>> 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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(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello 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 story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management 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. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, >> but if not in person, >> virtually will have to work. >>In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry >> about data. >> Now more than ever, with the pandemic, you 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 not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all 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 cetra, et cetra. 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 decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at 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 is 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-premises 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 CLAIRE 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, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a 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. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, 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 the 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 stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it'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 essentially 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 analytic stack. And that's really where our CLAIRE 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 data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life 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. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And 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 foundational 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 realized 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 for 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 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? 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 organizations 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 datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> 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. This 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, and 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 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 out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and 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. 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 CLAIRE, 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 capability. 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 to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> 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 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 Jitesh. 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 Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

(ambient music) >> Narrator: From the cube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello 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 story especially during this COVID pandemic. Great conversation here Jitesh Ghai, Senior Vice President and General Manager of Data Management 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. Jitesh great to see you. Thanks for coming on. >> Hey, great to see you again. We weren't able to make it happen in person this year, but if not in person, virtually will have to work. >> One of the things, I'm a half glass half full kind of guy but you can't look at this without saying man, it's bad. But it really highlights how things are going on. So first, how are you doing? How's everyone Informatica doing over there? You guys are doing okay? >> We are well, we are well, families well, the Informatica family is well. So overall, can't complain can't complain, I think it was remarkable how quickly we were able to transition to a work from home environment for our global 5000 plus organization. And really, the fact that we're Cloud First Cloud Native, both in our product offerings, as well as an IT organization really helped make that transition seamless. >> In our past conversations on theCUBE and through all the Informatica employees it's always been kind of an inside baseball, kind of inside the ropes conversation in the industry about data. Now more than ever, with the pandemic, you 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 not ignore it. It's happening. It's also highlighting what's working, what's not. I have to ask you in the current environment Jitesh what are you seeing as some of those opportunities that your customers are dealing with approach to data? 'Cause clearly, you're working with that data layer, there's a lot of innovation opportunities, you've got CLAIRE on the AI side, all 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 cetra, et cetra. 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 decision. So a big shift and appreciation for the importance and trustworthiness in their data, their data state and their insights. >> So as the GM of data management and Senior Vice President at 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 is 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-premises 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 CLAIRE 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, love that. The question I want to ask you, I'll put you on the spot here because I think this is an important conversation we've been having and also writing a lot about it on siliconangle.com and that is customers say to us, "Hey, John, I'm investing in Cloud Native technologies, using Cloud data warehouse as a 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. It's opening the eyes of leadership of decision makers to be truly Cloud First and Cloud Native, now more than ever. And so organizations, traditional banking organizations, 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 the 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 stuff which I agree with hundred percent. Cloud Native is where it goes but you aren't they're (laughs) yet. Still on Hybrid and Multi-cloud is a big discussion. I want to get your thoughts >> Completely. >> On how that's going to play up because if you put Hybrid cloud and Multi-cloud I see Public cloud it'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 essentially 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 analytic stack. And that's really where our CLAIRE 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 data, bricks, all partners, all deep partnerships that we have. >> Jitesh, I want to get your thoughts on something. I was having a Zoom call a couple weeks ago, with