and looking at how data would be part of a real part of the developer mindset. Um, early on, and this more of the development. Now it's mainstream, you're seeing value being created in graph structures. Okay. Not just relational. This has been, uh, very well verified. You guys are in this business. So this is a really hot area, a lot of value being created. It's cool. And it's relevant. So tell us first, what is star dog doing? What's uh, what is the company about? >>Yeah, so I mean, we are an enterprise knowledge graph platform company. We help people be successful at standing up knowledge graphs of the data that they have both inside their company and using public data and tying that all together in order to be able to leverage that connected data and really turn it into knowledge through context and understand it. >>So how did this all come about this from a tech standpoint? What is the, what is the, uh, what was the motivation around this? Because, um, obviously the unstructured wave hit, you're seeing successes like data bricks, for instance, just absolutely crushing it on, on their valuation and their relevance. You seeing the same kind of wave hit almost kind of born back on the Hadoop days with unstructured data. Is that a big part of it? Is it just evolution? What's the big driver here? >>Yeah, no, I think it's a, it's a great question. The driver early is as these data sets have increased for so many companies trying to really bring some understanding to it as they roll it out in their organizations, you know, we've tried to just try to centralize it and that hasn't been sufficient in order to be able to unlock the value of most organization status. So being able to step beyond just, you know, pulling everything together into one place, but really putting that context and meaning around it that the graph can do. So that's where we've really got started at, uh, back in the day is we really looked at the inference and reasoning part of a knowledge graph. How do we bring more context and understanding that doesn't naturally exist within the data? And that really is how we launched off the product. >>I got to ask you around the use cases because one of the things that's really relevant right now is you're seeing a lot of front end development around agile application. Dev ops is brought infrastructure as code. You're seeing kind of this huge tsunami of new of applications, but one of the things that people are talking about in some of the developer circles and it's kind of hits the enterprise is this notion of state because you can have an application calling data, but if the data is not addressable and then keeping state and in real time and all these kinds of new, new technical problems, how do you guys look at that? When you look at trying to create knowledge graphs, because maintaining that level of connection, you need data, a ton of it it's gotta be exposed and addressable and then deal dealt with in real time. How do you guys look at it? >>Yeah, that's, that's a great question. What we've done to try to kind of move the ball forward on this is move past, trying to centralize that data into a knowledge graph that is separate from the rest of your data assets, but really build a data virtualization layer, which we have integrated into our product to look at the data where it is in the applications and the unstructured documents and the structure repositories, so that we can observe as state changes in that data and answer questions that are relevant at the time. And we don't have to worry about some sort of synchronous process, you know, loading information into the graph. So that ability to add that virtualization layer, uh, to the graph really enables you to get more of a real time, look at your data as it evolves. >>Yeah. I definitely want to double, double click on that and say, but I want to just drop step back and kind of set the table for the folks that aren't, um, getting in the weeds yet on this. There's kind of a specific definition of enterprise knowledge graph. Could you like just quickly define that? What is the enterprise knowledge graph? Sure. >>Yeah, we, we really see an enterprise knowledge graph as a connected set of data with context. So it's not just storing it like a graph, but connect again and putting meaning around that data through structure, through definitions, et cetera, across the entire enterprise. So looking at not just data within a single application or within a single silo, but broadly through your enterprise, what does your data mean? How is it connected and what does it look like within context each other? >>How should companies reuse their data? >>Boy, that's a broad question, right? Uh, you know, I mean, one of the things, uh, that I think is very important as so many companies have just collected data assets over the years, they collect more and more and more. We have customers that have eight petabytes of data within their data Lake. And they're trying to figure out how to leverage it by actually connecting and putting that context around the data. You can get a lot more meaning out of that old data or the stale data or the unknown data that the people are getting right today. So the ability to reuse the data assets with in context of meeting is where we see people really be able to make huge licks for in their organization like drug companies be able to get drugs to market faster. By looking at older studies, they've done where maybe the meeting was hidden because it was an old system. Nobody knew what the particular codes and meaning were in context of today. So being able to reuse and bring that forward brings real life application to people solving business problems today. >>Rob, I