(upbeat music) >> We're back talking about the path to sustainable IT and now we're going to get the perspective from Mattia Ballerio who is with Elmec Informatica, an IT services firm in the beautiful Lombardi region, of Italy, north of Milano. Mattia, welcome to theCUBE. Thanks so much for coming on. >> Thank you very much, Dave. Thank you. >> All right, before we jump in, tell us a little bit more about Elmec Informatica. What's your focus? Talk about your unique value add to customers. >> Yeah! So basically Elmec Informatica is middle company from the north part of Italy. And is managed service provider in the IT area. Okay, so the, the main focus area of Elmec is, rich digital transformation, and innovation to our clients with the focus on infrastructure services, workplace services, and also cybersecurity services, okay. And we try to follow the path of our clients to the digital transformation and innovation through technology and sustainability. >> Yeah, obviously very hot topics right now. Sustainability, environmental impact, they're growing areas of focus among leaders across all industries, particularly acute right now in, in Europe, with the, you know, the energy challenges. You've talked about things like sustainable business. What does that mean? What does that term, you know, speak to, and, and what can others learn from it? >> Yeah, at Elmec, our approach to sustainability is grounded in science and, and values. And also in a customer territory, but also employee centered. I mean, we conduct regular assessments to understand the most significant environment and social issues for our business with, with the goal of prioritizing what we do for a sustainability future. Our service delivery methodology, employee care, relationship with the local supplier, and local area and institution are a major factor for us to, to build a such a responsibility strategy. Specifically during the past year, we have been particularly focused on define sustainability governance in the company based on stakeholder engagement, defining material issues, establishing quantitative indicators, to monitor and setting medium to long term goals. >> Okay, so you have a lot of data. You can go into a customer, you can do an assessment, you can set a baseline, and then you have other data by which you can compare that and, and understand what's achievable. So what's your vision for sustainable business? You know, that strategy, you know, how has it affected your business in terms of the evolution? 'Cause this was, hasn't always been as hot a topic as it is today, and, and is it a competitive advantage for you? >> Yeah, yeah. For, for all intense and proposed sustainability is a competitive advantage for Elmec. I mean, it's so, because at the time of profound transformation in the work, in the world of work, CSR issues make a company more attractive when searching for new talent to enter in the workforce of our company. In addition, efforts to ensure people's proper work life balance are a strong retention factor. And, regarding our business proposition, Elmec's attempts is to meet high standard of sustainability and reliability. Our green data center, you said is a prime example of this approach, as at the same time, is there a conditioning activity that is done to give a second life to technology devices that come from, back from rental? I mean, our customer inquiries with respect to Elmec sustainability are increasingly frequent, and in depth. And which is why we monitor our performance, and invest in certification, such as, EcoVadis or ISO 14,001. Okay? >> Got it! So in a previous life, I actually did some work with, with power companies, and there were two big factors in IT, that affected the power consumption. Obviously virtualization was a big one, if you could consolidate servers, you know, that was huge. But the other was the advent of flash storage, and that was all we used to actually go in with the, the engineers and the power company put in alligator clips to measure of, of of an all flash array versus, you know, the spinning disk and it was a big impact. So, you want to talk about, your, your experience with Pure Storage. You use Flash Array, and the Evergreen architecture. Can you talk about your experience there? Why did you make that decision to select Pure Storage? How does that help you meet sustainability and operational requirements? Do those benefits scale as your customers grow? What's your experience been? >> Yeah! It was basically, an easy, an easy answer to our, to our business needs. Okay, because you said before that, in Elmec, we manage a lot of data, okay. And in the past we, we, we see, we see that, the constraints of managing so many, many data was very, very difficult to manage in terms of power consumption or simply for the, the space of storing the data. And, when, when Pure came to us and share our, their products, their vision, to the data management journey for Elmec Informatica, it was very easy to choose Pure, why? With values and the numbers, we, we create a business case and, we said, we see that our power consumption usage was much less, more than 90% of previous technology that we used in the past. Okay? And so of course you have to manage a gradual deploy of flash technology storage, but it was a good target. So we have tried to monitoring the adoption of flash technology, and monitor, monitoring also the power consumption, and the efficiency that the pure technology bring to our, to our IT systems, and of course the IT systems of our clients. And so this is one, the first part, the first good part of our trip with, with Pure. And after that, we approach also the sustainability in long term of choosing Pure technology storage. You mentioned the evergreen models of Pure, and of course this was, a game challenge for us because it allows, it allow us to extend the life cycle management of our data centers, but also the, it allows us to improve the facility, of the facilities of using technology from our technical side, okay. So we are much more efficient than in the past with the choose of Pure Storage Technologies, okay. Of course, this easy users, easy usage mode, let me say, it allow us to bring this value to our, to all our clients that put their data in our data centers. >> So, you talked about how you've seen, 90% improvement relative to previous technologies. I always, I haven't put you on the spot. Because I, I, I was on Pure's website, and I saw in their ESG report some com, you know, it was a comparison with a generic competitor. I'm presuming that competitor was not, you know 2010 spinning disk system. But, but, so I'm curious, as to the results that you're seeing with Pure, in terms of footprint and power usage. You, you're referencing some of that. We heard some metrics from Nicole and Ajay earlier in the program. Do you think, again I'm going to put you in the spot, do you think that Pure's architecture, and the way they've applied, whether it's machine intelligence or the Evergreen model, et cetera, is more competitive than other platforms, that you've seen? >> Yeah, of course. Is more competitor, more competitive. Because basically it allows to service provider to do much more efficient value proposition and offer services that are more that brings more values to, to the customers. Okay, so the customer is always at the center of a proposition of service provider. And the trying to adopt the methodology and also the, the value that Pure as inside, by design in the technology is, is for us very, very important and very, very strategic. Because, because, with like a glass, we can ourself transfer, try to transfer the values of Pure, Pure technologies to our service provider client. >> Okay Mattia, let's wrap and talk about sort of near term 2023 and then longer term. It looks like sustainability is a topic that's here to stay. Unlike when we were putting alligator clips on storage arrays, trying to help customers get rebates, that just didn't have legs. It was too complicated. Now it's a, a topic that everybody's measuring. What's next for Elmec, in its sustainability journey? What advice would you might have for sustainability leaders that want to make a meaningful impact on the environment but also on the bottom line? >> Okay. So, sustainability is fortunately a widely spread concept. And our role in, in this great game is to define a strategy, align with the common and fundamentals goals for the future of planet, and capable of expressing our inclination, and the particularities. Elmec sustainability goals in the near future, I can say that are will be basically free. One define sustainability plan, okay. It's fundamentals to define a sustainability plan. Then it's very important to monitor the, its emissions and we will calculate our carbon footprint, okay. And list, button list, produce a certifiable and comprehensive sustainability report, with respect to the demands of customers, suppliers, and also partners. Okay, so I can say that, this three target will be our direction in the, in the future. Okay? >> Yeah, so I mean, pretty straightforward. Make a plan. You got to monitor and measure. You can't improve what you can't measure. So you going to set a baseline, you're going to report on that. You're going to analyze the data and you're going to make continuous improvement. >> Yep. >> Mattia, thanks so much for joining us today and sharing your perspectives from the, the northern part of Italy. Really appreciate it. >> Yep. Thank you for having me on board. Thank you very much. >> It was really our pleasure. Okay, in a moment, I'm going to be back to wrap up the program, and share some resources , that could be valuable in your sustainability journey. Keep it right there. (upbeat music)

>>Hey everyone. Welcome back to the cube. Lisa Martin here with Dave ante, we're covering snowflake summit 22. This is Dave two of our wall to wall cube coverage of three days. We've been talking with a lot of customers partners, and we've got some more partners to talk with us. Next. Informatica two of our guests are back with us on the program. Rick TA Daniels joins us the G P global ecosystems and technology at Informatica and Peter COO vice president and chief strategist banking and financial services. Welcome guys. >>Thank you guys. Thanks for having us, Peter, >>Talk to us about what some of the trends are that you're seeing in the financial services space with respect to cloud and data and AI. >>Absolutely. You know, I'd say 10 years ago, the conversation around cloud was what is that? Right? How do we actually, or no way, because there was a lot of concerns about privacy and security and so forth. You know, now, as you see organizations modernizing their business capabilities, they're investing in cloud solutions for analytics applications, as well as data data being not only just a byproduct of transactions and interactions in financial services, it truly fuels business success. But we have a term here in Informatica where data really has no value unless it's fit for business. Use data has to be accessible in the systems and applications you use to run your business. It has to be clean. It has to be valid. It has to be transparent. People need to understand where it comes from, where it's going, how it's used and who's using it. It also has to be understood by the business. >>You can have all the data in the world and your business applications, but people don't know what they need it to use it for how they should use it. It has no value as well. And then lastly, it has to be protected when it matters most what we're seeing across financial services, that with the evolution of cloud now, really being the center of focus for many of the net new investments, data is scattered everywhere, not just in one cloud environment, but in multiple cloud environments, but they're still dealing with many of the on premise systems that have been running this industry for many, many years. So organizations need to have the ability to understand what they need to do with their data. More importantly, tie that to a measurable business outcome. So we're seeing the data conversation really at the board level, right? It's an asset of the business. It's no longer just owned by it. Data governance brings both business technology and data leaders together to really understand how do we use manage, govern and really leverage data for positive business outcomes. So we see that as an imperative that cuts across all sectors of financial services, both for large firms, as well as for the mid-market so >>Quick follow up. If I, may you say it's a board level. I totally agree. Is it also a line of business level? Are you seeing increasingly that line of businesses are leaning in owning the data, be building data products and the like >>Absolutely. Because at the end of the day business needs information in order to be successful. And data ownership now really belongs in the front office. Business executives understand that data again is not just a bunch of zeros and ones. These are critical elements for them make decisions and to run their business, whether it's to improve customer experience, whether it's to grow Wallace share, whether it's to comply with regulations, manage risks in today's environment. And of course being agile business knows that data's important. They have ownership of it and technology and data organizations help facilitate that solutions. And of course the investments to ensure that business can make the decisions and take the appropriate actions. >>A lot of asks and requirements on data. That's a big challenge for organizations. You mentioned. Well, one of the things that we've mentioned many times on this program recently is every company has to be a data company. There is no more, it's not an option anymore. If you wanna be successful, how does Informatica help customers navigate all of the requirements on data for them to be able to extract that business value and create new products and services in a timely fashion? >>So Informatica announced what we call the intelligent data management cloud platform. The platform has capabilities to help organizations access the data that they need, share it across to applications that run their business, be able to identify and deal with data, quality issues and requirements. Being able to provide that transparency, the lineage that people need across multiple environments. So we've been investing in this platform that really allows our customers to take advantage of these critical data management, data governance and data privacy requirements, all in one single solution. So we're no longer out there just selling piecemeal products. The platform is the offering that we provide across all industries. >>So how has that affected the way Informatica does business over the last several years? Snowflake is relatively new. You guys have been around a long time. How has your business evolved and specifically, how are you serving the snowflake yeah. Joint customers with >>Informatica? Yeah, I think then when I've been talking with folks here at the event, there are two big areas that keep coming up. So, so data governance, data governance, data governance, right? It's such a hot topic out there. And as Peter was mentioning, data governance is a critical enabler of access to data. In fact, there is an IDC study for last year that said that, you know, 80, 84% of executives, you know, no surprise, right? They wanna have data driven outcomes, data driven organizations, but only 30% of practitioners actually use data to make decisions. There's a huge gap there. And really that's where governance comes in and creating trust around data and not only creating trust, but delivering data to and users. So that's one big trend. The other one is departmental user adoption. We're seeing a, a huge push towards agility and rapid startup of new projects, new data driven transformations that are happening at the departmental level, you know, individual contributors, that sort of thing. So Informatica, we did a made announcement yesterday with snowflake of a whole host of innovations that are really targeting those two big trend areas. >>I wanna get into the announcements, but you know, the point about governance and, and users, business users being reluctant, it's kind of chicken and egg, isn't it. If, if I don't have the governance, I'm, I'm afraid to use it. But even if I do have it, there's the architecture of my, my, my company, my, my data organization, you know, may not facilitate that. And so I'm gonna change the architect, but then it's a wild west. So it has to be governed. Isn't that a challenge that company companies >>Absolutely, and, and governance is, is a lot more than just technology, right? It's of a people process problem. And there really is a community or an ecosystem inside every organization for governance. So it's really important that when you think about deploying governance and being successful, that every stakeholder have the ability to interact with this common framework, right. They get what they need out of it. It's tailored for how they wanna work. You've got your it folks, you got your chief data officer data stewards, you have your privacy folks and you have your business users. They're all different personas. So we really focus on creating a holistic, single pane of glass view with our cloud data governance and catalog offering that that really takes all the way from the raw technical data and actually delivers data in, in a shopping cart, like experience for actual enterprise users. Right? And, and so I think that's when data governance goes from historically data, governments was seen as an impediment. It was seen as a tax, I think, but now it's really an accelerator, an enabler and driving consumption of data, which in turn for our friends here at snowflake is exactly what they're looking for. >>Talk about the news. So data loader, what does that do? >>Well, it's all in the name. We say, no, the data loader it, it's a free utility that we announced here at, at snowflake summit that allows any user to sign up. It's completely free, no capacity limits. You just need an email address, three simple steps start rapidly loading data into snowflake. Right? So that first step is just get data in there. Start working with snowflake. Informatica is investing and making that easy for every single user out there. And especially those departmental users who wanna get started quickly. >>Yeah. So, I mean, that's a key part point of getting data into the snowflake data cloud, right? It's like any cloud, you gotta get data in. How does it work with, with customers? I mean, you guys are, are known, you have a long history of, you know, extract transform ETL. How does it work in the snowflake world? Is it, is it different? Is it, you remember the Hadoop days? It was, it was E LT, right? How are customers doing that today in this environment? >>Yeah, it's different. I mean, there, there are a lot of the, the same patterns are still in play. There's a lot more of a rapid data loading, right. Is a key theme. Just get it into snowflake and then work on the data, transform it inside of snowflake. So it's, it's a flavor of T right. But it's really pushing down to the snowflake data cloud as opposed to Hado with spark or something like that. Right. So that, that's definitely how customers are using it. And, you know, majority of our customers actually with snowflake are using our cloud technology, but we're also helping customers who are on premise customers, automate the migration from our on-premises technology to our cloud native platform as well. Yeah. >>And I'd say, you know, in addition to that, if you think about building a snowflake environment, Informatica helps with our data loader solution, but that's not enough. Then now you need to get value out of your data. So you can put raw data into the snowflake environment, but then you realize the data's not actually fit for business use, what do we need to do actually transform it to clean it, to govern it. And our customers that use Informatica with snowflake are managing the entire data management and data governance process so that they can allow the business to get value out of the snowflake investment. >>How quickly can you enable a business to get value from that data to be able to make business decisions that can transform right. Deliver competitive advantage? >>I think it really depends on an organization on a case by case basis. At the end of the day, you need to understand why are you doing this in the first place, right? What's the business outcome that you're trying to achieve next, identify what data elements do you actually need to capture, govern and manage in order to support the decisions and the actions that the business needs to take. If you don't have those things defined, that's where data governance comes into play. Then all you're doing is setting up a technical environment with a bunch of zeros in ones that no one knows what to do with. So we talk about data governance more holistically, say, you need to align it to your business outcomes, but ensure that you have people, processes, roles, and responsibilities, and the underlying technology to not just load data into snowflake, but to leverage it again for the business needs across the organization. >>Oh, good, please. >>I just wanted to add to that real quickly. Yeah. One of the things Informatica we're philosophically focused on is how do you accelerate the entire business of data management? So with our, our cloud platform, we have what's called our clear AI engine, right? So we use AI techniques, machine learning recommendations to accelerate with the, the knowledge of the metadata of what's gone on the organization. For example, that when we discover data assets figure out is this customer data, is it product data that dramatically shortens the time to find data assets deliver