a bunch of CXO friends, people, practitioners, probably some of them are probably your customers. It was kind of a social get together. But we were talking about, how the world we're living in pandemic, from COVID data, fake news, and one of the comments was, finally the whole world now realized what my life 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. >> Right. This is back down to some of the things you're talking about, trust. We've talked about metadata services in the past. This authenticity, the duck democratization has been around for a while in the enterprise, so that dealing with bad data or fake data or too much data, you can make data (laughs) into whatever you want. You got to make sense of it. What's your thoughts on the reaction to his comment? I mean, what does it make you feel? >> Completely agree, completely agree. And 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 foundational 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 realized 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 for 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 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? 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 organizations 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 datasets to wherever you want to analyze them, whether it's in Tableau Power BI, an S3 market, what have you. And that is what I mean by a foundation of intelligent data governance. That is enabling data democratization. >> 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. This 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, and 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 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 out data pipelines to support their AI to operationalize AIML, to enable data science, and to enable analytics. >> Jitesh great insight. I really appreciate you explaining all this concept and 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. 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 CLAIRE, 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 capability. 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 to the applications for AI. That's the future. >> Exactly right. Data 4.0, (laughs) AI power data management. >> 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 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 Jitesh. 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 Jitesh Ghai Senior Vice President General Manager, Data Manager at Informatica. Thanks for watching. (upbeat music)

>> Live from Las Vegas, it's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight along with my co-host John Furrier, we are joined by Jitesh Ghai, he is the Senior Vice President and General Manager Data Quality, Security and Governance at Informatica. Thank you so much for coming or returning to the show Jitesh. >> My pleasure, happy to be here. >> So, this is a real moment for data governance, we have the anniversary of GDPR and the California Privacy Act it's a topic at Dabos, there is growing concern among the public and lawmakers over security and privacy, give us the lay of the land from your perspective. >> Right, you know it is a moment for data governance, what's exciting in the space is governance was born out of risk and compliance and managing for risk and compliance, but really what it was mandating was healthy data management practices, how do we give the regulators comfort that our data is of high quality, that we know the lineage of where data is coming from that we know how the business relies on the data what is critical data? And while it was born to give the regulators comfort, what organizations very quickly realized is well when you democratize data, you need to give everybody that comfort, you need to give your data scientists, your data analysts, that same level of contextual understanding of their data right, where did it come from? What's the quality of it? How does the business use it, rely on it? And so that has been a tremendous opportunity for us, we've supported organizations, financial services from a BCBS 239 CCAR, counterparty credit risk, but what's happened is from a data democratization, data scale perspective, self-service analytics perspective, is what moved from terabytes to petabytes. We've moved from data warehouses, to data lakes and you can't democratize data unless there's a governed framework. I don't know, it sounds kind of like wait, democratizing data is supposed to be free data everywhere, but without some governed framework, it's a bit of a mess, and so what we're enabling organizations is the effective consumption and understanding of where their data is, discovering it, so that the right people can consume the data that they care about, the right data scientists can build the right models, the right analysts can build the right reports and the executives get the right confidence on what reports they're getting, what KPI's they're getting. >> One of the things that we talked last year, you had a couple customers on, you had told a great story, you guys had had the benefit as a long-standing company, 25 years in the private for large-customer base, but the markets changed, you mentioned governance I mean we're in the one year-anniversary of GDPR. >> Right. >> And I think everyone's kind of like OK what happened last year? More privacy laws are coming and one of the themes this year is clarity with data, but also in the industry you know access to data, making data addressable, because AI needs data sets, cloud has proven that, SAS business models, using data winning formula, that's clear if you're born in the cloud. Enterprises now want that same kind of SAS-like execution on the applications side, whether it's SAS or using AI for instance, >> Right. >> So when you have more regulation, inherent nature is to oh like more complexity, how are customers dealing with the complexity of this, because they want to free it up, but at the same time they want to make sure that they can respect the laws for individuals, but also governments aren't that smart either so you know, the balance there, what's the strategy? >> And therein lies the challenges with privacy specifically, it's not just about quality counterparty credit risk in like five or seven systems in a data warehouse, it's all the data in your enterprise, it's the data in production, there's the data in your DevOps environment, it's all your data literally, structured all the way to unstructured data like Word, PDFs, Powerpoints. And you need a governing framework around it, you need to enable organizations to be able to discover where is there sensitive information, how is there sensitive information proliferating through the organization? Is it protected? Is it not protected? And what's particularly, you know, we're all consumers, I'm pretty confident some or all of our