(upbeat electronic music) >> As we've previously reported on theCUBE, Alation was an early pioneer in the data, data governance, and data management space, which is now rapidly evolving with the help of AI and machine learning, and to what's often referred to as data intelligence. Many companies, you know, they didn't make it through the last era of data. They failed to find the right product market fit or scale beyond their close circle of friends, or some ran out of money or got acquired. Alation is a company who did make it through, and has continued to attract investor support, even in a difficult market where tech IPOs have virtually dried up. Back with me on theCUBE is Satyen Sangani, who's the CEO and co-founder of Alation. Satyen, good to see you again. Thanks for coming on. >> Great to see you, Dave. It's always nice to be on theCUBE. >> Hey, so remind our audience why you started Alation 10 years ago, you and your co-founders, and what you're all about today. >> Alation's vision is to empower a curious and rational world, which sounds like a really, I think, presumptuous thing to say. But I think it's something that we really need, right? If you think about how people make decisions, often it's still with bias or ideology, and we think a lot of that happens because people are intimidated by data, or often don't know how to use it, or don't know how to think scientifically. And we, at the core, started Alation because we wanted to demystify data for people. We wanted to help people find the data they needed and allow them to use it and to understand it better. And all of those core consumption values around information were what led us to start the company, because we felt like the world of data could be a little easier to use and manage. >> Your founding premise was correct. I mean, just getting the technology to work was so hard, and as you well know, it takes seven to 10 years to actually start a company and get traction, let alone hit escape velocity. So as I said in the open, you continue to attract new investors. What's the funding news? Please share with us. >> So we're announcing that we raised 123 million from a cohort of investors led by Thoma Bravo, Sanabil Investments, and Costanoa. Databricks Ventures is a participant in that round, along with many of our other existing investors, which would also include Salesforce amongst others. And so, super excited to get the round done in this interesting market. We were able to do that because of the business performance, and it was an up round, and all of that's great and gives our employees and our customers the fuel they need to get the product that they want. >> So why the E Round? Explain that. >> So, we've been accelerating growth over the last five quarters since our Series D. We've basically increased our growth rate to almost double since the time we raised our last round. And from our perspective, the data intelligence market, which is the market that we think we have the opportunity to continue to be the leading platform in, is growing super fast. And when faced with the decision of decelerating growth in the face of what might be, what could be a challenging macroeconomic environment, and accelerating when we're seeing customers increase the size of their commitments, more new customers sign on than ever, our growth rates increasing. We and the board basically chose to take the latter approach and we sort of said, "Look, this is amazing time in this category. This is an amazing time in this company. It's time to invest and it's time to be aggressive when a lot of other folks are fearful, and a lot of other folks aren't seeing the traction that we're seeing in our business. >> Why do you think you're seeing that traction? I mean, we always talk about digital transformation, which was a buzzword before the pandemic, but now it's become a mandate. Is that why? Is it just more data related? Explain that if you could. >> I think there's this potentially, you know, somewhat confusing thing about data. There's a, maybe it's a dirty secret of data, which is there's the sense that if you have a lot of data, and you're using data really well, and you're producing a ton of data, that you might be good at managing it. And the reality of it is that as you have more people using data and as you produce more data, it just becomes more and more confusing because more and more people are trying to access the same information to answer different questions, and more workloads are produced, and more applications are produced. And so the idea of getting more data actually means that it's really hard to manage and it becomes harder to manage at scale. And so, what we're seeing is that with the advent of platforms like AWS, like Snowflake, like Databricks, and certainly with all of the different on-premise applications that are getting born every single day, we're just seeing that data is becoming really much more confusing, but being able to navigate it is so much more important because it's the lifeblood for any business to build differentiation and satisfy their customers. >> Yeah, so last time we talked, we talked about the volume and velocity bromide from the last decade, but we talked about value and how hard it is to get value. So that's really the issue is the need and desire for more organizations to get more value out of that data is actually a stronger tailwind than the headwinds that you're seeing in the macroeconomic environment. >> Right. Because I think in good times you need data in order to be able to capitalize off all the opportunities that you've got, but in bad times you've got to make hard choices. And when you need to make hard choices, how do you do that? Well, you've got to figure out what the right decisions are, and the best way to do that is to have a lot of data and a lot of people who understand that data to be able to capitalize on it and make better insights and better decisions. And so, you don't see that just, by the way, theoretically. In the last quarter, we've seen three companies that have had cost reductions and force reductions where they are increasing at the same time their investment with Alation. And it's because they need the insight in order to be able to navigate these challenging times. >> Well, congratulations on the up round. That's awesome. I got to ask you, what was it like doing a raise in this environment? I mean, sellers are in control in the public markets. Late stage SaaS companies, that had to be challenging. How did you go about this? What were the investor conversations like? >> It certainly was a challenging fundraise. And I would say even though our business is doing way better and we were able to attract evaluation that would put us in the top quartile of public companies were we trading as a public company, which we aspire to do at some point, it was challenging because there was a whole slew of investors who were basically sitting on their hands. I had one investor conversation where an investor said to me, "Look, we think you're a great business, but we have companies that are able to give us 2.5 liquidation preference, and that gives us 70%, 75% of our return day one. So we're just going to go do those companies that may have been previously overvalued, but are willing to give us these terms because they want to keep their face valuation." Other investors said, "Look, we'd really rather that you ran a lower growth plan but with a potentially lower burn plan. But we think the upside is really something that you can capitalize on." From our perspective, we were pretty clear about the plan that we wanted to run and didn't want to necessarily totally accommodate to the fashion of the current market. We've always run a historically efficient business. The company has not burned as much as many of the data peers that we've seen to grow to get to our scale, but our general view was, look, we've got a really clear plan. The board, and the company, and the management team know exactly what we'd like to do. We've got customers that know exactly what they want from us, so we really just have to go execute. And the luck is that we found investors who were willing to do that. Many investors, and we picked one in Thoma Bravo that we felt could be the best partner for the coming phase of the company. >> So I love that because you see the opportunity, you've had a very efficient business. You're punching above your weight in terms of your use of capital. So you don't want to veer off. You know your business better than anybody. You don't want to veer off that plan. The board's very supportive. I could see you, you hear it all the time, we're going to dial down the growth, dial up the EBIT, and that's what markets want today. So congratulations on sticking to your beliefs and your vision. How do you plan to use the funds? >> We are planning to invest in sales and marketing globally. So we've expanded in Asia-Pacific over the most recent year, and also in (indistinct) and we plan to continue to do that. We're going to continue to expand in public sector with fed. And so, you would see us basically just increase our presence globally in all of the markets that you might expect. In particular, you're going to see us lean in heavily to many of the partners Databricks invested alongside this particular round. But you would have seen previously that Snowflake was a fabulous, and has been a fabulous partner of ours, and we are going to continue to invest alongside these leading data platforms. What you would also expect to see from us, though, is a lot of investment in R&D. This is a really nascent category. It's a really, really hard space. People would call it a crowded market because there are a lot of players. I think from our perspective, our aspirations to be the leading data intelligence platform, platform being a really key word there because it's not like we can do it all ourselves. We have a lot of different use cases in data intelligence, things like data quality and data observability, things like data privacy and data access control. And we have some really great partners that we walk alongside in order to make the end customer successful. I think a lot of folks in this market think, "Oh, we can just be master of all. Sort of jack of all trades, master of none." That is not our strategy. Our strategy is to really focus on getting all our customers super successful, really focused on engagement and adoption, because the really hard thing with these platforms is to get people to use them, and that is not a problem Alation has had historically. >> You know, it's really interesting, Satyen, you talk about, I mean, Thoma Bravo, obviously, very savvy investors, deep pockets, they've been making some moves. Certainly we've seen that in cyber security and data. So you got some quasi patient capital there. But the interesting thing to me is that the previous Snowflake investment last year and now Databricks, a lot of people think of them as sort of battling it out, but my view is it's not a zero sum game, meaning, yes, there's overlap, but they're filling a lot of gaps in the marketplace, and I think there's room, there's so much opportunity, and there's such a large tam, that partnering with both is a really, really smart idea. I'll give you the last word. Going forward, what can we expect from Elation? >> Well, I think that's absolutely true, and I think that the biggest boogeyman with all of this is that people don't use data. And so, our ability to partner together is really just a function of making customers successful and continuing to do that. And if we can do that, all companies will grow. We ended up ultimately partnering with Databricks and deepening our partnership, really, 'cause we had one already, primarily because of the fact that we have over a hundred customers that are jointly using the products today. And so, it certainly made sense for us to continue to make that experience better 'cause customers are demanding it. From my perspective, we just have this massive opportunity. We have the ability and the insight to run a really efficient, very, very high growth business at scale. And we have this tremendous ability to get so many more companies and people to use data much more efficiently and much better. Which broadly is, I think, a way in which we can impact the world in a really positive way. And so that's a once in a lifetime opportunity for me and for the team. And we're just going to get after it. >> Well, it's been fun watching Alation over the years. I remember mid last decade talking about this thing called data lakes and how they became data swamps, and you were helping clean that up. And now, the next 10 years, and data's not going to be like the last, you know, simplifying things and and really democratizing data is the big theme. Satyen, thanks for making time to come back on theCUBE, and congratulations on the raise. >> Thank you, Dave. It's always great to see you. >> And thank you for watching this conversation with the CEO in theCUBE, your leader in enterprise and emerging tech coverage. (gentle electronic music)

(tranquil music) >> Alation was an early pioneer in solving some of the most challenging problems in so-called big data. Founded early last decade, the company's metadata management and data catalog have always been considered leading examples of modern tooling by customers and analysts alike. Governance is one area that customers identified as a requirement to extend their use of Alation's platform. And it became an opportunity for the company to expand its scope and total available market. Alation is doing just that today, announcing new data governance capabilities, and partner integrations that align with the market's direction of supporting federated governance. In other words, a centralized view of policy to accommodate distributed data in this world of an ever expanding data cloud, which we talk about all the time in theCUBE. And with me to discuss these trends and this announcement is Satyen Sangani, who's the CEO and co-founder of Alation. Satyen, welcome back to the CUBE. Good to see you. >> Thank you Dave, It's great to be back. >> Okay, so you heard my open, please tell us about the patterns that you were seeing in the market and what you were hearing from customers that led you in this direction and then we'll get into the announcement. >> Yeah, so I think there are really two patterns, right? I mean, when we started building this notion of a data catalog, as you said a decade ago, there was this idea that metadata management broadly classified was something that belonged in IT, lived in IT and was essentially managed by IT, right? I always liken it to kind of an inventory management system within a warehouse relative to Amazon.com, which has obviously broadly published for the business. And so, with the idea of bringing all of this data directly to the business and allowing people arbitrarily, depending on their role to use the data. You know, you saw one trend, which was just this massive, shift in how much data was available at any given time. I think the other thing that happened was that at the same time, data governance went through a real transitionary phase where there was a lot of demand often spurred by regulations. Whether that's GDPR, CCPA or more recently than that, certainly the Basel accord. And if you think about all of those regulations, people had to get something in a place. Now what we ended up finding out was when we were selling in to add accounts, people would say, well guess what? I've got this data governance thing going on, but nobody's really using it. I built this business glossary, it's been three years. Nothing's been really very effective. And we were never able to get the value and we need to get value because there are so many more people now accessing and using and leveraging the data. And so with that, we started really considering whether or not we needed to build a formal capability in the market. And that's what we're today that we're doing. >> I liked the way you framed that. And if you think back, we were there as you were in the early big day-to-days. And all the talk was about volume, variety and velocity. And those are sort of IT concepts. How do you deal with all these technical challenges? And then the fourth V which you just mentioned was value. And that's where the line of business really comes in. So let's get into the news. What are you announcing today? >> So we're announcing a new application on top of Alation's Catalog platform, which is an Alations data governance application. That application will be released with our 2021.3 release on September 14th. And what's exciting about that is that we are going to now allow customers to discreetly and elegantly and quickly consume a new application to get data governance regimes off the ground and initiatives off the ground, much more quickly than they've ever been able to do. This app is really all about time to value. It's about allowing customers to be able to consume what they need when they need it in order to be able to get successful governance initiatives going. And so that's what we're trying to deliver. >> So maybe you could talk a little bit about how you think about data governance and specifically your data governance approach. And maybe what's different about Alation's solution. >> Yeah, I think there's a couple of things that are different. I think the first thing that's most