them? And so across our whole portfolio, we're taking things that were traditionally months to do. We're taking 'em down to weeks and days and even hours, right? So that's the whole goal is just accelerate that entire journey and life cycle through cloud native approaches and AI. Yeah, >>You kind of just answered my question. I think Rick, so you have this joint value statement together. We help customers. This is informatic and snowflake together. We help customers modernize their data. Architecture enable the most critical workloads, provide AI driven data governance and accelerate added value with advanced analytics. I mean, you definitely touched on some of those, but kind of unpack the rest of that. What do you mean by modernize? What is their data architecture? What is that? Let's start there. What does that look like? Modernizing a data. Yeah. >>So, so a lot with so many customers, right? They, they built data warehouses, core data and analytics systems on premises, right? They're using ETL technology using those, those either warehouse, appliances or databases. And what they're looking for is they wanna move to a cloud native model, right. And all the benefits of cloud in terms of TCO elasticity, instant scale up agility, all those benefits. So we're looking, we're looking to do with our, our modernization programs for our, for our current customer base that are on premises. We automate the process to get them to a fully cloud native, which means they can now do hybrid. They can do multi-cloud elastic processing. And it's all also in a consumption based model that we introduced about about a year and a half ago. So, so they're looking for all those elements of a cloud native platform and they're, but they're solving the same problems, right? We still have to connect data. We still have to transform data, prepare it, cleanse it, all those things exist, but in a, in a cloud native footprint, and that's what we're helping them get to. >>And the modern architecture these days, quite honestly, it's no longer about getting best breed tools and stitching them together and hoping that it will actually work. And Informatica is value proposition that our platform has all those capabilities as services. So our customers don't have to deal with the costs and the risks of trying to make everything work behind the scenes and what we've done with IDMC or intelligent data management cloud for financial services, retail, CPG, and healthcare and life sciences. In addition to our core capabilities and our clear AI machine learning engine, we also have industry accelerators, prebuilt data, quality rules for certain regulations in within banking. We've got master data management, customer models for healthcare insurance industry, all prebuilt. So these are accelerators that we've actually built over the years. And we're now making available to our customers who adopt informatic as intelligent data management cloud for their data management and governance needs. >>And then, and then the other part of this statement that that's interesting is provide AI driven data governance. You know, we are seeing a move toward, you know, decentralized data architectures and, and, and organizations. And we talk to snowflake about that. They go, yeah, we're globally distributed cloud. Okay, great. So that's decent place, but what we see a lot of customers doing to say, okay, we're gonna give lines of business responsibility for data. We're gonna argue about who owns what. And then once we settle that here's your own, here's your own data lake. Maybe they they'll try to cobble together a catalog or a super catalog. Right. And then they'll try to figure out, you know, some algorithms to, to determine data quality, you know, best, you know, okay. Don't use. Right, right. So that, so if I understand it, you automate all that. >>So what we're doing with AI machine learning is really helping the data professional, whether in the business, in technology or in between not only to get the job done faster, better, and cheaper, but actually do it intelligently. What do we mean by that? For example, our AI engine machine learning will look at data patterns and determine not only what's wrong with your data, but how should you fix it and recommend data quality rules to actually apply them and get those errors addressed. We also infer data relationships across a multi-cloud environment where those definitions were never there in the beginning. So we have the ability to scan the metadata and determine, Hey, this data set is actually related to that data set across multiple clouds. It makes the organization more productive, but more importantly, it increases the confidence level that these organizations have the right infrastructure in place in order to manage and govern their data for what they're trying to do from a business perspective. >>And I add that as well. I think you're talking a lot about data mesh architectures, right? That, that are really kind of popular right now. And I think those kind of, they live or die on, on data governance. Right? If you don't have data governance to share taxonomy, these things, it's very hard to, I think, scale those individual working groups. But if you have a platform where they, the data owners can publish out visibility to what their data means, how to use it, how to interpret it and get that insight, that context directly to the data consumers that's game changing. Right. And that's exactly what we're doing with our cloud data governance and catalog. >>Well, the data mesh, you talk about data mesh, there's four principles, right? It's like decentralized architecture data products. So if, once you figure out those two yep. You just created two more problems, which is the other two parts of the Princip four, two parts of the four principles, self service infrastructure, and computational governance. And that's like the hardest part of federated, federated, computational governance. That's the hardest part. That's the problem that you're solving. >>Yeah. Yeah, absolutely. I mean, think about the whole decentralization and self-service, well, I may be able to access my data in mesh architecture, but if I don't know what it means, how to use it for what purpose, when not to use it, you're creating more problems than what you originally expected to solve. So what we're doing is addressing the data management and the governance requirements, regardless of what the architecture is, whether it's a mesh architecture, a fabric architecture or a traditional data lake or a data store. >>Yeah. Mean, I say, I think data mesh is more of an organizational construct than it is. I, I'm not quite sure what data fabric is. I think Gartner confused the issue that data fabric was an old NetApp term. Yeah. You're probably working in NetApp at the time and it made sense in the NetApp context. And then I think Gartner didn't like the fact that Jamma Dani co-opted this cool term. So they created data fabric, but whatever. But my, my point being, I think when I talk to customers that are they're, they're trying to get more value outta data and they recognize that going through all these hyper specialized roles is time consuming and it's not working for them. And they're frustrated to your points and your joint statement. They want to accelerate that. And they're realizing, and the only way to do that is to distribute responsibility, get more people involved in the process. >>And, and that's, it kind of dovetails with some, the announcements we made on data governance for snowflake, right, is you're taking these, these operational controls of the snowflake layer that are typically managed by SQL and you, and that decentralized architecture data owner doesn't know how to set those patterns and things like that. Right. So we're saying, all right, we're, we're creating these deep integration so that again, we have a fit for persona type experience where they can publish data assets, they can set the rules and policies, and we're gonna push that down to snowflake. So when it actually comes to provisioning data and doing data sharing through snowflake, it's all a seamless experience for the end user and the data owner. Yeah. >>That's great. Beautiful, >>Seamless experience absolutely necessary these days for everybody above guys. Thanks so much for joining David me today, talking about Informatica what's new, what you're doing with snowflake and what you're enabling customers to do in terms of really extracting value from that data. We appreciate your insights. >>Thank you. Yep. >>Thank you for having us >>For our guests and Dave ante. I'm Lisa Martin. You're watching the cubes coverage of snowflake summit day two of the cubes coverage stick around Dave. And I will be right back with our next guest.

>>Hey everyone. Welcome back to the cube. Live in Las Vegas, Lisa Martin, with Dave Nicholson, we are covering AWS reinvent 2021. This was probably one of the most important and largest hybrid tech events this year with AWS and its enormous ecosystem of partners. We're going to be talking with a hundred guests in the next couple of days. We started a couple of days ago and about really the innovation that's going to be going on in the cloud and tech in the next decade. We're pleased to welcome Rick Tam Daniel's as our next guest VP of strategic ecosystems at Informatica. Rick. Welcome to >>The program. Thank you for having me. It's a, it's a pleasure to be back. >>Isn't it nice to be back in person? Oh, it's amazing. All these conversations you just can't replicate by video conferencing. Absolutely >>Great to reconnect with folks haven't seen in a few years as well. >>Absolutely. That's been the sentiment. I think one of the, one of the sentiments that we've heard the last three days, so one of the things thematically that we've also been hearing about in, in between all of the plethora of AWS announcements, typical reinvent is that every company has to become a data company, public sector, private sector, small business, large business. Talk to us about how Informatica and AWS are helping companies become data companies so that they don't get left behind. >>But one of the biggest things that we're hearing at reinvent is that customers are really concerned with data, fragmentation, data silos, access to trusted data, and how do they, how do they get that information to really affect data led transformation? In fact, we did a survey earlier in the year of chief, the chief data officers were found that up to 80, almost 80% of organizations had 50% or more of their data in hybrid or multi-cloud environments. And also a 79% are looking to leverage more than 100 data sources. And 30% are looking to leverage more than 1000 data sources. So Informatica we, with our intelligent data management cloud, we're really focused on enabling customers to bring together the data assets, no matter where they live, what format they're in, on-premise cloud, multi-cloud bringing that all together. >>Well, we sold this massive scatter 22 months ago now, right? Of everyone just, and the edge exploded and data exploded and volumes and data sources exploded hard for organizations to get their head around that, to go or that the data is going to be living in all these different places. You talked about a lot of customers and every industry being hybrid multi-cloud because based on strategy, based on acquisition, but to get their arms around that data and to be able to actually extract value from it fast is going to be the difference between those businesses that succeed and those that don't >>Absolutely. And our partnership with AWS, that's a long standing partnership and we're very much focused on addressing the challenges you're talking about. Uh, and in fact, earlier this year we announced our cloud first, our cloud native, uh, data governance and data catalog on AWS, which is really focused on creating that central point of trusted data access and visibility for the organization. And just today, we had an announcement about how we're bringing data democratization and really accelerating data democratization for AWS lake formation. >>What is, when you, when you, we talk about data democratization often, what does that mean to you? What does that mean to Informatica? How do you deliver that to customers so that they can really be able to extract as much value as they can? >>Yeah. So a great question. And really that whole data management journey is a big piece of this. So it starts with data discovery. How do I even begin to find my data assets? How do I get them from where they are to where they need to go in the cloud? How do I make sure they're clean, they're ready to use. I trust them. I understand where they came from. And so the solution that we announced today is really focused on how do we provide a business users with a self-service way of getting access to data lake data, sitting in Amazon S3 with lake formation governance, but doing it in a way that doesn't create an undue burden on those business users, around data compliance and data policies. And so what we've done is we brought our business user-friendly self-service experience an axon data marketplace together with AWS lake formation. >>So Informatica has had a long history in the data world. Um, I think of terms like MDM and ETL. Yes. Where does, where does Informatica is history dovetail with the present day in terms of cloud the con does the concept of extract translate load? I think that's what ETL stood for too many TLAs running as far as trying to transform, uh, w where does that play in today's world? Are you focused separately on cloud from on-premise data center or do you, or do you link the two? Yeah, >>So we focus on, uh, addressing data management, uh, when, no matter where the data lives. So on-premise cloud multi-cloud, uh, on our intelligent data management cloud platform is a, is the industry's first end-to-end cloud native as a service data management platform that delivers all those capabilities. I mentioned before, uh, to customers. So we can manage all those workloads that are distributed from a single cloud-based as a service data management platform. So >>The platform is, is as a service in the cloud, but you could be managing data assets that are in traditional on premises, data centers, the like, absolutely. >>Okay. >>So congratulations on the IPO. Of course we can't, we can't not talk to Informatica without that. I imagined the momentum is probably pretty great right about now when we think of AWS, I, when I think of AWS, I always think of momentum. We, I mean the, the volume of announcements, but also when I think about AWS, I think about their absolute focus on the customer, that working backwards approach from a partnership perspective. Is there alignment there? I imagine, like I said, with the IPO, a lot of momentum right now, probably a lot of excitement are, is infant medical also was focused and customer obsessed as AWS's. >>Yeah. So, um, first of all, thank you so much. Congratulations. Uh, so we had a very successful IPO last month. And in fact, just yesterday, our CEO I'm at Wailea presented our Q3 results, uh, which showcase the continued growth of our subscription revenue or cloud revenue. And in fact, our cloud revenue grew 44% year over year, which is really reflective of our big shift to being a cloud first company and also the success of our intelligent data management cloud platform. Right. And, and that platform, again, as I mentioned, it's spanning all those aspects of data management and we're delivering that for more than 5,000 customers globally. Uh, and just from an adoption perspective, we processed about 23 trillion transactions a month for customers in our cloud platform. And that's doubling every six to 12 months. So it's incredible amount of adoption. Some of the biggest enterprises in the world like Unilever, Sanofi folks like that are using the cloud is their preferred data management platform of choice in the cloud. >>Well, you know, of course, congratulations is in order for the IPO, but also really on what you just mentioned, the trajectory of where Informatica is going, because Informatica wasn't born yesterday. Right. And, uh, we shouldn't overlook the fact that there are challenges associated with moving from the world as it exists on premises for still 80% of it spend at least navigating that transition, going from private to public, getting the right kind of investment where people realize that cloud is a significant barrier to entry, uh, for, for a lot of companies. I think it's, it's, you know, you have a lot of folks cheering for you as you navigate this transition. >>Well, one thing I do I say is, yes, we have it in the business of data for a long time, but we also then the business of cloud quite a long time. So this is true. This is the 10th reinvent. This is also the ten-year anniversary of the Informatica AWS partnership, right? So we've been working in the cloud with AWS for, for that long innovating all of these different, different core services. So, um, and from that perspective, you know, I think we're doing a tremendous amount of innovation together, you know, solutions like when we talked about for lake formation, but we also announced today a couple of key programs that we partnered with AWS around, around modernization and migration, right? So that's a big area of focus as well is how do we help customers modernize and take advantage of all the great services that AWS offers? So that's how we announced our membership and what's called the workload migration program and also the data lead migrations program, which is part of the public sector focus at AWS as well. >>The station perspective that was talked a lot about by Adam yesterday. And we've talked about it a lot today, every organization needs to monitorize, even some of those younger ones that you think, oh, aren't, they already, you know, fairly modern, but where, where are your customer conversations happening from a modernization perspective is that elevated up the, the C stat that we've got to modernize our or our organization get better handle of our data, be able to use it more protected, secure it so that we can be competitive and deliver outstanding customer experiences. >>What happens is the pain points that the legacy infrastructure has from the business perspective really do elevate the conversation to the C-suite. They're looking at saying, Hey, especially with the pandemic, right? We have to transform our business. We have to have data. We have to have trust in data. How do we do that? And we're not going to get there >>On rigid on-premise infrastructure. We need to be in a cloud native footprint. And so we've been focused on helping customers get to those cloud native end points, but also to a truly cloud native data management, we talked about earlier can manage all those different workloads, right? From a single that SAS serverless type experience. Right? What have been some of the interesting conversations that you've had here? Again, we are in person yep. Fresh off the IPO, lots of announcements coming out. You guys made announcements today. What's been the sentiment from the, those customers and partners that you've talked about. >>Well, I'll give you guys actually a little sneak preview of another announcement we have coming tomorrow, uh, with our friends at Databricks. So we, uh, we are announcing a data, data democratization solution with Databricks accelerating some of the same, you know, addressing some of the same challenges we were talking about here, but in the data breaks in the Lakehouse environment. Um, so, so, but around that, and I had a great conversation with some partners here, some of the global system integrators, and they're just so happy to see that, right, because a lot of the infrastructure that's around data lakes are lake formation. It's pretty technical it's for a technical audience. And, and Informatica has really been focused on how do we expand the base of users that are able to tap into data and that's through no code experiences, right? It's through visual experiences. And we bring that tightly coupled together with the performance and the power and scale of platforms like Databricks and the AWS Redshift and S3, it's really transformative for customers. >>What are some of the things that here we are wrapping up the 10th, re-invent almost as tomorrow, but also wrapping up the end of 2021. What are some of the things that th th that there's obviously a lot of momentum with Informatica right now that from a partnership perspective, anything that you, you just gave us some breaking news. Thank you. We always love that. What are some of the things that you're looking forward to in 2022 that you think are really going to help Informatica customers just be incredibly competitive and utilizing data in the cloud on prem to their maximum? >>Well, I think as we go into the next year data complexity data fragmentation, it's gonna continue to grow. It's, it's, it's exploding out there. Uh, and one of the key components of our platform or the IDMC platform is we call it Clare, which is the industry first kind of metadata driven AI engine. And what we've done is we've taken the intelligence of machine learning and AI, and brought that to the business of data management. And we truly believe that the way customers are going to tame that data, they're going to address those problems and continue to scale and keep up is leveraging the power of AI in a cloud native cloud, first data management platform. >>Excellent. Rick, thank you so much for joining us today. Again, congratulations on last month, Informatica IPO, great solid, strong, deep partnership with AWS. We thank you for your insights and best of luck next year. >>Awesome. Thank you so much. Pleasure being here. Our >>Pleasure to have you for my co-host David Nicholson, I'm Martin. You're watching the cube, the global leader in live tech coverage.