data has been breached at some point, enabling organizations, what these privacy regulations are doing is they are giving us, as individuals, rights to go to the organizations we transact with and ask them, what are you doing with out data? Forget my data or at least tell me how you're processing it and get my consent for the data. >> Yeah, I mean policy and business models are certainly driving that and with regulation, I see that, but the question is that when you move the impact to the enterprise, you got storage drives. You store it on drives as a storage administrator you've got software abstractions with data, like you guys do. So, it's complicated, so the question is, for you, is what are customers doing now? What's the answer to all this? >> The answer really comes down to you need to scale to the scope of the problem, it's a thousand x-increase, you're going from terabytes to petabytes right? And so, you need an AI, an ML, an intelligent solution that can discover all of this information, but it can map it to John Furrier, this is where John Furrier's information is, it's in the human capital management system, the CRM system, organizations know, may start knowing whether sensitive data is, but hey don't know who it belongs to, so when you go to invoke your right to be forgotten or portability, today, what we're enabling organizations with is hey, we'll help you discover the sensitive information, but we'll also tell you who it belongs to, so that when John shows up or Rebecca, you show up, you just have to punch in their name and we'll tell you all the systems, that it's in. That is something that requires teams of database administrators, lawyers, system administrators that needs to be automated, to truly realize the potential of these privacy regulations, while enabling organizations to continue to innovate and disrupt with data. >> What's your take on whether or not consumers truly understand the scope of these privacy regulations, I mean talking about GDPR and you get the pop-ups that say do you consent and you just say yes, I just need to get to this site and so you blithely, just press yes, yes, yes so you are technically giving your consent, but do you, I mean what's your take, do consumers truly understand what they're doing here? >> You know, I think historically, we've all said yes, yes, yes, over the last, I would say two years with growing regulations and significant breaches, there is a change in customer expectations, you know, there's a stat out there in the event of a data breach, two-thirds of consumers of a particular organization blame the organization for the breach, not the hackers, right, so it's a mindshift in all of us, where you're the custodian of my data I'm counting on you, whatever organization I'm transacting with ,to ensure and preserve my privacy, ensure my data's protected. So, that's a big shift that's happened, so whether you're doing it for regulatory reasons, CCPA North America, there's several other state-wide regulations coming out or GDPR, the consumer expectation, forget regulations, it's brand preservation, it's customer trust, it's customer experience, that organizations are really having to solve for from a privacy standpoint. >> Tell what the news around yesterday around the shift of the trust pieces, because that's a huge deal. Because trust is shifting, expectations are shifting, so when you have shifting expectations, with users and buyers, customers, the experience has to shift. So, take us through what's the new things? >> Well, the new things are, you know, you look at we're enabling organizations to be data-driven, we're enabling organizations to transform, build new products, new services, be more efficient and for that, you need to enable them to get access to data. The counter, the tension on the other end is how do we get them broad-based access while ensuring privacy, right, and that's the balance. How do we enable them to be customer-centric and optimal in engaging with their customers while preserving the privacy of their customers and that really comes down to having a detailed understanding of what your critical data is, where is it in the organization and how an organization is using that data. Enabling an organization to know that they're processing data with the appropriate consent. >> What's interesting to me, when I was with press yesterday, is also the addition of how the cloud players are coming onboard, because you know, one constituent that's not mentioned in that statement is that you guys are kind of keeping an eye on, that are impacted by this, is developers, because you know developers like infrastructures coded with DevOps. Don't want to be provisioning networks and storage, they just write to the API's. Data is kind of going through that similar experience where, if I'm a developer doing an IOT app, I'm just going to use the cloud. I put the data there, I don't need to have a mismatch of mechanisms to deal with some governance compliance rules. >> Correct and that's why it needs to be built-in by design. And you know there's this connotation that- >> Explain that, what does built-in by design mean. >> Well you need to have privacy built-into how you as a business operate, how you as a DevOps team or development team, build products, if that's built-in to how you operate, you enable the innovation without falling into the pitfalls of oh you know what we broke some privacy regulations there we breached our customers trust there, we used data or engaged with them in manner that they weren't comfortable with. >> So, don't retro-fit after the fact? Think holistically on the front-end of the transformation in architecture. >> It's an enabler, in that if you do it right to begin with, you can continue to innovate and engage effectively, versus bolting it on as an afterthought and retro-fitting. >> It really seems like it is this evolution in thinking from this risk and compliance, overdoing this to check all the boxes, versus here are our constraints, but our constraints are actually liberating, is what you're saying. >> Right, but you can't democratize data, without giving the consumers of that data an understanding of the quality of that data, the trustworthiness of that data, the relevance of the data to the business, you give them that and now you're enabling your analytics, your data scientists, your analytics organizations to innovate with that data with confidence and if you do it within a framework of privacy, you're ensuring that you're preserving customer trust while you're automating and building intelligent and engaging customer experiences. >> What I love about the data business right now, is it's exciting because it's real specific examples of impact, security, you know, national security, to hackers, to just general security, privacy of the laws, But, I've seen the development angles interesting