critically different is that we move beyond this notion of sort of policy declaration into the world of policy application and policy enforcement, right? I think a lot of data governance regimes basically stand up and say, look you know, it's all about people and then process and then technology. And what we need to do is declare who all the governors are and who all the stewards are. And then we're going to get all our policies in the same place and then the business will follow them. And the reality is people don't change their workflows to go off and arbitrarily follow some data governance policy that they don't know exists, or they don't want to actually have to follow up. And so really what you've got to do is make sure that the policy and the knowledge exists as in where the data exists. And that's why it's so critical to build governance into the catalog. And so what we're doing here is we're basically saying, look, you could declare policies with a new policy center inside of Alation. Those policies will get automatically created in some cases by integrating with technologies like Snowflake. But beyond that, what we're also doing is we're saying, look, we're going to move into the world of taking those policies and applying them to the data on an automated basis using ML and AI and basically saying that now it doesn't have to be some massive boil the ocean three-year regime to get very little value in a very limited business loss rate. Rather all of your data sets, all of your terms can be put into a single place on an automated basis. That's constantly being used by people and constantly being updated by the new systems that are coming online. And that's what's exciting about it. >> So I just want to follow up on that. So if I'm hearing you correctly, it's the humans are in the loop, but it's not the only source of policy, right? The machines are assisting. And in some cases managing end-to-end that policy. Is that right? >> You've got it. I think the the biggest challenge with data governance today is that it basically relies a little bit like the Golden Gate Bridge. You know, you start painting it and by the time you're done painting it, you've got to go back and start painting it again, because it relies upon people. And there's just too much change in the weather and there's too much traffic and there's just too much going on in the world of data. And frankly in today's world, that's not even an apt analogy because often what happens is midway through. You've got to restart painting from the very beginning because everything's changed. And so there's so much change in the IT landscape that the traditional way of doing data governance just doesn't work. >> Got it, so in winning through the press release, three things kind of stood out. I wonder if we could unpack them, there were multi-cloud, governance and security. And then of course the AI or what I like to call machine intelligence in there. And what you call the people centric approach. So I wonder if we could dig in into these and help us understand how they fit together. So thinking about multi-cloud governance, how do you think about that? Why is that so challenging and how are you solving that problem? >> Yeah, well every cloud technology provider has its own set of capabilities and platforms. And often those slight differences are causing differences in how those technologies are adopted. And so some teams optimize for certain capabilities and certain infrastructure over others. And that's true even within businesses. And of course, IT teams are also trying to diversify their IT portfolios. And that's another reason to go multi-cloud. So being able to have a governance capability that spans, certainly all of the good grade called megascalers, but also these new, huge emerging platforms like Snowflake of course and others. Those are really critical capabilities that are important for our customers to be able to get a handle on top of. And so this idea of being cloud agnostic and being able to sort of have a single control plane for all of your policies, for all of your data sets, that's a critical must have in a governance regime today. So that's point number one. >> Okay and then the machine learning piece, the AI, you're obviously injecting that into the application, but maybe tell us what that means both maybe technically and from a business stand point. >> Yeah, so this idea of a data policy, right? Can be sometimes by operational teams, but basically it's a set of rules around how one should and should not be able to use data, right? And so those are great rules. It could be that people who are in one country shouldn't be able to access the data of another country, very simple role, right? But how do you actually enforce that? Like you can declare it, but if there is a end point on a server that allows you to access the data, the policy is effectively moot. And so what you got to go do is make sure that at the point of leverage or at the point of usage, people know what the policy happens to be. And that's where AI come in. You can say let's document all the data sets that happened to be domiciled in Korea or in China. And therefore make sure that those are arbitrarily segregated so that when people want to use that as datasets, they know that the policy exists and they know that it's been applied to that particular dataset. That's somewhere where AI and ML can be super valuable rather than a human being trying to document thousands of databases or tens of thousands of data sets, which is really kind of a (mumbles) exercise. And so, that application of automation is really critical and being able to do governance at the scale that most enterprises have to do it. >> You got it 'cause humans just can't do that at scale. Now what do you mean by people-centric approach? Can you explain that? >> Yeah, often what I find with governance is that there's this notion of kind of there's this heavy notion of how one should deal with the data, right? So often what I find is that there are certain folks who think, oh well, we're going to declare the rules and people are just going to follow them. And if you've ever been well, a parent or in some cases seeing government operate, you realize that that actually isn't how things work. And involve them in how things are run. And if you do that, right? You're going to get a lot more success in how you apply rules and procedures because people will understand that and people know why they exist. And so what we do within this governance regime is we basically say, look, we want to make sure that the people who are using the data, leveraging the data are also the people who are stewarding the data. There shouldn't be a separate role of data steward that is arbitrarily defined off, just because you've been assigned to a job that you never wanted to do. Rather it should be a part of your day job. And it should be something that you do because you really want to do it. And it's a part of your workflow. And so this idea of being people centric is all about how do you engage the analyst, the product managers, the sales operation managers, to document those sales data sets and those product data sets. So that in fact, those people can be the ones who are answering the questions, not somebody off to the side who knows nothing about the data. >> Yeah, I think you've talked in previous CUBE interviews about context and that really fits to this discussion. So these capabilities are part of an application, which is what? it's a module onto your existing platform. And it's sort of it's a single platform, right? I mean, we're not bespoke products. Maybe you can talk about that. >> Yeah, that's exactly right. I mean, it's funny because we've evolved and built a relation with a lot of capability. I mean, interestingly we're launching this data governance application but I would say 60% of our almost 300 customers would say they do a form or a significant part of data governance, leveraging relations. So it's not like we're new to this market. We've been selling in this market for years. What's different though, is that we've talked a lot about the catalog as a platform over the last year. And we think that that's a really important concept because what is a platform? It's a capability that has multiple applications built on top of it, definitionally. And it's also a capability where third party developers can leverage APIs and SDKs to build applications. And thirdly, it has all of the requisite capabilities and content. So that those application developers, whether it's first party from Alation or third party can really build those applications efficiently, elegantly and economically well. And the catalog is a natural platform because it contains all of the knowledge of the datasets. And it has all of the people who might be leveraging data in some fundamental way. And so this idea of building this data governance module allows a very specialized audience of people in governance to be able to leverage the full capabilities of the platform, to be able to do their work faster, easier, much more simply and easily than they ever could have. And that's why we're so excited about this launch, because we think it's one example of many applications, whether it's ourselves building it or third parties that could be done so much more elegantly than it previously could have been. Because we have so much knowledge of the data and so much knowledge of how the company operates. >> Irrespective of the underlying cloud platform is what I heard before. >> irrespective of the underlying cloud platform, because the data as you know, lives everywhere. It's going to live in AWS, it's going to live in Snowflake. It's going to live on-premise inside of an Oracle database. That's not going to be changed. It's going to live in Teradata. It's going to live all over the place. And as a consequence of that, we've got to be able to connect to everything and we've got to be able to know everything. >> Okay, so that leads me to another big part of the announcement, which is the partnership and integration with Snowflake. Talk about how that came about. I mean, why snowflake? How should customers think about the future of data management. In the context of this relationship, obviously Snowflake talks about the data cloud. I want to understand that better and where you fit. >> Yeah, so interestingly, this partnership like most great partnerships was born in the field. We at the late part of last year had observed with Snowflake that we were in scores of their biggest accounts. And we found that when you found a really, really large Snowflake engagement, often you were going to be complementing that with a reasonable engagement with Alation. And so seeing that pattern as we were going out and raising our funding route at the beginning of this year, we basically found that Snowflake obviously with their Snowflake Ventures Investment arm realized how strategic having a great answer in the governance market happened to be. Now there are other use cases that we do with Snowflake. We can certainly get into those. But what we realized was that if you had a huge scale, Snowflake engagement, governance was a rate limiter to customers' ability to grow faster. And therefore also Snowflake's ability to grow faster within that account. And so we worked with them to not only develop a partnership but much more critically a roadmap that was really robust. And so we're now starting to deliver on that roadmap and are super excited to share a lot of those capabilities in this release. And so that means that we're automatically ingesting policies and controls from Snowflake into Alation, giving full transparency into both setting and also modifying and understanding those policies for anybody. And so that gives you another control plane through which to be able to manage all of the data inside of your enterprise, irrespective of how many instances of Snowflake you have and irrespective of how many controls you have available to you. >> And again, on which cloud runs on. So I want to follow up with that really interesting because Snowflake's promise of the data cloud, is it essentially abstracts the underlying complexity of the cloud. And I'm trying to understand, okay, how much of this is vision, how much is is real? And it's fine to have a Northstar, but sometimes you get lost in the marketing. And then the other part of the promise, and of course, big value proposition is data sharing. I mean, I think they've nailed that use case, but the challenge when you start sharing data is federated governance. And as well, I think you mentioned Oracle, Teradata that stuff's not all in the cloud, a lot of that stuff on-prem and you guys can deal with that as well. So help us sort of to those circles, if you can. Where do you fit into that equation? >> I think, so look, Snowflake is a magical technology and in the sense that if you look at the data, I mean, it reveals a very, very clear story of the ability to adopt Snowflake very quickly. So any data team with an organization can get up and running with the Snowflake instance with extraordinary speed and capability. Now that means that you could have scores, hundreds of instances of Snowflake within a single institution. And to the extent that you want to be able to govern that data to your point, you've got to have a single control plane through which you can manage all of those various instances. Whether they're combined or merged or completely federated and distinct from each other. Now, the other problem that comes up on governance is also discoverability. If you have all these instances, how do you know what the right hand is doing if the left hand is working independently of it? You need some way to be able to coordinate that effort. And so that idea of discoverability and governance is really the value proposition that Alation brings to the table. And the idea there is that people can then can get up and running much more quickly because, hey, what if I want to spin up a Snowflake instance, but there's somebody else, two teams over those already solved the problem or has the data that I need? Well, then maybe I don't even need to do that anymore. Or maybe I can build on top of that work to be able to get to even better outcome even faster. And so that's the sort of kind of one plus one equals three equation that we're trying to build with them. >> So that makes sense and that leads me to one of my favorite topics with the notion is this burgeoning movement around the concept of a data mesh in it. In other words, the notion that increasingly organizations are going to push to decentralize their data architectures and at the same time support a centralized policy. What do you think about this trend? How do you see Alation fitting in? >> Yeah, maybe in a different CUBE conversation. We can talk a little bit about my sort of stylized history of data, but I've got this basic theory that like everybody started out what sort of this idea of a single source of truth. That was a great term back in the 90s where people were like, look, we just need to build a single source of truth and we can take all of our data and physically land it up in a single place. And when we do that, it's going to all be clean, available and perfect. And we'll get back to the garden of Eden, right? And I think that idea has always been sort of this elusive thing that nobody's ever been able to really accomplish, right? Because in any data environment, what you're going to find is that if people use data, they create more data, right? And so in that world, you know, like that notion of centralization is always going to be fighting this idea of data sprawl. And so this concept of data mesh I think is, you know, there's formal technical definitions. But I'll stick with maybe a very informal one, which is the one that you offered. Which is just sort of this decentralized mode of architecture. You can't have decentralization if nobody knows how to access those different data points, 'cause otherwise they'll just have copies and sprawl and rework. And so you need a catalog and you need centralized policies so that people know what's available to them. And people have some way of being able to get conformed data. Like if you've got data spread out all over the place, how do you know which is the right master? How do you know what's the right customer record? How do you know what's your right chart of accounts? You've got to have services that exist in order to be able to find that stuff and to be able to leverage them consistently. And so, to me the data mesh is really a continuation of this idea, which the catalog really enabled. Which is if you can build a single source of reference, not a single source of truth, but a single place where people can find and discover the data, then you can govern a single plane and you can build consistent architectural rules so that different services can exist in a decentralized way without having to sort of bear all the costs of centralization. And I think that's a super exciting trend 'cause it gives power back to people who want to use the data more quickly and efficiently. >> And I think as we were talking about before, it's not about just the IT technical aspects, hey, it works. It's about putting power in the hands of the lines of business. And a big part of the data mesh conversation is around building data products and putting context or putting data in the