got to get your thoughts on something that we always riff on here on the cube, which is, um, you know, do you take down the data silos or do you leverage them? And you know, this came up a lot, many years ago when we first started discussing containers, for instance, and then that we saw that you didn't have to kill the old to bring in the new, um, there's one mindset of, you know, break the silos down, go horizontal scalability on the data, critical data, plane control, plane, other saying, Hey, you know what, just put it, you know, put a wrapper around those, those silos and you know, I'm oversimplifying, but you get the idea. So how should someone who's really struggling with, or, or not struggling, we're putting together an architecture around their future plans around dealing with data and data silos specifically, because certainly as new data comes in there's mechanism for that. But as you have existing data silos, what do companies do? What's the strategy in your opinion? >>Yeah, you know, it is a really interesting question. I was in data warehouse and for a long, long time and a big proponent of moving everything to one place. And, uh, then I really moved into looking into data virtualization and realized that neither of those solutions are complete, that there are some things that have to be centralized and moved the old systems aren't sufficient in order to be able to answer questions or process them. But there are many data silos that we've created within organizations that can be reused. You can leverage the compute, you can leverage the storage that already exist within us. And that's the approach we've taken at start off. We really want to be able to allow you to centralize the data that makes sense, right. To get it out of those old systems, that should be shut down from just a monetary perspective, but the systems that are have actual meeting or that it's too expensive in order to, to remove them, leverage those data silos. And by letting you have both approaches in the same platform, we hope to make this not an either or architectural decision, which is always the difficult question. >>Okay. So you got me on that one. So let me just say that. I want to leverage my data silos. What do I do? Take me through the playbook. What if I got the data silos? What is the star dog recommendation for me? >>Sure. So what, what we generally recommend is you start off with building kind of a model, uh, in the, in the lingo, we sometimes say ontology Euro, some sort of semantic understanding that puts context around what is my data and what does it mean? And then we allow you to map those data silos. We have a series of connectors in our product that whether it's an application and you're connecting through a rest connector, or whether it's a database and you're connecting through ODBC or JDBC map that data into the platform. And then when you issue queries to the startup platform, we federate those queries out to the downstream systems and answer as if that data existed on the graph. So that way we're leveraging the silos where they are without you having to move the data physically into the platform. So you guys are essentially building a >>Data fabric. >>We are, yeah. Data fabric is really the new term. That's been popping up more and more with our customers when they come to us to say, how can we kind of get past the traditional ways of doing data integration and unified data in a single place? Like you said, we don't think the answer is purely all about moving it all to one big Lake. We don't think the answer is all about just creating this virtualization plane, but really being able to leverage the festival. >>All right. So, so if you, if you believe that, then let's just go to the next level then. So if you believe that they can, don't have to move things around and to have one specific thing, how does a customer deal with their challenge of hybrid cloud and soon to be multi-cloud because that's certainly on the horizon. People want choice. There's going to be architectural. I mean, certainly a cloud operations will be in play, but this on-premise and this cloud, and then soon to be multiple cloud. How do you guys deal with that? That question? >>Yeah, that's a great question. And this is really a, an area that we're very excited about and we've been investing very heavily in is how to have multiple instances of StarTalk running in different clouds or on prem on the clown, coordinate to answer questions, to minimize data movement between the platforms. So we have the ability to run either an agent on prem. For example, if you're running the platform in the cloud or vice versa, you can run it in the cloud. You are two full instances that start off where they will actually cope plan queries to understand where does the data live? Where is it resident and how do I minimize moving data around in order to answer the question? So we really are trying to create that unified data fabric across on-prem or multiple cloud providers, so that any of the nodes in the platform can answer question from any of the datas >>S you know, complexity is always the issue. People cost go up. When you have complexity, you guys are trying to tame it. This is a huge conversation. You bring up multi-cloud and hybrid cloud. And multi-cloud when you think about the IOT edge, and you don't want to move data around, this is what everyone's saying, why move it? Why move data? It's expensive to move data processes where it is, and you kind of have this kind of flexibility. So this idea of unification is a huge concept. Is that enough? And how should customers think about the unification? Because if you can get there, it almost, it is the kind of the Holy