 From the CUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, welcome to the CUBE Studio. I'm John Furrier here in Palo Alto in our remote coverage of the tech industry. We are in our quarantine crew here, getting all the stories in the technology industry, from all the thought leaders and all the news makers. We've got a great story here about data, data compliance, and really about the platforms around how enterprises are using data. We've got two great guests and some news to announce. Kira Narsu is the Vice President of Business Development with Elation, and William Murphy, Vice President of Technology Alliance is a big ID. Got some interesting news, a integration partnership between the two companies. Really kind of compelling, especially now as people have to look at the cloud scale of what's happening in our world, certainly in the new realities of COVID-19 and going forward, the role of data, new kinds of applications, and the speed and agility are going to require more and more automation and more reality around making sure things are in place. So guys, thanks for coming on. Appreciate it, Kira Narsu. William, thanks for joining me. Thank you. Thank you. So let's take a step back, Elation. You guys have been on theCUBE many times. We've been following you guys. You've been a leader in enterprise catalog, a new approach. It's a real new technology approach and methodology and team approach to building out the data catalogs. So talk about the alliance here. What's the news? Why are you guys creating this integration partnership? Well, let me start and thank you for having us today. As you know, Elation launched the data catalog category seven years ago, and even today we're acknowledging as a leader in that space. And we really began with the core belief that ultimately data management will be driven more and more by business demand and less by information suppliers. So another way to think about that is how people behave with data will drive how companies manage data. So our philosophy put very simply is to start with people and not data. And our customers really seem to agree with this approach and we've got close to 200 brands using our tool every single day to drive vibrant data communities and foster a real data culture in the environment. So one of the things that was really exciting to us is the investment in data privacy by large corporate customers to get their arms around this. And we really strive to improve our ability to use the tool inside these enterprises across more use cases. So the partnership that we're announcing with Big ID today is really, Big ID is the leading modern data intelligence platform for privacy. And what we're trying to do is to bring a level of integration between our two technologies so that enterprises can better manage and scale their data privacy compliance capability. William, talk about Big ID, what you guys are doing. You guys obviously have a data intelligence platform. We've been covering GDPR for a very long time. I once called, I won't say it again because this wasn't really that complimentary but the reality has sit in and the users now understand more than ever, privacy is super important, and companies have to deal with this. You guys have a solution. Take a minute to explain Big ID and what you guys are doing. Yeah, absolutely. So our founders, Dmitri Sarota and Nimrod Vax founded Big ID in 2016. The same year that GDPR was authored. And the big reason there is that data change and how companies and enterprises dealt with data is changing pretty much forever. That profound change meant that the status quo could no longer exist. And so privacy was going to have to become a day-to-day reality to these enterprises. So what Big ID realized is that to start to do anything with privacy, you actually have to understand where your data is, what it is, and who's it is. And so that's really the genesis of what Dmitri and Nimrod created which is a privacy-centric data discovery and intelligence platform that allows our enterprise customers, and we have over 70 customers in the enterprise space, many within the Fortune 100, to be able to find, classify, and correlate sensitive data as they define it across data sources, whether it's on-prem or in the cloud. And this gives our users a kind of unprecedented ability to look into their data, to get better visibility, which it both allows for collaboration and also allows for real-time decision-making to take place with better accuracy and confidence that regulations are not being broken and that customer's data is being treated appropriately. Great, I'm just reading here from the release I want to get you guys' thoughts and unpack some of the concepts behind here. But the headline is, Elation Strengthens Privacy Capabilities with Big ID Partnership, Empowering Organizations to Mitigate Risk, Delivering Privacy-Aware Data Use and Improved Adherence to Data Privacy Regulations. It's a mouthful, but the bottom line is, is that there's a lot of complexity around these rules and these platforms. And what's interesting, you mentioned Discovery. The enterprise discovery side of the business has always been a complex nightmare. I think what's interesting about this partnership from my standpoint is that you guys are bringing an interface into a complex platform and creating an easy abstraction to kind of make it usable. I mean, at the end of the day, we're seeing