hands of the people who have the context. And so it seems to me that Alation, okay, so you could have a catalog that is of the line of businesses catalog, but then there's an Uber catalog that sort of rolls up. So you've got full visibility. It seems that you've fit perfectly into that data mesh. And whether it's a data hub, a data warehouse, data lake, I mean, you don't care. I mean, that's just another node that you can help manage. >> That's exactly right. I mean, it's funny because we all look at these market scapes where people see these vendor landscapes of 500 or 800 different data and AI and ML and data architecture vendors. And often I get asked, well, why doesn't somebody come along and like consolidate all this stuff? And the reality is that tools are a reflection of how people think. And when people have different problems and different sets of experiences, they're going to want to use the best tool in order to be able to solve their problem. And so the nice thing about having a mesh architecture is you can use whatever tool you want. You just have to expose your data in a consistent way. And if you have a catalog, you can be able to have different teams using different infrastructure, different tools, different fundamental methods of building the software. But as long as they're exposing it in a consistent way, it doesn't matter. You don't necessarily need to care how it's built. You just need to know that you've got good data available to you. And that's exactly what a catalog does. >> Well, at least your catalog. I think the data mesh, it should be tools that are agnostic. And I think there are certain tools that are, I think you guys started with that principle. Not every data catalog is going to enable that, but I think that is the trend Satyen. And I think you guys have always fit into that. It's just that I think you were ahead of the time. Hey, we'll give you the last word. Give us the closing thoughts and bring us home. >> Well, I mean that's exactly right. Like, not all the catalogs are created equal and certainly not all governance is created equal. And I think most people say these words and think that are simple to get into. And then it's a death by a thousand cuts. I was literally on the phone with a chief data officer yesterday of a major distributor. And they basically said, look, like we've got sprawl everywhere. We've got data everywhere. We've got it in every type of system. And so having that sophistication turned into something that's actually easy to use is a super hard problem. And it's the one that we're focused on every single day that we wake up and every single night when we go to sleep. And so, that's kind of what we do. And we're here to make governance easy, to make data discovery easy. Those are the things that we hold our hats on. And we're super excited to put this release out 'cause we think it's going to make customers so much more capable of building on top of the problems that they've already solved. And that's what we're here to do. >> Good stuff, Satyen. Thanks so much, congratulations on the announcement and great to see you again. >> You too, Dave. Great talking. >> All right, thanks for watching this CUBE conversation. This is Dave Vellante, we'll see you next time. (tranquil music)

(upbeat music) >> Announcer: From theCUBE studios in Palo Alto, in Boston connecting with all leaders all around the world, this is theCUBE conversation. >> Lisa Martin here with theCUBE conversation. One of our alumni is joining me Satyen Sangani the CEO and Co-Founder of Alation is back. Satyen, it's great to see you this morning. >> I know it's so great to see you especially so soon after we last talked. >> Yeah, we only spoke a couple of months ago when you guys launched the Alation Cloud Service and now big news raising 110 million in Series D led by Riverwood Capital from participation with some new investors, including Snowflake Ventures. Talk to us about this new funding raise. >> Yeah, it's so funny. I mean, we've seen market demand pick up ever since the sort of tail end of last year. And it's just been incredible. And quarter after quarter we keep on hitting and exceeding our numbers and we keep on hiring faster and faster and faster and it just doesn't seem like it's ever been fast enough. And so we've been aggressive since the beginning of the year. And even actually before that in spending and, taking the company from roughly 275 people at the end of the year to now, by the end of this year, 525 people. So with that kind of growth we definitely wanted to have the capital to, carry us to this year and then certainly beyond. And, so we went out and raised around and, obviously we're able to do that on great terms and to find a phenomenal partner in Riverwood. And so super excited about the outcome. >> Exactly saw a lot of demand as you and I talked about just a couple of months ago the acceleration of the business during the pandemic. Talk to me about, as you mentioned the demand has never been higher. Let's talk about the demand for the data intelligence platform how the funding is going to help. What are some of the things that you're specifically going to do? >> Yeah, so there's you know we're going to grow the business in a pretty balanced way. And so from our perspective, that means a couple of things right? So starting with sales and marketing, we've got just a need for more feet on the street. Everybody understands generally that they've got problems in data governance, data management, data search and discovery, enablement to people around data. These are things that people are now starting to understand but they don't always necessarily know how to solve the problem in the most efficient and best way. And many of the traditional approaches that sort of command and control top down, you know, let's go hire an army of consultants to figure this stuff out, tends to be the first thing that comes to mind. And so we're building our sales organization is one thing that we're going to do. The second thing that we're going to do is invest in our customer success and customer journey because everybody's looking for best practice and last but not least workforce investing in product and R&D. And so we're going to be growing the R&D organization by almost a factor of two, and that's going to be globally. And, just being the best in the market means you've got to still solve all these unsolved problems. And we're going to do that. >> Sounds like a tremendous amount of momentum kind of igniting this next era for Alation. When we talk about customers, I love that you're doubling down on the customer success. That's absolutely critical. That's why you're in business. But one of the things that we talk about with customers in every industry is being data-driven. And as we see data intelligence emerging as a very, very critical technology investment to enable an enterprise to become more data-driven or actually data-driven, what are some of the things that you're seeing that those customers are saying Alation help us with XYZ? >> Yeah, so I think everybody feels like they need to be on this. So let's first of all, talk about data intelligence. Like, what is this category? So historically there has been these sort of data management categories where the general approach has been let's curate or manage or clean the data in this manual way in order to be able to get good data in front of people so they can start to use it, right. And that data cleaning, that data work that data stewardship has lived often in IT sometimes with very technical people in the business. And it just doesn't scale. There's just too much data out there and there's too much demand for data. So the demand for data is increasing, the supply for data is increasing. So now there's this category of data intelligence. And basically what it's doing it's saying, look all these things that we're talking about machine learning, AI, all of that can be applied to actually the management of data. People can be way more intelligent about how they do this work. They can be more intelligent how they search. They can be more intelligent about how they curate the data. And so what we're seeing is that people are saying, look, I've got so much data. My entire business relies upon data, and now I need you Alation or somebody to help me do this better to do this faster, to do this more efficiently. And all of these really traditional approaches where you use, you know, predominantly workflows and all this stuff it's just not working. And so that's why people are coming after us. >> Well, that need for data in real time is something that we saw during the pandemic. It's for many industries and many different types of situations. It's no longer a nice to have. It's really going to be the defining element between those businesses that succeed in really kind of leveraged COVID as an accelerant versus those that don't succeed. But I'm curious where your conversations are going within the customer base. As we see the need for data across an organization, but the need to access data that they can trust quickly, data that tells the truth, data that can be shared. Are you seeing this elevate up to C-suite in terms of your customer conversations? >> Yeah, and it is and it is because of one really critical reason because a lot of these data projects both fail and under exceed expectations and they do it for reasons that the C-suite doesn't understand. And so now the C-suite is getting forced to say, well, why is this happening? Why are these not going like, wow, you know the boardroom is saying like, well, we need to do more AI. Well, why aren't we doing more AI? Well, it's 'cause your data isn't really clean 'cause you don't actually have the data that you think you have. Because people don't share your data because people are, you know, your data is locked in some on-premise instance in, some access database that nobody's ever heard of. And so all of these reasons are things that now because they're impeding the business or getting to more senior levels in the organization >> That's kind of what I was thinking. I want to talk now about the investment this particular Series D that we talked about. So you've got investment, as I mentioned from a couple of new partners, but talk to me about the Snowflake and the Salesforce Ventures and how that is helping to catalyze what Alation is doing. >> Yeah, so we've, you know had a long time relationship with Salesforce but we found in the last year in particular that our relationship with Snowflake has just taken off in a way that I have seen few partnerships taking off in in certainly in my career. And, you know, it started really with just scores of customers. I mean, literally scores of customers that are all global to 1000s and fortune 500s where we would often just say, hey, what's your data source. And, you know, let's start with Alation and they'd be like, yeah we are either about to invest in Snowflake or we're invested in Snowflake or, something like that. So we'd often see customers on the journey with Snowflake and Alation at the exact same time. And then the next order conversation became well, you know if we're expanding and rolling out with Snowflake, which customers, you know, everybody looks at Snowflakes 168 net percent net expansion rate where every customer is spending a dollar 68 more than they were last year on average. And, you know, says, wow, if I'm going to scale that much we need to govern all of that data. And so Snowflake customers came to Snowflake and to Alation at the same time, and we've been the natural solution of choice. And so that kind of marriage has been quite symbiotic and we're super excited to partner with them. You know, they think exclusively about data consumption. We think about, you know, finding, discovering understanding data. So it's a really natural marriage. And so we're really excited to partner with them and you're going to see a lot from the two companies moving forward. >> So it sounds like that really was driven from joint customers in terms of facilitating maybe an expansion of the partnership that Alation and Snowflake have. Talk to me a little bit more about what some of the things are that we can expect in the next year. >> Yeah, so I won't take away from the stories that we're about to release, but you are going to see really exciting innovations and product between Snowflake and Alation over the course of the next couple of months. And in particular, you're going to see, you know some fun announcements at the snowflake summit coming up next week. So stay tuned for that. Not surprisingly data governance is going to be a big topic for us. Data search and discovery is going to be a big topic for us. Data privacy and security is going to be a big topic us. And so those are all areas where you're going to see lots of fun products innovation. And then on the other side, you're going to see a lot of go to market innovation. So customers are moving data to the cloud, obviously and that's going to be a big place of discussion just enabling all of the field sales forces getting the stories and the customer stories to market. You're going to see a lot of that from us. >> In the last year, I'm curious if you saw any verticals in particular that really have pivoted with fuel from Alation. I think healthcare, life sciences, manufacturing anything that you, that really stood out to you in the last year >> You know, it's, I mean I think there's been the pandemic certainly hurt certain industries more than others transportation, travel and hospitality. And so we definitely saw a trend where there were dips in some of those industries but those were really temporary. And what we're finding is in a lot of those industries are now coming back bigger than ever. And the other industries in manufacturing and pharma in financial services, you know those are just as strong as they've ever been. And interestingly through the pandemic, what we found is that our user account within the company doubled. So even though the customer base itself didn't double the number of users on the platform across all of our customers, literally doubled on an active basis. And so, it's just been, interestingly enough it's just that across the board the growth has been consistent. And I think, really speaks to the fact that everybody's working from home and needs more data to do their job. >> Well, hopefully that's something that's going to be temporary. This, I was telling you, this is my first day back in the studio and not sitting in the home office. So in terms of the demand we talked about the demand we're customers, you're more than 250 customers now, big names, including one of the I think last year's most used terms household terms of Pfizer. Talk to me about the customer perspective on the funding and in terms of the things that you're going to be able to do to go to market. What are you hearing from your customer? >> Yeah I mean, literally the first thing I hear from 80 to 90% of my customers is go faster. You know, like there's this fun story, right? Where there's two people, they meet in the forest, they start walking together and then all of a sudden they both see a big bear. And the bear is, right about to come right after them. One person sits down and like puts on their running shoes. And they're like, well, you know, the other guy says, oh, there's no way you're going out run the bear. And they're like, well, I (indistinct)the bear. I've got to out run you. Right, and our customers are basically saying to us, look the bear of the data problem is gigantic. And yeah, you might be better than everything else out there, but I still have to as a customer contend with this massive data problem. And you know, if I have to do that, I need you to go faster because data's coming after me faster than ever. And I've got to contend with all of that work. And so they just want us to go faster and they want us to go faster in product. And they want us to go faster in developing the customer journey. And they want us to go faster in developing the ecosystem because many of our customers are you leveraging us as a platform. They want to see data on top of Alation. They want to see data privacy on top of Alation. They want to see data migration on top of Alation. So building out all these capabilities with our partners in our ecosystem and with partners like Snowflake and Salesforce, I mean, they just want us to move faster >> Moving faster, I think we all want that in certain senses but in any industry, consumers, users are getting more and more demanding as you're helping customers achieve their desire of going faster. How do you do that and help them foster a data culture that's, that supports that speed. >> Yeah, it's so interesting because cultural transformation, as you all know, like as we all know, that's like that's certainly slow work, right? Like you're not going to show up at an enterprise and say, hey, I installed Alation. You know what? You're going to have a totally different area culture. Everybody's going to start asking questions with data and the world's going to change, right. And so that, that, you know I'd love for that to be the eventual vision that we achieve. But it's certainly not where we are at today. I think, one of the things that I believe is that you can't go fast and big things you've got to break up big problems and turn them into small problems. And so one of the habits that