grail you're talking about here. So, so this is kind of the prospect of, of having kind of an ideal architecture of unification. So take us, take me through that one step deeper. >>Well, it is, it is kind of interesting because as you really think about unifying your data and really bringing it together, of course it is the Holy grail. And that's what people have been talking about. Um, gosh, since I started in the industry over 20 years ago, how do I get this single plain view of my data, regardless of whether it's physically located or, uh, somehow stitched together, but what are the things that, you know, our founders really strongly believed on when they started the company? Was it isn't enough. It isn't sufficient. There is more value in your data that you don't even know. And unlocking that through either machine learning, which is, of course, we all know it's very hot right now to look at how do I derive new insights out of the data that I already have, or even through logical reasoning, right? And inference looking at, what do I understand about how that data is put together and how it's created in order to create more connections within the data and answer more questions. All those are ways to grow beyond just unifying your data, but actually getting more insights out of it. And I think that is the real Holy grail that people are looking for, not just bringing all the data together, but actually being able to get business value and insights out of that data. Yeah. >>Looking for it. You guys have obviously a pretty strong roster of clients that represent that. Um, but I got to ask you, since you brought up the founders, uh, the company, obviously having a founders' DNA, uh, mindset, um, tends to change the culture or drive the culture of the covenant change with age drives the culture of the company. What is the founder's culture inside star, dog? What is the vibe there, if you could, um, what do they talk about the most when you, when they get in that mode of being founders like, Hey, you know, this is the North star, what is, what's the rap like? What's the vibe share? It takes that, take us through some star star, dog culture. >>Sure. So our three founders came out of the rusty of Maryland, all in a PhD program around semantic reasoning and logical understanding and being able to understand data and be able to communicate that as easily as possible is really the core and the fiber of their being. And that's what we see continually under discussion every single day. How can we push the limits to take this technology and your gift easier to use more available, bring more insights to the customers beyond what we've seen in the past. And I find that really exciting to be able to constantly have conversations about how do we push the envelope? How do we look beyond even what Gartner says is five or eight years in the future, but looking even further ahead. So there >>They're into they're into this whole data scene. Then big time they are >>That they are very active in the conferences and posts and you know, all that great. >>They love this agility. They got to love dev ops. I mean, if you're into this knowledge graph scene, so I gotta, I gotta ask you, what's the machine learning angle here, obviously, AI, we know what AI is. AI is essentially combination of many things, machine learning and other computer science and data access. Um, what is the secret sauce behind the machine learning and, and the vibe and the product of, of, uh, >>Yeah, a lot of times w we, the way that we leverage machine learning or the way that we look at it is how do we create those connections between data? So you have multiple different systems and you're trying to bring all that data together. Yeah. It's not always easy to tell, is this rod Harris the same as that rod Harris is this product the same as that product. So when possible we will leverage keys or we'll leverage very, uh, you know, systematic type of understanding of these things are the same, but sometimes you need to reach beyond that. And that's where we leverage a lot of machine learning within the platform, looking at things like linear regression or other approaches around the graph, you know, connectivity, analysis, page rank, things like that to say, where are things the same so that we can build that connections in that connectivity as automatically as possible. >>You don't get a lot of talks on the cube. Also. Now that's new news, new clubhouse app, where people are talking about misinformation, obviously we're in the media business. We love the digital network effect. Everything's networks, the network economy. You starting to see this power of information and value. You guys carved the knowledge graph. So I gotta, I gotta ask you, when you look at this kind of future where you have this, um, complexity and the network effect, um, how are you guys looking at that data access? Because if you don't have the data, you're not going to have that insight, right? So you need to have that, that network connection. Is that a limitation or for companies? Is that an, um, cause usually people aren't necessarily their blind spot is their data or their lack of their data. So having things network together is going to be more of the norm in the future. How do you guys see that playing out? Yeah, >>I think you're exactly right. And I think that as you look beyond where we are today, and a lot of times we focus today on the data that a company already has, what do I know? Right. What do I know about you? What, how do I interact with you? How have I interacted with you? I think that as we look at the future, we're going to talk more about data sharing, but leveraging publicly available information