too, so when you got these two things moving, customers can ignore this, it's not like back-up and recovery where same kind of ethos is there, you don't want to think about it after the fact, you want to build it in, you know, there's certainly reasons why you do that, in case there's a disaster, but data is highly impactful all the time. This is a challenge, you guys can pull this off. >> Well you know, it's a, with privacy, it's no longer about a few systems, it's all your data and so the scope is the challenge and the scale applies for privacy, the scale applies for making data available enterprise wide and that's where you need and you know we spoke about AI needs data, well data also needs AI. And that's where we're leveraging AI and ML. Building out intelligence, to help organizations solve that problem and not do it manually. >> You know, I've said it on theCUBE, you've probably heard it many times, I say it all the time, scale is the new competitive advantage. Value is the new lock-in. No proprietary software anymore, but technology is needed. I want to ask you, you've been talking about this with some of your customers last year around data is that you need more scale, because AI needs more access to data, because the more visibility into data, the smarter, machine learning and AI applications can become. So Scale is real. What is the, what are you, you guys have some scalosity in your customers, you got the end-to-end, got the catalog and everything is kind of looking good, but you have competition How would you compare to the competition, when people say hey Jitesh, a start-up just popped out or XYZ company's got the solution, why should I go with them or you? What's the difference, what's the competitive angle? >> You know, the way we're thinking the problem is founded on governance is an enabler it's not about locking things down for risk and compliance, because you know, the regulators want to know that this particular warehouse is highly tightly controlled, it's about getting the data out there, it's about enabling end-users to have a contextual understanding when you're doing that for all of your data, within around, that's a thousand X-increase in the data, it's a thousand X-increase in your constituents, you're not supporting, the risk and compliance portions of the organization, you're supporting marketing, you're supporting sales, you're supporting business operations, supply chain, customer-onboarding and so with the problem of scale, practices of the past, which were typically manual laborious, but hey at the risk of non-compliance, we just had to deal with them, don't practically in any way scale, to the requirements of the future which is a thousand X-increase in consumers and that's where intelligence and AI and ML come in. >> The question I have for you is, where should customers store their data? Is there an answer to that on premises or in the cloud? What are they doing? >> The answer is yes, (Knight laughs) the customer should store their data, what we see, the world is going to be hybrid, mainframes are still here, on-premise will still be here many years from now. >> So you're taking the middle of the road here, so >> There's Switzerland. >> You're saying whatever they want on-premise or cloud, is there a preference you see with customers? >> Well, you know it depends on the applications , depends on regulations, historically regulations especially in financial services, have mandated a more on-premise stance, but those regulations, are also evolving and so we see, the global investment banks all of a sudden, we're having all sorts of conversations about enabling them to move select portions of their data estate to the cloud, enabling them to be more agile, so the answer is yes and it will be for a very long time to come. >> Final question, one of the most pressing problems in the technology industry is the skills gap. I want to hear your thoughts on it, how as a Senior Executive at Informatica, how worried are you about finding qualified candidates for your open-roles? >> You know, it is a challenge, good news is, we're a global organization, my teams are globally-distributed. I have teams in Europe, North America and Asia and the good part about that is if you can't find it in the valley, you can certainly find the talent elsewhere, and so while, it is a challenge, we're able to find talented engineers, software developers, data scientists, to help us innovate and build the intelligence capabilities to solve the productivity challenges, the scale challenges of data consumption. >> Jitesh, talk about the skills required for people coming out of school, take your Informatica hat off, put your expertise hat on, data guru hat, knowing that data is going to continue to grow, continue to have more impact across the board, from coding to society affix, whatever, what are some of the key skills in training, classes or courses or areas of expertise that people an dial-up or dig into that might be beneficial to them that may or may not be on the radar curriculum or, say is, part of school curriculum, >> you know we engage with universities in North America, in Europe, in Asia, we have a large development center in India and we're constantly, engaging with them. We're on various boards at various universities, advisory standpoint, big data standpoint and what we're seeing is as we engage with these organizations, we're able to feed back on where the market is going, what the requirements are, the nature of data science, the enabling technologies such as platforms like Spark, languages like Python and so we're working with these schools to share our perspectives, they in turn, are incorporating this into their curriculums and how they train future data scientists. >> When you see a young gun out there that's kicking butt and taking names and data, what are some of the backgrounds? Is it math, is it philosophy, is there a certain kind of pattern that you've seen as the makeup of just the killer data person? >> You know, it's interesting, you mention philosophy, I'm a big, I've hired many philosophy majors that have been some of the best architects, having said that, from a data science perspective, it's all about stats, it's all about math and while that's an important skillset to have, we're also focused on making their lives easier, they're spending 70% of their time, doing data engineering versus data science and so while they are being educated from a stats, from a data science foundation, when they come into the industry, they end up spend 70% of their time doing data engineering, that's where we're helping them as well. >> So study your Socrates and study your stats. >> I like that. (Knight and Furrier laugh) >> Jitesh, thank you so much for coming on theCUBE. >> My pleasure, happy to be here, thank you. >> I'm Rebecca Knight for John Furrier, you are watching theCUBE.