we've seen within the organization, and one of the things that I talked to our team about every single day is look, you know make small promises and deliver on them. If you got to connect to data source, do that faster. If you're going to train a set of employees do that more quickly because customers have intent with data, but if they don't get the data in front of themselves quickly then they're just going to go to their gut decision. And so capturing that moment of intent and building a sort of velocity is where we see our best customer engagements go. And so that sort of incremental success approach, as opposed to the boil the ocean three month engagement, you know never see the finish line approach is really what I think makes us special and different. >> Tell me a little bit about speaking of culture, about Alations culture. What are some of the things that have changed in the last year? And it sounds like with the Series D round that you've just raised a lot of growth opportunities you mentioned that. Talk to me about the culture, how it's transformed in the last year and what you are excited for going forward. >> Yeah, it's so funny 'cause I always think about culture. You know, people think about culture and they say, companies (indistinct) culture and they think of that culture as being a fixed thing. And it's totally true that, yeah, there's got to be some shared vision, shared values shared ideals within a company in order for it to grow at the pace that we're growing, right. Adding 250 people in a 12 month period is not easy. But it's also the case that, you know, what we found is that there's a lot more specialization within the company. And so people now really, you know where you found the company on generalist you scale a company on specialists. And so getting those specialists inside of the company and respecting them and letting them do their jobs and really kind of building that expertise in the company is something that's been really fabulous and just wonderful to see the team work that way. I think the other thing that's been really interesting obviously is just the remote first work. I mean, we've seen zero loss in productivity and I've talked to CEOs who were like, yeah we need to get people back in the office. I don't really care where my team works. They're getting the job done and they're doing it fabulously for customers. And so if customers want them in front of them, totally great. Obviously love to see the team all the time but it is so wonderful to see how productive people can be when they don't have to spend two hours in a car every day. And so those have been two small things. I mean, at the core, there are other aspects of our culture that have been more permanent, but those two have been slightly different. >> That's great to hear that about the productivity. I was actually very excited to commute this morning for the first time. Although there was no traffic to navigate. As we look at the current market valuation, 1.2 billion the growth rate, the demand for the technologies. What are some, you mentioned some of the events that you're going to be at you mentioned Snowflakes event. Where can folks go to hear more information about this? >> Yeah, absolutely. You can come to our website, of course, at alation.com. There's a ton of information there. Anybody who's watching this interview obviously is a experienced and thoughtful enterprise IT buyers. So certainly, you know, this is a fairly expert audience but we do have tons of field resources that are available. The Alation Cloud instance allows you to get up and running super quickly. And you're going to see that speed increase further over the coming 12 months, but, you know start with alation.com and go from there. And then there's a whole bunch of people who are sitting behind that front door waiting to help you. >> Excellent, alation.com. Well, Satyen congratulations on the funding announcement. Thank you for joining me today helping us unpack what at means the impact, the demand from the customers and how we're going see Alation go even faster. I'm excited to see what happens next in the next couple of months. I'm sure I'll see you again. >> I know. Me too. Thank you Lisa, it's always great to talk. >> Likewise, for Satyen Sangani, I'm Lisa Martin. You're watching this CUBE conversation. (upbeat music)

(soft music) >> Hey, welcome to this "CUBE Conversation". I'm Lisa Martin today talking to a CUBE alumni who's been on many times talking about data, all things data. Please welcome Satyen Sangani the Co-Founder and CEO of Alation. Satyen, it's great to have you back on theCUBE. >> Hi Lisa, it's great to see you too. It's been a while. >> It has been a while. And of course in the last year we've been living in this virtual world. So, I know you've gotten to be on theCUBE during this virtual world. Hopefully someday soon, we'll get to actually sit down together again. There's some exciting news that's coming out of Alation. Talk to us about what's going on. What are you announcing? >> So we're announcing that we are releasing our Alation Cloud Service which actually comes out today, and is available to all of our customers. And as a consequence are going to be the fastest, easiest deploy and easiest to use data catalog on the Marketplace, and using this release to really double down on that core differentiation. >> So the value prop for Alation has always been about speed to deployment, time to value. Those have really been, what you've talked about as the fundamental strengths of the platform. How does the cloud service double down on that value prop? >> Well, if you think about data, our basic premise and the reason that we exist is that, people could use data with so many of their different decisions. People could use data to inform their thinking. People can use data in order to figure out what decision is the best decision at any given point in time. But often they don't. Often gut instinct, or whatever's most fast or easy to access is the basis off of which people decide to do what they do. And so if you want to get people to use data more often you've got to make sure that the data is available that the data is correct, and that the data is easy to find and leverage. And so everything that we can do at Alation to make data more accessible, to allow people to be more curious, is what we get excited about. Because unlike, paying your payables or unlike, figuring out whether or not you want to be able to have greater or lesser inventory, those are all things that a business absolutely has to do but people don't have to use data. And to get people to use data, the best thing you can do is to make it easy and to make it fast. >> And speaking of fast, that's one of the things I think the last year has taught us is that, real-time access to data is no longer a nice to have. It's really a competitive differentiator. Talk to me about how you enable customers to get access to the right data fast enough, to be able to do what so many companies say, and that is actually make data-driven decisions. >> Yeah, that's absolutely right. So, it really is a entire continuum. The first and most obvious thing that we do is we start with the user. So, if you're a user of data, you might have to hunt through a myriad of reports, thousands of tables in a database, hundreds of thousands of files in a data lake, and you might not know where to find your answer and you might have the best of intentions but if you don't have the time to go through all of those sources, the first thing you might do is, go ask your buddy down the hall. Now, if your buddy down the hall or your colleague over Zoom can't give you the time of day or can't answer your question quickly enough then you're not going to be able to use that data. So the first thing, and the most obvious thing that we do is we have the industry's best search experience and the industry's best browse experience. And if you think about that search experience, that's really fueled by our understanding of all of the data patterns in your data environment. We basically look at every search. We look at every log within a company's data environment to understand what it is that people are actually doing with the data. And that knowledge just like Google has page rank to help it inform which are the best results for a given webpage. We do the exact same thing with data. And so great search is the basis of what we do. Now, above and beyond that, there's a couple of other things that we do, but they all get to the point of getting to that end search experience and making that perfect so that people can then curate the data and leverage the data as easily as possible. >> Sounds like that's really kind of personalized based on the business, in terms of the search, looking at what's going on. Talk to me a little bit more about that, and how does that context help fuel innovation? >> Yeah. So, to build that context, you can't just do, historically and traditionally what's been done in the data management space. Lots of companies come to the data management world and they say, "Well, what we're going to do is we're going to hire... "We've got this great software. "But setting the software up is a journey. "It takes two to three to four years to set it up "and we're going to get an army of consultants "and everybody's going to go and assert quality of data assets "and measure what the data assets do "and figure out how the data assets are used. "And once we do all of that work, "then in four years we're going to get you to a response." The real key is not to have that context to be built, sort of through an army of consultants and an army of labor that frankly nine times out of 10 never gets to the end of the road. But to actually generate that context day one, by understanding what's going on inside of those systems and learning that by just observing what's happening inside of the company. And we can do that. >> Excellent. And as we've seen the acceleration in the last year of digital transformation, how much of that accelerant was an accelerator revelation putting this service forward and what are customers saying so far? >> Yeah, it's been incredible. I mean, what we've seen in our existing accounts is that, our expansions have gone up by over 100% year over year with the kind of crisis in place. Obviously, you would hypothesize that these catalogs, these, sort of accessibility and search tools and data in general, would be leveraged more when all of us are virtual and all of us can't talk to each other. But, it's been amazing to see that we've found that that's actually what's happening. People are actually using data more. People are actually searching for data more. And that experience and bringing that to our customers has been a huge focus of what we're trying to do. So we've seen the pandemic, in many cases obviously been bad for many people but for us it's been a huge accelerant of customers using our product. >> Talk to me about Alation with AWS. What does that enable your customers to achieve that they maybe couldn't necessarily do On-Prem? >> Yeah, so, customers obviously don't really care anymore, or as much as they used to, about managing the software internally. They just want to be able to, get whatever they need to get done and move forward with their business. And so by leveraging our partnership with AWS, one, we've got elastic compute capability. I think that's obviously, something that they bring to the table, better than perhaps any other in the market. But much more fundamentally, the ability to stand up Alation and get it going, now means that all you have to do is go to the AWS Marketplace or call up an Alation rep. And you can, within a matter of minutes, get an Alation instance that's up and running and fit for purpose for what you need. And that capability is really quite powerful because, now that we have that elasticity and the speed of deployment, customers can realize the value, so much more quickly than they otherwise might've. >> And that speed is absolutely critical as we saw a lot last year that was the difference between the winners and those that were not going to make it. Talk to me a little bit about creating a data culture. We talk about that a lot. It's one thing to talk about it, it's a whole other thing to put it in place, especially for legacy institutions that have been around for a while. How do you help facilitate the actual birth of a data culture? >> Yeah, I mean, I think we view ourselves as a technology, as a catalyst, to our best customers and our best customer champions. And when we talk to chief data officers and when we talk to data leaders within various organizations that we service, organizations like Pfizer, organizations like Salesforce, organizations like Cisco, what they often tell me is, "Look, we've got to build products faster. "We've got to move at the speed and the scale "of all of the startups that are nipping at our heels. "And how do we do that? "Well, we've got to empower our people "and the way that we empower our people "is by giving them context. "And we need to give them the data "to make the right decisions, "so that they can build those products "and move faster than they ever might've." Now those are amazing intentions but those same leaders also come and say, "I've just been mired in risk "and I've been mired in compliance, "and I've been mired in "doing all of these data janitorial projects. "And it's really hard for me to get "on the offense with data. "It's really hard for me to get proactive with data." And so the biggest thing that we do, is we just help companies be more proactive, much more easily, because what they're able to do, is they're able to leave a lot of that janitorial work, lead a lot of that discovery work, lead a lot of that curation work to the software. And so what they get to focus on is, how is it that I can then drive change and drive behavioral change within my organizations so that people have the right data at their disposal. And that's really the magic of the technology. >> So I was reading the "Alation State of Data Culture Report" that was just published a few weeks ago. This is this quarterly assessment that Alation does, looking at the progress that enterprises have made in creating this data culture. And the number that really struck out at me was 87% of respondents say, data quality issues are a barrier to successful implementation of AI in their organizations. How can Alation help them solve that problem? >> Yeah, I think the first is, whenever you've got a problem, the first thing you've got to do is acknowledge that you've got a problem. And a lot of the time people, leaders will often jump to AI and say, "well, hey, everybody's talking about AI. "The board level conversation is AI. "McKinsey is talking about AI, let's go do some AI." And that sounds great in theory. And of course we all want to do that more, but the reality is that many of these projects are stymied by the basic plumbing. You don't necessarily know where the data's coming from. You don't know if people have entered it properly in the source systems or in the systems that are online. Those data often get corrupted in the transformation processes or the processes themselves don't run appropriately. And so you don't have transparency. You don't have any awareness of what people are doing, what people are using, how the data is actually being manipulated from step to step, what that data lineage is. And so that's really where we certainly help many of our customers by giving them transparency and an understanding of their data landscape. Ironically, what we find is that, data leaders are super excited to get data to the business but they often don't themselves have the data to understand how to manage the data itself. >> Wow, that's a conundrum. Let's talk about customers because I was looking on the website and there's some pretty big metrics-based business outcomes that Alation is helping customers drive. I wanted to kind of pick through some examples from your perspective. First one is 364% ROI. Second one is 70% less time for analysts to complete projects. Workforce productivity is huge. Talk to me about how Alation is helping customers achieve business outcomes like that. >> Yeah, so if you think about a typical analytical project you would think that most of the time is spent inside of the analytical tool, inside of your Excel, inside of your Tableau, that where you're thinking about the data and you're analyzing it, you're thinking deep thoughts. And you're trying to hypothesize you're trying to understand. But the reality is going back to the data quality issue that most of the time is spent with figuring out which are the right datasets. Because at one of our customers, for example, there were 4,000 different types of customer transaction datasets, that spoke to the exact same data. Which data set do I actually use out of a particular database? And then once I figured out which ones to use, how do I construct the appropriate query and assumptions in order to be able to get the data into a format that makes sense to me. Those are the kinds of things that most analysts and data scientists struggle with. And what we do is we help them by not having them reinvent the wheel. We allow them to figure out what the right dataset is fast, how to manipulate it fast, so that they can focus most of their time on doing that end analytical work. And that's where