>> Narrator: From the Cube Studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hello and welcome back to this CUBE Conversation, I'm John Furrier here in theCUBE Studios, your hosts for our remote interviews as part of our coverage and continue to get the interviews during COVID-19. Great talk and session here about data warehouses, data lakes, data everything, hybrid cloud, and back on theCube for a return Cube alumni, virtual alumni, Jitesh Ghai senior vice president general manager of data management, Informatica. Great to see you come back. We had a great chat about privacy in the last session and data scale. Great to see you again. >> Likewise John, great seeing you is 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 we 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, 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 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 does, 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, but 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, they were these things called data marts that the finance teams would report against, for financial reporting, regulatory compliance, et cetera. Then there was this, 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, we're also in many cases, data swamps because of the lack of governance, 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 realize that, hey, you know what? Managing these 5,100, several hundred node, Hadoop lakes, Sparked lakes on-premise is extremely expensive and hardware extremely expensive and people extremely expensive and 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 a last to can 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 Informatica, 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 ingestion, 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 data 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 are really truly understand how important what you just said is the 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 can 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 to various applications and various sources of data on-premise in the cloud from SaaS apps, from a cloud PaaS 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 datasets that you can democratize. And 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. AXA XL 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, realize these, realize that 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 fashion. 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 it, 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 them I'm good. I got a cloud strategy with Microsoft. I've 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 that, well unless they're really in the legacy world, but may be they're locked in, but for the most part, they're saying, hey, I'm not ready to move. >> We see, we see both. We see the spectrum. We of course, to us data management, being cloud-first being cloud-native, necessitates that your capability support hybrid architectures. So there is a, there are a class of customers that for potentially regulatory compliance reasons, typically financial services, certainly comes to mind where they're decidedly, align state of their estate is on-premise. It's an old fashioned data centers. Well, those customers, we have market leading capabilities that we've had for many, many, many, many, many years. And that's fine. That works too. But we're naturally seeing organizations, even banks and financial services awakened 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, it has modernized to, I want to leverage the past 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 were speaking about earlier. >> Well, I know you mentioned a Snowflake. I think they're actually hot company in Silicon Valley. They filed to go public. Everyone I've talked 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's using old school data warehouses, Oh, they look at Snowflake is great. How does a customer who wants to get to that kind of experience set up for that? There's some that you guys do. We've had many conversations with some of the leaders at Informatica about this and your board members, and you've got to, you've got to set the foundation and you've got to get this done right. Take us through what it takes to do that. I mean, timetable, are we talking months, weeks, days, is that a migration for a year? It 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, 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, but 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 they service capabilities. And that's where we enable and support them. We also see customers recognizing that their on-premise data and analytics stack their cloud data Lake or 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 metaphors, 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 serverless and so forth. >> Jitesh, always great to speak with you. You've got a 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 as well, you've got a PNL responsibility. Thanks for coming on. I do want to ask you while I got you here to react to some of the news, and how it means what it means for the enterprise. So I just did a panel session on Sunday. My new, "meet the analysts 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? And responsibility. Well, I'm going to surveil you. You're going to say, it's not necessarily 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 kinds of the policies you need to be adaptive to. It 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 you know, 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 that 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 enabled you to have an organized view of your data state, enable you to plan on how you want the 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 governance framework. So extremely critical, but to democratize data, to be more data-driven you also need the 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've 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 nontechnical 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, or what we call a CLAIRE engine, and we have built the fit for use deliberate experiences. What are the appropriate personas, the deeply technical ones, wanting more technical experiences, all the way to nontechnical 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 for there's a varied group of data consumers. >> 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 a 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 and other goes with Azure. And so the world of data analytics is decidedly multicloud. It's, while we recognize that data is proliferating everywhere, and there are multiple technologies and multiple PaaS 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 power data management, so that you can get faster insights and be more data-driven and deliver more business outcomes. >> Jitesh Ghai, 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. 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