the trends with Amazon, they have Kendra which they announced and they're going to have a ship soon. Fast speed of insights has to be there. So unifying data interfaces with backend is really what seems to be the pattern. Is that the magic going on here? Can you guys explain what's going on with this and what's the outcome going to be for customers? Yeah, I guess I'll kick off and will please chime in. I think really there's three overarching challenges that I think enterprises are facing as they're grappling with these regulations as Will talked about. Number one, it's really hard to both identify and classify private data, right? It's not as easy as it might sound. And Will can talk a little bit more about that. It's also very difficult to flag at the point of analysis when somebody wants to find information, the relevant policies that might apply to the given data that they're looking to run an analysis on. And lastly, enterprises are constantly in motion as enterprises change and buy new businesses and enter new markets and launch new products. These policies have to keep up with that change. And these are real challenges to address. And with big ID and elation, we're trying to really accelerate that compliance with the combination of our tools, reduce the cost and complexity of compliance and fundamentally keep up through a single interface so that users can know what to do with data at the point of consumption. And I think that's the way to think about it. Will, I don't know if you want to add something to that. Absolutely, I think when Kieran and I have been working on this for actually many months at this point, but most companies don't have a business plan of just saying, let's store as much data as possible without getting anything out of it. But in order to get something out of it, the ability to find that data rapidly and then analyze it so that decision makers can make up to date decisions is pretty vital. A lot of these things, when they have to be done manually take a long time, they're huge business issues there. And so the ability to both automate data discovery and then cataloging across elation and big ID gives those decision makers, whether they did the data steward, the data analyst, the chief data officer, an ability to really dive deeper than they have previously with better speed. You know, one of the things that we've been talking about for a long time with big data is these data lakes. And they're fairly easy to deploy. I mean, you can put a bunch of data into a corpus and you act on them. But as you start to get across these silos is a need for, you know, getting a process down around managing just not only the data wrangling, but the policies behind it. And platforms are becoming more complex. Can you guys talk about the product market fit here? Because there's sass involved. So there's also customer activity. What's the product market fit that you guys see with this integration? What are some of the things that you're envisioning to emerge out of this value proposition? I can start. I think you're exactly right. Enterprise have made huge investments in, you know, historically data warehouses, data marts, data lakes, all kinds of other technology infrastructure aimed at making the data easier to get to, but they've effectively just layered onto the problem. So Alation's catalog has made it incredibly much more effective at helping organizations to find, understand, trust, to reuse and use that data so that stewards and people who know about the data can inform users who need to run a particular report or conduct a specific analysis can accelerate that process and compress the time to insights much more than are possible with today's technologies. And if you overlay that onto the data privacy challenge, it's compounded. And I think, you know, Will, it would be great for you to comment on what the data discovery capabilities of big ID do to improve that even further. Yeah, absolutely. So as two companies, we're trying to bridge this gap between data governance and privacy. And John, as you mentioned, there's been a proliferation of a lot of tools, whether they're data lakes, data analysis tools, et cetera. What big ID is able to do is we're looking across over 70 different types of data platforms, whether they be legacy systems, like SharePoint and SQL, whether they be on-prem or in the cloud, whether it's data at rest or in motion, and we're able to auto-populate our metadata findings into Alation's data catalog, the main purpose there being that those data stewards then have access to the most authentic, real-time data possible. So on the terms of the customer value, they're going to see what more built-in, privacy-aware features, is it speed? But, you know, what, I mean, obviously the problem is compounded with the data, getting that catalog and getting insights out of it. But for this partnership, is it speed to outcome? What is the outcome that you guys are envisioning here for the customer? I think it's a combination of speed. As you said, you know, they can much more rapidly get up to speed. So an analyst who needs to make a decision about a specific data set, whether they can use it or not, can know at the point of analysis if this data is governed by policies that has been informed by big IDs. So the Alation catalog user can make a much more rapid decision about how to use that. The second piece is the complexity