>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

>> Narrator: It's theCUBE covering Sapphire Now 2017 brought to you by SAP Cloud Platform and HANA Enterprise Cloud. >> Welcome back everyone to our special Sapphire Now 2017 coverage in our Palo Alto Studios. We have folks on the ground in Orlando. It's the third day of Sapphire Now and we're bringing our friends and experts inside our new 4500 square foot studio where we're starting to get our action going and covering events anywhere they are from here. If we can't get there we'll do it from here in Palo Alto. Our next guest is Satyen Sangani, CEO of Alation. A hot start-up funded by Custom Adventures, Catalyst Data Collective, and I think Andreessen Horowitz is also an investor? >> Satyen: That's right. >> Satyen, welcome to the cube conversation here. >> Thank you for having me. >> So we are doing this special coverage, and I wanted to bring you in and discuss Sapphire Now as it relates to the context of the biggest wave hitting the industry, with waves are ones cloud. We've known that for a while. People surfing that one, then the data wave is coming fast, and I think this is a completely different animal in the sense of it's going to look different, but be just as big. Your business is in the data business. You help companies figure this out. Give us the update on, first take a minute talk about Alation, for the folks who aren't following you, what do you guys do, and then let's talk about data. >> Yeah. So for those of you that don't know about what Alation is, it's basically a data catalog. You know, if you think about all of the databases that exist in the enterprise, stuff on Prem, stuff in the cloud, all the BI tools like Tableau and MicroStrategy, and Business Objects. When you've got a lot of data that sits inside the enterprise today and a wide variety of legacy and modern tools, and what Alation does is, it creates a catalog, crawling all of those systems like Google crawls the web and effectively looks at all the logs inside of those systems, to understand how the data is interrelated and we create this data social graph, and it kind of looks >> John: It's a metadata catalog? >> We call you know, we don't use the word metadata because metadata is the word that people use when you know that's that's Johnny back in the corner office, Right? And people don't want to talk about metadata if you're a business person you think about metadata you're like, I don't, not my thing. >> So you guys are democratizing what data means to an organization? That's right. >> We just like to talk about context. We basically say, look in the same way that information, or in the same way when you're eating your food, you need, you know organic labeling to understand whether or not that's good or bad, we have on some level a provenance problem, a trust problem inside of data in the enterprise, and you need a layer of you know trust, and understanding in context. >> So you guys are a SAS, or you guys are a SAS solution, or are you a software subscription? >> We are both. Most of this is actually on Prem because most of the people that have the problem that Alation solves are very big complicated institutions, or institutions with a lot of data, or a lot of people trying to analyze it, but we do also have a SAS offering, and actually that's how we intersect with SAP Altiscale, and so we have a cloud base that's offering that we work with. >> Tell me about your relation SAP because you kind of backdoored in through an acquisition, quickly note that we'll get into the conversation. >> Yeah that's right, So Altiscale to big intersections, big data, and then they do big data in the cloud SAP acquired them last year and what we do is we provide a front-end capability for people to access that data in the cloud, so that as analysts want to analyze that data, as data governance folks want to manage that data, we provide them with a single catalog to do that. >> So talk about the dynamics in the industry because SAP clearly the big news there is the Leonardo, they're trying to create this framework, we just announced an alpha because everyone's got these names of dead creative geniuses, (Satyen laughs) We just ingest our Nostradamus products, Since they have Leonardo and, >> That's right. >> SAP's got Einstein, and IBM's got Watson, and Informatica has got Claire, so who thought maybe we just get our own version, but anyway, everyone's got some sort of like bot, or like AI program. >> Yep. >> I mean I get that, but the reality is, the trend is, they're trying to create a tool chest of platform re-platforming around tooling >> Satyen: Yeah. >> To make things easier. >> Satyen: Yeah. >> You have a lot of work in this area, through relation, trying to make things easier. >> Satyen: Yeah. >> And also they get the cloud, On-premise, HANA Enterprise Cloud, SAV cloud platform, meaning developers. So the convergence between developers, cloud, and data are happening. What's your take on that strategy? You think SAP's got a good move by going multi cloud, or should they, should be taking a different approach? >> Well I think they have to, I mean I think the economics in cloud, and the unmanageability, you know really human economics, and being able to have more and more being managed by third-party providers that are, you know, effectively like AWS, and how they skill, in the capability to manage at scale, and you just really can't compete if you're SAP, and you can't compete if your customers are buying, and assembling the toolkits On-premise, so they've got to go there, and I think every IT provider has to >> John: Got to go to the cloud you mean? >> They've got to go to the cloud, I think there's no question about it, you know I think that's at this point, a foregone conclusion in the world of enterprise IT. >> John: Yeah it's pretty obvious, I mean hybrid cloud is happening, that's really a gateway to multi-cloud, the submission is when I build Norton, a guest in latency multi-cloud issues there, but the reality is not every workloads gone there yet, a lot of analytics going on in the cloud. >> Satyen: Yeah. >> DevTest, okay check the box on DevTest >> Satyen: That's right. >> Analytics is all a ballgame right now, in terms of state of the art, your thoughts on the trends in how companies are using the cloud for analytics, and things that are challenges and opportunities. >> Yeah, I think there's, I think the analytics story in the cloud is a little bit earlier. I think that the transaction processing and the new applications, and the new architectures, and