 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Welcome back to this CUBE Conversation. I'm John Furrier here in theCUBE Studios, your host for our remote interviews as part of our coverage and continue to get the interviews during COVID-19. Great talking session here about data warehouses, data lakes, data everything, hybrid cloud, and back on theCUBE for a return CUBE alumni, virtual alumni, Chadesh Guy, senior vice president, general manager of data management, Informatica. Great to see you come back. We had a great chat about privacy in our last session and data scale. Great to see you again. Likewise, John, great seeing you. It's always a pleasure to join you and discuss some of the prevailing topics in the space of data. Well, it's great that you're available on remote and thanks for coming back again because I want to dig into really the digital transformation aspect of the challenges that your customers have specifically around data warehouses and data lakes because this has become a big topic. What are the biggest challenges that you guys see your customers facing with digital transformation? Yeah, great question. Really, you know, it comes down to ensuring every digital transformation should be data-driven. There is a data work stream to help inform thoughtful insights that drive decisions to embark on and realize outcomes from the transformation. And for that, you need a healthy, productive, modern, agile, flexible data and analytics stack. And so what we are enabling our customers to realize is a modern, cloud-native, cloud-first data and analytics stack built on modern architectures of data lakes and data warehouses, all in the cloud. So you mentioned the data warehouse, modern cloud and the data lake. Tell us more about that. What's going on there? How do customers approach that? Because it's not the old fashioned way and data lakes been around for a while too, by the way. Some people call it the data swamp if they don't take care of it. Talk about those two things and how customers attack that strategic imperative to get it done right. Yeah, there's been a tremendous amount of disruption and innovation in the data and analytics stack. And what we're really seeing, I think you mentioned it is 15, even 20 years ago, there were these things called data marks that the finance teams would report against for financial reporting, regulatory compliance, et cetera. Then there was these things called data warehouses that were bringing together data from across the enterprise for comprehensive enterprise views to run the business as well as to perform reporting. And then with the advent of big data about five years ago, we had Hadoop-based data lakes, which as you mentioned, were also in many cases data swamps because of the lack of governance, a lack of cataloging and insights into what is in the lake who should and shouldn't access the lake. And very quickly that itself got disrupted from Hadoop to Spark. And very quickly customers realized that, hey, you know what managing these 5100, several hundred node Hadoop lakes, Spark lakes on-premises, extremely expensive in hardware, extremely expensive in people, extremely expensive in maintaining and patching and et cetera, et cetera. And so the demand very rapidly shifted to cloud-first, cloud-native data lakes. Equally, we're seeing customers realize the benefits of cloud-first, cloud-native, the flexibility, the elasticity, the agility, and we're seeing them realize their data warehouses and reporting in the cloud as well for the same elastic benefits for performance as well as for economics. So what is the critical capabilities needed to be successful with kind of a modern data warehouse or a data lake that's elastic and scaling and providing value? What are those critical capabilities required to be successful? For sure, exactly. It's first and foremost cloud-first, cloud-native. But why are we in Formatica uniquely positioned and excited to enable this modernization of the data and analytics stack in the cloud as it comes down to foundational capabilities that we're recognized as a leader in across the three magic quadrants of metadata management, data integration and data quality. Oftentimes, when folks are prototyping, they immediately start hand-coding and putting some data together through some basic ingestion capability and they think that they're building a data lake or populating a data warehouse. But to truly build a system of record, you need comprehensive metadata management, integration and data quality capabilities. And that's really what we're offering to our customers as a cloud-first, cloud-native. So that it's not just your data lakes and data warehouses that are cloud-first, cloud-native, so is your data management stack so that you get the same flexibility, agility, resiliency benefits. I don't think many people really truly understand how important what you just said is cloud-native capabilities, in addition to some of those things, it's really imperative to be built for the future. So with that, can you give me a couple of examples of customers that you could showcase to illustrate the success of having the critical capabilities from Informatica? Yeah, what we've found is an enabler to be data-driven requires organizations to bring data together through various applications, various sources of data on-premise in the cloud, from SaaS apps, from cloud-pass databases, as well as from on-premise databases, on-premise applications. And that's typically done in a data lake architecture. It's in that