why from Las Vegas it's the cube covering implementing a world 2018 machito by informatica okay welcome back everyone's the cube live here in Las Vegas at the Venetian ballroom is the cubes exclusive coverage of informatica world 2018 I'm John for your host in analyst here with Peter Baris host and analyst here for two days of coverage our next two guests are jitesh guy who's the senior vice president general manager data quality security and governance for informatica and barry green the chief data officer for bank of ireland great to see you attached great to have you on the cube and great to be here so love having two to smart people talking about data GPRS right around the corner and friday you're at the bank of ireland so in the middle of it while you're in you're in this in the territory you're in the heart get any sleep what talk about your role at the bank what are you guys doing I want to get into the GDP RS right on our doorstep it's going to major implications for data as a strategic asset talk about what you do so for me we've created a daily management framework frameworks pretty simple map process get context for data put it into the business data model or sign ownership put data quality over it and then maintain it using a risk model operational risk model now it doesn't matter with GDP our or becbs whatever it is it's about adding value to data understanding day they're using it for them and making sure you've got better customer experience all the good things you know GDP are is important but it's not the only thing you guys are new to managing data and certainly complies your financials bank so it's not a new thing what is how is GDP are being rolled out how is it impacting you guys what are you paying attention to what's the impact so the big thing about GDP are is we're having to understand where our key customer data's sits in the physical systems we're looking at mapping key processes something to see and what it's used for we're assigning ownership to people who own data so we can basically make decisions about it in the future GDP ours a bit like becbs that's going to evolve right you're not going to be GDP are compliant on May 25th you're gonna have to put in place the infrastructure the tooling the governance the management to make sure that as an organization you know you were using data the way it's supposed to be if you want to be a digital organization you have to manage data this is just pushing along they had evolution of data being important to an organization but just as y2k wasn't about making the world safe for mainframes in the year 2000 it forced a separation and understanding of the separation that's required between applications and data so gdpr is another one of those events it's forcing a separation in this case between data and the notion of data assets great so take us through how the thought process of gdpr has catalyzed new thinking within the bank about how we think about data differently as a consequence I think what it's done so we've developed the framework so we can apply it to any problem right I think what it's done is it's raised up data's the risk of data more generally so people talk about data as an asset I've talked about data as a liability right so it's a contingent liability if you think about gdpr it's raise that awareness up that we can't continue to operate and tricked out of the way we have in the past so there's a whole cultural change going on around how we treat data and there's a big understanding training going on about everyone knowing why they use data making sure that they don't use it for the purpose it's not used for and generally it's a big education cultural change very how would you describe the mindset for this new thinking it certainly I agree with you it's at the strategic nature center the center of the center of the value proposition right now on all aspects not just some department what's the mindset that people should be thinking about when they think of data okay should I have access to this data but do I need it for the role I'm undertaking and if it was my data would I be treating it you know how would I shred it how would I want it to be treated even if you're the subject yeah exactly it's almost like you know if I had my data being used for certain thing context is that the way I'd want my data treated there's almost in the old adage you know do unto others as you would have you done to you yeah ethics is important yeah to church talk about the informatics opportunity because you guys really timings pretty awesome for informatica with the catalog you guys have an interesting opportunity right now to come in and do a lot of good things for clients that's that's exactly right we've we've been working very hard with our clients over the last 18 months to help them on this gdpr journey what we you know think of as supporting their privacy and protection and and you mentioned catalog you know our we have our enterprise data catalog powered by Claire our AI machine learning capabilities and metadata and that helps you get an organized view of all your data assets within the enterprise leveraging that same technology we have a security source offering which is effectively a data subject catalog to help our customers understand where exactly is the data subject sensitive data not where the organization's data is but the data subject sensitive data within the organization where their national identifiers information is how where their personal home address email phone etc is and how many occurrences and what systems why so that our customers can take that information and more effectively respond to the data subject if the data subject wants to invoke you know the right to be forgotten or right for data portability etc as well as take that same information and demonstrate to the regulator that they are processing this sensitive data with the appropriate with the appropriate consent from the data subject as well as have the systems I presume to then be able to expose to the subject the reasons why the data may in fact still be part of the asset of the bank correct so I I hadn't heard that before we've had other company cells that they're going to help companies find subject data but you guys are taping us taking a step