new integrations, certainly if you're going to build a new project, you're going to do that in the cloud, but I think the analytics in a stack, first of all there's like data gravity, right, you know there's a lot of gravity to that data, and moving it all into the cloud, and so if you're transaction processing, your behavioral apps are in the cloud, then it makes sense to keep the data in an AWS, or in the cloud. Conversely you know if it's not, then you're not going to take a whole bunch of data that sits on Prem and move it whole hog all the way to the cloud just because, right, that's super expensive, >> Yeah. >> You've got legacy. >> A lot of risks too and a lot of governance and a lot of compliance stuff as well. >> That's exactly right I mean if you're trying to comply with Basel II or GDPR, and you know you want to manage all that privacy information. How are you going to do that if you're going to move your data at the same time >> John: Yeah. >> And so it's a tough >> John: Great point. >> It's a tough move, I think from our perspective, and I think this is really important, you know we sort of say look, in a world where data is going to be on Prem, on the cloud, you know in BI tools, in databases and no SQL databases, on Hadoop, you're going to have data everywhere, and in that world where data is going to be in multiple locations and multiple technologies you got to figure out a way to manage. >> Yeah. I mean data sprawls all over the place, it's a big problem, oh and this oh and by the way that's a good thing, store it to your storage is getting cheaper and cheaper, data legs are popping out, but you have data links, for all you have data everywhere. >> Satyen: That's right. >> How are you looking at that problem as a start-up, and how a customer's dealing with that, and what is this a real issue, or is this still too early to talk about data sprawl? >> It's a real issue, I mean it, we liken it to the advent of the Internet in the time of traditional media, right, so you had you had traditional media, there were single sort of authoritative sources we all watched it may be CNN may be CBS we had the nightly news we had Newsweek, we got our information, also the Internet comes along, and anybody can blog about anything, right and so the cost of creating information is now this much lower anybody can create any reality anybody can store data anywhere, right, and so now you've got a world where, with tableau, with Hadoop, with redshift, you can build any stack you want to at any cost, and so now what do you do? Because everybody's creating their own thing, every Dev is doing their own thing, everybody's got new databases, new applications, you know software is eating the world right? >> And data it is eating software. >> And data is eating software, and so now you've got this problem where you're like look I got all this stuff, and I don't know I don't know what's fake news, what's real, what's alternative fact, what doesn't make any sense, and so you've got a signal and noise problem, and I think in that world you got to figure out how to get to truth, right, >> John: Yeah. And what's the answer to that in your mind, not that you have the answer, if you did, we'd be solving it better. >> Yeah. >> But I mean directionally where's the vector going in your mind? I try to talk to Paul Martino about this at bullpen capital he's a total analytics geek he doesn't think this big data can solve that yet but they started to see some science around trying to solve these problems with data. What's your vision on this? >> Satyen: Yeah you know so I believe that every I think that every developer is going to start building applications based on data I think that every business person is going to have an analytical role in their job because if they're not dealing with the world on the certainty, and they're not using all the evidence, at their disposable, they're not making the best decisions and obviously they're going to be more and more analysts and so you know at some level everybody is an analyst >> I wrote a post in 2008, my old blog was hosted on WordPress, before I started SilicionANGLE, data is the new developer kid. >> That's right. >> And I saw that early, and it was still not as clear to this now as obvious as least to us because we're in the middle, in this industry, but it's now part of the software fabric, it's like a library, like as developer you'd call a library of code software to come in and be part of your program >> Yeah >> Building blocks approach, Lego blocks, but now data as Lego blocks completely changes the game on things if you think of it that way. Where are we on that notion of you really using data as a development component, I mean it seems to be early, I don't, haven't seen any proof points, that says, well that company's actually using the data programmatically with software. >> Satyen: Yeah. well I mean look I think there's features in almost every software application whether it's you know 27% of the people clicked on this button into this particular thing, I mean that's a data based application right and so I think there is this notion that we talked a lot about, which is data literacy, right, and so that's kind of a weird thing, so what does that exactly mean? Well data is just information like a news article is information, and you got to decide whether it's good or it's bad, and whether you can come to a conclusion, or whether you can't, just as if you're using an API from a third-party developer you need documentation, you need context about that data, and people have to be intelligent about how they use it. >> And literacies also makes it, makes it addressable. >> That's right. >> If you have knowledge about data, at some point it's named and addressed at some point in a network. >> Satyen: Yeah. >> Especially Jada in motion, I mean data legs I get, data at rest, we start getting into data in motion, real-time data, every piece of data counts. Right? >> That's exactly right. And so now you've got to teach people about how to use this stuff you've got to give them the right data you got to make that discoverable you got to make that information usable you've got to get people to know who the experts are about the data, so they can ask questions, you know these are tougher problems, especially as you get more and more systems. >> All right, as a start up, you're a growing start-up, you guys are, are lean and mean, doing well. You have to go compete in this war. It's a lot of, you know a lot of big whales in there, I mean you got