>> Announcer: From around the globe, it's "theCUBE" with digital coverage of AWS "re:Invent" 2020. Sponsored by Intel, AWS and our community partners. >> Hi, everyone, welcome back to theCUBE's virtual coverage of AWS "re:Invent" virtual 2020. It's not an in-person event this year. It's remote, it's virtual, "theCUBE" is virtual and our guests and our interviewers will be remote as well. And so we're here covering the event for the next three weeks, throughout the next three cause we're weaving in commentary from "theCUBE", check out the cube.net and all of our coverage. And here at AWS we have special feature programming, we got a great segment here talking about big data in the cloud, governance, data lakes, all that good stuff. Rik Tamm-Daniels, vice-president strategic ecosystems and technology for Informatica, and Rick Turnock, head of enterprise data services, Invesco, customer of Informatica. Welcome to the cube. >> Hey John, thanks for having us. >> So Rik, with a K from Informatica, I want to ask you first, we've been covering the company journey for many, many years. Always been impressed with the focus on data and specifically cloud and all the things that you guys have been announcing over the years, have been spot on the mark. You know, AI with CLAIRE, you know, making things, cloud native, all that's kind of playing out now with the pandemic, "re:Invent", that's the story here. Building blocks with high level services, cloud native, but data is the critical piece again. More machine learning, more AI, more data management. What's your take on this year's "re:Invent". >> Absolutely John and again, we're always excited to be here at "re:Invent", we've been here since the very first one. You know, it's a deep talk to a couple of key trends there, especially the era of the global pandemic here. There's so many challenges that so many enterprises are experiencing. I think the big surprise has been, that has actually translated into a tremendous amount of demand for digital transformation, and cloud modernization in particular. So we've seen a huge uptake in our cloud relationships with AWS when it comes to transformational architecture solutions around data and analytics, and using data as a fundamental asset for digital transformation. And so some of those solution areas are things like data warehouse, modernization of the cloud, or end-to-end data governance. That's a huge topic as well for many enterprises today. >> Before coming into "re:Invent", I had a chance to sit down an exclusive interview with Andy Jassy. I just spoke with Matt Garman who's now heading up sales and marketing, who ran EC too. Rick, you're a customer of Informatica. Their big talking point to me and validation to the trends is, there's no excuse to go slow anymore because there's a reason to go fast cause there's consequences and the pandemic has highlighted that you got to move faster otherwise, you know, you're going to be on the wrong side of history and necessity is the mother of all invention. Okay, great. I buy that by the way. So I have no complaints on talking point there from Amazon Web Services. The problem is, you got to manage the data. (John chuckles) To go fast. The gas in the tank is data, and if it's screwed up, it's not going to go well, all right? So it's like putting gas in a car. So, this is where I see the data lake coming into the cloud and all the benefits and look at the successes of companies. The cloud is a real enabler. What's your take on this? The importance of data governance, because cloud scale is here, people want to go faster, data is like the key thing. >> Yeah. The data governance was a critical component when we started our enterprise data platform and looking at, you know, how can we build a modern-day architecture with scale, bringing our enterprise data, but doing it in a governed fashion. So, when we did it, we kind of looked at, you know, what are critical partners? How can we apply data governance and the full catalog capabilities of knowing what data's coming in, identifying it, and then really controlling the quality of it to meet the needs of the organization. It was a critical component for us because typically it's been difficult to get access to that right data. And as we look in the future and even current needs, we really need to understand our data and bring the right data in and make it easily accessible and governance, and quality of that is a critical component of it. >> I want to just follow up with that if you don't mind cause you know, I've done so many of these interviews, I've been on the block now 30 years in the industry, I've seen the waves come and go, and you see a lot of these mandates, you know, "Data governance, we're adding data governance." From the Ivory tower, or you hear, "Everything got to be a service." But when you peel back and look under the hood to make that happen, it's complicated. You've got to have put things in place and it's got to be set up properly, you got to do your work. How important it is to have... And well what's under the covers to this? Cause governance, yeah, it's a talking point, I get that. But to make it actually happen well, it's hard. >> We started really with the operating models from the start. So I kind of took over data governance seven years ago and had a governing global architecture that's been around for 40 years, and it was hard. So this was really our shot and time to get it right. So we did an operating model, a governance model, and it really ingrained it through the whole build and execution process. And so it was part with technology and it was foundational to the process to really deliver it. So it wasn't governance from a governance saying, it was really part of our operating model and process to build this out and really succeed at it. >> Rik, on the Informatica side, I got to get your take on the new solution you guys announced, "The Governed Data Lake", I think it was solution. Does this tie into that? Take a minute to explain the announcement, and how does this tie in? >> Yeah, absolutely John. So I think you take a step back, look at... We talked about some of the drivers of why companies are investing in cloud data lakes. And I think what comes down to is, when you think about that core foundation of data analytics, you know, they're really looking at, you know, how do we go ahead and create a tremendous leap forward in terms of their ability to leverage data as an asset. And again, as we talked about, one of the biggest challenges is trust around the data. And what the solution does though, is it really looks to say, "Okay, first and foremost, "let's create that foundation of trust "not just for the cloud data lake, "but for the entire enterprise. "To ensure that when we start to build this "new architecture, one, we understand the data assets "that are coming in at the very get-go." Right? It's much harder to add data governance after the fact, but you put it in upfront, you understand your existing data landscape. And once that data is there, you make sure you understand the quality of the information, you cleanse the data, you also make sure you put it under the right data management policies. So many policies that enterprises are subject to now like CCPA and GDPR. They have to be concerned about consumer privacy and being able as part of your governance foundational layer, to make sure that you're in compliance as data moves through your new architectures. It's fundamental having that end trust and confidence to be able to use that data downstream. So our solution looks to do that end-to-end across a cloud environment, and again, not just the cloud environment, but the full enterprise as well. One thing I do want to touch on if you don't mind is on the AI side of things and the tooling side of things. Because I think data governance has been around a while, as you said, it's not that it's a new concept. But how do you do it efficiently in today's world? >> John: Yeah. >> And this is where Informatica is focused on a concept of data 4.0. Which is the use of metadata and AI and machine learning and intelligence, to make this process much, much more efficient. >> Yeah that's a good point, Rik, from these two Rickes, I got to go, one's with a K, one with a C, and CK. So Rick, CK and from Invesco customer, I want to just check that with you because I was your customer of Informatica, by they brought up a good point about governance. And I saw this movie before, we've all seen this before, people just slap on solutions or tooling to a pre-existing architecture. You see that with security, you know, now it's, you can't have a conversation without saying, "Oh security's got to be baked in from the beginning." Okay cool, I get that. There's no debate there. Governance, same kind of thing, you know, you're hearing this over and over again, if you don't bake governance into the beginning of everything, you're going to be screwed. Okay? So how important is that foundation of trust for this peace. (Rick mumbling) >> It's critical and to do it at beginning, right? So you're profiling the data, you're defining entitlements and who has access to it, you're defining data quality rules that you can validate that, you define the policies, is there a PII data, all of that, as you do that from the start, then you have a well-governed and documented data catalog and taxonomy that has the policies and the controls in place to allow that to use. If you do it after the fact, then you're always going to be catching up. So a part of our process and policies and where the really Informatica tools delivered for us is to make it part of that process. And to use that as we continue to build out our data platform with the quality controls and all the governance processes built in. >> I got to ask on your journey, that's seven years ago, you took over the practice. You were probably right in the middle of the sea change when everyone kind of woke up and said, "Hey, you know, Amazon, you go back seven years, "look at Amazon where they were to where they are today." Okay? Significantly strong then, total bellwether now in terms of value opportunity. So, how did you look at the cloud migration? How do you think about the cloud architecture? Because I'm sure, and I'd love to get your story here about how you thought about cloud, in the midst of architecting the data foundational platform there. >> Yeah, we're a global company that had architecture, we grew it by acquisition. So a lot of our data architecture was on-prem, difficult really to pull that enterprise data together to meet the business needs. So when we started this, we really wanted to leverage cloud technology. We wanted a modern stack. We wanted scale, flexibility, agility, security, all the things that the cloud brought us too. So we did a search, and looking at that, and looked at competitors, but really landed on to Amazon just bought by core capabilities and scale they have innovation and just the services to bring a lot what we're looking at and really deliver on what we wanted from a platform. >> Why Informatica and AWS, why the combination? Can you share some of the reasons why you went with Informatica with AWS? >> Yeah, again, when we started this off, we looked at the competitors, right? And we were using IVQ. So we had an Informatica product on-prem, but we looked at a lot of the different governance competitors, and really the integrated platform that Informatica brings to us, what was the key deliverer, right? So we can really have the technical metadata with EDC and enterprise data client, catalog, scan our sources, our file, understanding the data and lineage of what it is. And we can tie that into axon and the governance tools to really define business costs returns. We were very critical of defining all our key data elements business glossary, and then we can see where that is by linking that to the technical metadata. So we know where our PII data, where are all our data and how it flows, both tactically and from a business process. And then the IDQ. So when we've defined and understand the data, we want to bring in the delight and how we want to conform it, to make it easily accessible, we can define data quality rules within the governance tool, and then execute that with IDQ, and really have a well-defined data quality process that really takes it from governance in theory to governance in really execution. >> That's awesome. Hey, you are using the data, you're using the cloud, you're getting everything you need out of it. That's the whole idea, isn't it? >> Yeah. >> That's good stuff, Rik at Informatica, tell us about what's going on, you mentioned data 4.0, I think people should pay attention to some of the interviews I've done with your team. They're online also, it's part of that next-gen, next level thinking. Here at "re:Invent", what should customers pay attention to, that you guys are doing? Great customer example here of cloud scale. What's the story for "re:Invent" this year for Informatica. >> But what John, it comes down to when customers think about their cloud journey, right? And the difference, especially with their data centric workloads and priorities and initiatives, all the different hurdles that they need to overcome. I think Informatica we're uniquely positioned to help customers address all those different challenges and you heard Rick speak about a whole bunch of those along the way. And I think particularly at "re:Invent", first of all, I just welcome folks to... They want to learn more about our data governance solution. Please come by our virtual booth. We also have a great interactive experience that encouraged folks to check out. One of the key components of our solution is our enterprise data catalog. And attendees at "re:Invent" can actually get hands on with our data catalog through the demo jam, the AWS demo jam as part of "re:invent". So I'd encourage folks to check that out as well, just to see what we're talking about yet actually. >> Awesome. Final question for you guys, as "re:Invent" is going on, a lot app stores are popping up, you seeing obviously the same trends, machine learning and you know, outpost is booming, so a cloud operations is clearly here, Rick from Invesco, what do you think the most important story is for your peers as they're here? It's a learning conference and you guys have seven years in the cloud working together with Informatica, in your opinion, what should people be paying attention to as they looked at this pandemic and what they got to get through? And then coming out of it with the growth strategy, it's all got to be more about the data, there's more data coming in, more sources, IoT data, certainly the work at home is causing these workloads, workplace, workflows, everything's changed, the future of work. What's your advice to peers out there on what to pay attention to and what to think about? >> We really started with a top-down strategy, right? To really the vision and the future. What do we want to get out of our data? Data is just data, right? But it's the information, it's the analytics, it's really delivering value for our clients, shareholders, and employees to really do their job, simplify our architecture. So really defining that vision of what you want and approach, and then executing on it, right? So how do you build it in a way to make it flexible and scalable, and how do you build an operating and governance model really to deliver on it because, you know, garbage in is garbage out, and you really got to have those processes, I think to really get the full value of what you're building. >> Get the data out there at the right place, at the right time and the right quality data. That's a lot more involved now and you need to be agile. And I think agile data is a way to go. Rick Turnock... >> And then with channels and capabilities that makes it easier, right? It makes it doable. And I think that's what cloud and the Informatica tools, right? Where in the past, you know, it was people hard coding and doing it right? The capability of that cloud and these tools give us makes it really achievable. >> You know, we have an old saying here in our CUBE team, you know, "If there's a problem, "you got to see if it's important, "and then look at the consequences "of not solving that problem, quantify the value of "solving or not solving that problem, "and then look and deploy solutions to do it." I think now with the data, you can actually do that and say, "This ain't quite the consequences of not doing this "or doing this, have a quantifiable value." I just loved that because it brings the whole ROI back to the table. And, you know, it's a dark art, it used to be, you mentioned the old days, you know, you got to do all this custom work, it was like a dark art. Oh yeah, the ROI calculation, payback. I mean, it was a moving train. That's the way it used to be. Not anymore. >> You got to do it to survive, really, if you're not doing it, you know, I don't know. >> Necessity is the mother of all inventions I think, now more than ever, data's going to be the key. Rik final word from Informatica. What should people pay attention to? >> Yeah, I mean, I think as you mentioned there, data is obviously a critical asset, right? And, and to your point with cloud, you can not only realize ROI quickly, but, you can actually iterate so much more quickly, where you can get that ROI immediately or you can validate that ROI, you can adjust your approach. But again, from an Informatica standpoint, we are seeing such a huge uptake in demand for customers who want to go to the cloud, who are modernizing. Every day we're investing heavily and how do we make sure that customers can get there quickly? They can maximize the ROI from their data assets, and we're doing it with all things, data management, from traditional data integration, all the way to the data governance, all the capabilities we talked about today. >> Yeah. Congratulations. That's the benefit of investing in a platform and having a set of out of the box tooling with SaaS, platform as a service, really it can enable success. And I think the pandemic is pretty obvious who's taking advantage of it, so congratulations and continued success. Thanks for coming on. Appreciate it. Rick Turnock, head of data service, enterprise data services at Invesco, customer of Informatica sharing his insight. Great insight there. Necessity is the mother of all inventions, baking it in from the beginning data governance foundational, it's not a bolt on, that's the message. I'm John Furrier with theCUBE. Thanks for watching. (soft music)

