(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)

>> 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)