(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)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto studios today for theCUBE conversation. We're talking about data. We're always talking about data and it's really interesting. You know we like to go out and get you the first person insight from the people that start the companies, run the companies, the practitioners and, and, and get the insight directly from them. We also like to go out and get original research and hear from original research. And this is a great opportunity to hear from both. So we're excited to have, and welcome back into the studio. He's Aaron Kalb. He's the co founder of Alation, many time CUBE alumni. Aaron. Great to see you. >> Yeah, thanks for having me. It's good to be here. >> Yeah, it's very cool. But today it's a special, a special thing. We've never done this before with you. You guys are releasing a brand new report called, the Alation State of Data Culture Report. So really interesting report. A lot of great information that we're going to dig in here for the next few minutes. But before we do, tell us kind of the history of this report. This is a, the kind of the inaugural release. What was kind of behind it, why did you guys do this? And give us a little background before we get into the details. >> Absolutely. So, yes, that's exactly right. It's debuting today that we plan to kind of update this research quarterly we going to see the trends over time. And this emerged because, you know, I, part of my job, I talk to chief data officers and chief analytics officers across our customer base and prospects. And I keep hearing anecdotally over and over that establishing a data culture, is often the number one priority for these data leaders and for these organizations. And so we wanted to really say, can we quantify that? Can we agree upon a definition of data culture? And can we create sort of a simple yardstick to more objectively measure where organizations are on this sort of data maturity curve to get it into culture. >> Right. I love it. So you created this data, data index right? The data culture index. And, and I think it's important to look at methodology. I think people, a lot of times go right to the results on reports before talking about the methodologies. And let's talk about the methodologies cause we're supposed to be talking about data, right? So you talked to 300, some odd executives, correct. And I think it's really interesting and you broke it down into three kind of buckets of data literacy, if you will. Data search and discovery, number one, data, two kind of literacy in terms of their ability to work with the data. And then the third bucket is really data governance. And then in, in the form ABCD, you gave him a four point score and basically, are they doing it well? Are they doing it in the majority of the time? Are they doing it about half, they got one or they got a zero and you get this four point scale and you end up with a 12 point scale which we're all familiar with from, from school, from an A to an, A minus and B, et cetera. Just dig it a little bit on those three categories and how you chose those. So the first one again is kind of the data search and discovery, you know can they find it and then their competency, if you will and then a governance and compliance. Kind of dig into each of those three buckets a little bit. >> For sure. So, so the, the end goal in data culture, is to have an organization in which data is valued and decisions are made based on data and evidence, right? Versus a culture in which we go with the highest paid person's opinion or what we did last quarter or any of these other ways things get done. And so the idea is to make that possible, as you said you've to be able to find the data when you need it. That's the data search and discovery. You've to be able to interpret that data correctly and draw valid conclusions from it. And that's a data literacy, excuse me. And both of those are contingent upon having data governance in place. So that data is well-defined and has high data quality, as well as other aspects, so that it is possible to find it and understand it properly. >> Right. And what are the things too that I think is really important that we call that, and again, we're going to dive into the details, is your perceived execution versus the reported execution by the people that are actually providing data. And I think you've found and you've highlighted on specific slides that you know, there's not necessarily a match there. And sometimes that you know, what you perceive is happening, isn't necessarily what's happening when you go down and query the people in the field. So really important to come up with a number. And I think a, I think you said this is going to be an ongoing thing over a period of time. So you kind of start to see longitudinal changes in these organizations. >> Absolutely. And we're very excited to see those, those trends over time. But even at the outset is this you know, very striking effect emerges which is, as you said, if we ask one of these you know, 300 data leaders, you know, all around the world actually, you know, if we ask, how is the data culture at your company overall, and this is very broad general top down way and have them graded on the sort of SaaS scale. You know, we get results where there's a large gap between kind of that level of maturity and what emerges in a bottom up methodology excuse me, in which you ask about, you know governance and literacy and, and such kind of by department and in a more bottom up way. And so we do see that that, you know, it can be helpful, even for data people to have a, a more granular metric and framework for quantifying their progress. >> Right? Let's jump into some of the results. It's, it's a fascinating, they're kind of all over the map, but there's some definite trends. One of the trends you talked about is that there's a lot of questions on the quality of the data. But that's a real inhibitor to people. Whether that suspicion is because it's not good data. And I don't know, this question for you, is, is, do they