architecture that you have multiple zones of curation, you have a landing zone, a prep zone, and then it's certified data sets that you can democratize. We spoke about some of this previously under the topic of data governance and privacy. What we are enabling with these capabilities of metadata management, data integration, data quality is onboarding all of this data comprehensively, processing it and getting it ready for analytics teams, for data science teams. Kelly Services, for example, is managing the recruitment of over a half a million candidates using greater data-driven insights within their data lake architecture, leveraging our integration quality metadata management capabilities to realize these outcomes. AXAXL is doing very similar things with their data lake and data warehousing architecture to inform the data science teams or more productive underwriting. So a tremendous amount of data-driven insights, being data-driven, being a data-driven organization really comes down to this foundational architecture of cloud data warehousing and data lakes and the associated cloud-first cloud-native data management that we're enabling our customers to realize becoming a data-driven organization. Okay, Jitesh, I got to put you on the spot on this one. I'm a customer, pretend for a minute, I'm a customer. I say, okay, I'm comfortable with my old-fashioned, my grandfather's data warehouse. Had it for years, it spits out the reports it needs to spit out. Data lake, I'm really not, I got a bunch of servers. Maybe we'll put our toe in the water there and try it out, but I'm good right now. I'm not sure I'm ready. To go there. My boss is telling me, I'm telling him, I'm good. I got a cloud strategy with Microsoft. I got a cloud strategy with AWS on paper. We're going to go that way, but I'm not going to move. I need to just stay where I'm at. What do you say to that customer? First of all, I don't think anyone's that kind of, well, unless they're really in the legacy world, but maybe they'll be locked in. But for the most part, they're saying, hey, I'm not ready to move. We see both. We see the spectrum. We of course, to us, data management, being cloud native necessitates that your capabilities support hybrid architectures. So there are a class of customers that for potentially regulatory compliance reasons, typically financial services, certainly comes to mind where they're decidedly aligned state of their estate is on premise. And that's what happens in old-fashioned data centers. For those customers, we have market leading capabilities that we've had for many, many, many, many years. And that's fine. That works too. But we're naturally seeing organizations, even banks and financial services, awaken to all the obvious benefits of a cloud-first strategy and are starting to modernize various pieces. First, it was just decommissioning data centers and moving their application and analytics and data estate to the cloud as it's bring your own licenses as we refer to it. That very quickly has modernized to, I want to leverage the PAS data offerings within an AWS, within an Azure, within a GCP. I want to leverage this modern data warehouse from Snowflake. And there, that's when customers are realizing this benefit and realizing the acceleration of value they can get by unshackling themselves from the burden of managing servers, managing the software, the operating system, as well as the associated applications databases that need to be administered, upgraded, et cetera. Abstracting away all of that so that they can really focus on the problem of data, collecting it, processing it and enabling the larger lines of business to be data-driven, enabling those digital transformations that we're speaking about earlier. Well, you may have mentioned Snowflake. I think they're actually hot company in Silicon Valley. They filed to go public. Everyone I talk to loves working with them. They're easy to use and I think they're eating into Redshift a little bit from Amazon side. Certainly anyone who's using old school data warehouses is, oh, they look at Snowflake, it's great. How does a customer who wants to get to that kind of experience set up for that? This is something that you guys do. We've had many conversations with some leaders at Informatica about this and your board members. You got to set the foundation and you got to get this done right. Take us through what it takes to do that. I mean, timetable, we talk in months, weeks, days. Is it a migration for a year? Depends on how big it is. But if I do want to take that step to set my company up for these kinds of large cloud-scale, cloud-native benefits. Yeah, great question, John. Really how customers approach it varies significantly. We have a segment of the market that really just picks up our trial version free. We have a freemium embedded within the Snowflake experience so that you can select us within as a Snowflake administrator and select us as the data management tooling that you want to use to start ingesting and onboarding and processing data within the Snowflake platform. We have customers that are building net new data warehouses for a line of business like marketing, where they need enterprise class, enterprise scale, data management as a service capabilities. And that's where we enable and support them. We also see customers recognizing that they're on-premise data and analytics stack. They're Cloud Data Lake where their Cloud Data Warehouse is too expensive, is not delivering on the latest and greatest features or the necessary