 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello, and welcome to this CUBE Conversation. I'm John Furrier, host of theCUBE here in our Palo Alto Studios during this quarantine crew, doing all the interviews, getting all the top stories, especially during this COVID pandemic. Great conversation here. Jetesh Guy, Senior Vice President, General Manager of Data Manager with Informatica, CUBE alumni multi-time. We can't be in person this year because of the pandemic, but a lot of great content. We've been doing a lot of interviews with you guys. Jetesh, great to see you. Thanks for coming on. I'm great to see you again. We weren't able to make it happen in person this year, but if not in person, virtually we'll have to work. In our past conversations on theCUBE and through all the Informatica employees, it's always been kind of an inside baseball and kind of inside the ropes conversation in the industry about data. Now more than ever with the pandemic, you're starting to see people seeing it. Oh, I get it now. I get why data is important. I can see why cloud-first, mobile-first, data-first strategies, and now virtual-first is now this transformational scene. Everyone's feeling it. You can't help but not ignore it. It's happening. And it's also highlighting what's working, what's not. I have to ask you in the current environment, Jetesh, what are you seeing as some of those opportunities that your customers are dealing with with the approach to data? Because clearly, you're working with that data layer. There's a lot of innovation opportunities. We've got Claire on the AI side. Oh, great. But now with the pandemic, it's really forcing that conversation. I got to rethink about what's going to happen after and have a really good strategy. Yeah, you're exactly right. There's a broad-based realization that, I'll take a step back. First, we all know that as global 2000 organizations or in general, we all need to be data-driven. We need to make fact-based decisions. And there is a lot of that good work that's happened over the last few years as organizations have realized just how important data is to innovate and to deliver new products and services, new business models. What's really happened is that during this COVID pandemic, there is a greater appreciation for trust in data. Historically, organizations became data-driven, we're on the journey of being increasingly data-driven. However, there was some element of, oh, gut or experience. And that combined with data will get us to the outcomes we're looking for, will enable us to make the decisions. In this pandemic world of great uncertainty, supply chains falling apart on occasion, groceries not getting delivered on time, et cetera, et cetera. The appreciation and critical importance on the quality, on the trust of data is greater than ever to drive the insights for organizations. Leaders are less hesitant, or sorry, leaders are more hesitant to just go with your gut type of approaches. There is a tremendous reliance on data. And we're seeing it in particular more than ever, as you can imagine, in the healthcare provider sector, in the public sector with federal, state, and local, as all of these organizations are having to make very difficult decisions and are increasingly relying on high-quality, trustworthy, governed data to help them make what can be life or death decisions. So a big shift and appreciation for the importance and trustworthiness in their data, their data estate, and their insights. So as the GEM of data management, and you're very present, obviously, Informatica, you get a good view of things. I got to ask you, love this data 4.0 concept. Talk about what that means to you, because you got customers have been doing data management with you guys for a while, but now it's data 4.0 that has a feeling of agility to it, it's got kind of a DevOps vibe. It feels like a lot of automation being discussed and you mentioned trust. What does data 4.0 mean? So data 4.0 for us is where AI and ML is powering data management. And so what do I mean by that? There is a greater insight and appreciation for high-quality, trustworthy data to enable organizations to make fact-based decisions to be more data-driven. But how do you do that when data is exponentially growing in volume, where data types are increasing, where data is moving increasingly between clouds, between on-premise and clouds, between various ecosystems, new data sources are emerging, the internet of things is yet another exploding source of data. This is a lot of different types of data, a lot of volume of data, a lot of different locations and gravity of data where data resides. So the question becomes, how do you practically manage this data without intelligence and automation? And that's what the era of data 4.0 is, where AI and ML is powering data management, making it more intelligent, automating more and more of what was historically manual to enable organizations to scale, to enable them to scale to the breadth of data that they need to get a greater understanding of their data landscape within the enterprise, to get a greater understanding of the quality of the data within their landscape, how it's moving, and the associated privacy implications of how that data is being used, how effectively it's protected, so on and so forth, all underpinned by our Clare engine, which is AI and ML applied to metadata to deliver the intelligence and enable the automation of the data management operations. Awesome, thanks for taking the time to define that, I love that. Question I want to ask you, I don't want to put you on the spot here because I think this is an important conversation we've been having and also writing a lot about on SiliconANGLE.com. And that is customers say to us, hey John, I'm investing in cloud native technologies using cloud data warehouses and data lakes. I