think it's not relevant to the decision that's being made? Is it an incomplete data set or the wrong data set? It seems to be that keeps coming up over and over about, decision-makers not necessarily having confidence in the data. What, can you share a little bit more color around that? >> Yeah, it's quite interesting actually. So what we find is that 90%. So 90 people, 10 executives (indistinct) to question the data sometimes often or always. But the part that's maybe disappointing or concerning is the two thirds of executives are believed to ignore the data and make a decision kind of pushing the data aside which is really quite striking when you think about it, why have all this data, if more often than not you're sort of disregarding it to make your final answer. And so you're absolutely correct when we dug into why, what are the reasons behind pushing it aside. Data quality was number one. And I think it is a question of, Oh, is the data inaccurate? Is it out of date, these sort of concerns sort of we, we hear from customers and prospects. But as we dig in deeper in the survey results, excuse me, we, we see some other reasons behind that. One is a lack of collaboration between the data analytics folks and the business folks. And so there's a question of, I don't know exactly where this data came from or to your point kind of how it was produced. What was the methodology? How was it sourced? And maybe because of that disconnect is a lack of trust. So trust really is the ultimate I think, failure to having data culture really take root. >> Right? And it's trust in this trust, as you said, not only in the data per se, the source of the data, the quality of the data, the relevance of the data but also the people who are providing you with the data. And obviously you get, you get some data sets. Sometimes you didn't get other data sets. So, that's really I'm a little bit disconcerting. The other thing I thought was kind of interesting is, it seems to be consistent that the, the primary reason that people are using big data projects is around operations and operations efficiency, a little bit about compliance, but, you know, it's interesting we had you on at the MIT CDOIQ, Chief Data Information Officer quality symposium, and you talked about the goodness of people moving from kind of a defensive posture to an offensive posture, you know using data in terms of product development and innovation. And, and what comes across in this survey is that's kind of down the list behind you know, kind of operational efficiency. We're seeing a little bit of governance and regulation but the, the quest for data as a tool for innovation, didn't really shine through in this report. >> Well, you know, it's very interesting. It depends whether you look at the aggregate level or you break things down a little bit more. So one thing we did after we got that zero to 12 scale on the data culture index or DCI, is it actually, we were able to break it down into thirds. And among the sort of bottom third, it has the least well-established data culture by this yardstick. We've found that governance and regulatory compliance, was the number one application of data. But among the top third of respondents, we actually found the opposite where things like providing a great customer experience, doing product innovation, those sort of things actually came to the fore and governance fell behind. So I think there is this curve where, It's table stakes to get the sort of defense side of data figured out. And then you can move on to offense in using data to make your organization meet its meet its other goals. >> Right. Right. And then I wanted to get your take on kind of the democratization of data, right? This is a, this is a trend that's been going on, and really, I think you said before you know, your guys' whole mission is to empower curious and rational world to give people the ability to ask the right questions have the right data and get the right answer. So, you know, we've seen democratization in terms of the access to the data, the access to the tools, the ability to do something with the data and the tool, and then the actual authority to execute business decision based on that. The results on that seem a little bit split here because a lot of the problems seem to be focused on leadership, not necessarily taking a data based decision move, but on the good hand a lot of people trying to break down data silos and make data more accessible for a larger group of people. So that more people in the organization are making data based decisions. This seems kind of like this little bit of a bifurcation between the C suite and everybody else trying to get their job done. >> Absolutely. There's always this question of you know, sort of the, that organizational wide initiative and then what's happening on the ground. One thing we saw that was very heartening and aligns with our customers index success, is a real emphasis being placed on having data governance and data context and data literacy factors sort of be embedded at the point of use. To not expecting people, to just like take a course and look things up and kind of end up with their workflow to be able to use data quickly and accurately and, and interpret it in varied ways. So that was really exciting to see as, as, as a initiative. It sort of bridges that gap along with initiatives to have more collaboration and integration between the data people and the business people. because really you know, they exist to serve one another. But in terms of the disconnect between the C suite and other parts of the org, there was a really interesting inverse correlation. Well, or maybe it's not interesting how you look at it, but basically, you know, when we talk to C level executives and ask, you know, does the C suite ignore data? Do they question data et cetera, those numbers came in lower than when we talked to, you know, senior director about the C suite right? It's sort of the farther you get, and there's a difference there, you know, from my perspective, I almost wonder whether that distance is actually is more objective viewpoint. And