>> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE studios, in Palo Alto, California for another CUBE conversation where we go in depth with though leaders driving innovation across tech industry. I'm your host, Peter Burris. The whole concept of self service analytics has been with us decades in the tech industry. Sometimes its been successful, most times it hasn't been. But we're making great progress and have over the last few years as the technologies matures, as the software becomes more potent, but very importantly as the users of analytics become that much more familiar with what's possible and that much more wanting of what they could be doing. But this notion of self service analytics requires some new invention, some new innovation. What are they? How's that going to play out? Well, we're going to have a great conversation today with Stephanie McReynolds, she's Senior Vice President of Marketing, at Alation. Stephanie, thanks again for being on theCUBE. >> Thanks for inviting me, it's great to be back. >> So, tell us a little, give us an update on Alation. >> So as you know, Alation was one of the first companies to bring a data catalog to the market. And that market category has now been cemented and defined depending on the industry analyst you talk to. There could be 40 or 50 vendors now who are providing data catalogs to the market. So this has become one of the hot technologies to include in a modern analytics stacks. Particularly, we're seeing a lot of demand as companies move from on premise deployments into the cloud. Not only are they thinking about how do we migrate our systems, our infrastructure into the cloud but with data cataloging more importantly, how do we migrate our users to the cloud? How do we get self-service users to understand where to go to find data, how to understand it, how to trust it, what re-use can we do of it's existing assets so we're not just exploding the amount of processing we're doing in the cloud. So that's been very exciting, it's helped us grow our business. We've now seen four straight years of triple digit revenue growth which is amazing for a high growth company like us. >> Sure. >> We also have over 150 different organizations in production with a data catalog as part of their modern analytics stack. And many of those organizations are moving into the thousands of users. So eBay was probably our first customer to move into the, you know, over a thousand weekly logins they're now up to about 4,000 weekly logins through Alation. But now we have customers like Boeing and General Electric and Pfizer and we just closed a deal with US Air Force. So we're starting to see all sorts of different industries and all sorts of different users from the analytics specialist in your organization, like a data scientist or a data engineer, all the way out to maybe a product manager or someone who doesn't really think of them as an analytics expert using Alation either directly or sometimes through one of our partnerships with folks like Tableau or Microstrategy or Power BI. >> So, if we think about this notion of self- service analytics, Stephanie, and again it's Alation has been a leader in defining this overall category, we think in terms of an individual who has some need for data but is, most importantly, has questions they think data can answer and now they're out looking for data. Take us through that process. They need to know where the data is, they need to know what it is, they need to know how to use it, and they need to know what to do if they make a mistake. How is that, how are the data catalogs, like Alation, serving that, and what's new? >> Yeah, so as consumers, this world of data cataloging is very similar if you go back to the introduction of the internet. >> Sure. >> How did you find a webpage in the 90's? Pretty difficult, you had to know the exact URL to go to in most cases, to find a webpage. And then a Yahoo was introduced, and Yahoo did a whole bunch of manual curation of those pages so that you could search for a page and find it. >> So Yahoo was like a big catalog. >> It was like a big catalog, an inventory of what was out there. So the original data catalogs, you could argue, were what we would call from an technical perspective, a metadata repository. No business user wants to use a metadata repository but it created an inventory of what are all the data assets that we have in the organizations and what's the description of those data assets. The meta- data. So metadata repositories were kind of the original catalogs. The big breakthrough for data catalogs was: How do we become the Google of finding data in the organization? So rather than manually curating everything that's out there and providing an in- user inferant with an answer, how could we use machine learning and AI to look at patterns of usage- what people are clicking on, in terms of data assets- surface those as data recommendations to any end user whether they're an analytics specialist or they're just a self- service analytics user. And so that has been the real break through of this new category called data cataloging. And so most folks are accessing a data catalog through a search interface or maybe they're writing a SQL query and there's SQL recommendations that are being provided by the catalog-- >> Or using a tool that utilizes SQL >> Or using a tool that utilizes SQL, and for most people in a- most employees in a large enterprise when you get those thousands of users, they're using some other tool like Tableau or Microstrategy or, you know, a variety of different data visualization providers or data science tools to actually access that data. So a big part of our strategy at Alation has been, how do we surface