>> 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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>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello, everyone. Welcome to this CUBE conversation. I'm John Furrier, host of theCUBE. We're here in our Palo Alto studios in California for a CUBE conversation with Suresh Menon, who's the senior vice president and general manager of Informatica of the master data group. Suresh, great to see you. We couldn't see you in person. Three-time CUBE alumni at Informatica World, industry executive. We're remote. Great to see you. >> Good to see you, John. Great to be back. Wish this was in person, but I think this is fantastic. >> Well, one of the things that's clear in my interviews over the past four months, we've been doing our best to hit the road and we've got a quarantine crew here. We're doing our part telling the stories that matter. Data now more than ever, COVID-19 has shown that the companies that are prepared, that have done the work, for the digital transformation, you know, putting the cliche aside, is real and the benefits are definitely there. And you're seeing things like reaction time, war rooms are being put together, because business still needs to go on. This is the reality. And so companies are seeing some exposure and some opportunities, and so a lot of things are going on. So I want to get your reaction to that, because there are changes on how customers are evolving with data. You guys have been at the forefront of that, pioneering this horizontal data fabric, data 4.0, amidst talks about. What are you seeing from customers? How are they approaching this? Because at the end of the day, they got to come out of the pandemic with a growth strategy and they got to solve the problems they've got to do today and be in position. What are you seeing for changes? >> So one of the most important things that we started seeing, there are about three big trends that we began to see starting in about late March, and share some of the data points that we saw across the world, starting with Italy, which was in the news earlier this year with the pandemic. We saw that in one week, the stats were that online or digital sales increased by 81% in a single week. And it's obvious when you lock down a large population, commerce moves to, away from the brick and mortar kind of model to being completely online and digital. The other part of it that we started seeing is we had already started seeing a lot of our customers starting to struggle with supply chain issues. As borders started closing, opening, and then closing again, how do you maintain a resilient supply chain? And a resilient supply chain also means being able to be really agile in terms of trying to identify alternate supply sources, be able to quickly onboard new suppliers, maybe in different parts of the world that are not so affected. And then finally, the last piece that we saw were every single CFO, chief financial officer, people who ran finance organizations at all of these companies, for them, it is almost as if you're driving down the highway and you suddenly run into, enter this fog bank. The first reaction is to hit the brakes, of course, because you don't know what's (microphone cuts out) so every CFO around the world started saying, I need to be able to understand what my cash flow situation is. Where is it coming in from? Where is it going out of? How do I reconcile across the geographies, lines of business? Because everybody realized that without an adequate cash reserve, who knows how long this thing is going to carry on? We need to be able to survive. And then the fourth element that has always been important for our customers is all about customer engagement, getting the best possible customer experience. That's just being turned up to 11, the volume, because as organizations are saying, there's disruption happening now. There are new ways in which consumers are going out there and buying products and services, and these things might stick. There's also an opportunity for some of these organizations to go out and enter into markets, gain market share, that they were not able to do in the past. And then how do you come out of this, whenever it is, how do we come out of it? It's always by making sure you're retaining your customers and getting more of them. So the underpinnings across all of this, whether it's supplier data, whether it's getting the most accurate product information delivered to your online channels, whether it is being able to understand your supply chain holistically with our data platform under it, and then finally customer experience depends on understanding everything end to end, including everything you need to know about your customer. So data continues to become top of mind for all of these organizations. >> You know, Suresh, we've had conversations over the past three years, and I can remember them vividly all about, and we've been really geeking out, but also getting very industry focused around, oh, the enablement of data and doing all these things, horizontal scalability, application enablement, AI CLAIRE, all these things are very relevant. But now with COVID-19, that that future's been pulled to the present. It's accelerated so fast that everything's impacted the business model. You mentioned supply chain and cash flow. The business is right there visible, and all these things are exposed and heightens the volume, as you said, and so everyone's seeing it happen. They can see the consequences, right? So this is like the most reality view of all time in any kind of is digital transformation, will it happen? So I want to get your thoughts on this, because I've been riffing on this idea of the future of work, the word work, workplaces, workforce, workloads, and workflows, right? So they all have work in them, right? We talk about workflows and workloads. That's a cloud term and a tech term. Workplace is the physical place, now home. Workforce are people, their emotional stability, their engagement. These things are all now exposed and all this new data's coming in. Now the executives have to make these decisions. This has really been a forcing function. So first, I'm sure you agree with all that, but what's your reaction to that? Because this brings up challenges that customers are facing. What's your thoughts on this massive reality? >> Yeah, I mean, this is where I think the other domain that is very important, which is most important for organizations if you have to be successful is really that employee or workforce understanding. We talk about customer 360s. We have to talk about employee 360s, right? And tie that to locations. And there are very few enlightened organizations, I would say, maybe three, four, five years ago, who had said, we really do need to understand everything about employees, where they work from, what are the different locations they go to, whether it's home and whether it's the multiple office locations that the organization might have. And it started quite realistically in the healthcare organization. There's a large healthcare provider here in California who many, many years ago decided that they want to create an employee 360, and considering it's doctors, it's nurses, it's hospital technicians and so on, who move from one hospital to another different outpatient clinics. And we are in a disaster-prone state, and what they said is I need to build this data foundation about my employees to understand where someone is at any given point in time and be able to place them so that if there is, let's say, an earthquake in one part of the state, I want to know who's affected, and more importantly, who's not affected who can go out and help. And we started seeing that mindset now go across every single organization, organizations that said, hey, I was not able to keep track, when the lockdowns were started, I was not able to keep track of which one of my employees were in the air at that time, crossing borders, stuck in different parts of the world. So as much as we talk about product, customer financial data, supplier data, employee data, and an employee 360, and now with a lot of state and local governments creating citizens 360s has also now become top of mind because being able to pull all of this data together, and it's not just your traditional structured data. We're also talking about all the data that you're getting, the interaction data from folks carrying their phones, mobile devices, the swipes that people are doing in and out of locations, being able to capture all of that, tie it all together. Again, we talk about an explosion in volume, which I think is to your point, bringing in more automation with CLAIRE, with artificial intelligence, machine learning techniques, is really the only way to get ahead of this, because it's not humanly possible to say, as your data scales, we need to get the same linearly, the same number of people. That's not going to happen. So technology, AI, has to solve it. >> Well, I want to get to AI in a second. It's on my list to ask you about CLAIRE, get the update there. But you mentioned 360 view of business and the employee angle's definitely relevant. Talk more about this 360 business approach, how are customers approaching it across the enterprise. Certainly now more than ever, it's critical. >> Right, so the 360s have always been around, John, and I think we've had these conversations about 360s now, for the last few years now, and a lot of organizations have gone out and said, create a 360 around a particular, whichever one specific business-critical domain that they want to create a 360 out of. So typically for most organizations, you're buying parts, raw materials from a supplier. So create a supplier 360. You really need to understand is there risk there in the supply chain? Am I allowed to do business with a lot of these suppliers? It's data that helps them create that supplier 360. The product is always important, whether you're manufacturing your own, or if you're a retailer, you're buying these from your suppliers and then selling them via your different channels. And then finally, the third one was always customers, without which none of those organizations would be in business. So customer 360 was always top of mind. But, and there are ancillary domains, whether it's that's the employee 360 we just talked about, finance 360, which are of interest maybe to specific lines of business. These are all being done in silos. If you think about creating a full 360 profile of your suppliers, of your products, of your customers, the industry has been doing it now for a few years, but where this pandemic has really taught a lot of organizations is now it's important to use that platform to start connect (microphone cuts out) a line all the way from your customers via their experience all the way back to your suppliers and all the different functions and domains and 360s that it needs to touch. And the most, I guess real-world example a lot of us had to deal with was the shortages in the grocery stores, right? And that ties all the way back to the supply chain. And you're not providing your best possible customer experience if the goods and products and services that customers want to buy from you are not available. That's when organizations started realizing, we need to start connecting the customer profiles, their preferences, to the products, our inventory, all the way back down to suppliers, and are, for example, can we turn up the production in a particular factory, but maybe that location is under one of the most stringent lockdown conditions and we're not able to bring in or increase capacity there. So how do you get a full 360 across your entire business starting with customer all the way back to supplier. That is what we are saying, the end-to-end 360 view of a business, or as we, there's too many words, we just call it business 360. >> Yeah, it's interesting, and I'm interviewing a lot of your customers lately and talking some of the situations around COVID. There's the pre-COVID, before COVID, during COVID, now looking after COVID. Some have been very happy and well-prepared because they have been using, say, Informatica, and had done the work and are taking advantage of those benefits. I've talked to other practitioners who are struggling with trying to figure out how to architect, because what your customers who've been successful have been telling me is that, look at, we're in good shape right now because we did the work prior to COVID, and now they are being forced to have a 360 view not because it's a holistic corporate mission. It's they have to, right? People are at home, so it's not like, hey, let's get a 360 view of the business and do an assessment and do better and enable things. No, no, no. There's business pressure. So they're enabled. Now new types of data's coming in. So again, back to the catalog and back to some of the things that you guys have been working on. How do you talk to your customers now that they're in COVID for the ones that have been set up before COVID and the ones now that are coming to the table saying, okay, I need to now get quickly deployed with Informatica while I'm in, during the state of COVID so I can have a growth strategy coming out of it, so I don't make these mistakes again. What's your thoughts? >> Absolutely, and I think that the, whether an organization has already, a customer has already laid the groundwork, has the foundation before COVID, and the ones who are now moving full steam ahead because they're missing capabilities in those functions. The conversation is in reality more or less the same, because even for those who have the foundation, what they're starting to see is new forms of data coming in, new forms of, new requirements being placed on the, by the business on that infrastructure, the data infrastructure, and being able to, most importantly, react very, very quickly. And even for those who are starting off right now from scratch, it's the same thing. It's need to get up and running, need to get the answers to these questions, need to get the, we need to get the problems to these solutions as soon as possible. And that the theme, or I guess the talking points for both of those customers is really two things. One is you need agility. You need to be able to bring these solutions up to life and delivering as soon as possible, which means that the capabilities, the solutions you need, whether it's bringing the catalog, understanding where your data is very, very quickly, your business critical information. How do you bring that in, all of that data, and integrate that data into a 360 solution, be able to make sure it's of the highest quality, enrich it, master it, create those 360 profiles by joining it to all of this interaction, transaction data. All that has to be done with the power of technologies like CLAIRE, with artificial intelligence, so that you are up and running in a matter of days or weeks, as opposed to months and years, because you don't have that time. And then the other one which is quite important is cloud, because all of this capability needs infrastructure, hardware to run on. And we've started seeing a lot of, let's say cloud-hesitant verticals, entire verticals now in the last two to three months suddenly going from yeah, cloud is maybe somewhere down the road, as far as our future's concerned. But to now saying, we understand that we have to go to a cloud when our technicians are not able to get access to our data centers to add new machinery in there to take care of the new demands, that migration to cloud. So it's that agility and cloud which really is the common theme when we talk to customers, both- >> Yeah, and now more than ever, they need it, 'cause it's an important time, and it's going to be an inflection point, for sure. There'll be winners and losers, and people want to be on the right side of history here. Suresh, I got to ask you about AI. Obviously CLAIRE's been a big part of it. Now more than ever, if you have bad data, AI can be bad too. So understanding the relationship between data and AI is super important. This is going to be critical to help people move faster and deal with more data as soon as they're dealing with now. What's your thoughts on the role AI will play? >> Oh, AI has a huge role to play. It's already begun to play a huge role in our solutions, whether we start from catalog to integration to 360 solutions. The first thing that AI can really do very, very well is, we've gone from folks who said, let's take supply chain. There were maybe three sources of supplier data that used to come into creating a supplier 360. Today, there are hundreds of sources. If you go all the way to the customer 360, and we are talking about 1,300, 1,400 different sources of data with 90% of them sitting up in the cloud. How is it humanly possible to bring all of that data together? First of all, understand where customer information is sitting across all of those different places, whether it's your clickstream data, call log data, whether it's the actual interaction data that customers are having with in-store, online, collecting all of that information, and from your traditional systems like CRM, ERP, and billing, and all of that, bringing all that together for understanding where it is, catalog gives you that Google for the enterprise view, right? It tells you where all this data is. But then once you've got that there, it also tells you what its relative quality is, what needs to be done to it, how usable is it. To your point of if it's bad data, at least what AI can do first of all is tell you that these are unreliable attributes, these are ones that can be enriched. And then, and this is where AI now moves to the next level, which is to start inferring what kind of rules that are in our, let's say, repository across integration, quality, and mastering, and bring, and matching, bring all that together and say, here, you as the developer who's been tasked with making this happen in a matter of days, we are going to infer for you what you need to do with this data, and then we will be able to go in and bring all these sources in, connect it, load it up into a 360 solution, and create those 360 profiles that everybody downstream, whether it's your engagement systems and other. So it's really about that discovery, that automation, as well as the ability to refine and suggest new rules in order to make your data better and better as you go along. I think that's really the power of CLAIRE and AI. >> I love the Google for the enterprise or data, because the metaphor really is about finding what you're looking for. It's the discovery piece, as you said, to make it easy, and Google did make it easy to find things, which is what their search engine did. But if you look at what Google did after that, they had to have large scales. SREs is what they call them, site reliability engineers, one engineer for thousands and thousands of servers, which they, revolutionizing IT and cloud. You guys are kind of thinking the same way, data scale, right? So it's Google in terms of discovery, right? Find what you're looking for, catalog, get it in, and get it out quest, make it available for applications. But you're kind of teasing out this other point where the AI comes in. That's scale. >> Yes. >> That's super important nuance. >> Absolutely. >> But it's key to the future. >> Absolutely, because when we are starting to talk about now not just tens of millions of records when it comes to customer data or product experience dat and so on. We are already talking about organizations like Dell, for example, with our customer 360, with billions of records going in, which would be equivalent to the scale of, if you look at Google search engine business back maybe 10, 12 years ago. So yes, we are talking about within the context of a single organization or a single company, we're already talking about volumes that were unthinkable even five years ago. So being able to manage that scale, be able to have architectures, technologies that are able to autoscale, and the advantage of course is now we've got an architectural platform that has microservices. As loads start increasing, be able to spawned new instances of those microservices seamlessly. Again, this is another part where AI comes in, AI being able to say, in the old days it was somebody had to see that the CPUs are overloaded to about 100% before someone realized that we have to go out and do something about it. In this new world with AI managing the ops layer, being able to look at is this customer bringing in another, in the cloud rack, cloud world, in a SaaS world, bringing in a billion records that they want to push through in the next 10 minutes, be able to anticipate that, spawn the new infrastructure and the microservices, and be able to take care of that load and then dial those back down when the work is done. This again, from an ops perspective as well, from, so we are able to scale instead of sort of having, let's say, 1000 SREs, I think, to your example, John, have only 10 SREs to make sure that every, look at the dashboard and make sure everything is going well. >> Well, I've been covering you guys for a long time. You guys know that. And I'm a big fan. I always had been a fan of the vision that's playing out. Large scale data, large scale discovery, fast and easy, integrating that into applications for business value. It's not just the data warehouse and just park something over here. This is a mindset. It's a foundational enablement model. You guys have done an amazing job. And now more than ever, it's I think more understood because of the pandemic. >> Absolutely, and people are making that direct connection between the business outcome and the value of having this data foundation that does all the things we described. >> Suresh, great to see you, and bummer we couldn't be in person, but hey, the pandemic hit. Informatica World when virtual. A lot of different events. I know you guys have a lot of things going on virtually, and you're engaging well. Everyone's working at home. Not a problem. Most of the techies can work at home. It's not a big deal. But you've got remote customers. You guys are engaging with them. And congratulations and great to see you. >> Same here. Thank you so much. >> All right, Suresh Menon. He is senior vice president, general manager of master data at Informatica. Data's more important than ever. We're seeing it, this is a foundational thing. If it's not enabling value, then it's not going to be a good solution. This is the new criteria. This is where action matters. People who need data and need to integrate into new workloads, new applications across workforces and new workplaces. This is the reality of the future. I'm John Furrier with theCUBE. Thanks for watching. (bright 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. >>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)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to again sunset legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, This is John Furrier here inside theCUBE studios we serve 85 of the Fortune 100, over half the global 2000. for an update with Informatica's digital conference. The reason why customers come back and speak on our behalf Take a look at it, check it out online. and literally thousands of customers speak on our behalf, Join the community. Be part of those thousands of customers that they have, it's humbling, is because we have the best and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. intelligent data platform in the market. I'm John Furrier, thanks for watching. And we also understand our customers aren't buying software. (soft music) They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight.