when you're in that role, it's hard to even see your cognitive biases and your tendency to ignore a data when it doesn't suit you. >> Right. Right. So there's, there's some other interesting things here. So one of them is, you know, kind of predictors, right? One of the whole reasons to do studies and collect data so that we can have some predictive ability. And, and it comes out here that the reporting structure is a strong predictor of a company's data tier structure. So, you know, there's the whole rise of the chief data officers and the chief analytics officer and the chief data and analytics officer and lots of conversations about those roles and what exactly are those roles and who do they report to. Your study finds a pretty compelling leading indicator that if that role is reporting to either the CEO or the executive board, which is often a one in the same person, that that's actually a terrific indicator of success in moving to a more data centric culture. >> That's absolutely correct. So we found that that top third of organizations on the data culture index were much more likely to have a chief data executive, a CDO, CAO or CDAO. In fact, they're more likely to have folks with the analytics in their title because in some organizations, data is thought to mean sort of raw data, infrastructural defense and analytics is sort of where it gets you know, infused into business processes and value. But certainly that top third is much more likely to have the chief data executive reporting into the executive board or CEO when the highest ranking data executive is under the CIO or some other part of the organization, those orgs tend to score a far lower on the DCI. >> Right. Right. So it's interesting, you know you're a really interesting guy even doing this for a while. You were at Siri before you were at Alation. So you have a really good feel for kind of what data can do and can't do and natural human or natural language processing and, and, and human voice interaction with these devices, a really interesting case study, and they can do a really good job within a small defined data set and instruction set, but they don't do necessarily so well once you kind of get outside how, how they're trained. And you've talked a lot about how metaphor shaped the way that we think and I know you and Dave talked about data oil and data lakes I don't want to necessarily go down that whole path but I do think it's important. And what came out of the study and the way people think about data. You know, there's a lot of conversation. How do you value data? Is data, you know it used to just be an expense that we had to buy servers to store the stuff we weren't sure what we ever did with it. So I wonder if there's any, you know, kind of top level metaphors level, kind of a thought or process or framing in the companies that you study that came out. maybe not necessarily in the top line data, but maybe in some of the notes that help define why some people, you know are being successful at making this transition and putting, you know kind of data out front of their decision processing versus data, either behind as a supporting thing or maybe data, I just don't have time with it or I don't trust it, or God knows where you got that, and this is not the data that I wanted. You know, was there any, you know, kind of tangental or anecdotal stuff that came out of this study that's more reflective of, of the softer parts of a data culture versus the harder parts in terms of titles and roles and, and, and job responsibilities. >> Yeah. It's a really interesting place to explore. I do think there's a, I don't want to make this overly simplistic group binary, but at the end of the day you know, like anything else within an organization, you can view data as a liability to say, okay, we have for example, you know, customer's names and phone numbers and passwords, and we just need to prevent an adverse event in which there's a leak or some sort of InfoSec problem that could cause, you know, bad press and fines and other negative consequences. And I think the issue there is if data's a liability, the most you know, the best case is that it's worth zero as opposed to some huge negative on your company's balance sheet. And, and I think, you know, intuitively, if you really want to prevent data misuse and data problems, one fail safe, but I think ultimately in its own way risky way to do that was just not collect any data, right. And not store it. So I think that the transition is to say, look data must be protected and taken care of that's step zero. But you know, it's really just the beginning and data is this asset that can be used to inform the huge company level strategic decisions that are made in annual planning at the board level, down to the millions of little decisions every day in the work of people in customer support and in sales and in product management and in, you know, various roles that just across industries. And I think once you have that, that shift, you know the upside is potentially, you know, unbounded. >> Right. And, and it just changes the way, the way you think. And suddenly instead of saying, Oh, data needs to be kind of hidden away, it's more like, Oh, people need to be trained on data use and empowered with data. And it's all about not if it's used or if it's misused but really how it's used and why it's used, what it's being used for to make a real impact. >> Right. Right. And it's funny when I just remember it being back in business school one of the great things that help teach is to think in terms of data, right. And you always have the infamous center consulting interview question, How many manhole covers are in Manhattan. Right. So, you know, to, to, to start to think about that problem from a data centric, point of view really gives you a leg up and, and even, you know where to start and how to attack those types of problems. And I thought it was interesting you know, talking about challenges for people to have a more data centric, point of view. It's interesting. The reports says, basically everybody said there's all kinds of challenges around data quality and compliance, and they had democratization. But the bottom companies, the bottom companies said that the biggest challenge was lack of buy in from company leadership. So I guess the good news bad news is that there's a real opportunity to make a significant change and get your company from the bottom third to a middle third or a top third, simply by taking a change in attitude about putting data in a much more central role in your decision making process. 