need to make this work because this is a scale opportunity. I need to come out of this pandemic with really agile scalable solutions that I can move fast on my applications. How do you comment on that? What's your thoughts on this? Because you guys are in the middle of all this with the data management. I couldn't agree more. Increasingly, data workloads are moving to the cloud. It's projected that by 2022, 75% of all databases will be in the cloud. And COVID-19 is really accelerating. It's opening the eyes of leadership, of decision makers to be truly cloud first in cloud native, now more than ever. And so organizations, traditional banking organizations that highly regulated industries that have been hesitant to move to the cloud are now aggressively embarking on that journey. And industries that were early adopters of the cloud are now accelerating that journey. I mentioned earlier that we had a very seamless transition as we moved to a work-from-home environment. And that's because our IT is cloud first, cloud native. And why is that? It's because it's through being cloud first and cloud native that you get the resiliency, the agility, the flexibility benefits in these uncertain times. And we're seeing that with the data and analytics stack as well. Customers are accelerating their move to cloud data warehouses, to cloud data lakes and become cloud native for their data management stack in addition to the data analytics platforms. Great, great stuff, but I agree with 100%. Cloud native is where it's going, but a lot of people aren't there yet. Still on hybrid and multi-cloud is a big discussion. I want to get your thoughts on how that's going to play out because if you put hybrid cloud and multi-cloud, I'll say public cloud's amazing, we know that, but hybrid and multi-cloud as the next generation of kind of interoperability framework of cloud services, you're going to have to overlay and manage data governance and privacy. It's going to get more complicated, right? So how are you seeing your customers approach that piece on the public side and then with hybrid because that's become a big discussion point. So hybrid is an absolutely critical enabling capability as organizations modernize their on-premise estate into the cloud. You need to be able to move and connect to your on-premise applications, databases and migrate the data that's important into the cloud. So hybrid is an essential capability. When I say Informatica is cloud-first, cloud-native, being cloud-first, cloud-native as a data management as a service provider, if you will, requires essential capabilities of being able to connect to on-premise data sources and therefore be hybrid. So hybrid architecture is an essential part of that. Equally, it's important to enable organizations to understand what needs to go to the cloud. As you're modernizing your infrastructure, your applications, your data and analytics stack, you don't need to bring everything to the cloud with you. So there's an opportunity for organizations to introduce efficiencies. And that's done by enabling organizations to really scan the data landscape on-premise, scan the data that already exists in the various public clouds that they partner with and understand what's important, what's not, what can be decommissioned and left behind to realize savings and what is important for the business and needs to be moved into a cloud-native analytics stack. And that's really where our clear metadata intelligence capabilities come to bear. And that's really what serves as the foundation of data governance, data cataloging and data privacy to enable organizations to get the right data into the cloud to do so while ensuring privacy and to ensure that they govern that data in their new, now cloud-native analytics stack, whether it's AWS, Azure, GCP, Snowflake, Databricks, all partners, all deep partnerships that we have. Jatash, I want to get your thoughts on something. I was having a Zoom call a couple of weeks ago with a bunch of CXO friends, people who are practitioners, probably some of them are probably your customers. It was kind of a social get-together, but we were talking about how the real world we're living in pandemic from COVID data, fake news. And one of the comments was, finally the whole world now realizes what my life's like. And in referring to how we're seeing fake news and misinformation kind of screw up an election and you got COVID's got 10 zillion different data points and people are making it to tell stories and what does it really mean? There's a lot of trust involved. People are confused and all that's going on. Again, in that backdrop, he said that, that's my world. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This site of authenticity, the duck democratization has been around for a while in the enterprise. So then dealing with bad data or fake data or too much data, you can make data and do whatever you want. You got to get, you got to make sense of it. What's your thought on the reaction to his comment? I mean, how do you, what does that make you feel? Completely agree, completely agree. And that's, that goes back to the earlier comment I made about making fact-based decisions that you can have confidence in because the insight is based on trusted data. And so you mentioned data democratization. Our point of view is to democratize data. You have to do it on a foundation of governance. Right? There's a reason why traffic lights exist. It's to facilitate or at least attempt to facilitate the optimal free flow of traffic without getting into accidents, without causing congestion, so on and so forth. Equally, you need to have a foundation of governance. And I realize that there's an optical tension of democratized data, which is free data for everybody consume it whenever and however you want. And then governance which seems to imply locking