>> Presenter: From theCUBE Studios in Palo Alto and Boston, connecting with Dot leaders all around the world. This is a CUBE conversation. >> Everyone welcome to theCUBE studio here in Palo Alto. I'm John Furrier, host of theCUBE. We're here with our quarantine crew. We've been here for three months quarantining but we're getting the stories out. We're talking to all of our favorite guests and most important stories in technologies here remotely and we have a great conversation in store for you today with Amit Walia CEO of Informatica. Cube alumni, frequent guest of theCUBE, now, the CEO of Informatica. Amit, great to see you. Thank you for coming on this CUBE conversation. >> Good to see you John. It's different to be doing this like this versus being in the studio with you but I'm glad that we could leverage technology to still talk to each other. >> You're usually right here, right next to me, but I'm glad to get you remotely at least and I really appreciate you. You always have some great commentary and insights. And Amit, before we get into the real meaty stuff that I'd love around the data, I want to get your thoughts on this COVID-19 crisis. It's a new reality, it's highlighted as we've been reporting on SiliconANGLE for the past few months. The at scale problems that people are facing but it's also an opportunity. People are sheltered in place, there's a lot of anxiety on what their work environment is going to look like but the world still runs. Your thoughts on the current crisis and how you're looking at it, how you're navigating it as a leader. >> No doubt, it is a very unique situation we all live in. We've never all faced something like this. So I think first of all, I'll begin by expressing my prayers for anyone out there who has been impacted by it and of course, a huge round of thank you to all the heroes out there at the front lines. The healthcare workers, the doctors, the nurses (mumbles) so we can't forget that. These are very unique situations but as you said, let's not forget that this is a health crisis first and then it becomes an economic crisis. And then, as you said there is a tremendous amount of disruption and (mumbles) I think all of them will go through some phases and I think you can see already while there is disruption in front of us, you see the digital contents of organizations who are ready for that have definitely faced it lot better but as obviously the ones that have been somewhat in the previous generations, let's just say business models or technologies models are struggling through it. So there is a lot data chain. I think they're still learning. We're absolutely still learning and we will continue to learn til the end of this year and we'll come out very different for the next decade for sure. >> If anyone who's watching goes to YouTube on the SiliconANGLE CUBE and look at your videos over the years, we've been talking about big data and these transformational things. It's been an inside the industry kind of discussion. Board room for your clients and your business and Informatica but I think this is now showing the world this digital transformation. The future has been pulled forward faster than people have been expecting it and innovation strategy has been on paper, maybe some execution but now I think it's apparent to everyone that the innovation strategy needs to start now because of this business model impact, the economic crisis is exposed. The scale of opportunities and challenges, there will be winners and losers and projects still need to get done or reset or reinvented to come out of this with growth. So this is going to be the number one conversation. What are your thoughts around this? >> No, so I've talked to hundreds of customers across the globe and we see the same thing. In fact actually, in some ways as we went through this, something very profound dawned on me. We, John, talked about digital transformation for the last few years and clearly digital transformation will accelerate but as I was talking to customers, I came to this realization that we actually haven't digitally transformed. To be honest, what happened in the last three to four years is that it was more digital modernization. A few apps got tweaked, a few front-ends got tweaked but if you realize, it was more digital modernization, not transformation because in my opinion, there are four aspects to digital transformation. You think of new products and services, you think of new models of engaging with your customers, you think of absolutely new operating models and you think of fundamentally new business models. That's a whole rewrite of an organization, which is not just creating a new application out there, fundamental end to end transformation. My belief is, our belief is that, now starts a whole new era of transformation, digital transformation. We've just gone through digital modernization. >> Well, that's a great point and the business model impacts create... And in times of these inflection points, and again, you're a student of history in the tech industry, PC revolution, TCP IP. These are big points in time. They're not transitions. The big players tend to win the transitions. When you have a transformation, it's a Cambrian explosion of new kinds of capabilities. This is really, I would agree with your point but I think it's going to be a Cambrian explosion because the business model forcing function is there. How do you see it play, 'cause you're in the middle of all this, 'cause you guys are the control plane for data in the industry as a company. You enable these new apps. Could you share your-- >> So, we see a lot of that and I think the way to think about it, I think first of all, you said it right. This is a step function changing orbit. This is a whole new... You get to a new curve, you go to a different model. It's a whole new equation you're hiking for the curve you're going to be on. It's not just changing the gradient of the curve you've been on, this is going to be a whole journey. And when we think of the new world of digital transformation, there are four elements that are taught. First of all, it has to be strategic. It has to be Board, CEO, executive topped down, fundamentally across the whole organization, across every function of an organization. Second one you talked about scale. I believe this is all about innovating at scale. It's not about, hey, let me go put a new application in some far plans of my business. You've got to innovate at scale, end to end change does not happen in bits and pieces. Third one, this is cloud native, absolutely cloud native. If there was any minuscule of doubt, this is taking it away. Cloud nativity is the fundamental differentiator and the last but not the least is digital natives, which is where everybody wants to go become a digitally transformed company that are data-led. You got to make data-led decisions. So for competence, strategic mindset, innovation at scale cloud nativity and being data-led is going to define digital transformation. >> I think that encapsulates absolutely innovation strategy. I agree with you 100%, that's really insightful. I want to also get your thoughts on some things that you're talking about and you have always had some really kind of high level conversations around this and theCUBE has been a very social organization. We'd love to be that social construct between companies and audiences but you use a term, the digital transformation, the soul of digital transformation is data 4.0. This idea of having a soul is interesting because the apps all have personalization built in. You have CLAIRE, you've been doing CLAIRE AI for a while. So this idea of social organizations, a soul is kind of an interesting piece of metadata you're putting out in the messaging. What do you mean by that? How can digital transmission have a soul? >> I think we talked about it a lot and I think it just came to me that, look at the end of the day, any transformation is so fundamental to anything that anybody does and I think if you think about, you can go to a fundamental transformation that is just qualitative, it's qualitative and quantitative. It's about a human body, it's about a human body transforming itself and then something doesn't have a soul, John, it does not have life. It cannot truly move to the next paradigm. So I believe that, any transformation has to have a soul and the digital world is all about data. So obviously, we believe that we're walking into a data-for-data world where, as I said, the four pillars of digital transformation would be data-led and I believe data is the soul of that transformation and data itself is moving into a new paradigm. You've heard us talk about 1.0, 2.0, 3.0, and this is the new world of 4.0, a data 4.0 which basically is all about cloud nativity, intelligent automation, AI powered, focusing on data, trust in data ethics and operations and innovation at scale. When you bring these elements together, then that enables digital transformation to happen on the shoulders of data 4.0, which in my opinion, is the soul of digital transformation. >> All right, so just rewind on data 4.0 for a minute. Pretend I'm a CIO, I'm super busy. I don't have time to read up about it. Give me the bottom line, what is data 4.0? Describe it to me in basic terms, is it just an advancement, acceleration? What's the quick elevator pitch on 4.0, data 4.0? >> Very simple?. We're all walking into a world where we're going to be digital. Digital means that we're basically going to be creating tons of data. By the way, and data is everywhere. It's not just within the four walls of us. It's basically what I call transaction and interaction and with the scale and volume of data increasing, the complexity of it increasing. We want to make decisions. I say, tomorrow's decision, today and with data that is available to us yesterday, so I can be better at that decision. So we need intelligence, we need automation, we need flexibility, which is where AI comes in. These are all very fundamental rewrites of the technology stack to enable a fundamental business transformation. So in that world, data is front and center and you look at the amount of data we are going to collect, the whole concept of data ethics and data trust become very important, not just Goodwill governance, governance is important but data privacy, data trust becomes very important. Then we're going to do things like contact tracing, it's very important for the society but the ethics, trust and privacy of what you and I will give to the government is going to become very much important. So to me, that world that we go in, every enterprise has to think data first, data led, build an infrastructure to support the business in that context and then, as I said, then the soul, which is data will give life to digital transformation. >> That's awesome. Love the personalization and the soul angle on it. I always believe that you guys had that intelligent automation fabric and to me, you said earlier, cloud native is apparent to everyone now. I think out of all this crisis, I think the one thing that's not going to be debated anymore is that cloud native is the operating model. I think that's pretty much a done deal at this point. So having this horizontally scalable data, you know I've been on this rant for years. I think that's the killer app. I think having horizontally scalable data is going to enable a lot, souls and more life. So I got to ask you the real, the billion dollar question. I'm a customer of yours or prospect or a large enterprise. I'm seeing what's happening at scale, provisioning of VPNs for 100% employees at home, except for the most needed workers. I now see all the things I need to either process, I need to cancel and projects that double down on. I still got to go out and build my competitive advantage. I still have to run my business. So I need to really start deploying right out of the gate data centric, data first, virtual first, whatever you want to call it, the new reality first, this inflection point. What do I do? What is the things that you see as projects or playbook recipes that people could implement? >> First of all, we see a very fundamental reevaluation of the entire business model. In fact, we have this term that we're using now that we have to think of business has a business 360 and if I think about it in this new world, that the businesses that stood the test is that had basically what I call, a digital supply chain or in a very digital scalable way of interacting with their customers, being able to engage with their customers. A digital fabric often making sure that they can bring their product and services to the customers very quickly or in some cases, if they were creating new products and services, they had the ability for a whole new supply chain to reach that end customer. And of course, a business model that is flexible so they dont obviously, they can cater to the needs of their customers. So in all of these worlds, customers are a building digital, scalable data platforms and when I say platforms, it's not about some monolithic platform. These are, as you and I have talked about, very modular microservices based platform that reside on what we call metadata. Data has to be the soul of the digital enterprise. Metadata is the nervous system, that makes it all work. That's the left brain, right brain, that makes it all work, which is where we put AI on top. AI that works for the customers and then they leverage it but AI applied to that metadata allows them to be very flexible, nimble and make these decisions very rapidly, whether they are doing analytics for tomorrow's offering to be brought in front of a customer or understanding the customer better to give them something that appeals to them in changing times or to protect the customer's data or to provide governance on top of it. Anything that you would like to do has to ride on top of what I call a, AI led metadata driven platform that can scale horizontally. >> Okay, so I got to go to the next level on this, which is, okay, you got me on that. I hear what you're saying, I agree, great. But I got to put my developers to work and I got insight, I got analytics teams, I got competencies but Amit, my complexities don't go away. I still got compliance at scale, I got governance at scale but I also got, now my developers not just to get analytical insight, there's great dashboards and there's great analytic data out there, you guys do a good job there. I got to get my developers coding so I can get that agility of the data into the apps for visualization in the app or having a key ingredient of the software. How do I do that? What's your answer to that one? >> So, that's a critic use case. If you think about it, for a developer, one of the biggest challenge for analytics project is how do I bring all the data that is in sites across the enterprise so then I can put it in any kind of visualization analytics tool and things are happening at scale. An enterprise is spread across the globe. It's so many different data sources available everywhere. Again, what we've done is that as a part of the data platform when you focus upon the metadata, that allows you to go to one place where you can have full access to all of the data assets that are available across (mumbles). Do you remember at theCUBE years ago, we unveiled the launch of our enterprise data catalog, which as I said, was the Google for enterprise data through metadata. Now, developers don't have to go start wasting their time, trying to find whether data has (mumbles), through the catalog that CLAIRE is in-built, they have access to it. They can start putting that to work and figuring out how do I take different kinds of data? How do I put it in some data times tool? Through which we have the in-built integrations. Do what I call the valuable last mile work, which is where the intelligence is needed from them versus spend their energy trying to figure out where good data, clean data, all kinds of data sets. We have eliminated all of that complexity with the help of metadata data platform, CLAIRE, to let the developers do what I call value-added productive work. >> Amit, final question for you. I know you talk to customers a lot, you're always on the road, you got a great product background, that's where you came from, good mix understanding of the business but now your customers and prospects are trynna put the fires out. The big room that... No one's going to talk about their kitchen appliances when the house is burning down and in some cases on the business model side or if it's a growth strategy, they're going to put all their energies where the action is. So getting mind share with them is going to be very difficult. How are you as a leader and how is Informatica getting in front of these folks and saying, "Look, I know things are tough "but we're an important supplier for you." How do you differentiate? How are you going to get that mind share? What are some of those conversations? 