Oracle, SAP, IBM, they're all trying to transform, everybody is transforming all the incumbent winners, potential buyers of your company, or potentially you displacing this, as a young CEO, they you know eat their lunch, you have to go compete in a big game. How are you guys looking at that compass, I see your focus so I know a little bit about your plan, but take us through the mindset of a start-up CEO, that has to go into this world, you guys have to be good, I mean this is a big wave, see it's a big wave. >> Yeah. Nobody buys from a start-up unless you get, and a start-up could be even a company, less than a 100-200 people, I mean nobody's buying from a company unless there's a 10x return to value relative to the next best option, and so in that world how do you build 10x value? Well one you've got to have great technology, and then that's the start point, but the other thing is you've got to have deep focus on your customers, right, and so I think from our perspective, we build focus by just saying, look nobody understands data in your company, and by and large you've got to make money by understanding this data, as you do the digital transformation stuff, a big part of that is differentiating and making better products and optimizing based upon understanding your data because that helps you and your business make better decisions, >> John: Yeah. >> And so what we're going to do is help you understand that data better and faster than any other company can do. >> You really got to pick your shots, but what you're saying, if I hear you saying is as a start-up you got to hit the beachhead segment you want to own. >> Satyen: That's right. >> And own it. >> Satyen: That's exactly. >> No other decision, just get it, and then maybe get to a bigger scope later, and sequence around, and grow it that way. >> Satyen: You can't solve 10 problems >> Can't be groping for a beachhead if you don't know what you want, you're never going to get it. >> That's right. You can't solve 10 problems unless you solve one, right, and so you know I think we're at a phase where we've proven that we can scalably solved one, we've got customers like, you know Pfizer and Intuit and Citrix and Tesco and Tesla and eBay and Munich Reinsurance and so these are all you know amazing brands that are traditionally difficult to sell into, but you know I think from our perspective it's really about focus and just helping customers that are making that digital analytical transformation. Do it faster, and do it by enabling their people. >> But a lot going on this week for events, we had Informatica world this week, we got V-mon. We had Google I/O. We had Sapphire. It's a variety of other events going on, but I want to ask you kind of a more of a entrepreneurial industry question, which is, if we're going through the so-called digital transformation, that means a new modern era an old one movie transformed, yet I go to every event, and everyone's number one at something, that's like I was just at Informatica, they're number one in six squadrons. Michael Dell we're number in four every character, Mark Hurr at the press meeting said they're number one in all categories, Ross Perot think quote about you could be number one depends on how you slice the market, seems to be in play, my point is I kind of get a little bit, you know weirded out by that, but that is okay, you know I guess theCUBE's number one in overall live videos produced at an enterprise event, you know I, so we're number one at something, but my point is. >> Satyen: You really are. >> My point is, in a new transformation, what is the new scoreboard going to look like because a lot of things that you're talking about is horizontally integrated, there's new use cases developing, a new environment is coming online, so if someone wanted to actually try to keep score of who number one is and who's winning, besides customer wins, because that's clearly the one that you can point to and say hey they're winning customers, customer growth is good, outside of customer growth, what do you think will be the key requirements to get some sort of metric on who's really doing well these are the others, I mean we're not yet there with >> Yeah it's a tough problem, I mean you know used to be the world was that nobody gets fired for choosing choosing IBM. >> John: Yeah. >> Right, and I think that that brand credibility worked in a world where you could be conservative right, in this world I think, that looking for those measures, it is going to be really tough, and I think on some level that quest for looking for what is number one, or who is the best is actually the sort of fool's errand, and if that's what you're looking for, if you're looking for, you know what's the best answer for me based upon social signal, you know it's kind of like you know I'm going to go do the what the popular kids do in high school, I mean that could lead to you know a path, but it doesn't lead to the one that's going to actually get you satisfaction, and so on some level I think that customers, like you are the best signal, you know, always, >> John: Yeah, I mean it's hard, it's a rhetorical question, we ask it because, you know, we're trying to see not mystical with the path of fact called the fashion, what's fashionable. >> Satyen: Yeah. >> That's different. I mean talk about like really a cure metro, in the old days market share is one, actually IDC used a track who had market shares, and they would say based upon the number of shipments products, this is the market share winner, right? yeah that's pretty clean, I mean that's fairly clean, so just what it would be now? Number of instances, I mean it's so hard to figure out anyway, I digress. >> No, I think that's right, I mean I think I think it's really tough, that I think customers stories that, sort of map to your case. >> Yeah. It all comes back down to customer wins, how many customers you have was the >> Yeah and how much value they are getting out of your stuff. >> Yeah. That 10x value, and I think that's the multiplier minimum, if not more and with clouds and the scale is happening, you agree? >> Satyen: Yeah. >> It's going to get better. Okay thanks for coming on theCUBE. We have Satyen Sangani. CEO, co-founder of Alation, great start-up. Follow them on Twitter, these guys got some really good focus, learning about your data, because once you understand the data hygiene, you start think about ethics, and all the cool stuff happening with data. Thanks so much for coming on CUBE. More coverage, but Sapphire after the short break. (techno music)