further and allowing the bank for in this case do we have to look at that data from the subjects perspective exactly right because it's not just with some regulations financial regulations you need to demonstrate the quality and trustworthiness of the data here at to the regulator here it's demonstrating to the data subject themselves the individual themselves how you're processing how you're treating their data how protected or unprotected it is and and how you're using it to market to them how you using to become part of the metadata that's exactly right it's using the same metadata foundation too but focused on the data subject specifically interesting interpret ection aspect of it if I say I want my right to be forgotten and you can hold data for something mean where's the where's the protection aspect for the business and the user is there conflict there how do you guys handle that yes that's interesting there is a conflict so there's a conflict already with an existing regulation so you know um the thing that a lot of people aren't talking about is you can hold data so if someone can't just delete data if you want to hold an account or you know these reasons for using it you got a legitimate use for using it you can still hold it you have to tell a customer why you're using it so there's a lot of context here which they didn't have before so it's giving the customer the power to understand what the data is being used for the context is being used for and so they know it's not gonna be used for sort of spiritless marketing campaigns it's being used for you know the reason that does that extra work for you guys is that automated this is where we start to get into the question next yeah which is a context the context is the metadata and you're going to be able to capture that context explicitly as these data elements have this context in metadata allows you to do that with some degree of certainty and you know relatively low cost I assume it's all about reuse right so a lot of what we've done in the past and on its way at the bank um to me everyone's done in the past is they've understood something and then thrown it away so with Exxon you can record it you know record it then with the metadata you can join the metadata in Exxon so you can do in a high level process understand what data is used at the context is used for who owns that quality all these kind of business relevant things then you put the metadata out and you've got a system view it's very very powerful so the technology is starting to allow us to automate but it's all about gathering it reusing it and making sure you understand it right that's for you know from a from a data subject catalog standpoint you get the technical metadata it tells you across your data landscape where all the sensitive information is for Barry green you marry that up with the business metadata of how is that sensitive information being used in every step of let's say customer onboarding your mission critical business processes within the organization and that's what you demonstrate to a data subject or a regulator if this is how I'm processing it based on this consent now if they invoke the right to be forgotten there's various things you can do there because there's conflicts you can just mask the data using our masking capabilities and then it's true forgotten or you can archive the data and remove it from a particular business process that is marketing or selling to them if that's so yeah choice is it some flexibility correct or or slight maybe slightly differently Mystere forgot that's right you can get work out of that data in an appropriate way so the customer can be forgotten so that this this kind of work now that you cannot apply that data to marketing whatever else it might be for when it comes to understanding better products or building better products whatever else through masking you can apply the data still to that work because it's a legitimate use under the law exactly also think about the fact you've mask key critical data right so the thing about data privacy in general was you know if you can't understand a data subject so if you can hide certain pieces of data and you can't identify them you didn't aggregate it you can it's not personal data anymore so you know there's this some real nuance there's a lot of people aren't talking about these things but these new icers will be surfaced yeah yeah because certainly it's a it's the beginning of a generational shift there gonna be some pain points coming online I mean we're hearing some people complaining here and there you guys are you know used to this some industries are like used to dealing with Brad you know compliance like no big deal some people are fast and loose with their data like wait a minute I said you can't be a digital wanker we can't be a head of digital propositions you don't understand your data you know you and you don't understand it and manage it so this is an opportunity to do this across the enterprise it exposes companies that have not planned for an architected data whether that's investment in data engineering or have staff this is a huge issue and pools and tools that can't support that process I mean if you got a I mean people are looking in their organization going oh man we've really don't have it or they're ready the exciting part is you know organizations have focused on quality and trustworthiness of their data we're now taking that same data and focusing on the privacy and protection and the ethical treatment of it and leveraging the appropriate technologies which happen to be very similar fundamentally for quality and Trust and privacy and protection and and in the absence of a global standard for GDP our we're we're seeing organizations without GDP our as a de facto standard in fact Facebook just announced that they're treating all users data you know that was one of our research predict yes yeah very obvious I mean we'll see how eleven have any teeth or anything but you know Facebook's got their own challenge but it's an opportunity for a clean sheet of paper Friday May 27 I'm sure there's gonna be a ton of class-action lawsuits against Facebook jitesh Barry thanks for coming on great to see you thanks for everything in Ireland we're here on the open and informatica world right and written the solutions expose the cue bringing you all the data right here in the catalog you got the cube dotnet check it out I'm people John free with Peterborough's stay with us for more day to coverage at different Matic world after this short break