about being able to take these insights and leverage them, not just within the walls of my own organization, but being able to share them and, uh, work together with other organizations to bring up a better understanding of you as a person or as a consumer that we could all interact with. Yeah, you're absolutely right. You know, Metcons law still holds true that, you know, more network connections bring more value. I certainly see that growing in the future, probably more around, you know, more data sharing and more openness about leveraging publicly available. >>You know, it's interesting. You mentioned you came from a data warehouse background. I remember when I broken the businessmen 30 years ago, when I started getting computer science, you know, it was, it was, there was, there was pain having a product and an enabling platform. You guys seem to have this enabling platform where there's no one use case. I mean, you, you have an unlimited use case landscape. Um, you could do anything with what you guys have. It's not so much, I mean, there's, low-hanging fruit. So I got to ask you, if you have that, uh, enabling platform, you're creating value for customers. What are some of the areas you see developing, like now in terms of low-hanging fruit and where's the possibilities? How do you guys see that? I'm sure you've probably got a tsunami of activity around corner cases from media to every vertical we do. And that's, you know, >>The exciting part of this job. Uh, part of the exciting part of knowledge press in general is to see all the different ways that they are allowed to use. But we do see some use cases repeated over and over again. Uh, risk management is a very common one. How do I look at all the people and the assets with an organization, the interactions they have to look at hotspots for risk, uh, that I need to correct within my organization for the pre-commercial pharma, that has been a very, very hot area for us recently. How do we look at all the that's available with an organization that's publicly available in order to accelerate drug development in this post COVID world, that's become more and more relevant, uh, for organizations to be able to move forward faster and the kind of bio industry and my sciences. Um, that's a use case that we've seen repeated over and over again. And then this growing idea of the data fabric, the data fabric, looking at metadata within the organization to improve data integration processes, to really reduce the need for moving data without or around the organization as much. Those are the use cases we've seen repeated over and over again over the last >>Awesome Rob. My last question before we wrap up is for the solution architect that's out there that has, you know, got a real tall order. They have to put together a scalable organization, people process and technology around a data architecture. That's going to be part of, um, the next gen, the next gen next level activity. And they need headroom for IOT edge and industrial edge, uh, and all use cases. Um, what's your advice to them as they have to look out at and start thinking about architecture? >>Yeah, that's, it's a great question. Uh, I really think that it's important to keep your options open as the technology in the space continues to evolve, right? It's easy to get locked into a single vendor or a single mindset. Um, I've been an architect most of my career, and that's usually a lot of the pitfalls. Things like a knowledge graph are open and flexible. They adhere to standards, which then means you're not locked into a single vendor and you're allowed to leverage this type of technology to grow beyond originally envisioned. So thinking about how you can take advantage of these modern techniques to look at things and not just keep repeating what you've done in the past, the sins of the past have, uh, you know, a lot of times do reappear. So fighting against that as much as possible as gritty is my encouragement. >>Awesome, great insight. And I love this. I love this area. I know you guys got a great trend. You're riding on a very cool, very relevant final minute. Just take a quick minute to give a plug for the company. What's the business model. How do I deploy this? How do I get the software? How do you charge for it? If I'm going to buy this solution or engage with star DOE what do I do? Take me through that. Sure. >>Yeah. We, uh, we are like, uh, you've sat through this whole thing. We are enterprise knowledge graph platform company. So we really help you get started with your business, uh, uh, leveraging and using a knowledge graph fricking organization. We have the ability to deploy on prem. We have on the cloud, we're in the AWS marketplace today. So you can take a look at our software today, who generally are subscription-based based on the size of the install. And we are happy to talk to you any time, just drop by our website, reach out we'll we'll get doctors. >>Rob. Great. Thanks for coming. I really appreciate it. That gradients said, looking forward to seeing you in person, when we get back to real life, hopefully the vaccines are coming on. Thanks to, uh, companies like you guys providing awesome analytics and intelligence for these drug companies and pharma companies. Now you have a few of them in your, on your client roster. So congratulations, looking forward to following up great, great area. Cool and relevant data architecture is changing. Some of it's broken. Some it's being fixed started off as one of the hot startups scaling up beautifully in this new era of cloud computing meets applications and data. So I'm John. Forget the cube. This is a cube conversation from Palo Alto, California. Thanks for watching.