this data recommendation engine in those third party products. And then if you think about it, once you're surfacing that information and providing some value to those end users, the next thing you want to do is make sure that they're using that data accurately. And that's a non- trivial problem to solve, because analytics and data is complicated. >> Right >> And metadata is extremely complicated-- >> And metadata is-- because often it's written in a language that's arcane and done to be precise from a data standpoint, that's not easily consumable or easily accessible by your average human being. >> Right, so a label, for example, on a table in a data base might be cust_seg_257, what does that mean? >> It means we can process it really quickly in the system. >> Yeah, but as-- >> But it's useless to a human being-- >> As a marketing manager, right? I'm like, hey, I want to do some customer segmentation analysis and I want to find out if people who live in California might behave differently if I provide them an offer than people that live in Massachusetts, it's not intuitive to say, oh yeah, that's in customer_seg_ so what data catalogs are doing is they're thinking about that marketing manager, they're thinking about that peer business user and helping make that translation between business terminology, "Hey I want to run some customer segmentation analysis for the West" with the technical, physical model, that underlies the data in that data base which is customer_seg_257 is the table you need to access to get the answer to that question. So as organizations start to adapt more self- service analytics, it's important that we're managing not just the data itself and this translation from technical metadata to business metadata, but there's another layer that's becoming even more important as organizations embrace self- service analytics. And that's how is this data actually being processed? What is the logic that is being used to traverse different data sets that end users now have access to. So if I take gender information in one table and I have information on income on another table, and I have some private information that identifies those two customers as the same in those two tables, in some use tables I can join that data, if I'm doing marketing campaigns, I likely can join that data. >> Sure. >> If I'm running a loan approval process here in the United States, I cannot join that data. >> That's a legal limitation, that's not a technical issue-- >> That's a legal, federal, government issue. Right? And so here's where there's a discussion, in folks that are knowledgeable about data and data management, there's a discussion of how do we govern this data? But I think by saying how we govern this data, we're kind of covering up what's actually going on, because you don't have govern that data so much as you have to govern the analysis. How is this joined, how are we combining these two data sets? If I just govern the data for accuracy, I might not know the usage scenario which is someone wants to combine these two things which makes it's illegal. Separately, it's fine, combined, it's illegal. So now we need to think about, how do we govern the analytics themselves, the logic that is being used. And that gets kind of complicated, right? For a marketing manager to understand the difference between those things on the surface is doesn't really make sense. It only makes sense when the context of that government regulation is shared and explained and in the course of your workflow and dragging and dropping in a Tableau report, you might not remember that, right? >> That's right, and the derivative output that you create that other people might then be able to use because it's back in the data catalog, doesn't explicitly note, often, that this data was generated as a combination of a join that might not be in compliance with any number of different rules. >> Right, so about a year and a half ago, we introduced a new feature in our data catalog called Trust Check. >> Yeah, I really like this. This is a really interesting thing. >> And that was meant to be a way where we could alert end users to these issues- hey, you're trying to run the same analytic and that's not allowed. We're going to give you a warning, we're not going to let you run that query, we're going to stop you in your place. So that was a way in the workflow of someone while they're typing a SQL statement or while they're dragging and dropping in Tableau to surface that up. Now, some of the vendors we work with, like Tableau, have doubled down on this concept of how do they integrate with an enterprise data catalog to make this even easier. So at Tableau conference last week, they introduced a new metadata API, they introduced a Tableau catalog, and the opportunity for these type of alerts to be pushed into the Tableau catalog as well as directly into reports and worksheets and dashboards that end users are using. >> Let me make sure I got this. So it means that you can put a lot of the compliance rules inside Alation and have a metadata API so that Alation effectively is governing the utilization of data inside the Tableau catalog. >> That's right. So think about the integration with Tableau is this communication mechanism to surface up these policies that are stored centrally in your data catalog. And so this is important, this notion of a central place of reference. We used to talk about data catalogs just as a central place of reference for where all your data assets lie in the organizations, and we have some automated ways to crawl those sources and create a centralized inventory. What we've added in our new release, which is coming out here shortly, is the ability to centralize all your