(upbeat music) >> Announcer: Live, from Las Vegas, It's theCube! Covering Informatica World 2018, brought to you by Informatica. >> Welcome back everyone. It's theCube's live coverage of Informatica World 2018, here in Las Vegas. I'm John Furrier, your host and analyst, with Peter Burris, co-host and analyst at Wikibon and still going on theCube. Our next two guests is Gitesh Ghai, C Vice President, General Manager of Data Quality Security and Governance for Informatica, and Smail Haddad who is the Senior IT Director of Data Governance and Data Delivery Architecture at Toyota, company wide, Great to have you on Gitesh. Great to have you on Smail. So we were just talking before coming on camera, before we went on live about the massive role that you have at Toyota with data. You are looking at everything now. You're touching all the data. But it wasn't always like that. >> Smail: Yeah it wasn't always like that... >> Tell us about your journey and your role at Toyota. >> Yeah thank you. So Toyota, again, started business in North America. People know, maybe not, 65 years ago. And we started as a little dealership in North Hollywood. Bringing these Japanese cars. So we grew from that single dealership in North Hollywood to this big company we are today, with almost 25 plants around North America, Canada, US, and Mexico. And almost 2,600 dealerships across nationwide. So what that came with, it came with a big responsibility, in terms of understanding our customer base and trying to be more closer to what the customer needs. So our supply chains, where we produce the vehicles, it really was mostly a push supply chain, where we build a car and we push it to the customer to buy it. The model works very well, all the way to 2008. Where things change and we all understand what happened back in the financial meltdown and the crisis, that was a worldwide crisis. And that was a turning point for Toyota because we start seeing a shift in the demand. The customers becoming more savvy. Demanding for example, more electrical cars, less gas guzzlers vehicles and so on. The marketing department, which was a different company back then, understood that but the production companies, which was producing the vehicles, they didn't have that knowledge. So the journey to bring these two together became really critical after that 2008 crisis. Because what it forced us to do was the vehicles were being produced everyday, the dealers were not able to sell, and we were just stuck in vehicles around the lot. So why the digital disruption was so key for us, is the data was always there. Data always told us the truth. And that's what the facts are. Where we started looking at, back after that, is hey, if we look at the data and the data always predicted that the shift in the market will happen that way. And we should've have throttled down maybe, our production system better. Why we didn't do it that way? We were not looking at the data. Data was available. So what we undertook, under Toyota IS, we said, "Can we bring all this data across all these silos, "into one place?" So we build our big data solution, where the data is coming from various departments and various business lines. And it's being blended together and correlated. What that gives us is really that 360 view of our business, which we were missing. 'Cause we were looking at the business in silo, in pieces. And with that explosion of data, that we were gathering, obviously that brings a lot of questions about where this data, how good it is, if I'm going to make decisions on it, can I trust it? All that was a good takeaway into the business I'm in, which is the Data Governance. It's basically how can we govern this data that we are collecting on a daily basis today? And so my department is leading basically, the North American Governance and Quality across all the business line in North America. So as we are gathering these data points everyday, on a daily basis, even today we are gathering. What made it even, made it go even further in terms of volume, is we started capturing data coming from the cost, on a real time basis. So this is not just sales data where we capture the experience, the sales, and configuration of the vehicles on a daily basis... >> John: That's a lot of data coming in. >> A lot of it, a lot of it. So the volume exploded. With that, the responsibility to put a solution, where people can go quickly, find the right data. So basically, the time to data became so critical. How can we shorten that time to find the right data you want? And understand it, and trust it, and use it? >> John: So last... >> Sorry John, the Toyota story that you're telling us is especially interesting 'cause Toyota is legendary for empirical based management, lean manufacturing, so you have plants and marketing organizations, and sales organizations who, because of the Toyota way, have grown up on the role that data needs to play in their function. And what you're doing is you're saying, "That was great. "But we had to take it to a next level "and organize our data differently so we could look at it "across the entire company." >> Across the entire company. So absolutely, there are four, basically, goals that Toyota is trying to achieve today. One is understanding our customer in a more personalized way. Understand today's demand and hopefully predict tomorrow's demand. The second important pillar, empower our employees and our team members. By the way, Toyota, we call employees team members. And the third one is optimize our operations. And the fourth is transform our product. In order to achieve all these four goals, data is at the middle of all this. Why it's so important, we understand that today, in this day and age of digital disruption. And by the way, the automotive industry is being disrupted. Not our competition right now, Toyota, is no more the GM, and the Ford, the traditional automotive companies. But our new competition is all the technology companies, Google, Apple, Amazon. And