all the ROI or a lot of the ROI is coming from because they don't know how to reinvent the wheel. They can do the work and they can move on with the much faster business decision which means that that business moves significantly faster. And so what we find is that for these very highly priced resources, some data scientists who make 200, 300, $400,000 fully load it for a company, those people can do their job 74% faster which means they can get not only the answer faster but they can get many more tasks done, for over a given period of time. >> Well, that just opens up a potential suite of benefits that the organization will achieve, not just getting the analyst productivity cranked up in a big way, but also allowing your organization to be more agile which many organizations are striving to be. to be able to identify new products, new services, what's happening, especially, in a changing chaotic environment like we've been living in the last year. >> Yeah, absolutely. And they also can learn... Not only can they help themselves figure out what new products to launch, but they can also help themselves figure out where their risks happen to be, and where they need to comply, because it could be the case that analysts are using datasets that they ought not to be using or the businesses using the data incorrectly. And so you can find both the patterns but also the anti-patterns, which means that you're not only moving faster, but you're moving forward with less risk. And so we've seen so many failures with data governance, regimes, where people have tried to assert the quality of data and figure out the key data elements and develop a business glossary. And there's that great quote, "I wanted data governance but all I got is a data glossary." That all happens because, they just don't have enough time in the day to do the value added work. They only have enough time in the day to start doing the data cleaning and all of the janitorial work that we, as a company, really strive to allow them to completely eliminate. >> So wrapping things up here, Alation Cloud Service. Tell me about when it's available, how can customers get it? >> So it's available today, which is super exciting. Customers can get it either through the AWS Marketplace or by calling your Alation representative. You can do that coming to our website. And that's super easy to do and getting a demo and moving forward. But we try to make it as easy as possible. And we really want to get out of the way, of allowing people to have a seamless frictionless experience and are super excited to have this cloud service that allows them to do that, even faster than they were able to do before. >> And we all know how important that speed is. Well, Satyen, congratulations on the announcement of Alation Cloud Service. We appreciate you coming on here and sharing with us the news and really what's in it for the customers. >> Thank you, Lisa. It's been phenomenal catch up and great seeing you. >> Likewise. For Satyen Sangani, I'm Lisa Martin. You're watching this "CUBE Conversation." (soft music)

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're coming to you today from our Palo Alto studios with theCUBE conversation, talking about data, and we're excited to have our next guest. He's been on a number of times, many times, CUBE alum, really at the forefront of helping companies and customers be more data centric in their activities. So we'd like to welcome onto the show Satyen Sangani. He is the co founder and CEO of Alation. Satyen, great to see you. >> Great to see you, Jeff. It's good to see you again in this new world, a new format. >> It is a new world, a new format, and what's crazy is, in March and April we were talking about this light switch moment, and now we've just turned the calendar to October and it seems like we're going to be doing this thing for a little bit longer. So, it is kind of the new normal, and even I think when it's over, I don't think everything's going to go back to the way it was, so here we are, but you guys have some exciting news to announce, so let's just jump to the news and then we'll get into a little bit more of the nitty gritty. So what do you got coming out today, right? >> Yeah its so. >> What we are announcing today is basically Alation 2020, which is probably one of the biggest releases that I've been with, that we've had since I've been with the company. We with it are releasing three things. So in some sense, there's a lot of simplicity to the release. The first thing that we're releasing is a new experience around what we call the business user experience, which will bring in a whole new set of users into the catalog. The second thing that we're announcing is basically around Alation analytics and the third is around what we would describe as a cloud-native architecture. In total, it brings a fully transformative experience, basically lowering the total cost of getting to a data management experience, lower and data intelligent experience, much lower than previously had been the case. >> And you guys have a really simple mission, right? You're just trying to help your customers be more data, what's the right word? Data centric, use data more often and to help people actually make that decision. And you had an interesting quote in another interview, you talked about trying to be the Yelp for information which is such a nice kind of humanizing way to think about it because data isn't necessarily that way and I think, you mentioned before we turned on the cameras, that for a lot of people, maybe it's just easier to ignore the data. If I can just get the decision through, on a gut and intuition and get onto my next decision. >> Yeah, you know it's funny. I mean, we live in a time where people talk a lot about fake news and alternative facts and our vision is to empower a curious and rational world and I always smile when I say that a little bit, because it's such a crazy vision, right? Like how you get people to be curious and how do you get people to think rationally? But you know, to us, it's about one making the data really accessible, just allowing people to find the data they need when and as they want it. And the second is for people to be able to think scientifically, teaching people to take the facts at their disposal and interpret them correctly. And we think that if those two skills existed, just the ability to find information and interpret it correctly, people can make a lot better decisions. And so the Yelp analogy is a perfect one, because if you think about it, Yelp did that for local businesses, just like Amazon did it for really complicated products on the web and what we're trying to do at Alation is, in some sense very simple, which is to just take information and make it super usable for people who want to use it. >> Great, but I'm sure there's the critics out there, right? Who say, yeah, we've heard this before the promise of BI has been around forever and I think a lot of peoples think it just didn't work whether the data was too hard to get access to, whether it was too hard to manipulate, whether it was too hard to pull insights out, whether there's just too much scrubbing and manipulating. So, what is some of the secret sauce to take? What is a very complex world? And again and you got some very large customers with some giant data sets and to, I don't want to say humanize it, but kind of humanize it and make it easier, more accessible for that business analyst not just generally, but more specifically when I need it to make a decision. >> Yeah I mean, it's so funny because, making something, data is like a lot of software death by 1000 cuts. I mean you look at something from the outside and it looks really, really, really simple, but then you kind of dwell into any problem and that can be CRM something like Salesforce, or it can be something like service now with ITSM, but these are all really, really complicated spaces and getting into the depths and the detail of it is really hard. And data is really no different, like data is just the sort of exhaust from all of those different systems that exist inside of your company. So the detail around the data in your company is exhaustingly minute. And so, how do you make something like that simple? I think really the biggest challenge there is progressively revealing complexity, right? Giving people the right amount of information at the right amount of time. So, one of the really clever things that we do in this business user experience is we allow people to search for and receive the information that's most relevant to them. And we determined that relevance based upon the other people in the enterprise that happen to be using that data. And we know what other people are using in that company, because we look at the logs to understand which data sources are used most often, and which reports are used most often. So right after that, when you get something, you just see the name of the report and it could be around the revenues of a certain product line. But the first thing that you see is who else uses it. And that's something that people can identify with, you may not necessarily know what the algorithm was or what the formula might be, how the business glossary term relates to some data model or data artifact, but you know the person and if you know the person, then you can trust the information. And so, a lot of what we do is spend time on design to think about what is it that a person expects to see and how do they verify what's true. And that's what helps us really understand what to serve up to somebody so that they can navigate this really complicated, relevant data. >> That's awesome, cause there's really a signal to noise problem, right? And I think I've heard you speak before. >> Yeah >> And of course this is not new information, right? There's just so much data, right? The increasing proliferation of data. And it's not that there's that much more data, we're just capturing a lot more of it. So your signal to noise problem just gets worse and worse and worse. And so what you're talking about is really kind of helping filter that down to get through a lot of that, a lot of that noise, so that you can find the piece of information within the giant haystack. That is what you're looking for at this particular time in this particular moment. >> Yeah and it's a really tough problem. I mean, one of the things that, it's true that we've been talking about this problem for such a long time. And in some instance, if we're lucky, we're going to be talking about it for a lot longer because it used to be that the problem was, back when I was growing up, you were doing research on a topic and you'd go to the card catalog and you'd go to the Dewey decimal system. And in your elementary school or high school library, you might be lucky if you were to find, one, two or three books that map to the topic that you were looking for. Now, you go to Google and you find 10,000 books. Now you go inside of an enterprise and you find 4,000 relational database tables and 200 reports about an artifact that you happened to be looking for. And so really the problem is what do I trust? And what's correct and getting to that level of accuracy around information, if there's so much information out there is really the big problem of our time and I think, for me it's a real privilege to be able to work on it because I think if we can teach people to use information better and better then they can make better decisions and that can help the world in so many different. >> Right, right, my other favorite example that everybody knows is photographs, right? Back when you only got 24 and a roll and cost you six bucks to develop it. Those were pretty special and now you go buy a fancy camera. You can shoot 11, 11 frames a second. You go out and shoot the kids at the soccer game. You come home with 5,000 photos. How do you find the good photo? It's a real, >> Yeah. >> It's a real problem. If you've ever faced something like that, it's kind of a splash of water in the face. Like where do I even begin? But the other piece that you talk about a lot, which is slightly different but related is context, and in favorite concept, it's like 55, right? That's a number, but if you don't have any context for that number, is it a temperature? Is it cold inside the building? Is it a speed? Is it too slow on i5? Or is it fast because I'm on a bicycle going down a Hill and without context data is just, it's just a number. It doesn't mean anything. So you guys really by adding this metadata around the data are adding a lot more contextual information to help figure out kind of what that signal is from the noise. >> Yap, you'll get facts from anywhere, right? Like, you're going to have a Hitchcock, you've got a 55 or 42, and you can figure out like what the meaning of the universe is and apparently the answer is 42 and what does that mean? It might mean a million different things and that, to me, that context is the difference between, suspecting and knowing. And there's the difference between having confidence and basically guessing. And I think to the extent that we can provide more of that over time, that's, what's going to make us, an ever more valuable partner to the customers that we satisfy today. >> Right, well, I do know why 42 is always the answer 'cause that's Ronnie Lot and that's always the answer. So, that one I know that's an easy one. (both chuckles) But it is really interesting and then you guys just came out. I heard Aaron Kalb on, one of your co-founders the other day and we talked about this new report that you guys have sponsored the Data Culture Report and really, putting some granularity on a Data Culture Index and I thought it was pretty interesting and I'm excited that you guys are going to be doing this, longitudinally because whether you do or do not necessarily agree with the method, it does give you a number, It does give you a score, It's a relatively simple formula. And at least you can compare yourself over time to see how you're tracking. I wonder if you could share, I mean, the thing that jumps out right off the top of that report is something we were talking about before we turned the cameras on that, people's perception of where they are on this path doesn't necessarily map out when you go bottoms up and add the score versus top down when I'm just making an assessment. >> Yeah, it's funny, it's kind of the equivalent of everybody thinks they're an above average driver or everybody thinks they're above average in terms of obviously intelligence. And obviously that mathematically is not possible or true, but I think in the world of data management, we all talk about data, we all talk about how important it is to use data. And if you're a data management professional, you want people in your company to use more data. But ironically, the discipline of data management doesn't actually use a lot of data itself. It tends to be a very slow methodical process driven gut oriented process to develop things like, what data models exist and how do I use my infrastructure and where do I put my data and which data quality is best? Like all of those things tend to be, somewhat heuristic driven or gut driven and they don't have to be and a big part of our release actually is around this product called Alation Analytics. And what we do with that product is really quite interesting. We start measuring elements of how your organization uses data by team, by data source, by use case. And then we give you transparency into what's going on with the data inside of your landscape and eco-system. So you can start to actually score yourself both internally, but also as we reveal in our customer success methodology against other customers, to understand what it is that you're doing well and what it is that you're doing badly. And so you don't need necessarily to have a ton of guts instinct anymore. You can look at the data of yourselves and others to figure out where you need to improve. And so that's a pretty exciting thing and I think this notion that says, look, you think you're good, but are you really good? I mean, that's fundamental to improvement in business process and improvement in data management, improvement in data culture fundamentally for every company that we work with. >> Right, right and if you don't know, there's a problem, and if you're not measuring it, then there's no way to improve on it, right? Cause you can't, you don't know, what you're measuring is. >> Right. >> But I'm curious of the three buckets that you guys measured. So you measured data search and discovery was bucket number one, data literacy, you know what you do once you find it and then data governance in terms of managing. It feels like that the search and discovery, which is, it sounds like what you're primarily focused on is the biggest gap because you can't get to those other two buckets unless you can find and understand what you're looking for. So is that JIve or is that really not problem, is it more than manipulation of the data once you get it? >> Yeah, I mean we focus really. We focus on all three and I think that, certainly it's the case that it's a virtuous cycle. So if you think about kind of search and discovery of data, if you have very little context, then it's really hard to guide people to the right bit of information. But if I know for example that a certain data is used by a certain team and then a new member of that