'Cause all the other stuff's kind of operational, execution challenges that we all have, not enough people, blah, blah, blah. But in terms of attitude of leadership and prioritization, that's something that's very easy to change if you so choose. And really seems to be the key to unlock this real journey as opposed to the minutiae of a lot of the little details that that are a challenge for everybody. >> Absolutely. In your changing attitudes might be the easiest thing or the hardest thing depending on (indistinct). But I think you're absolutely right. The first step, which, which which could, maybe it should be easy, is admitting that you have a problem or maybe to put it more positively, realizing you have an opportunity. >> I love that. And then just again, looking at the top tier companies, the other thing that I thought was pretty interesting in this study is, I'm looking at it here, is getting champions in each of the operational segments. So rather than, I mean, a chief data officer is important and you know, somebody kind of at the high level to shepherd it in the executive suite, as we just discussed, but within each of the individual tasks and functions and roles, whether that's operations or customer service or product development or operational efficiency, you need some type of champion, some type of person, you know, banging the gavel, collecting the data, smoothing out the complexities, helping people get their thing together. And again, another way to really elevate your position on the score. >> Absolutely. And I think this idea of again, bridging between, you know, if data is centralized you have a chance to try to really get excellent practices within the data org. But even it becomes even more essential to have those ambassadors, people who are in the business and understand all the business context who can sort of make the data relevant, identify the key areas where data can really help, maybe demystify data and pick the right metaphors and the right examples to make it real for the people in their function. >> Right. Right. So Aaron has a lot of great stuff. People can go to the website at alation.com. I'm sure you'll have a link to this, a very prominently displayed, but, and they should and they should check it out and really think about it and think about how it applies to their own situation, their own department, company et cetera. I just wanted to give you the last word before we before we sign off, you know, kind of what was the most you know, kind of positive affirmation or not the most but one or two of the most outcome affirming outcomes of this exercise. And what were one or two of the things that were a little concerning or, you know, kind of surprises on the downside that, that came out of this research? >> Yeah. So I think one thing that was maybe surprising or concerning the biggest one is sort of where we started with that disconnect between, you know, what people would, say as an off the cuff overall assessment and the disconnect between that and what emerges when we go department by department and (indistinct) to be pillars of data culture from such a discovery to data literacy, to data governance. I think that disconnect, you know, should give one pause. I think certainly it should make one think, Hmm. Maybe I shouldn't look from 10,000 feet, but actually be a little more systematic. And considering the framework I use to assess data culture that is the most important thing to my organization. I think though, there's this quote that you move what you measure, just having this hopefully simple but not simplistic yardstick to measure data culture and the data culture index should help people be a little bit more realistic in their quantification and they track their progress, you know, quarter over quarter. So I think that's very promising. I think another thing is that, you know sometimes we ask, how long have you had this initiative? How much progress have you made? And it can sometimes seem like pushing a boulder uphill. Obviously the COVID pandemic and the economic impacts of that has been really tragic and really hard. You know, a tiny silver lining in that is the survey results showed that organizations have really observed a shift in how much they're using data because sometimes things are changing but it's like a frog in boiling water. You don't realize it. And so you just assume that the future is going to look like the recent past and you don't look at the data or you ignore the data or you miss parts of the data. And a lot of organizations said, you know COVID was this really troubling wake up call, but they could even after this crisis is over, producing enduring change which people were consulting data more and making decisions in a more data driven way. >> Yeah, certainly an accelerant that, that is for sure whether you wanted it, didn't want it, thought you had it at the time, didn't have time. You know COVID is definitely digital transformation accelerant and data is certainly the thing that powers that. Well again, it's the Alation State of Data Culture Report available, go check it at alation.com. Aaron always great to catch up and again, thank you for, for doing the work and supporting this research. And I think it's really important stuff. And it's going to be interesting to see how it changes over time. 'Cause that's really when these types of reports really start to add value. >> Thanks for having me, Jeff and I really look forward to discussing some of those trends as the research is completed. >> All right. Thanks a lot, Aaron, take care. Alright. He's Aaron and I'm Jeff. You're watching theCUBE, Palo Alto. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto studios today for theCUBE conversation. We're talking about data. We're always talking about data and it's really interesting. You know we like to go out and get you the first person insight from the people that start the companies, run the companies, the practitioners and, and, and get the insight directly from them. We also like to go out and get original research and hear from original research. And this is a great opportunity to hear from both. So we're excited to have, and welcome back into the studio. He's Aaron Kalb. He's the co founder of Alation, many time CUBE alumni. Aaron. Great to see you. >> Yeah, thanks for having me. It's good to be here. >> Yeah, it's very cool. But today it's a special, a special thing. We've never done this before with you. You guys are releasing a brand new report called, the Alation State of Data Culture Report. So really interesting report. A lot of great information that we're going to dig in here for the next few minutes. But before we do, tell us kind of the history of this report. This is a, the kind of the inaugural release. What was kind of behind it, why did you guys do this? And give us a little background before we get into the details. >> Absolutely. So, yes, that's exactly right. It's debuting today that we plan to kind of update this research quarterly we going to see the trends over time. And this emerged because, you know, I, part of my job, I talk to chief data officers and chief analytics officers across our customer base and prospects. And I keep hearing anecdotally over and over that establishing a data culture, is often the number one priority for these data leaders and for these organizations. And so we wanted to really say, can we quantify that? Can we agree upon a definition of data culture? And can we create sort of a simple yardstick to more objectively measure where organizations are on this sort of data maturity curve to get it into culture. >> Right. I love it. So you created this data, data index right? The data culture index. And, and I think it's important to look at methodology. I think people, a lot of times go right to the results on reports before talking about the methodologies. And let's talk about the methodologies cause we're supposed to be talking about data, right? So you talked to 300, some odd executives, correct. And I think it's really interesting and you broke it down into three kind of buckets of data literacy, if you will. Data search and discovery, number one, data, two kind of literacy in terms of their ability to work with the data. And then the third bucket is really data governance. And then in, in the form ABCD, you gave him a four point score and basically, are they doing it well? Are they doing it in the majority of the time? Are they doing it about half, they got one or they got a zero and you get this four point scale and you end up with a 12 point scale which we're all familiar with from, from school, from an A to an, A minus and B, et cetera. Just dig it a little bit on those three categories and how you chose those. So the first one again is kind of the data search and discovery, you know can they find it and then their competency, if you will and then a governance and compliance. Kind of dig into each of those three buckets a little bit. >> For sure. So, so the, the end goal in data culture, is to have an organization in which data is valued and decisions are made based on data and evidence, right? Versus a culture in which we go with the highest paid person's opinion or what we did last quarter or any of these other ways things get done. And so the idea is to make that possible, as you said you've to be able to find the data when you need it. That's the data search and discovery. You've to be able to interpret that data correctly and draw valid conclusions from it. And that's a data literacy, excuse me. And both of those are contingent upon having data governance in place. So that data is well-defined and has high data quality, as well as other aspects, so that it is possible to find it and understand it properly. >> Right. And what are the things too that I think is really important that we call that, and again, we're going to dive into the details, is your perceived execution versus the reported execution by the people that are actually providing data. And I think you've found and you've highlighted on specific slides that you know, there's not necessarily a match there. And sometimes that you know, what you perceive is happening, isn't necessarily what's happening when you go down and query the people in the field. So really important to come up with a number. And I think a, I think you said this is going to be an ongoing thing over a period of time. So you kind of start to see longitudinal changes in these organizations. >> Absolutely. And we're very excited to see those, those trends over time. But even at the outset is this you know, very striking effect emerges which is, as you said, if we ask one of these you know, 300 data leaders, you know, all around the world actually, you know, if we ask, how is the data culture at your company overall, and this is very broad general top down way and have them graded on the sort of SaaS scale. You know, we get results where there's a large gap between kind of that level of maturity and what emerges in a bottom up methodology excuse me, in which you ask about, you know governance and literacy and, and such kind of by department and in a more bottom up way. And so we do see that that, you know, it can be helpful, even for data people to have a, a more granular metric and framework for quantifying their progress. >> Right? Let's jump into some of the results. It's, it's a fascinating, they're kind of all over the map, but there's some definite trends. One of the trends you talked about is that there's a lot of questions on the quality of the data. But that's a real inhibitor to people. Whether that suspicion is because it's not good data. And I don't know, this question for you, is, is, do they think it's not relevant to the decision that's being