and cost of compliance. They can really reduce and start to winnow down their technology footprint, because with the combination of the discovery that big ID provides and the ongoing discovery that big ID provides and the enterprise data catalog provided by Alation, we give the framework for being able to keep up with these changes in policies as rules and as companies change. So they don't have to keep reinventing the wheel every time. So we think that there's a significant speed and time to market advantage as well as an ability to really consolidate technology footprint. Well, I want to add to that. Yeah, just one moment. So Alation, when they helped create this marketplace seven years ago, one of the goals there, and I think where big ID is assisting as well, is the trust and confidence that both the users of these softwares, the data stewards, the analysts have and the data that they're using. And then the trust and confidence are building with their end consumers is much better knowing that there is the, this is both bidirectional and ongoing continuously. You know, I've always been impressed with Alation's vision. It's big vision around the role of the human and data and it's always been impressive. And yeah, I think the world's spinning in that direction. You're starting to see that now. William, I want to get your thoughts with big ID because, you know, one of the things is challenging out there from what we're hearing is, you know, people want to protect the sensitive data obviously with the hacks and everything else and personal information. There's all kinds of regulations and believe me, state by state, nation by nation, it's crazy complex. At the same time, they got to ensure this compliance tripwires everywhere, right? So you have this kind of nested, complex web of stuff and some real security concerns. At the same time, you want to make data available for machine learning and for things like that. This is the real kind of things, the problem that's twisted around. So if I'm an enterprise, I'm like, oh man, this is a pain in the butt. So how are you guys seeing this evolve because this solution is one step in that direction. What are some of the pain points? What are some of the examples? Can you share any insights around how people are overcoming that because they want to get the data out there. They want to create applications that are going to be modern, robust and augmented with whether it's augmented AI of some sort or some sort of application. At the same time, protecting the information and compliance. It's a huge problem challenge. Your thoughts. Absolutely. So to your point, regulations and compliance measures, both state by state and internationally, they're growing. I mean, I think when we saw GDPR four years ago and the proliferation of other things, whether it be in Latin America, in Asia Pacific or across the United States, potentially even at the federal level in the future, it's not making it easier to add complexity to that. Every industry and many companies individually have their own policies in the way that they describe data, whether what's sensitive to them. Is it patent numbers? Is it loyalty card numbers? Is it any number of different things where they could just that that enterprise says that this type of data is particularly sensitive? The way we're trying to do this is we're saying that if we can be a force multiplier for the individuals within an organization that are in charge of the stewardship over their data, whether it be on the privacy side, on the security side, or on the data and analytics side, that's what we want to do and automation is a huge piece of this. So yes, big ID has a number of patents in the machine learning area around data discovery and classification, cluster analysis, being able to find duplicative data out there. And when we put that in conjunction with what Elation's doing and actually gave the users of the data the kind of unprecedented ability to curate, deduplicate, secure sensitive data all by a policy-driven automated platform, that's actually, I think the magic here is we want to make sure that when humans get involved their actions can be made, how do I say this? Minimum human interaction and when it's done, it's done for a reason of remediation. So they're the second step, not the first step. Kira, I want to get your thoughts. I always riff on the idea of DevOps and it's a cloud term. And when you apply that to data, you're talking about programmability scale, automation, but the humans are making calls whether you're a programmer in DevOps world or to a data customer of the catalog and Elation, I'm making decisions with my business. I'm a human, I'm taking action at the point of design or whatever. This is where I think the magic can happen. Your thoughts on how this evolves for that use case because what you're doing is you're augmenting the value for the user by taking advantage of these things. Is that right or am I around the right area? I mean, I think so. I think the one way to think about Elation in that analogy is that the biggest struggle that enterprise business users have and we target the consumers of data. We're not a provider to the information suppliers, if you will, but the people who need to make decisions every single day on the right set of data, we're here to empower them to be able to do that with the data that they know has been given the thumbs up