team comes on board. Then I can go ahead and serve them with exactly that bit of data, because I know that the human relationships are quite tight in the context graph on the back end. And so that comes from basically building more context over time. Now that context can come from a stewardship process implemented by a data governance framework. It can come from, building better data literacy through having more analytics. But however, that context is built and revealed, there tends to be a virtuous cycle, which is you get more, people searching for data. Then once they've searched for the data, you know how to necessarily build up the right context. And that's generally done through data governance and data stewardship. And then once that happens, you're building literacy in the organization. So people then know what data to search for. So that tends to be a cycle. Now, often people don't recognize that cycle. And so they focus on one thing thinking that you can do one to the exclusion of the others, but of course that's not the case. You have to do all three. >> Great and I would presume you're using some good machine, Machine Learning and Artificial Intelligence in that process to continue to improve it over time as you get more data, the metadata around the data in terms of the usage and I think, again I saw in another interview there talking about, where should people invest? What is the good data? What's the crap data? what's the stuff we shouldn't use 'cause nobody ever uses it or what's the stuff, maybe we need to look and decide whether we want to keep it or not versus, the stuff that's guiding a lot of decisions with Bob, Mary and Joe, that seems to be a good investment. So, it's a great application of applied AI Machine Learning to a very specific process to again get you in this virtuous cycle. That sounds awesome. >> Yeah, I know it is and it's really helpful to, I mean, it's really helpful to think about this, I mean the problem, one of the biggest problems with data is that it's so abstract, but it's really helpful to think about it in just terms of use cases. Like if I'm using a customer dataset and I want to join that with a transaction dataset, just knowing which other transaction datasets people joined with that customer dataset can be super helpful. If I'm an analyst coming in to try to answer a question or ask a question, and so context can come in different ways, just in the same way that Amazon, their people who bought this product also bought this product. You can have all of the same analogies exist. People who use this product also use that product. And so being able to generate all that intelligence from the back end to serve up simple seeming experience on the front end is the fun part of the problem. >> Well I'm just curious, cause there's so many pieces of this thing going on. What's kind of the, aha moment when you're in with a new customer and you finish the install and you've done all the crawling and where all the datasets are, and you've got some baseline information about who's using what I mean, what is kind of the, Oh, my goodness. When they see this thing suddenly delivering results that they've never had at their fingertips before. >> Yeah, it's so funny 'cause you can show Alation as a demo and you can show it to people with data sets that are fake. And so we have this like medical provider data set that, we've got in there and we've got a whole bunch of other data sets that are in there and people look at it and interestingly enough, a lot of time, they're like, Oh yeah, I can kind of see it work and I can kind of like understand that. And then you turn it on against their own data. The data they have been using every single day and literally their faces change. They look at the data and they say, Oh my God, like, this is a dataset that Steven uses, I didn't even know that Steven thought that this data existed and, Oh my God, like people are using this data in this particular way. They shouldn't be using that data at all, Like I thought I deprecated that dataset two years ago. And so people have all of these interesting insights and it's interesting how much more real it gets when you turn it on against the company's systems themselves. And so that's been a really fun thing that I've just seen over and over again, over the course of multiple years where people just turn on the cup, they turn on the product and all of a sudden it just changes their view of how they've been doing it all along. And that's been really fun and exciting. >> That's great yeah, cause it means something to them, right? It's not numbers on a page, It's actually, it's people, it's customers, it's relationships, It's a lot of things. That's a great story and I'm curious too, in that process, is it more often that they just didn't know that there were these other buckets of reports and other buckets of data or was it more that they just didn't have access to it? Or if they did, they didn't really know how to manipulate it or to integrate it into their own workflow. >> Yeah, It's kind of funny and it's somewhat role dependent, but it's kind of all of the above. So, if you think about it, if you're a data management professional, often you kind of know what data sources might exist in the enterprise, but you don't necessarily know how people are using the data. And so you look at data and you're like, Oh my God, I can't believe this team is using this data for this particular purpose. They shouldn't be doing that. They should be using this other data set. I deprecated that data set like two years ago. And then sometimes if you're a data scientist, you're you find, Oh my gosh, there's this new database that I otherwise didn't realize existed. And so now I can use that data and I can process that for building some new machine learning algorithms. In one case we've had a customer where they had the same data set procured five different times. So it was a pure, it was a data set that cost multiple hundreds of thousands of dollars. They were spending $2 million overall on a data set where they could have been spending literally one fifth of that amount. And then you had a sort of another case finally, where you're basically just looking at it and saying, Hey, I remember that data set. I knew I had that dataset, but I just don't remember exactly where it was. Where did I put that report? And so it's exactly the same way that you would use Google. Sometimes you use it for knowledge discovery, but sometimes you also use it for just remembering the thing you forgot. >> Right but, but the thing, like I remember when people were trying to put Google search in that companies just to find records not necessarily to support data efforts and the knock was always, you didn't have enough traffic to drive the algorithm to really have effective search say across a large enterprise that has a lot of records, but not necessarily a lot of activity. So, that's a similar type of problem that you must have. So is it really extracting that extra context of other people's usage that helps you get around kind of that you just don't have a big numbers? >> Yeah, I mean that kind of is fundamentally the special sauce. I mean, I think a lot of data management has been this sort of manual brute force effort where I get a whole bunch of consultants or a whole bunch of people in the room and we do this big documentation session. And all of a sudden we hope that we've kind of, painted the golden gate bridge is at work. But, knowing that three to six months later, you're going to have to go back and repaint the golden gate bridge overall all over again, if not immediately, depending on the size and scale of your company. The one thing that Google did to sort of crawl the web was to really understand, Oh, if a certain webpage was linked to super often, then that web page is probably a really useful webpage. And when we crawled the logs, we basically do the exact same thing. And that's really informed getting a really, really specific day one view of your data without having to have a whole bunch of manual effort. And that's been really just dramatical. I mean, it's been, it's allowed people to really see their data very quickly and new different ways and I think a big part of this is just friction reduction, right? We'd all love to have an organized data world. We'd love to organize all the information in a company, but for anybody has an email inbox, organizing your own inbox, let alone organizing every database in your company just seems like a specificity in effort. And so being able to focus people on what's the most important thing has been the most important thing. And that's kind of why we've been so successful. >> I love it and I love just kind of the human factors kind of overlay, that you've done to add the metadata with the knowledge of who is accessing these things and how are they accessing it. And the other thing I think is so important Satyen is, we talk about innovation all the time. Everybody wants more innovation and they've got DevOps so they can get software out faster, et cetera, et cetera. But, I fundamentally believe in my heart of hearts that it's much more foundational than that, right? That if you just get more people, access to more information and then the ability to manipulate and clean knowledge out of that information and then actually take action and have the power and the authority to take action. And you have that across, everyone in the company or an increasing number of people in the company. Now suddenly you're leveraging all those brains, right? You're leveraging all that insight. You're leveraging all that kind of First Line experience to drive kind of a DevOps type of innovation with each individual person, as opposed to, kind of classic waterfall with the Chief Innovation Officer, Doing PowerPoints in his office, on his own time. And then coming down from the mountain and handing it out to everybody to go build. So it's a really a kind of paradox that by adding more human factors to the data, you're actually making it so much more usable and so much more accessible and ultimately more valuable. >> Yeah, it's funny we, there's this new term of art called data intelligence. And it's interesting because there's lots of people who are trying to define it and there's this idea and I think IDC, IDC has got a definition and you can go look it up, but if you think about the core word of intelligence, it basically DevOps down to the ability to acquire information or skills, right? And so if you then apply that to companies and data, data intelligence then stands to reason. It's sort of the ability for an organization to acquire, information or skills leveraging their data. And that's not just for the company, but it's for every individual inside of that company. And we talk a lot about how much change is going on in the world with COVID and with wildfires here in California. And then obviously with the elections and then with new regulations and with preferences, cause now that COVID happened everybody's at home. So what products and what services do you have to deliver to them? And all of this change is, basically what every company has to keep up with to survive, right? If capitalism is creative destruction, the world's getting destroyed, like, unfortunately more often than we'd like it to be,. >> Right. >> And so then you're say there going, Oh my God, how do I deal with all of this? And it used to be the case that you could just build a company off of being really good at one thing. Like you could just be the best like logistics delivery company, but that was great yesterday when you were delivering to restaurants. But since there are no restaurants in business, you would just have to change your entire business model and be really good at delivering to homes. And how do you go do that? Well, the only way to really go do that, is to be really, really intelligent throughout your entire company. And that's a function of data. That's a function of your ability to adapt to a world around you. And that's not just some CEO cause literally by the time it gets to the CEO, it's probably too late. Innovations got to be occurring on the ground floor. And people have got to repackage things really quickly. >> I love it, I love it. And I love the other human factor that we talked about earlier. It's just, people are curious, right? So if you can make it easy for them to fulfill their curiosity, they're going to naturally seek out the information and use it versus if you make it painful, like a no fun lesson, then people's eyes roll in and they don't pay attention. So I think that it's such an insightful way to address the problem and really the opportunity and the other piece I think that's so different when you're going down the card catalog analogy earlier, right? Is there was a day when all the information was in that library. And if you went to the UCLA psych library, every single reference that you could ever find is in that library, I know I've been there, It was awesome, but that's not the way anymore, right? You can't have all the information and it's pulling your own information along with public information and as much information as you can. where you start to build that competitive advantage. So I think it's a really great way to kind of frame this thing where information in and of itself is really not that valuable. It's about the context, the usability, the speed of these ability and that democratization is where you really start to get these force multipliers and using data as opposed to just talking about data. >> Yeah and I think that that's the big insight, right? Like if you're a CEO and you're kind of looking at your Chief Data Officer or Chief Data and Analytics Officer. The real question that you're trying to ask yourself is, how often do my people use data? How measurable is it? Like how much do people, what is the level at which people are making decisions leveraging data and that's something that, you can talk about in a board room and you can talk about in a management meeting, but that's not where the question gets answered. The question gets really answered in the actual behaviors of individuals. And the only way to answer that question, if you're a Chief Analytics Officer or somebody who's responsible for data usage within the company is by measuring it and managing it and training it and making sure it's a part of every process and every decision by building habit and building those habits are just super hard. And that's, I think the thing that we've chosen to be sort of the best in the world at, and it's really hard. I mean, we're still learning about how to do it, but, from our customers and then taking that knowledge and kind of learning about it over time. >> Right, well, that's fantastic. And if it wasn't hard, it wouldn't be valuable. So those are always the best problems to solve. So Satyen, really enjoyed the conversation. Congratulations to you and the team on the new release. I'm sure there's lots of sweat, blood and tears that went into that effort. So congrats on getting that out and really great to catch up. Look forward to our next catch up. >> You too Jeff, It's been great to talk. Thank you so much. >> All right, take care. All righty Satyen and I'm Jeff, you're watching theCUBE. We'll see you next time. Thanks for watching. (ethereal music)