made? Is it an incomplete data set or the wrong data set? It seems to be that keeps coming up over and over about, decision-makers not necessarily having confidence in the data. What, can you share a little bit more color around that? >> Yeah, it's quite interesting actually. So what we find is that 90%. So 90 people, 10 executives (indistinct) to question the data sometimes often or always. But the part that's maybe disappointing or concerning is the two thirds of executives are believed to ignore the data and make a decision kind of pushing the data aside which is really quite striking when you think about it, why have all this data, if more often than not you're sort of disregarding it to make your final answer. And so you're absolutely correct when we dug into why, what are the reasons behind pushing it aside. Data quality was number one. And I think it is a question of, Oh, is the data inaccurate? Is it out of date, these sort of concerns sort of we, we hear from customers and prospects. But as we dig in deeper in the survey results, excuse me, we, we see some other reasons behind that. One is a lack of collaboration between the data analytics folks and the business folks. And so there's a question of, I don't know exactly where this data came from or to your point kind of how it was produced. What was the methodology? How was it sourced? And maybe because of that disconnect is a lack of trust. So trust really is the ultimate I think, failure to having data culture really take root. >> Right? And it's trust in this trust, as you said, not only in the data per se, the source of the data, the quality of the data, the relevance of the data but also the people who are providing you with the data. And obviously you get, you get some data sets. Sometimes you didn't get other data sets. So, that's really I'm a little bit disconcerting. The other thing I thought was kind of interesting is, it seems to be consistent that the, the primary reason that people are using big data projects is around operations and operations efficiency, a little bit about compliance, but, you know, it's interesting we had you on at the MIT CDOIQ, Chief Data Information Officer quality symposium, and you talked about the goodness of people moving from kind of a defensive posture to an offensive posture, you know using data in terms of product development and innovation. And, and what comes across in this survey is that's kind of down the list behind you know, kind of operational efficiency. We're seeing a little bit of governance and regulation but the, the quest for data as a tool for innovation, didn't really shine through in this report. >> Well, you know, it's very interesting. It depends whether you look at the aggregate level or you break things down a little bit more. So one thing we did after we got that zero to 12 scale on the data culture index or DCI, is it actually, we were able to break it down into thirds. And among the sort of bottom third, it has the least well-established data culture by this yardstick. We've found that governance and regulatory compliance, was the number one application of data. But among the top third of respondents, we actually found the opposite where things like providing a great customer experience, doing product innovation, those sort of things actually came to the fore and governance fell behind. So I think there is this curve where, It's table stakes to get the sort of defense side of data figured out. And then you can move on to offense in using data to make your organization meet its meet its other goals. >> Right. Right. And then I wanted to get your take on kind of the democratization of data, right? This is a, this is a trend that's been going on, and really, I think you said before you know, your guys' whole mission is to empower curious and rational world to give people the ability to ask the right questions have the right data and get the right answer. So, you know, we've seen democratization in terms of the access to the data, the access to the tools, the ability to do something with the data and the tool, and then the actual authority to execute business decision based on that. The results on that seem a little bit split here because a lot of the problems seem to be focused on leadership, not necessarily taking a data based decision move, but on the good hand a lot of people trying to break down data silos and make data more accessible for a larger group of people. So that more people in the organization are making data based decisions. This seems kind of like this little bit of a bifurcation between the C suite and everybody else trying to get their job done. >> Absolutely. There's always this question of you know, sort of the, that organizational wide initiative and then what's happening on the ground. One thing we saw that was very heartening and aligns with our customers index success, is a real emphasis being placed on having data governance and data context and data literacy factors sort of be embedded at the point of use. To not expecting people, to just like take a course and look things up and kind of end up with their workflow to be able to use data quickly and accurately and, and interpret it in varied ways. So that was really exciting to see as, as, as a initiative. It sort of bridges that gap along with initiatives to have more collaboration and integration between the data people and the business people. because really you know, they exist to serve one another. But in terms of the disconnect between the C suite and other parts of the org, there was a really interesting inverse correlation. Well, or maybe it's not interesting how you look at it, but basically, you know, when we talk to C level executives and ask, you know, does the C suite ignore data? Do they question data et cetera, those numbers came in lower than when we talked to, you know, senior director about the C suite right? It's sort of the farther you get, and there's a difference there, you know, from my perspective, I almost wonder whether that distance is actually is more objective viewpoint. And when you're in that role, it's hard to even see your