by people who know about the data, connecting stewards who know about the subject matter at hand with the data that the analyst wants to use at the time of consumption. And that powerful connection has been so effective in our customers, enabling them to do analytical work that they just couldn't dream of before. So the key piece here is with the combination with big ID, we can now layer in a privacy-aware consumption angle, which means if you have a question about running some customer's propensity model and you don't know if you can use this data or that data, the big ID data discovery platform informs the Elation catalog of the usage capabilities of that given dataset at the moment the analyst wants to conduct his or her analysis with the appropriate dataset as identified by the stewards and as endorsed by the steward. So that point in time is really critical because that's where we can fundamentally shrink the decision site. Yeah, it's interesting. It's also the point of attack on the user, in this case the person in the business who's doing some real work. That's where the action is. It's a whole nother meaning of actionable data, right? So, you know, this seems to be where the value is. It's agility really. It's kind of what we're talking about here, isn't it? It is very agile and the differentiation between Elation and big ID and what we're bringing to the market now is we're also bringing flexibility and you mentioned that the point of agility there is because we allow our customers to say what their policies are, what their sense of data is, define that themselves within our platforms and then go out, find that data, classify it and catalog it, et cetera. Like that's giving them that extra flexibility that enterprises today need so that it can make the business decisions faster and actually operationalize data. Guys, great job, good news. I think this is kind of an interesting canyre in the coal mine around the trends that are going on around how data is evolving. What's next? How are you guys going to go to market? Partnership obviously makes a lot of sense. Technical integration, business model integration, good fit. What's next for you guys? I'll start. I mean, I think the great thing is that, you know, from the CEO down, our organizations are very much aligned in terms of how we want to integrate our two solutions and how we want to go to market. So myself and Will have been really focused on making sure that the skill sets of the various constituents within both of our companies have the level of education and knowledge to bring these results to bear, coupled with the integration of our two technologies. Will your thoughts? Yeah, absolutely. I mean, between our CEOs who have a good cadence to care to myself who probably spend too much time on the phone at this point, we might have to get them a guest bedroom or something. Alignment's a huge key here, ensuring that we've enabled our field to evangelize this out to the marketplace itself. And then doing, whether it's this or our webinars or however we're getting the news out, it's important that the markets know that these capabilities are out there because the biggest obstacle, honestly, to adoption is not that other solutions or build it yourself. It's just lack of knowledge that it could be easier. It could be done better that you could have, you could know your data better. You could catalog it better. Great, final question to end the segment. Message to the potential customer out there. What about their environment that might make them a great prospect for this solution? Is it a known problem? Is it a blind spot? When would someone know to call you guys up in this partnership and leverage this partnership? Is it too much data? Is it just too much many applications across geographies? I'm just trying to understand, for the folks watching, when is an opportunity to call you guys? Well, from an elation perspective, there can never be too much data. But I'll get it aside. A signal that may indicate an interest or a potential fit for us would be, the need to be compliant with one or more data privacy regulations. And as Will said, these are coming up left and right individual states in addition to countries are rolling out data privacy regulations that require a whole set of capabilities to be in place and a very rigorous framework of compliance. Those requirements and the ability to make decisions every single day, all day long about what data to use and when and under what conditions are a perfect set of conditions for the use of a data catalog calculation coupled with a data discovery and data privacy solution like big I. Absolutely. If you're an organization out there and you have a lot of customers, you have a lot of employees, you have a lot of different data sources in disparate locations, whether they're on-prime or the cloud, these are solid indications that you should look at purchasing best of breed solutions like elation and big I.D. as opposed to trying to build something internally. Guys, congratulations. Elation strengthening your privacy capabilities with the big I.D. partnership. Congratulations on the news and we'll be tracking it. Thanks for coming on, appreciate it. Thank you. Okay, so CUBE coverage here in Palo Alto on remote interviews as we get through this COVID crisis. We have our quarantine crew here in Palo Alto. I'm John Furrier, thanks for watching. Okay, guys.