[Music] [Applause] hi I'm Peter Burris and welcome again to another cube conversation from our wonderful studios here in Palo Alto California great conversation today branching out into the world of data governance a lot of things going on in the industry around data and what does it mean for digital business and how do we treat data increasingly as an asset and that obviously raises a lot of questions about how we govern those assets improve their value share them appropriately at the same time privatizing and make sure that they are corrupted and to have this conversation we've got two great guests first off we've got Kelly O'Neil who's the CEO of first San Francisco partners is an information management consultancy here in the Bay Area and Saatchi on sangani welcome back to the cube CEO of relation welcome so let's get started I kind of said in the preamble that this notion of data governance becomes especially acute especially important because we're now trying to treat data as an asset so we're not governing the resources to manage data we're actually trying to govern data itself utilizing resources so Sachin why don't we start with you what does data governance mean from a tool and process standpoint and then Kelly on rescue and let's go deeper into that process part >> yeah I mean I think so there's lots of different definitions of data governance that a wide variety of experts have put out and I'm not sure that I want to sort of put a new definition in the debate very generically it's a set of processes that institutions use to manage the data that's at their disposal and if you think about that generically in terms of where the problem is broadly stated how do I manage my information and in the consumption and the production and the storage of that information you know that is a super hard problem to deal with when you have you know hundreds of thousands of data sources potentially millions of different data sets and thousands of people who are constantly consuming that information and limited resources and so the process of data governance now in a world to your point where every business is trying to become a digital business and where the monetization of data is a huge part of that business is the fundamental problem right how do we have people discover the data how do we have people understand the data that they're seeing how do we have people trust the data that they're seeing that is the consort of province of data governance and that is what people are coming to realize but >> we do have tooling now that is specifically being built including elation which is a great catalog for performing some of these or to facilitate some of these governance activities so there's a enough of a standard set of definitions that we actually can put tooling in place which means now we can really liberate the power and the talent of people to appropriately govern data and use data so Kelly what are you doing with your clients to help them take the tools for data governance and turn it into ideally a strategic capability that really drives the digital business forward yeah >> absolutely so as a services organization we really focus on ensuring that the people in the process are in place so that they can take advantage of the technology right so you've got accountability around who has who's responsible to ensure the data is of a certain sort of quality or a certain sort of standard as well as who has the ability to access that data and use that data and I think one of the things that sahteen brought up is there's just this onslaught of data that's coming in so if you think about that as a construct it's entirely overwhelming there's too much data to be able to say this person owns this data field this person defines that data field it has to be much more organic it has to be much more shared and tools much more communal yeah and so it really is this concept of how do we have a certain level of trust in the data and what does trust mean to the organization to take advantage of that data and to use it as an asset and to use it in business context and so our services help organizations to see what that means to them to right-size that investment in the sense of how much effort do we put towards this and then also how do we make sure that those tools are used that they're adopted and that they're embedded into work processes that it's not a standalone repository that never gets used >> you know we had Aaron Cal bond not too long ago to talk about trust check and I know that's one of the things that's bringing you together is this notion of a communal approach to putting to imbuing data with trust so let's talk a bit about trust check and in particular how your companies are working together to accelerate the prop the processes that you so accurately described what starts at 10 when we start with you let's trust checking and what does it mean for you yeah so trust check is a very simple it's a very simple capability although very complicated to implement the idea behind trust check is that as and when somebody's communing consuming data whether that's in a Salesforce dashboard or in a tableau report or conversely even inside of elation that immediately as they're consuming that information they're presented with context around that data talking about the appropriateness of that data for the use that they particularly have now that could be about timeliness of the data that could be about the availability of the data that could be about the quality of the data that could be about you know the privacy regulations or the security surrounding that data there are lots of reasons why one might not trust the data but often that information is off to the side right and often that information is in a place where the consumer of the data has no awareness that the policy even exists much less where to go get it that information and so what trust check is saying look this notion of governance has to actually be actionable and immediate and available in order for it to be valuable to the person that's using the information yeah and you might say that also that it might be trusted in this context but not in other context as well so how does that inform well how does that facilitate how does that accelerate implementing these processes to make sure that communities of data in an evidence-based world are better able to apply data use data and share information about that data with each other yeah absolutely so it provides a number one just automation right so fundamentally that's a value add it means that it's more available it's more shared it's faster and that can make the governance organization more relevant to the business so that the data is actually used in a more appropriate and higher-value way so first things Automation and then the second thing is that as we start to automate there's this concept of kind of learning and expanding and so being able to leverage a tool within a services practice and phonology it means that we can kind of start within one area and to leverage that learning and extend and extend and extend because fundamentally data is pervasive right it's everywhere and which makes governance really intimidating and hard so that idea of focusing learning doing something well and being agile right and growing over time a tool really helps you to do that because it is a place where people can get focused for that learning and then repeat rinse and repeat rinse and repeat so it many respects it is a reflection of manifestation of some of these good processes absolutely the you guys obviously have an enormous amount of knowledge about data Government's about the tool infer data to government's about where this all goes but ultimately a lot of your customers are still very much in the formative stages of putting this in place so how are other than just having them license elations toolkit how are you coming together to put in place services or training or something else to help diffuse your knowledge I just want to come back to one point that you mentioned is I think I think there's been a shift in the tooling market okay so I wouldn't say that the tooling has not that there's never been any tooling to deal with the problem of data governance in fact I think there's been lots of tooling that hasn't worked particularly very well so so let me put some context on that so when I say tooling as I said kind of upfront to my mind it's tooling for the resources that handle the data not tooling for the data yeah but keep going if I'm wrong I want to hear well know I think even tooling for the resources that handle the data has largely been the province of either there is a category of software that one would traditionally described in the realm of data stewardship and data governance and broadly speaking it allows you to create forms and to administer workflows with those forms right so you know there and so that is a highly unauthorized and so what a traditional you know governance implementing regime might include would be the development of policies and the enactment of those policies through a set of people who have to vary manually check the data at their disposal it is generally speaking disconnected from the data right when you have small sets of data when you have limited quantities of data that could be a perfectly fine solution when you have a very small set of policies that you need to interact or interact with because you have to have a set of goals that are maybe regulatory in nature that is an okay thing to go do when you have petabytes of data across hundreds of thousands of data sets it's an impossible thing to go do right and so I think that that sort of inundation that Kelly was referring to is is is you know born out of this massive volume of data coming up where the traditional methods just just don't work right so your tanks are you talking about such an essentially that were that we're adding that metadata directly to the data itself and creating trusted objects that the organization can use and apply as assets wherever so that is exactly a solution and the analogy that I think will then inform you know most of the people who are sort of listening us today to us today would be the sort of shift from Yahoo to Google right so if you think about Yahoo Yahoo relied upon every single webmaster tagging every single webpage to make sure that the search engine knew which webpages to go look up right that required a whole bunch of trust in your webmasters first of all some of whom were bad actors right you may not have those in the stewardship regime regime inside of a enterprise but you could right people have their own perspectives and it also required for people to have enough knowledge to tag things right so you'd have to know what to tag and that a tag would have to be right for anybody who's developed a folder system you know that those folder systems are constantly changing right and so then Google comes along and says look if we just watch what people are doing with this information and we know what people are linking to then we can say hey what what's more valuable what's more useful by watching the behaviors right and I think that's the sort of shift of a Bottoms Up approach which is different from sort of that top-down declarative approach that's come in the technology for governance and fortunately and and I think that's what people have to understand which is that the problems always been there but what's happened is the volumes and the relevance and the timely the information have just been so critical that now we have to change the way we do things and not what we're working together on it's mercury it's it's it's it has scale issues but also the annoying technology has gotten to the point where we can actually do more automated discovery about how people are using things which means you have to change the process and the people great so let's let's come back to that question what are you guys doing together to ensure that you can in fact diffuse this knowledge and diffuse these insights into organizations faster so they can pick up on some of these changes better yeah so so for San Francisco is taking some of our methodologies and ensuring that they are right size and fit for the elation suite of products especially the trust checks suite of products and so what we're starting with is the data acquisition process and that's important because the supply chain for data is what has become inordinately complex it's no longer primarily internally created data most data is actually acquired and so if we start with that ingestion process and the data acquisition process that's a huge value both to the customers that are using it as well as to the mutual organizations right so right focusing on that as a as a case and then we'll move on to the concept of information stewardship itself so stewardship across the supply chain not just the data acquisition supply chain so we are adapting our methodologies to be specific and unique to alation to help their existing customer base and obviously potentially new customers together yeah so an a great example of that I was just talking to a chief dead officer of a very large financial institution in North America you know this individual was contending with the problem of making good data available to there and you know and business audience for analytical purposes right to solve exactly this problem he said we acquire companies all the time we're acquiring companies constantly and we're getting all of this data in and I have to figure out what this data is and do I already have this data in-house do I have systems that store this sort of data do I have systems that duplicate the data but incorrectly and are there multiple of these sets of data inside the company that I'm acquiring because they've got data duplication just like we do and how do we figure all of that out right so this would be a perfect example where the data acquisition problem is critical to solve in the process of being able to create available useful government data right and so this would be a perfect example for you know the two of our companies to be able to work together because we don't speak to the implementation and the process we speak to the technical capability of simply providing the inventory so that somebody can then figure out what to do with that information but there are practices that are probably going to do better or will generate greater value out of the elation toolkit than others would absolutely yeah and so in many respects we're looking into companies like yours to help correct or define what those practices are defuse them more broadly through C package consulting and through really good partnership that you guys have been working on yeah because I mean you know you know I mean I you know I think Larry Ellison is a controversial character right right but you know I'll quietly say that I worked at Oracle at one point in time what one of the things that Larry one of the things that Larry said is you know people when they buy software are constantly asking the question of how do I figure out how to take my existing business process and fit it on top of the software that exists out there and he's like no that's exactly wrong what people should be doing is figuring out what should my business process be given the capability that I've got right and so we now have a new capability and we're we're enabling people to have more or less super powers relative to what they would have had to do by hunting and pecking through every data set and tagging it manually right and what you know Kellyanne for San Francisco are bringing to the table is the ability to have a new process that would allow them to do that at scale and faster so that's where we see per sighted excellence so in a date of first world data governance becomes more important to thought leaders helping to make that happen Saachi and sigani CEO of elation Kelly O'Neil for San Francisco partners thanks very much for being on the queue thank you Peter thank you and once again this has been a cute conversation from our Palo Alto studios thank you very much for watching until next time [Music] you

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to theCUBE, I'm Lisa Martin with John Furrier. We are covering our second day of our event Big Data SV. We've had some great conversations, John, yesterday, today as well. Really looking at Big Data, digital transformation, Big Data, plus data science, lots of opportunity. We're excited to welcome back to theCUBE an alumni, Satyen Sangani, the co-founder and CEO of Alation. Welcome back! >> Thank you, it's wonderful to be here again. >> So you guys finish up your fiscal year end of December 2017, where in the first quarter of 2018. You guys had some really strong results, really strong momentum. >> Yeah. >> Tell us what's going on at Alation, how are you pulling this momentum through 2018. >> Well, I think we have had an enterprise focused business historically, because we solve a very complicated problem for very big enterprises, and so, in the last quarter we added customers like American Express, PepsiCo, Roche. And with huge expansions from our existing customers, some of whom, over the course of a year, I think went 12 X from an initial base. And so, we found some just incredible momentum in Q4 and for us that was a phenomenal cap to a great year. >> What about the platform you guys are doing? Can you just take a minute to explain what Alation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. >> Yeah. >> What's the update? Take a minute, talk about the update. >> Absolutely, so, you certainly know, John, but Alation's a data catalog and a data catalog essentially, you can think of it as Yelp or Amazon for data and information side of the enterprise. So if you think about how many different databases there are, how many different reports there are, how many different BI tools there are, how many different APIs there are, how many different algorithms there are, it's pretty dizzying for the average analyst. It's pretty dizzying for the average CIO. It's pretty dizzying for the average chief data officer. And particularly, inside of Fortune 500s where you have hundreds of thousands of databases. You have a situation where people just have too much signal or too much noise, not enough signal. And so what we do is we provide this Yelp for that information. You can come to Alation as a catalog. You can do a search on revenue 2017. You'll get all of the reports, all of the dashboards, all of the tables, all of the people that you might need to be able to find. And that gives you a single place of reference, so you can understand what you've got and what can answer your questions. >> What's interesting is, first of all, I love data. We're data driven, we're geeks on data. But when I start talking to folks that are outside the geek community or nerd community, you say data and they go, "Oh," because they cringe and they say, "Facebook." They see that data issues there. GDPR, data nightmare, where's the store, you got to manage it. And then, people are actually using data, so they're realizing how hard (laughs) it is. >> Yeah >> How much data do we have? So it's kind of like a tropic disillusionment, if you will. Now they got to get their hands on it. They've got to put it to work. >> Yeah. >> And they know that So, it's now becoming really hard (laughs) in their mind. This is business people. >> Yeah. >> They have data everywhere. How do you guys talk to that customer? Because, if