insights. And therefore they are migrating that on-premise data warehouse to a cloud-native data warehouse, like Snowflake, like Redshift, BigQuery, and so forth. And that's where we have technologies and capabilities that have helped them build this on-premise data warehouse, the business logic, all the ETL, the processing that was authored on-premise. We have a way of converting that and repurposing it within our cloud-first cloud-native metaphor so that they get the benefit of continued value from their existing estate, but within a modern cloud-first cloud-native paradigm that's elastic, that's serverless, and so forth. Shatash, always great to speak with you. You've got great thought leadership, just an expertise, but also leading a big group within Informatica around data warehouses and data management in general, that you're the GM is why you got a P&L responsibility. Thanks for coming on. I do want to ask you, well, I got you here to react to some of the news and what it means for the enterprise. I just did a panel session on Sunday. My new Meet the Analyst segment show I'm putting together around the EU's recent decision to shoot down the privacy shield law in the UK, mainly because of the data sharing, GDPR is kicking in, California is doing something here. It kind of teases out the broader trend of data sharing, right? Responsibility. Am I going to surveil you? It's not somebody related to Informatica, so to speak, but it does kind of give a tell sign that this idea of having your data to be managed so you can have kind of the policies you need to be adaptive to. Turns out no one knows what's going on. I got data over here, I got data over there. So it's kind of data all over the place and one law says this, the other law contradicts it, tons of loopholes, but it points out what can happen when data gets out of control. Yeah, and then that's exactly right. And that's why when I say metadata management is a critical foundational capability to build these modern data and analytics architectures, it's because metadata management enables cataloging and understanding where all your data is, how it's proliferating and ensuring that it enables, it also enables governance as a result because metadata management gives you technical metadata, it gives you business metadata. The combination on all of these different types of metadata enable you to have an organized view of your data estate, enable you to plan on how you want to process, manage, work with the data and who you can and cannot share that data with. And that's that governing framework that enables organizations to be data driven to democratize data, but within a governed framework. So extremely critical, but to democratize data, to be more data driven, you also need to govern data. And that's how metadata management with integration and quality really bring things together. And to have a user experience that's agile and modern, contemporary, you got to have the compliance, governance, but you got to enable the application developers or the use cases to not be waiting. You got to be fast. That's exactly right. In this new modern world, digital transformation, faster pace, everybody wants to be data driven and that spans a spectrum of deeply technical data engineers, data analysts, data scientists, all the way to non-technical business users that want to do some ad hoc analytics and want the data when they want it. And it's critical. We have built that on a foundation of intelligent metadata, what we call our Clare engine. And we have built the fit for use, deliberate experiences for the appropriate personas, the deeply technical ones wanting more technical experiences all the way to non-technical business users just want data in a simple data marketplace type of shopping paradigm. So critical to meet the UX requirements, the user experience requirements but there's a varied group of data consumers. Jatash, great to have you on. I'll let you have the last word. Talk to the people who are watching this that may be a customer of yours or may be in the need to be a customer of Informatica. What's your pitch? What would you say to that customer? Why Informatica? Give the pitch. Informatica is laser focused, singularly focused on the problem of data management. We are independent and neutral. So we work with your corporate standard, whether it's AWS, Azure, GCP, your best of breed selections, whether it's Snowflake or Databricks. And in many cases, we see the global 2000 select multiple cloud vendors. One division goes with AWS, another goes with Azure. And so the world of data analytics is decidedly multi-cloud. It's, while we recognize that data is proliferating everywhere and there are multiple technologies and multiple has offerings from various cloud vendors where data may reside, including on-premise. You want, and while all of that might be fragmented, you want a single data management capability within your organization that brings together metadata management, integration quality, and is increasingly automating the job of data management, leveraging AI and ML so that in this data 4.0 world, Informatica is enabling AI-powered data management so that you can get faster insights and be more data-driven and deliver more business updates. Jadash Gai, Senior Vice President and General Manager of Data Management at Informatica. You're watching our virtual coverage and remote interviews with all the Informatica, thought leaders and experts and senior executives and customers here on theCUBE. I'm John Furrier, thanks for watching.