policies in that catalog as well as the pointers to your data in that catalog. So you have a single source of reference for how this data needs to be governed, as well as a single source of reference for how this data is used in the organization. >> So does that mean, ultimately, that someone could try to do something, trust check and say, no you can't, but this new capability will say, and here's why or here's what you do. >> Exactly. >> A descriptive step that says let me explain why you can't do it. >> That's right. Let me not just stop your query and tell you no, let me give you the details as to why this query isn't a good query and what you might be able to do to modify that query should you still want to run it. And so all of that context is available for any end user to be able to become more aware of what is the system doing, and why is recommending. And on the flip side, in the world before we had something like Trust Check, the only opportunity for an IT Team to stop those queries was just to stop them without explanation or to try to publish manuals and ask people to run tests, like the DMV, so that they memorized all those rules of governance. >> Yeah, self- service, but if there's a problem you have to call us. >> That's right. That's right. So what we're trying to do is trying to make the work of those governance teams, those IT Teams, much easier by scaling them. Because we all know the volume of data that's being created, the volume of analysis that's being created is far greater than any individual can come up with, so we're trying to scale those precious data expert resources-- >> Digitize them-- >> Yeah, exactly. >> It's a digital transformation of how we acquire data necessary-- >> And then-- >> for data transformation. >> make it super transparent for the end user as to why they're being told yes or no so that we remove this friction that's existed between business and IT when trying to perform analytics. >> But I want to build a little bit on one of the things I thought I heard you say, and that is that the idea that this new feature, this new capability will actually prescribe an alternative, logical way for you to get your information that might be in compliance. Have I got that right? >> Yeah, that's right. Because what we also have in the catalog is a workflow that allows individuals called Stewards, analytics Stewards to be able to make recommendations and certifications. So if there's a policy that says though shall not use the data in this way, the Stewards can then say, but here's an alternative mechanism, here's an alternative method, and by the way, not only are we making this as a recommendation but this is certified for success. We know that our best analysts have already tried this out, or we know that this complies with government regulation. And so this is a more active way, then, for the two parties to collaborate together in a distributed way, that's asynchronous, and so it's easy for everyone no matter what hour of the day they're working or where they're globally located. And it helps progress analytics throughout the organization. >> Oh and more importantly, it increases the likelihood that someone who is told you now have self- service capability doesn't find themselves abandoning it the first time that somebody says no, because we've seen that over and over with a lot of these query tools, right? That somebody says, oh wow, look at this new capability until the screen, you know, metaphorically, goes dark. >> Right, until it becomes too complicated-- >> That's right-- >> and then you're like, oh I guess I wasn't really trained on this. >> And then they walk away. And it doesn't get adopted. >> Right. >> And this is a way, it's very human centered way to bring that self- service analyst into the system and be a full participant in how you generate value out of it. >> And help them along. So you know, the ultimate goal that we have as an organization, is help organizations become our customers, become data literate populations. And you can only become data literate if you get comfortable working with the date and it's not a black box to you. So the more transparency that we can create through our policy center, through documenting the data for end users, and making it more easy for them to access, the better. And so, in the next version of the Alation product, not only have we implemented features for analytic Stewards to use, to certify these different assets, to log their policies, to ensure that they can document those policies fully with examples and use cases, but we're also bringing to market a professional services offering from our own team that says look, given that we've now worked with about 20% of our installed base, and observed how they roll out Stewardship initiatives and how they assign Stewards and how they manage this process, and how they manage incentives, we've done a lot of thinking about what are some of the best practices for having a strong analytics Stewardship practice if you're a self- service analytics oriented organization. And so our professional services team is now available to help organizations roll out this type of initiative, make it successful, and have that be supported with product. So the psychological incentives of how you get one of these programs really healthy is important. >> Look, you guys have always been very focused on ensuring that your customers were able to adopt valued proposition, not just buy the valued proposition. >> Right. >> Stephanie McReynolds, Senior Vice President of Marketing Relation, once again, thanks for being on theCUBE. >> Thanks for having me. >> And thank you for joining us for another CUBE conversation. I'm Peter Burris. See you next time.