you don't have quality data, if you don't have data you can trust, if you don't have the right people, it's hard to get it going. >> Yeah. >> How do you guys solve that problem and how do you talk to customers? >> So we talk a lot about data literacy. There is a lot of data in this world and that data is just emblematic of all of the stuff that's going on in this world. There's lots of systems, there's lots of complexity and the data, basically, just is about that complexity. Whether it's weblogs, or sensors, or the like. And so, you can either run away from that data, and say, "Look, I'm going to not, "I'm going to bury my head in the sand. "I'm going to be a business. "I'm just going to forget about that data stuff." And that's certainly a way to go. >> John: Yeah. >> It's a way to go away. >> Not a good outlook. >> I was going to say, is that a way of going out of business? >> Or, you can basically train, it's a human resources problem fundamentally. You've got to train your people to understand how to use data, to become data literate. And that's what our software is all about. That's what we're all about as a company. And so, we have a pretty high bar for what we think we do as a business and we're this far into that. Which is, we think we're training people to use data better. How do you learn to think scientifically? How do you go use data to make better decisions? How do you build a data driven culture? Those are the sorts of problems that I'm excited to work on. >> Alright, now take me through how you guys play out in an engagement with the customer. So okay, that's cool, you guys can come in, we're getting data literate, we understand we need to use data. Where are you guys winning? Where are you guys seeing some visibility, both in terms of the traction of the usage of the product, the use cases? Where is it kind of coming together for you guys? >> Yeah, so we literally, we have a mantra. I think any early stage company basically wins because they can focus on doing a couple of things really well. And for us, we basically do three things. We allow people to find data. We allow people to understand the data that they find. And we allow them to trust the data that they see. And so if I have a question, the first place I start is, typically, Google. I'll go there and I'll try to find whatever it is that I'm looking for. Maybe I'm looking for a Mediterranean restaurant on 1st Street in San Jose. If I'm going to go do that, I'm going to do that search and I'm going to find the thing that I'm looking for, and then I'm going to figure out, out of the possible options, which one do I want to go to. And then I'll figure out whether or not the one that has seven ratings is the one that I trust more than the one that has two. Well, data is no different. You're going to have to find the data sets. And inside of companies, there could be 20 different reports and there could be 20 different people who have information, and so you're going to trust those people through having context and understanding. >> So, trust, people, collaboration. You mentioned some big brands that you guys added towards the end of calendar 2017. How do you facilitate these conversations with maybe the chief data officer. As we know, in large enterprises, there's still a lot of ownership over data silos. >> Satyen: Yep. >> What is that conversation like, as you say on your website, "The first data catalog designed for collaboration"? How do you help these organizations as large as Coca-Cola understand where all the data are and enable the human resources to extract values, and find it, understand it, and trust it? >> Yeah, so we have a very simple hypothesis, which is, look, people fundamentally have questions. They're fundamentally curious. So, what you need to do as a chief data officer, as a chief information officer, is really figure out how to unlock that curiosity. Start with the most popular data sets. Start with the most popular systems. Start with the business people who have the most curiosity and the most demand for information. And oh, by the way, we can measure that. Which is the magical thing that we do. So we can come in and say, "Look, "we look at the logs inside of your systems to know "which people are using which data sets, "which sources are most popular, which areas are hot." Just like a social network might do. And so, just like you can say, "Okay, these are the trending restaurants." We can say, "These are the trending data sets." And that curiosity allows people to know, what data should I document first? What data should I make available first? What data do I improve the data quality over first? What data do I govern first? And so, in a world where you've got tons of signal, tons of systems, it's totally dizzying to figure out where you should start. But what we do is, we go these chief data officers and say, "Look, we can give you a tool and a catalyst so "that you know where to go, "what questions to answer, who to serve first." And you can use that to expand to other groups in the company. >> And this is interesting, a lot of people you mentioned social networks, use data to optimize for something, and in the case of Facebook, they they use my data to target ads for me. You're using data to actually say, "This is how people are using the data." So you're using data for data. (laughs) >> That's right. >> So you're saying-- >> Satyen: We're measuring how you can use data. >> And that's interesting because, I hear a lot of stories like, we bought a tool, we never used it. >> Yep. >> Or people didn't like the UI, just kind of falls on the side. You're looking at it and saying, "Let's get it out there and let's see who's using the data." And then, are you doubling down? What happens? Do I get a little star, do I get a reputation point, am I being flagged to HR as a power user? How are you guys treating that gamification in this way? It's interesting, I mean, what happens? Do I become like-- >> Yeah, so it's funny because, when you think about search, how do you figure out that something's good? So what Google did is, they came along and they've said, "We've got PageRank." What we're going to do is we're going to say, "The pages that are the best pages are the ones "that people link to most often." Well, we can do the same thing for data. The data sources that are the most useful ones are the people that are used most often. Now on top of that, you can say, "We're going to have experts put ratings," which we do. And you can say people can contribute knowledge and reviews of how this data set can be used. And people can contribute queries and reports on top of those data sets. And all of that gives you this really rich graph, this rich social graph, so that now when I look at something it doesn't look like Greek. It looks like, "Oh, well I know Lisa used this data set, "and then John used it "and so at least it must answer some questions "that are really intelligent about the media business "or about the software business. "And so that can be really useful for me "if I have no clue as to what I'm looking at." >> So the problem that you-- >> It's on how you demystify it through the social connections. >> So the problem that you solve, if what I hear you correctly, is that you make it easy to get the data. So there's some ease of use piece of it, >> Yep. >> cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. >> Satyen: That's right. >> So this seems to be the challenge. So, if I'm a customer and I have a problem, the profile of your target customer or who your customers are, people who need to expand and operationalize data, how would you talk about it? >> Yeah, so it's really interesting. We talk about, one of our customers called us, sort of, the social network for nerds inside of an enterprise. And I think for me that's a compliment. (John laughing) But what I took from that, and when I explained the business of Alation, we start with those individuals who are data literate. The data scientists, the data engineers, the data stewards, the chief data officer. But those people have the knowledge and the context to then explain data to other people inside of that same institution. So in the same way that Facebook started with Harvard, and then went to the rest of the Ivies, and then went to the rest of the top 20 schools, and then ultimately to mom, and dad, and grandma, and grandpa. We're doing the exact same thing with data. We start with the folks that are data literate, we expand from there to a broader audience of people that don't necessarily have data in their titles, but have curiosity and questions. >> I like that on the curiosity side. You spent some time up at Strata Data. I'm curious, what are some of the things you're hearing from customers, maybe partners? Everyone used to talk about Hadoop, it was this big thing. And then there was a creation of data lakes, and swampiness, and all these things that are sort of becoming more complex in an organization. And with the rise of myriad data sources, the velocity, the volume, how do you help an enterprise understand and be able to catalog data from so many different sources? Is it that same principle that you just talked about in terms of, let's start with the lowest hanging fruit, start making the impact there and then grow it as we can? Or is an enterprise needs to be competitive and move really, really quickly? I guess, what's the process? >> How do you start? >> Right. >> What do people do? >> Yes! >> So it's interesting, what we find is multiple ways of starting with multiple different types of customers. And so, we have some customers that say, "Look, we've got a big, we've got Teradata, "and we've got some Hadoop, "and we've got some stuff on Amazon, "and we want to connect it all." And those customers do get started, and they start with hundreds of users, in some case, they start with thousands of users day one, and they just go Big Bang. And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that. We have other customers that say, "Look, we're going to start with a team of 10 people "and we're going to see how it grows from there." And, we can accommodate either model or either approach. From our prospective, you just have to have the resources and the investment corresponding to what you're trying to do. If you're going to say, "Look, we're going to have, two dollars of budget, and we're not going to have the human resources, and the stewardship resources behind it." It's going to be hard to do the Big Bang. But if you're going to put the appropriate resources up behind it, you can do a lot of good. >> So, you can really facilitate the whole go big or go home approach, as as well as the let's start small think fast approach. >> That's right, and we always, actually ironically, recommend the latter. >> Let's start small, think fast, yeah. >> Because everybody's got a bigger appetite than they do the ability to execute. And what's great about the tool, and what I tell our customers and our employees all day long is, there's only metric I track. So year over year, for our business, we basically grow in accounts by net of churn by 55%. Year over year, and that's actually up from the prior year. And so from my perspective-- >> And what does that mean? >> So what that means is, the same customer gave us 55 cents more on the dollar than they did the prior year. Now that's best in class for most software businesses that I've heard. But what matters to me is not so much that growth rate in and of itself. What it means to me is this, that nobody's come along and says, "I've mastered my data. "I understand all of the information side of my company. "Every person knows everything there is to know." That's never been said. So if we're solving a problem where customers are saying, "Look, we get, and we can find, and understand, "and trust data, and we can do that better last year "than we did this year, and we can do it even more "with more people," we're going to be successful. >> What I like about what you're doing is, you're bringing an element of operationalizing data for literacy and for usage. But you're really bringing this notion of a humanizing element to it. Where you see it in security, you see it in emerging ecosystems. Where there's a community of data people who know how hard it is and was, and it seems to be getting easier. But the tsunami of new data coming in, IOT data, whatever, and new regulators like GDPR. These are all more surface area problems. But there's a community coming together. How have you guys seen your product create community? Have you seen any data on that, 'cause it sounds like, as people get networked together, the natural outcome of that is possibly usage you attract. But is there a community vibe that you're seeing? Is there an internal collaboration where they sit, they're having meet ups, they're having lunches. There's a social aspect in a human aspect. >> No, it's humanal, no, it's amazing. So in really subtle but really, really powerful ways. So one thing that we do for every single data source or every single report that we document, we just put who are the top users of this particular thing. So really subtly, day one, you're like, "I want to go find a report. "I don't even know "where to go inside of this really mysterious system". Postulation, you're able to say, "Well, I don't know where to go, but at least I can go call up John or Lisa," and say, "Hey, what is it that we know about this particular thing?" And I didn't have to know them. I just had to know that they had this report and they had this intelligence. So by just discovering people in who they are, you pick up on what people can know. >> So people of the new Google results, so you mentioned Google PageRank, which is web pages and relevance. You're taking a much more people approach to relevance. >> Satyen: That's right. >> To the data itself. >> That's right, and that builds community in very, very clear ways, because people have curiosity. Other people are in the mechanism why in which they satisfy that curiosity. And so that community builds automatically. >> They pay it forward, they know who to ask help for. >> That's right. >> Interesting. >> That's right. >> Last question, Satyen. The tag line, first data catalog designed for collaboration, is there a customer that comes to mind to you as really one that articulates that point exactly? Where Alation has come in and really kicked open the door, in terms of facilitating collaboration. >> Oh, absolutely. I was literally, this morning talking to one of our customers, Munich Reinsurance, largest reinsurance customer or company in the world. Their chief data officer said, "Look, three years ago, "we started with 10 people working on data. "Today, we've got hundreds. "Our aspiration is to get to thousands." We have three things that we do. One is, we actually discover insights. It's actually the smallest part of what we do. The second thing that we do is, we enable people to use data. And the third thing that we do is, drive a data driven culture. And for us, it's all about scaling knowledge, to centers in China, to centers in North America, to centers in Australia. And they've been doing that at scale. And they go to each of their people and they say, "Are you a data black belt, are you a data novice?" It's kind of like skiing. Are you blue diamond or a black diamond. >> Always ski in pairs (laughs) >> That's right. >> And they do ski in pairs. And what they end up ultimately doing is saying, "Look, we're going to train all of our workforce to become better, so that in three, 10 years, we're recognized as one of the most innovative insurance companies in the world." Three years ago, that was not the case. >> Process improvement at a whole other level. My final question for you is, for the folks watching or the folks that are going to watch this video, that could be a potential customer of yours, what are they feeling? If I'm the customer, what smoke signals am I seeing that say, I need to call Alation? What are some of the things that you've found that would tell a potential customer that they should be talkin' to you guys? >> Look, I think that they've got to throw out the old playbook. And this was a point that was made by some folks at a conference that I was at earlier this week. But they basically were saying, "Look, the DLNA's PlayBook was all about providing the right answer." Forget about that. Just allow people to ask the right questions. And if you let people's curiosity guide them, people are industrious, and ambitious, and innovative enough to go figure out what they need to go do. But if you see this as a world of control, where I'm going to just figure out what people should know and tell them what they're going to go know. that's going to be a pretty, a poor career to go choose because data's all about, sort of, freedom and innovation and understanding. And we're trying to push that along. >> Satyen, thanks so much for stopping by >> Thank you. >> and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. >> I appreciate the time too. >> Thank you. >> And thanks John! >> And thank you. >> Thanks for co-hosting with me. For John Furrier, I'm Lisa Martin, you're watching theCUBE live from our second day of coverage of our event Big Data SV. Stick around, we'll be right back with our next guest after a short break. 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>> 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)