cognitive biases and your tendency to ignore a data when it doesn't suit you. >> Right. Right. So there's, there's some other interesting things here. So one of them is, you know, kind of predictors, right? One of the whole reasons to do studies and collect data so that we can have some predictive ability. And, and it comes out here that the reporting structure is a strong predictor of a company's data tier structure. So, you know, there's the whole rise of the chief data officers and the chief analytics officer and the chief data and analytics officer and lots of conversations about those roles and what exactly are those roles and who do they report to. Your study finds a pretty compelling leading indicator that if that role is reporting to either the CEO or the executive board, which is often a one in the same person, that that's actually a terrific indicator of success in moving to a more data centric culture. >> That's absolutely correct. So we found that that top third of organizations on the data culture index were much more likely to have a chief data executive, a CDO, CAO or CDAO. In fact, they're more likely to have folks with the analytics in their title because in some organizations, data is thought to mean sort of raw data, infrastructural defense and analytics is sort of where it gets you know, infused into business processes and value. But certainly that top third is much more likely to have the chief data executive reporting into the executive board or CEO when the highest ranking data executive is under the CIO or some other part of the organization, those orgs tend to score a far lower on the DCI. >> Right. Right. So it's interesting, you know you're a really interesting guy even doing this for a while. You were at Siri before you were at Alation. So you have a really good feel for kind of what data can do and can't do and natural human or natural language processing and, and, and human voice interaction with these devices, a really interesting case study, and they can do a really good job within a small defined data set and instruction set, but they don't do necessarily so well once you kind of get outside how, how they're trained. And you've talked a lot about how metaphor shaped the way that we think and I know you and Dave talked about data oil and data lakes I don't want to necessarily go down that whole path but I do think it's important. And what came out of the study and the way people think about data. You know, there's a lot of conversation. How do you value data? Is data, you know it used to just be an expense that we had to buy servers to store the stuff we weren't sure what we ever did with it. So I wonder if there's any, you know, kind of top level metaphors level, kind of a thought or process or framing in the companies that you study that came out. maybe not necessarily in the top line data, but maybe in some of the notes that help define why some people, you know are being successful at making this transition and putting, you know kind of data out front of their decision processing versus data, either behind as a supporting thing or maybe data, I just don't have time with it or I don't trust it, or God knows where you got that, and this is not the data that I wanted. You know, was there any, you know, kind of tangental or anecdotal stuff that came out of this study that's more reflective of, of the softer parts of a data culture versus the harder parts in terms of titles and roles and, and, and job responsibilities. >> Yeah. It's a really interesting place to explore. I do think there's a, I don't want to make this overly simplistic group binary, but at the end of the day you know, like anything else within an organization, you can view data as a liability to say, okay, we have for example, you know, customer's names and phone numbers and passwords, and we just need to prevent an adverse event in which there's a leak or some sort of InfoSec problem that could cause, you know, bad press and fines and other negative consequences. And I think the issue there is if data's a liability, the most you know, the best case is that it's worth zero as opposed to some huge negative on your company's balance sheet. And, and I think, you know, intuitively, if you really want to prevent data misuse and data problems, one fail safe, but I think ultimately in its own way risky way to do that was just not collect any data, right. And not store it. So I think that the transition is to say, look data must be protected and taken care of that's step zero. But you know, it's really just the beginning and data is this asset that can be used to inform the huge company level strategic decisions that are made in annual planning at the board level, down to the millions of little decisions every day in the work of people in customer support and in sales and in product management and in, you know, various roles that just across industries. And I think once you have that, that shift, you know the upside is potentially, you know, unbounded. >> Right. And, and it just changes the way, the way you think. And suddenly instead of saying, Oh, data needs to be kind of hidden away, it's more like, Oh, people need to be trained on data use and empowered with data. And it's all about not if it's used or if it's misused but really how it's used and why it's used, what it's being used for to make a real impact. >> Right. Right. And it's funny when I just remember it being back in business school one of the great things that help teach is to think in terms of data, right. And you always have the infamous center consulting interview question, How many manhole covers are in Manhattan. Right. So, you know, to, to, to start to think about that problem from a data centric, point of view really gives you a leg up and, and even, you know where to start and how to attack those types of problems. And I thought it was interesting you know, talking about challenges for people to have a more data centric, point of view. It's interesting. The reports says, basically everybody said there's all kinds of challenges around data quality and compliance, and they had democratization. But the bottom companies, the bottom companies said that the biggest challenge was lack of buy in from company leadership. So I guess the good news bad news is that there's a real opportunity to make a significant change and get your company from the bottom third to a middle third or a top third, simply by taking a change in attitude about putting data in a much more central role in your decision making process. 