you might have heard the news. Everyday, how they are disrupting these segments where you hear about autonomous driving cars and everybody's jumping on it. And behind all that, taking just the autonomous driving cars. The amount of data behind these so you can make the vehicle drive itself and take you from point a to point b in a safe manner and avoid all the road hazards. That needs a huge amount of data that's behind it, and fuels that. We're able to make huge stride. The new story of Data Governance at Toyota, is really, how we can enable that and not being just about compliance and risk management, which is kind of understood, that's part of the job. But we make that seamless. We wanted our business unit to focus more on the core business and goals, versus worrying about, "Am I in compliance, do I need to do this or that?" Try to seize the opportunities and put Toyota in a competitive way so they can compete with all these new disrupters like I said, Google, and the, the Apple of the world. Because what they have in common, those companies, >> John: They're data companies. >> Exactly. Data companies, technology. They understand how to use data. They understand how to analyze data. This is where traditional automotive companies like Toyota, and GM, and Ford, are basically bound to learn about that. >> But Waymo is not a car manufacturer, Uber is not a car manufacturer, they're companies that are providing a transportation service. And the only way that Toyota could provide a transportation service, is if you started organizing your data differently, in service to the idea of providing consumers a better, and businesses, with better transportation services. Whether you call it personal. I don't want to be the typical analyst that kind of goes off and starts renaming things. But that's fundamentally what you're trying to do. Is you're saying, "Our customers are mainly focused "on getting from point a to point b safely. "Let's make sure that we have products and services "that help them get there. "Perhaps through a lot of intermediaries along the way." But is that kind of how you're organizing things? >> Absolutely, so in order to achieve that goal. We wanted to bring the silos. Like I said, the data was always there but it was always built in silos, stored in silos. What we did in the next, last few years, we started breaking all the silos because we started looking at the data as an enterprise assets and no more as just a departmental assets or as a tool to get to a goal. It became the strategic assets for the company. And in order to achieve that, was to really break the silos. Bring it together so we can see across and understand how are business is operating. And hopefully, put the company in a competitive advantage to see the future coming to. >> It must be really frustrating to know that the data was there the whole time. And you're kind of kicking yourself. What did you do? I mean, you brought Informatica in. What's the Informatica connection, Gitesh? Get a word in, come on. With the Informatica connection, these guys. Are you the core supplier? Do you guys, the connective tissue between Toyota's groups? >> It's all about the data, right? It's all about the data. Informatica's role in all of this, it's a great story. Toyota's, Smail's story, is a great story. What Informatica brought to bear for Toyota, it's actually the promise of big data. The promise of big data is bringing together data that hasn't been analyzed together in a new context before. So breaking down these silos and bringing together the data. What's interesting is when you bring it together, you create a data lake. But there's a very big difference between a data lake and a data swamp. Which is why naturally, governance, quality, trustworthiness became a focus area of bringing all of this data together. >> Well last year, talking about data swamp and data lake as our core theme. This year governance and enterprise catalog is a bigger story because you guys easily could've been swamped out because of all this new data coming in, whether it's car telemetry or new data. 'Cause if you had set the table for your intercompany connective tissue, if you will, then you're like, "Oh, hey we're done, wait a minute." >> But Toyota was applying data to the work of manufacturing, to the work of marketing cars. And now you're trying to apply data to the work of providing better transportation. And the only way to think that through is to see how all this data can be reorganized and brought together. And at the same time, you can still, then turn that data around and still apply it for the work of manufacturing, the work of marketing, and the work of selling. >> Gitesh: Absolutely. >> Also I'd add, to be competitive in a new market, they are going to use their, leverage their assets. Not only data but their physical assets. To compete at a new level, a new playing field. >> Smail: Absolutely. >> With data at the center. >> And I think you said it earlier, you have to bring this data together in the lake. But you need an organized view of all the data that's out there, which starts with our data catalog. So the data catalog gives you a sense of what data do you want to bring in the lake and what data, frankly, is noise, doesn't matter? >> Whole 'nother level of operations, whole 'nother level of intelligence. Competitive advantage, competitive strategy. >> Peter: What a job. >> We're data geeks, geeking out here. Great story, I'd like to do a follow up. I think that this is a real big story of not only of digital transformation, digital evolution, digital disruption, digital business, great story... >> You used to be able to do this job in Southern California. >> Yes, absolutely. >> Thanks for bringing Toyota to the table. Thanks for coming on. >> My pleasure. Thank you for having me on. >> The beginning of a journey that's going to continue it's not ending anytime soon. Toyota company, really bringing data into the center of the action. Of course, we're in the center of the action as theCube, bringing you the data from Informatica World, right here, on theCube. More coverage after this short break. I'm John Furrier, Peter Burris. Stay with us, we'll be right back. (upbeat music)

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.