things down controlling them. And really when I say you need a foundation of data governance, you need to enable organizations to implement guardrails so that data can be effectively democratized so that data consumers can easily find data. They can understand how trustworthy it is, what the quality of it is, and they can access it in an easy way and consume it while adhering to the appropriate privacy policies that are fit for the use of that particular set of data that a data and data consumer wants to access. And so how do you practically do that? And that's where data 4.0 AI power data management comes into play in that you need to build a foundation of what we call intelligent data governance. A foundation of scanning metadata, combining it with business metadata, linking it into an enterprise knowledge graph that gives you an understanding of an organization and enterprises data language. It auto tags, auto curates. It gives you insight into the quality of the data and now enables organization to publish these curated data sets into a capability what we call a data marketplace so that much like amazon.com, you can shop for the data. You can browse home and garden electronics, various categories. You can identify the data sets that are interesting to you when you select them, you can look at the quality dimensions that have already been analyzed and associated with the data set. And you can also review the privacy policies that govern the use of that data set. And if you're interested in it, find the data sets, add them to your shopping cart like you would do with amazon.com and check out. And when you do that triggers off an approval workflow to enable organizations to that last mile of governing access. And once approved, we can automatically provision the data sets to wherever you want to analyze them, whether it's in Tableau, Power BI and S3 bucket, what have you. And that is what I mean by a foundation of intelligent data governance that is enabling data democratization. You know, a common metadata layer gives you capabilities to use AI. I get that. There's a concept that you guys are talking a lot about this augmentation to the data. There's augmented data management activities that go on. What does that mean? Can you describe and explain that further and unpack that this augmented data management activity? Yeah. What do we mean by augmented data management? It's a really a first step into full blown automation of data management. In the old world, a developer would connect to a source, parse the source schema, connect to another source, parse its source schema, connect to the target, understand the target schema, and then pick the appropriate fields from the various sources, structure it through a mapping and then run a job that transforms the data and delivers it to a target database in its structure, in its schema, in its format. Now that we have enterprise scale metadata intelligence, we know what the source of data looks like. We know what targets exist. As you simply pick sources and targets, we're able to automatically generate the mappings and automate this development part of the process so that organizations can more rapidly build our data pipelines to support their AI, to operationalize AI and ML, to enable data science and to enable analytics. To test great insight, I really appreciate you explaining all this constant unpacking that with me. Final point, I'd love you to have you just take a minute to put the plug in there for Informatica, what you're working on, what are your customers doing, what are some of the best practices coming out of the current situation. Take a minute to talk about that. Yeah, thank you, I'm happy to. It really comes down to focusing on enabling organizations to have a complete understanding of their data landscape. And that is where we're enabling organizations to build an enterprise knowledge graph of technical metadata, business metadata, operational usage metadata, social metadata, to understand and link and develop the necessary context to understand what data exists where, how it's used, what its purpose is and whether or not you should be using it. And that's where we're building the Google for the enterprise to help organizations develop that. Equally, leveraging that insight, we're building out the necessary, that insight and intelligence through Clare, we're building out the automation in the data quality capabilities, in the data integration capabilities, in the metadata management capabilities, in the master data management capabilities, as well as the data privacy capabilities. So things that our tooling historically used to do manually, we're just automating it so that organizations can more productively access data, understand it and scale their understanding and insight and analytics initiatives with greater trust, greater insight. It's all built on a foundation of our intelligent data platform. Love it, scaling data, it's that's really the future fast, available, highly available, integrated into the applications for AI, that's the future. Exactly right, we're data 4.0, AI power data management. You know, I could have these data, I love talking about data in the future because I think that's really valuable and I think developers, and I've always been saying this for over a decade now, data is a critical piece for the applications and AI really unlocks that of having it available and surface is critical. You guys doing a great job. Thanks for the insight. Appreciate you, Tesh. Thank you for coming on. Thanks for having me, pleasure to be here. You couldn't do it in person with Informatica World, but we're getting the conversations here on the remote cube, cube virtual. I'm John Furrier, you're watching Cube Conversation with Chadesh Guy, Senior Vice President, General Manager, Data Manager at Informatica. Thanks for watching.