'Cause all the other stuff's kind of operational, execution challenges that we all have, not enough people, blah, blah, blah. But in terms of attitude of leadership and prioritization, that's something that's very easy to change if you so choose. And really seems to be the key to unlock this real journey as opposed to the minutiae of a lot of the little details that that are a challenge for everybody. >> Absolutely. In your changing attitudes might be the easiest thing or the hardest thing depending on (indistinct). But I think you're absolutely right. The first step, which, which which could, maybe it should be easy, is admitting that you have a problem or maybe to put it more positively, realizing you have an opportunity. >> I love that. And then just again, looking at the top tier companies, the other thing that I thought was pretty interesting in this study is, I'm looking at it here, is getting champions in each of the operational segments. So rather than, I mean, a chief data officer is important and you know, somebody kind of at the high level to shepherd it in the executive suite, as we just discussed, but within each of the individual tasks and functions and roles, whether that's operations or customer service or product development or operational efficiency, you need some type of champion, some type of person, you know, banging the gavel, collecting the data, smoothing out the complexities, helping people get their thing together. And again, another way to really elevate your position on the score. >> Absolutely. And I think this idea of again, bridging between, you know, if data is centralized you have a chance to try to really get excellent practices within the data org. But even it becomes even more essential to have those ambassadors, people who are in the business and understand all the business context who can sort of make the data relevant, identify the key areas where data can really help, maybe demystify data and pick the right metaphors and the right examples to make it real for the people in their function. >> Right. Right. So Aaron has a lot of great stuff. People can go to the website at alation.com. I'm sure you'll have a link to this, a very prominently displayed, but, and they should and they should check it out and really think about it and think about how it applies to their own situation, their own department, company et cetera. I just wanted to give you the last word before we before we sign off, you know, kind of what was the most you know, kind of positive affirmation or not the most but one or two of the most outcome affirming outcomes of this exercise. And what were one or two of the things that were a little concerning or, you know, kind of surprises on the downside that, that came out of this research? >> Yeah. So I think one thing that was maybe surprising or concerning the biggest one is sort of where we started with that disconnect between, you know, what people would, say as an off the cuff overall assessment and the disconnect between that and what emerges when we go department by department and (indistinct) to be pillars of data culture from such a discovery to data literacy, to data governance. I think that disconnect, you know, should give one pause. I think certainly it should make one think, Hmm. Maybe I shouldn't look from 10,000 feet, but actually be a little more systematic. And considering the framework I use to assess data culture that is the most important thing to my organization. I think though, there's this quote that you move what you measure, just having this hopefully simple but not simplistic yardstick to measure data culture and the data culture index should help people be a little bit more realistic in their quantification and they track their progress, you know, quarter over quarter. So I think that's very promising. I think another thing is that, you know sometimes we ask, how long have you had this initiative? How much progress have you made? And it can sometimes seem like pushing a boulder uphill. Obviously the COVID pandemic and the economic impacts of that has been really tragic and really hard. You know, a tiny silver lining in that is the survey results showed that organizations have really observed a shift in how much they're using data because sometimes things are changing but it's like a frog in boiling water. You don't realize it. And so you just assume that the future is going to look like the recent past and you don't look at the data or you ignore the data or you miss parts of the data. And a lot of organizations said, you know COVID was this really troubling wake up call, but they could even after this crisis is over, producing enduring change which people were consulting data more and making decisions in a more data driven way. >> Yeah, certainly an accelerant that, that is for sure whether you wanted it, didn't want it, thought you had it at the time, didn't have time. You know COVID is definitely digital transformation accelerant and data is certainly the thing that powers that. Well again, it's the Alation State of Data Culture Report available, go check it at alation.com. Aaron always great to catch up and again, thank you for, for doing the work and supporting this research. And I think it's really important stuff. And it's going to be interesting to see how it changes over time. 'Cause that's really when these types of reports really start to add value. >> Thanks for having me, Jeff and I really look forward to discussing some of those trends as the research is completed. >> All right. Thanks a lot, Aaron, take care. Alright. He's Aaron and I'm Jeff. You're watching theCUBE, Palo Alto. Thanks for watching. We'll see you next time. (upbeat music)