'Cause this is really the psychology of the marketplace right now, the buyer and the customer. >> Well, first of all, obviously we had to adapt to reach our customers in a different way because, virtually based just like you and I are chatting right now and to be candid, our teams were fantastic in being able to do it. We've actually already had multiple pretty big sides of it. In fact, the first week before we started (mumbles), we had set up the MDM and Data Governance Summit up in New York and we expected thousands of customers to come there, ask them (mumbles) virtual and we did it virtually and we had three times more people attend the virtual event. It was much easier for people who attended from the confines of their living room. So we'd gone 100% virtual and good news is, that our customers are heavily engaged. We've actually had more participation of customers coming and attending our events. We've had obviously our customers speaking, talking about how they've created value. In light of that next week, we have the big event which we're calling, CLAIREview named after ClAIRE AI engine. It's basically a beautiful net-filled tech experience. We'll have a keynote, we'll have seasons and episodes, people can do bite-sized viewing at their own leisure. We'll talk about all kinds of transformation. In fact, we have Scott Guthrie who runs all of Azure and Cloud at Microsoft as a part of my Keynote. We have two great customers, CDO at XXL and a CEO of GDR nonprofit that does (mumbles) on diabetes work talk about the data journeys. We have Martin Byer from Gardner. So we've been able to pivot and our customers are heavily engaged because data is a P-zero or a P-one activity for them to invest in. So we haven't seen any drop-off in customer engagement with us and we've been very blessed that we have a very loyal and a very high retention rate customer base. >> Well, I would expect that being the center of the value proposition, where we've always said data has been. One more final question since this just popped in my head. You and I have been talking about the edge for years. Certainly now the edge is exposed, we all know what the edge is, it's working at home. It's the human, it's me, it's my IOT devices. More than ever, the edge is now the new perimeter. It's the edge and now the edges is there. There's something that you've been talking a while. This is another part of data fabric that's important. Your view on this new edge that's now visualized by everybody, realized this immersion. What's your thoughts on the edge? >> Oh, I think the edge is real now. You and me chatted about that almost four years ago and I (mumbles). Look, think of it this way. Think of how security is going to change. There's no more data center to which we route our traffic anymore. It's sitting over there somewhere where no human beings is going to have access. People are connecting to all kinds of cloud application directly from their offices or living rooms or their cultures and the world of security has to change in that context. And people are more going to be more, enterprise (mumbles) are more worried about, hey, how do I make sure that that data centric, privacy and security is there in my device and that connects to the third party cloud vendors versus I can't transfer traffic to mine, everything to my VPN. So the edge is going to become a lot more compute intensive as well as it will require a lot of the elements that are, to be honest, used to be data center centric. We have to lighten them and bring them to the edge so enterprises can feel assured and working because at the end of the day, they have to run a business by the standards that an enterprise is held to. So you will see a ton of innovation, by the way, robotics. Robotics is going to make edge even more interesting in live view. So I see the next couple of years, heavy IOP edge computing, just like the clients that are modeled to mainframe that the PC became like a mainframe in terms of compute capacity. I guarantee at the desktop, compute capacity will go down to the edge and we're going to see that happen in the next five years or so. >> The edge is the new data centers. I always say, it's the land is the way, the way is the land. Amit, great to see you and thanks for sharing and I'm sorry, we can't do it in person but this has been like a fireside chat meets CUBE interview, remote. Thanks for spending the time and sharing your insights and we've always had great interviews at your events, virtual again, this year. We're going to spread it out over time, good call. Thanks for coming on, I appreciate it. >> Thanks, John, take care. >> Okay, Amit, CEO of Informatica, always great to get the conversation updates from him on the industry and what Informatica, as at the center of the value proposition data 4.0. This is really the new transformation, not transition, data science, data, data engineering, all happening. theCUBE with our remote interviews, bringing you all the coverage here from our Palo Alto studios, I'm John Furrier. Thanks for watching. (gentle music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to gain some legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, we serve 85 of the Fortune 100, over half the global 2000. The reason why customers come back and speak on our behalf and literally thousands of customers speak on our behalf, it's humbling, is because we have the best intelligent data platform in the market. And we also understand our customers aren't buying software. They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight. One final personal question I want to ask you. I've been following you guys for a long time, and we've had you on theCUBE many times. You've been a seasoned veteran in the industry. You've seen cycles of innovation. You've seen the ups and downs over the years. You've been on boards, you've been a leader, a senior leader. What do you talk about with your friends and peers when you look at this current inflection point? As there's the candid conversations are happening, it's really an opportunity, but also there are serious challenges. As a leader, how should leaders be thinking about getting through this? What's your personal view? You've seen many cycles. You've see many waves. This wave coming is going to be big. This change is certainly going to create an uptick, we believe, exponentially a step function transformation. What's your view? What are some of the conversations that you're having with your friends, peers around what to do? >> Yeah, so I think in any situation like the one that we're in, it's important first and foremost to take care of the employees, take care of the customers, take care of the short term needs. That's critical. And yet at the same time in parallel, to be thinking longer term because there is an opportunity when you go through a situation like this to regroup and to think about, what will be the key markets that come back the fastest? What will be your differentiation, your company's differentiation so that you come out of this when the market does start to rebound and really thriving. So it's always this constant balance of how you deal with the short-term and the realities that we're in because people are making some tough decisions. And yet at the same time, make sure that you're very clear on your long-term strategy so that you can come out of this swinging. >> Great advice. That's a masterclass right there. Thank you for sharing that. Of course, check out Informatica's CLAIREview event. Of course, the digital events are always online. Check them out. Tracey, thanks for your time and thanks for that insight and update, appreciate it. >> Yeah, great to be here, John. Look forward to seeing you in person soon. >> Okay, take care. This is John Furrier here inside theCUBE studios for an update with Informatica's digital conference. Take a look at it, check it out online. Join the community. Be part of those thousands of customers that they have, and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. I'm John Furrier, thanks for watching. (soft music)

(upbeat music) >> Hello, everyone, welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here with a very special guest, Amit Walia CEO of Informatica. Newly appointed CEO, about a month ago, a little bit over a month ago. Head of product before that. Been with Informatica since 2013. Informatica went private in 2015, and has since been at the center of the digital transformation around data, data transformation, data privacy, data everything around data and value and AI. Amit, great to see you, and congratulations on the new CEO role at Informatica. >> Thank you. Always good to be back here, John. >> It's been great to follow you, and for the folks who don't know you, you've been a very product centric CEO. You're a product set CEO, as they call it. But also now you have a company in the middle of the transformation. CloudScale is really mainstream. Enterprise is looking to multicloud, hybrid cloud. This is something that you've been on for many, many years. We've talked about it. So now that you're in charge, you've got the ship, the wheel in your hands. Where are you taking it? What is the update of Informatica? Give us the update. >> Well, thank you. So look, business couldn't be better. I think to give you a little bit of color where we're coming from the last couple of years Informatica went through a huge amount of transformation. All things trying to transform a business model, pivoting to subscription, all things have really been into Cloud, the new workloads as we talked about and all things new like AI. To give a little bit of color, we basically exited last year with a a billion dollars of ARR, not just revenues. So we had a billion dollar ARR company and as we pivoted to subscription, our subscription business for the last couple of years has been growing North of 55%. So that's the scale at which we are running multimillion dollars and if you look at the other two metrics which we keep very clicked near and dear to heart, one is innovation. So we are participating in five Magic Quadrants and we are the leader in all five Magic Quadrants. Five on five as we like to call it Gartner Magic Quadrants, very critical to us because innovation in the tech is very important. Also customer loyalty, very important to us. So we again, we're the number one in customer sat from a TSI survey and Gartner publishes the vendor ratings. We basically have a very strong positioning in that. And lastly, our market share continues to grow. So last IDC survey, our market share continued to grow and with the number one in all our markets. So business couldn't be at a better place where we are right now. >> I want to get into some of the business discussion. We first on the Magic Quadrant front, it's very difficult for the folks that aren't in the Cloud as to understand that to participate in multiple Magic Quadrants, what many do is hard because Clouds horizontally scalable Magic Quadrants used to be old IT kind of categories but to be in multiple Magic Quadrants is the nature of the beast but to be a leader is very difficult because Magic Quarter doesn't truly capture that if you're just a pure play and then try to be Cloud. So you guys are truly that horizontal brand and technology. We've covered this on theCUBE so it's no secret, but I want to get your comments on to be a leader in today, in these quadrants, you have to be on all the right waves. You've got data warehouses are growing and changing, you got the rise of Snowflake. You guys partner with Databricks, again, machine learning and AI, changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were transforming analytics and operational workloads. This is really, really challenging. Can you just share your thoughts on why is it so hard? What are some of the key things behind these trends? We've got analytics, I guess you can do if it's just Analytics and Cloud, great, but this is a, this horizontal data play Is not easy. Can you share why? >> No, so yes, first we are actually I would say a very hidden secret. We're the only software company and I'll say that again, the only software company that was the leader in the traditional workloads legacy on premise and via the leader and the Cloud workloads. Not a single software company can say that they were the leader of and they were started 27 years ago and they're still the leader in the Magic Quadrants today. Our Cloud by the way runs at 10 trillion transactions a month scale and obviously we partnered with all the hyperscalers across the board and our goal is to be the Switzerland of data for our customers. And the question you ask is is a critical one, when you think of the key business drivers, what are customers trying to do? One of them is all things Cloud, all things AI is obviously there but one is all data warehouses are going to Cloud, we just talked about that. Moving workloads to Cloud, whether it is analytical, operational, basically we are front and center helping customers do that. Second, a big trend in the world of digital transformation is helping our customers, customer experience and driving that, fueling that is a master data management business, so on and so products behind that, but driving customer experiences, big, big driver of our growth and the third one is no large enterprise can live without data governance, data privacy. Even this is a thing today. You going to make sure that you would deliver a good governance, whether it's compliance oriented or brand oriented, privacy and risk management. And all three of them basically span the business initiatives that featured into those five Magic Quadrants. Our goal is to play across all of them and that's what we do. >> Pat Gelsinger here said a quote on theCUBE, many years ago. He said, "If you're not on the right wave, your could be driftwood," meaning you're going to get crashed over. >> He said very well. >> A lot of people have, we've seen a lot of companies have a good scale and then get washed away, if you will, by a wave. You're seeing like AI and machine learning. We talked a little bit about that. You guys are in there and I want to get your thoughts on this one. Whenever this executive changes, there's always questions around what's happening with the company. So I want you to talk about the state of Informatica because you're now the CEO, there's been some changes. Has there been a pivot? Has there been a sharpening focus? What is going on with Informatica? >> So I think our goal right now is to scale and hyperscale, that's the word. I mean we are in a very strong position. In fact, we use this phrase internally within the company, the next phase of great. We're at a great place and we are chartering the next phase of great for the company. And the goal that is helping our customers, I talked about these three big, big initiatives that companies are investing in, data warehousing and analytics, going to the Cloud, transforming customer experiences and data governance and privacy. And the fourth one that underpins all of them is all things AI. I mean, as we've talked about it before, right? All of these things are complex, hard to do. Look at the volume and complexity of data and what we're investing in is what we call native AI. AI needs, data, data needs AI, as I always said, right? And we had investing in AI to make these things easy for our customers, to make sure that they can scale and grow into the future. And what we've also been very diligent about is partnering. We partnered very well with the hyperscalers, like whether it's AWS, Microsoft, whether it's GCP, Snowflake, great partner of ours, Databricks great partner of ours, Tablo, great partners of ours. We have a variety of these partners and our goal is always customer first. Customers are investing in these technologies. Our goal is to help customers adopt these technologies, not for the sake of technologies, but for the sake of transforming those three business initiatives I talked about. >> You brought up, I was going to ask you the next question about Snowflake and Databricks. Databricks has been on theCUBE, Ali, >> And here's a good friend of ours. And he's got chops, I mean Stanford, Berkeley, he'll kill me with that, he's a cowl at Stanford but Databricks is doing well. They made some good bets and it's paying off for them. Snowflake, a rising star, Frank Slootman's over there now, they are clearly a choice for modern data warehouses as is, inhibits Redshift. How are you working with Snowflake? How do you take advantage of that? Can you just unpack your relationship with Snowflake? >> It's a very deep partnership. Our goal is to help our customers as they pick these technology choices for data warehousing as an example where Snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them. See, customers build this complex logic sitting in the old technologies. As they move to anything new, they want to make sure that their transition, migration is seamless, as seamless as it can be. And typically they'll start something new before they retire to something old. With us, they can carry all of that business logic for the last 27 years, their business logic seamlessly and run natively in this case, in the Cloud. So basically we allow them this whole from-to and also the ability to have the best of new technology in the context of data management to power up these new infrastructures where they are going. >> Let me ask you the question around the industry trends, what are the top trends, industry trends that are driving your business and your product direction and customer value? >> Look, digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed, so that remains a big vector of growth. I would say Clouded option is still relatively that an early innings. So now you love baseballs, so we can still say what second, third inning as much as we'd like to believe Cloud has been there. Customers more with that analytical workloads first, still happening. The operational workloads are still in its very, very infancy so that is still a big vector of growth and and a big trend to BC for the next five plus years. >> And you guys are in the middle of that because of data? >> Absolutely. Absolutely because if you're running a large operation workload, it's all about the data at the end of the day because you can change the app, but it's the data that you want to carry, the logic that you've written that you want to carry and we participate in that. >> I've asked you before what I want to ask you again because I want to get the modern update because PureCloud, born in the Cloud startups and whatever, it's easy to say that, do that, everyone knows that. Hybrid is clear now, everyone that sees it as an architectural thing. Multicloud is kind of a state of, I have multiple Clouds but being true multicloud a little bit different maybe downstream conversation but certainly relevant. So as Cloud evolves from public Cloud, hybrid and maybe multi or certainly multi, how do you see those things evolving for Informatica? >> Well, we believe in the word hybrid and I define hybrid exactly as these two things. One is hybrid is multicloud. You're going to have hybrid Clouds. Second is hybrid means you're going to have ground and Cloud inter-operate for a period of time. So to us, we in the center of this hybrid Cloud trail and our goal is to help customers go Cloud native but make sure that they can run whatever was the only business that they were running as much possible in the most seamless way before they can at some point contour. And which is why, as I said, I mean our Cloud native business, our Cloud platform, which we call Informatica Intelligent Cloud Services, runs at scale globally across the globe by the way, on all hyperscalers at 10 plus trillion transactions a month. But yet we've allowed customers to run their on-prem technologies as much as they can because they cannot just rip the bandaid over there, right? So multicloud, ground Cloud, our goal is to help customers, large enterprise customers manage that complexity. Then AI plays a big role because these are all very complex environments and our investment in AI, our AI being called Clare is to help them manage that as in an as automated way, as seamless a way and to be honest, the most important with them is, in the most governed way because that's where the biggest risk or risks come into play. That's when our investments are. >> Let's talk about customers for a second. I want to get your thoughts on this 'cause at Amazon reinvent last year in December, there was a meme going around that we starred on theCUBE called, "If you take the T out of Cloud native, it's Cloud naive," and so the point was is to say, hey, doing Cloud native makes sense in certain cases, but if you'd not really thinking about the overall hybrid and the architecture of what's going on, you kind of could get into a naive situation. So I asked Andy this and I want to ask you any chance and I want to ask you the same question is that, what would be naive for a customer to think about Cloud, so they can be Cloud native or operated in a Cloud, what are some of the things they should avoid so they don't fall into that naive category? Now you've being, hi, I am doing Cloud for Cloud's sake. I mean, so there's kind of this perception of you got to do Cloud right, what's your view on Cloud native and how does people avoid the Cloud naive label? >> It's a good question. I think to me when I talk to customers and hundreds of them across the globe as I meet them in a year, is to really think of their Cloud as a reference architecture for at least the next five years, if not 10. I mean technology changes think of a reference architecture for the next five years. In that, you've got to think of multiple best of breed technologies that can help you. I mean, you've got to think of best of breed as much as possible. Now, you're not going to go have hundreds of different technologies running around because you've got to scale them. But think as much as possible that you are best of breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different, let's say Cloud environments, analytical workload and operational workload, financial workload, each one of them will have something that will work best in somewhere else, right? So to me, putting the business focus on what the right business outcome is and working your way back to what Cloud environments are best suited for that and building that reference architecture thoughtfully with a five year goal in mind then jumping to the next most exciting thing, hot thing and trying to experiment your way through it that will not scale would be the right way to go. >> It's not naive to be focusing on the business problems and operating it in a Cloud architecture is specifically what you're saying. Okay so let's talk about the customer journey around AI because this has become a big one. You guys been on the AI wave for many, many years, but now that it's become full mainstream enterprise, how are the applications, software guys looking at this because if I'm an enterprise and I want to go Cloud native, I have to make my apps work. Apps are driving everything these days and you guys play a big role. Data is more important than ever for applicants. What's your view on the app developer DevOps market? >> So to me the big chains that we see, in fact we're going to talk a lot about that in a couple of months when we are at Informatica World, our user conference in May is how data is moving to the next phase. And it's what developers today are doing is that they are building the apps with data in mind first, data first apps. I mean if you're building, let's say a great customer service app, you've got to first figure out what all data do you need to service that customer before you go build an app. So that is a very fundamental shift that has happened. And in that context what happens is that in a Cloud native environment, obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complimented by what we see is data Ops, having all kinds of data available for you to make those decisions as you're building an application and in that discussion you and me are having before is that, there is so much data that you would not be able to understand that investing in metadata so you can understand data about the data. I call metadata as the intelligent data. If you're an intelligent enterprise, you've got to invest in metadata. Those are the places where we see developers going first and from there ground up building what we call apps that are more intelligent apps on the future not just business process apps. >> Cloud native versus Cloud naive discussion we were just having it's interesting, you talk about best of breed. I want to get your thoughts on some trends we're seeing you seeing even in cybersecurity with RSA coming up, there's been consolidation. You saw Dell just sold RSA to a private equity company. So you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with. You're seeing also skills gaps, but also skill shortages. There's not enough people. >> That is true. >> So now you have multiple Clouds, you got Amazon, you got Azure, you got Google GCP, you got Oracle, IBM, VMware, now you have a shortage problem. >> True. So this is putting pressure on the customers. So with that in mind, how are the customers reacting to this and what is best of breed really mean? >> So that is actually a really good one. Look, we all live in Silicon Valley, so we get excited about the latest technology and we have the best of skills here, even though we have a skills problem over here, right? Think about as you move up here from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere, that is not a whole lot of developers who understand the latest cutting edge technology that happens here. Our goal has been to solve that problem for our customers. Look, our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level. The underlying technology comes and goes and it'll come and go a hundred times. They don't have to worry about that. So our goal is abstract of the underlying changes in technology, focus at the business logically and you could move, you can basically run your business for over the course of 20 years. And that's what we've done for customers. Customers have invested with us, have run their businesses seamlessly for two decades, three decades while so much technology has changed over a period of time. >> And the Cloud is right here scaling up. So I want to get your thoughts on the different Clouds, I'll say Amazon Web Services number one in the Cloud, hyperscaler we're talking pure Cloud, they've got more announcements, more capabilities. Then you've got Azure again, hyperscale trying to catch up to Amazon. More enterprise-focused, they're doing very, very well in the enterprise. I said on Twitter, they're mopping up the enterprise because it's easy, they have an install base there. They've been leveraging it very well. So I think Nadella has done the team, has done a great job with that. You had Google try to specialize and figure out where they're going to fit, Oracle, IBM and everyone else. As you'd have to deal with this, you're kind of an arms dealer in a way with data. >> I would love to say I dance with it, not an arms dealer. >> Not an arms dealer, that's a bad analogy, but you get my point. You have to play well, you have to. It's not like an aspiration, your requirement is you have to play and operate with value in all the Clouds. One, how is that going and what are the different Clouds like? >> Well, look, I always begin with the philosophy that it's customer first. You go where the customers are going and customers choose different technologies for different use cases as deems fit for them. Our job is to make sure our customers are successful. So we begin with the customer in mind and we solve from there. Number two, that's a big market. There is plenty of room for everybody to play. Of course there is competition across the board, but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day, our joint customers, we have great success stories with all of them. Again, within mind, the end customer. So that has always been Informatica's philosophy, customer first and we partner with a critical strategic partners in that context and we invest and we've invested with all of them, deep partnerships with all of them. They've all been at Informatica well you've seen them. So again, as I said and I think the easiest way we obviously believe that the subset of data, but keep the customer in mind all the time and everything follows from there. >> What is multicloud mean to your customers if your customer century house, we hear people say, yeah, I use this for that and I get that. When I talk to CIOs and CSOs where there's real dollars and impact on the business, there tends to be a gravitational pull towards one Cloud. Why do people are building their own stacks which is why in-house development is shifted to be very DevOps, Cloud native and then we'll have a secondary Cloud, but they recognize that they have multiple Clouds but they're not spreading their staff around for the reasons around skill shortage. Are you seeing that same trend and two, what do you see is multicloud? >> Well, it is multicloud. I think people sometimes don't realize they're already in a multicloud world. I mean you have so many SaaS applications running around, right? Look around that, so whether you have Workday, whether you have Salesforce and I can keep going on and on and on, right. There are multiple, similarly, multi platform Clouds are there, right? I mean people are using Azure for some use cases. They may want to go AWS for certain other native use cases. So quite naturally customers begin with something to begin with and then the scale from there. But they realize as we, as I talked to customers, I realize, hey look, I have use cases and they're optimally set for some things that are multicloud and they'll end up there, but they all have to begin somewhere before they go somewhere. >> So I have multipleclouds, which I agree with you by the way and talking about this on theCUBE a lot. There's multi multiple Clouds and then this interoperability among Clouds. I mean, remember multi-vendor back in the old days, multicloud, it kind of feels like a multi-vendor kind of value proposition. But if I have Salesforce or Workday and these different Clouds and Amazon where I'm developing or Azure, what is the multi-Cloud interoperability? Is it the data control plane? What problems are the customers facing and the challenge that they want to turn into opportunities around multicloud. >> See a good example, one of the biggest areas of growth for us is helping our customers transform their customer experience. Now if you think about an enterprise company that is thinking about having a great understanding of their customer. Now just think about the number of places that customer data sits. One of our big areas of investment for data is a CRM product called salesforce.com right? Good customer data sits there but there could be where ticketing data sits. There could be where marketing data sits. There could be some legacy applications. The customer data sits in so many places. More often than not we realize when we talked to a customer, it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise. Clickstream data where people are social media data partner data. So in that context, bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there. So it is the customer. >> Is that the problem? >> It's a huge problem right now. Huge problem right now across the board where our customer like, hey, I want to serve my customer better but I need to know my customer better before I can serve them better. So we are squarely in the middle of that helping and we being the Switzerland of data, being fully understanding the application layer and the platform layer, we can bring all that stuff together and through the lens of our customer 360 which is fueled by our master data management product, we allow customers to get to see that full view. And from there you can service them better, give them a next best offer or you can understand the full lifetime value for customer, so on and so forth. So that's how we see the world and that's how we help our customers in this really fragmented Cloud world. >> And that's your primary value proposition. >> A huge value proposition and again as I said, always think customer first. >> I mean you got your big event coming up this Spring, so looking forward to seeing you there. I want to get your take as now that you're looking at the next great chapter of Informatica, what is your vision? How do you see that 20 mile stare out in the marketplace? As you execute, again, your product oriented CEO 'cause your product shops, now you're leading the team. What's your vision? What's the 20 mile stare? >> Well as simple as possible, we're going to double the company. Our goal is to double the company across the board. We have a great foundation of innovation we've put together and we remain paranoid all the time as to where and we always try to look where the world is going, serve our customers and as long as we have great customer loyalty, which we have today, have the foundations of great innovation and a great team and culture at the company, which we fundamentally believe in, we basically right now have the vision of doubling the company. >> That's awesome. Well really appreciate you taking the time. One final question I want to get your thoughts on the Silicon Valley and in the industry, is starting to see Indian-American executives become CEO. You now see you have Informatica. Congratulations. >> Amit: Thank you. >> Arvind over at IBM, Satya Nadella. This has been a culture of the technology for generations 'cause I remember when I broke into the business in the late 80s, 90s, this is the pure love of tech and the meritocracy of technology is at play here. This is a historic moment and it's been written about, but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there, future CEOs, what's it take to get there? What's it like? What's your personal thoughts? >> Well, first of all, it's been a humbling moment for me to lead Informatica. It's a great company and a great opportunity. I mean I can say it's the true American dream. I mean I came here in 1998. As a lot of the immigrants didn't have much in my pocket. I went to business school, I was deep in loans and I believed in the opportunity. And I think there is something very special about America. And I would say something really special about Silicon Valley where it's all about at the end of the day value, it's all about meritocracy. The color of your skin and your accent and your, those things don't really matter. And I think we are such an embracing culture typically over here. And, and my advice to anybody is that look, believe, and I genuinely used that word and I've gone through stages in my life where you sometimes doubt it, but you have to believe and stay honest on what you want and look, there is no substitute to hard work. Sometimes luck does play a role, but there is no substitute for hard work. And at the end of the day, good things happen. >> As we say, the for the love of the game, love of tech, your tech athlete, loved it, loved to interview and congratulate, been great to follow your career and get to know you and, and Informatica. It's great to see you at the helm. >> Thank you John, pleasure being here. >> I'm John Furrier here at CUBE conversation at Palo Alto, getting the update on the new CEO from Informatica, Amit Walia, a friend of theCUBE and of course a great tech athlete, and now running a great company. I'm John Furrier. Thanks for watching. (upbeat music)

[Music] hello and welcome to this cube conversation here in Palo Alto California I'm John for your host of the cube we're here the very special guest I met while he is CEO of informatica newly appointed CEO about a month ago a little over a month ago had a product before that been with informatics in 2013 informatica went private in 2015 and has since been at the center of the digital transformation around data data transformation data privacy data everything around data and value in AI that made great to see you and congratulations on the new CEO role at informatica so thank you all it's good to be back here John it's been great to follow you and for the folks who don't know you you've been a very product centric CEO your products and CEO as they call it but also now you have a company in the middle of the transformation cloud scale is really mainstream enterprises look at multi cloud hybrid cloud this is something that you've been on for many many years we've talked about it so now that you're in charge you get the ship you get the wheel and you're in your hands were you taking it what is the update of informatica give us the update well thank you solook business couldn't be better I think to give you a little bit of color wavy coming from the last couple of years informatica went through a huge amount of transformation all things trying to transform our business model pivoting to subscription all things heavily bet into cloud the new workloads as we talked about and all things new like AI to give a little bit of color we basically exited it last year with a billion dollars of ARR not just revenue so we're a billion-dollar AR our company and as we pivot it to subscription as subscription business for the last couple of years has been growing north of 55 percent so that's the scale at which we are running multi-billion dollars and if you look at the other two metrics which we keep very click near and dear to hard one is innovation so we are participated in five magic quadrants and we are the leader in all five magic quadrants five one five as we like to call it Gartner Magic Quadrant very very critical to us because innovation in the tech is you know is very important also customer loyalty very important to us so we again were the number one in customer satisfaction continues to grow sore last IDC survey our market share continue to grow and be the number one in all our markets so business couldn't be at a better place where we are and what again some of the business discussed which first method on the Magic Quadrant front it's very difficult the folks that aren't in the club is to understand that to participate in multiple magic quadrants with many many do is hard because clouds horizontally scalable magic partners used to be old IT kind of categories but to be in multiple magic quadrants is the nature of the beast but to be a leader is very difficult because magic question doesn't truly capture that if you just appear play and then try to be cloud so you guys are truly that horizontal brand and and technology we've covered this on the cube so there's no secret but I want to get your comments on to be a leader and today in these quadrants you have to be on all the right waves you've got data warehouses are growing and changing at the rise of snowflake you guys partner with data bricks again machine learning and AI changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were you know transforming you know analytics and operational workloads this is really really challenging can you just share your thoughts on why is it so hard what are the some of the key things behind these trends we can analytics I guess you can do if it's just analytics without great but this is a this horizontal data play is not easy can you share why no so yes first we are actually a I would say a very hidden secret we're the only software company and I'll say that again the only software company that was the leader in the traditional world traditional workloads legacy on-premise and via the leader in the cloud workloads not a single software company can say that they were the leader when they were started 27 years ago and there's still the leader in the magic quadrants today our cloud by the way runs at 10 trillion transactions a month scale and obviously we partner with all the hyper scalars across the board and our goal is to be the Switzerland of data for our customers and the question you ask is is a critical one when you think of he business drivers what a customer's trying to do one of them is all all things cloud all things the eye is obviously there but one is all data warehouses are going to cloud we just talked about that moving workloads to cloud whether it is analytical operational basically we have front and center helping customers do that second a big trend in the world of digital transformation is helping our customers customer experience and driving that fueling that is a master data management business so on and so products behind that but driving customer experiences big big driver of our growth and the third one is no large enterprise can live without data governance need a privacy man this is a thing today right you got to make sure that you deliver good governance whether it's compliance oriented or brand oriented privacy and risk management and all three of them basically span the business initiatives that feature into those five magic quadrants our goal is to play across all of them and that's what we do Pat Cal senior had a quote on the cube many years ago he said if you're not on the right wave you could be driftwood its meaning you're gonna get crashed oh sorry well a lot of people have we've seen a lot of companies have a good skill and then get washed away if you will by a wave you're seeing like AI and machine learning we talked a little bit about that you guys are in there I want to get your thoughts on this one is there whenever this executive changes there's always questions around you know what's happening with the company so I want you to talk about the state of informatica because you're now the CEO there's been some changes has there been a pivot has there been a sharpening focus what's going on with informatica so I think I'm cool right now is to scale and hyper scale that's the word I mean we're in a very strong position in fact we use this phrase internally within the company next phase of great we're at a great place and we are chartering the next phase of great for the company and the cool there is helping our customers I talked about these three big big initiatives that companies are investing in data warehousing and analytics going to the cloud transforming customer experiences and data governance and privacy and the fourth one that underpins all of them is all things a I mean as we've talked about before right all of these things are complex hard to do look at the volume and complexity of data and what we're investing in is what we call native ai ai needs data data needs AI as I always said right and we are investing in AI to make these things easy for our customers to make sure that they can scale and grow into the future and what we've also been very diligent about this partnering we partnered very well with the hyper scalars like whether it's AWS Microsoft whether it's GCP snowflake great partner of ours data brick skate part of ours tableau great partner of ours we have a variety of these partners and our cool is always customer first customers are investing in these technologies our goal is to help customers adopt these technologies not for the sake of technologies but for the sake of transforming those three business initiatives I thought you brought up I was gonna ask you the next question but snowflake and data versus data Brooks has been on the cube Holly a great that's a good friend of ours and he's got chops you Stan I'm not Stanford Berkeley he'll kill me with that if it's ow he's but beta Brooks is doing well they made some good bets and it's paying off of them snowflake a rising star Frank's Lubin's over there now they are clearly a choice for modern data warehouses as is any of us redshift how are you working with snowflake how do you take advantage of that can you just unpack your relationship with snowflake it's a it's a very deep partnership our goal is to help our customers you know as they pick these technology choices for data warehousing an example where snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them see customers build this complex logic sitting in the old technologies as they move to anything new they want to make sure that that transition migration is seamless as seamless as it can be and typically they'll start something new before they retire something old with us they can carry all of that business logic for the last 27 years their business logic seamlessly and run natively in this case in the cloud so basically we allow them this whole from tool and also the ability to have the best of breed technology in the context of data management to power up these new infrastructures where they are going let me ask you the question around the industry trends what are the top and trends industry trends that are driving your business and your product direction and customer value look digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed so that remains a big vector of growth I would say cloud adoption is still relatively literally inning so no you love these balls we can still say what second third inning as much as we would like to believe cloud has been their customers mode with their analytical workloads first still happening the operational workloads are still in its very very infancy so that is still a big vector of growth and and a big trend that we see for the next five plus years and you guys in the middle of that oh absolutely yeah absolutely because if you're running a large operational workload it's all about the data at the end of the day because you can change the app but it's the data that you want to carry the logic that you've written that you want to carry and we participate in that I have ashes before but I want to ask you again because I want to get the modern update because pure cloud born in the cloud like you know startups and whatever it's easy to say that do that everyone knows that hybrid is clear now everyone that sees that as an architectural thing Multi cloud is kind of a state of I have multiple clouds but being true multi-cloud a little bit different maybe downstream conversation but certainly relevant so as cloud evolves from public cloud hybrid and maybe multi or certainly multi how do you see those things evolving for informatica well we believe in the word hybrid and I define hybrid exactly as these two things one is hybrid is multi cloud you can have hybrid clouds second is hybrid means you're gonna have ground and cloud interoperate for a period of time so to us we sit in the center of this hybrid cloud trend and our goal is to help customers go cloud native but make sure that they can run whatever was the old business that they were running as much possible in the most seamlessly before they can at some point cut over and which is why as I said I've been our cloud native business a cloud platform which we call informatica intelligent cloud services runs at scale globally across the globe by the way on all hyper scalars at ten plus trillion transactions a month but yet we will allowed customers to run their own Prem technologies as much as they can because they cannot just rip the band-aid over there right so multi cloud ground cloud our goal is to help customers large enterprise customers manage that complexity their AI plays a big role because these are all very complex environments and our investment in AI our REI being called Claire is to help them manage that as in an as automated way as seen this away and to be honest the most important thing for them is in the most governed way because that's where the biggest risk risks come into play that's where our investments let's say what customers per second I want to get your thoughts on this because at Amazon reinvent last year in December it was a meme going around on the queue that we that we start on the cube called if you think the tea out of cloud native it's cloud naive and so the the the point was is to say hey doing cloud native makes sense in certain cases but if you're not really thinking about the overall hybrid and the architecture of what's going on you kind of could get into a night naive situation so I asked any of this and I want to ask you any chat so I'll ask you the same question is that what would be naive for a customer to think about cloud so they can be cloud native or operate in a cloud what are some of the things they should avoid so they don't fall into that naive category now you've been you know I hey I'm doing cloud yeah for clouds sake I mean so there's kind of this perception have you got to do cloud right mm-hmm what's your view on cloud native and how does people avoid the cloud naive label it's it's a good question I think to me when I talk to customers and hundreds of them across the globe is I meet them in a year is to really think of their cloud as a reference architecture for at least the next five years if not I'm a technology changes think of a reference architecture for the next five years and in that you got to think of multiple best-of-breed technologies that can help you I mean you got to think best-of-breed as much as possible now you're not going to go have hundreds of different technologies running around because you got to scale them but think as much as possible that you are Best of Breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different let's say cloud environments analytical workload and operation workload a financial workload each one of them will have something that will work best in somewhere else right so to me putting the business focus on what the right business outcome is and working you will be back to what cloud environments are best suited for that and building that reference architecture thoughtfully with a five-year goal in mind then jumping to the next most exciting thing hot thing and try to experiment your way through it that will not scale would be the right way to go yeah it's not naive to be focusing on the business problems and operating it in a cloud architecture this is what you're saying okay so let's talk about like the customer journey around AI because this has become a big one you guys been on the AI way for many many years but now that it's become full mainstream enterprise how are the applications software guys looking at this because if I'm an enterprise and I want to go cloud native app to make my apps work yes apps are driving everything these days and you guys play a big role data is more important than ever for applicants what's your view on the app developer DevOps market so to me the big chains of VC in fact we're gonna talk a lot about that in a couple of months when we are at informatica world our user conference in May is how data is moving to the next phase and it's what developers today are doing is that they are building the apps with data in mind first data first apps I mean if you're building let's see a great customer service app you gotta first figure out what all data do you need to service a customer before you go build an app so that is a very fundamental shift that has happened and and in that context what happens is that in a cloud native environment obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complemented by what we see is data ops having all kinds of data available for you to make those decisions as you build an application and in that discussion you're near having before is that there is so much data that you will not be able to understand that investing in metadata so you can understand data about the data I called metadata as the intelligent data if you're an intelligent enterprise you gotta invest in metadata those are the places where we see developers going first and from their ground up building what we call apps that are more intelligent apps of the future not just business process apps cloud native versus cloud naive connotation we were just having is interesting you talk about Best of Breed I want to get your thoughts on some trends we're seeing seeing even in cybersecurity with RSA coming up there's been consolidation you saw our Dell Jesolo RSA 2 private equity company so you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with you're seeing also skills gaps but also skills shortages there's not enough people oh now you have multiple clouds you got Amazon you got Azure you got Google GCP you've got Oracle IBM VMware now you have a shortage problem true so this is putting pressure on the customers so with that in mind how are the customers reacting to this and what is best to breed really mean so that is actually a very good point look we all live in Silicon Valley so we get excited about the latest technology and we have the best of skills here even though we have a skills problem over here right think about as you move away from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere there is not a whole lot of developers who understand the latest cutting-edge technology that happens here our goal has been to solve that problem for our customers look our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level the underlying technology comes and goes and you know it will come and go 100 times they don't have to worry about that so our goal is abstract away the underlying changes in technology focus at the business logic layer and you can move you can basically run your business for over the course of 20 years and that's what we've done for customers customers were invested with us have run their businesses seamlessly for two decades three decades while so much technology has changed with a period of time and the cloud is right here scaling up so I want to get your thoughts on the different clouds I see Amazon Web Services number one the cloud hyper scalar we're talking pure cloud that gets more announcements more capabilities then you got a sure again hyper scale trying to catch up to Amazon more Enterprise focused are doing very very well on the enterprise I was I said on Twitter they're mopping up the enterprise because it's easy to have an install base there they've been leveraging your very well stuff in atella has done team done a great job that you got Google trying to specialize and figure out where they're gonna fit Oracle IBM everyone else as you'd have to deal with this you're kind of an arms dealer in a way with data I would love to say no hands but not absolute I'm dealing that's the bad analogy but you get my point you have to play well you have to it's not like an aspiration show your requirements you have to play and operate with value in all the clouds one how is that going and what are the different clouds like well I always begin with the philosophy that its customer first you go with the customers a queen and customers choose different technologies for different use cases as deems fit for them our job is to make sure our customers are successful so we begin with the customer in mind and we solve from there number two that's a big market there is plenty of room for everybody to play of course there is competition across the board but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day our joint customers we have great success stories with all of them again you get in mind the end customer so that has always been informatic as philosophy customer first and we partner with a critical strategic partners in that context and and we invest and we've invested with all of them deep partnerships of all of them they've all been at informatica well you've seen them so again as I said and I think the easiest way we obviously believe they do this incident of data but keep the customer in mind all the time and everything follows from there what is multi-cloud me to your customers if your customer centric obviously we hear people say yeah I use this for that and I get that when I talk to CIOs and see says with his real dollars and interact on the business there tends to be a gravitational pull towards one cloud a lot of people are building their own stacks in house development has shifted to be very DevOps I'm cloud native and then I'll have a secondary cloud but they recognize that they have multiple clouds but they're not spreading their staff around for the reasons around skill shortage yeah are you seeing that same trend and to what do you see is multi cloud well it is 1d cloud I think I think people sometimes don't realize they're already in a multi cloud world I mean you have so many SAS applications running around right look around that so whether you have work day with your salesforce.com and I can keep going on and on and on right there are multiple similarly multi platform clouds are there right I mean people are using hash or for some use case they may want to go a dime us for certain other negative use cases so quite naturally customers begin with something to begin with and then the scale from there but they realize as we as I talk to customers I realize hey look I have use cases and they're optimally set for some things that are multi-cloud and they'll end up there but they all have to begin somewhere before they go somewhere so I have multiple clouds which I agree with you by the way and talking about this one cube a lot there's multi multiple clouds and then this interoperability among clouds I mean remember multi-vendor back in the old days multi-cloud it kind of feels like a multi vendor kind of value proposition but if I have Salesforce or workday in these different clouds in Amazon where I'm developing or Azure what is the multi cloud interoperability is it the data control plane what problems are the customers facing and the challenge that they want to turn into opportunities do a good example multi-cloud see a good example one of the biggest areas of growth for us is helping a customers transform the customer experience now if you think about an enterprise company that is thinking about having a great understanding of their customer now just think about the number of places that customer data sets one of the one of the big areas of investment viability the CRM product called salesforce.com right good customer data sits there but there could be where ticketing data sets there could be where marketing data sits there could be some legacy applications the customer data sits in so many places more often than not we realize when we talk to a customer it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise right clickstream data where people had parts or shared a partner data so in that context bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there so it is the cost the customers have the problem it's a huge problem right now huge problem right now across the board where cup a per customer like hey I want to serve my customer better but I need to know my customer better before I can serve them better so we are squarely in the middle of that helping and B being the Switzerland of data being fully understanding the application layer and the platform layer we can bring all that stuff and through the lens of our customer 360 which is fueled by our master data management product we allow customers to get to see that full view and from there you can service them better give them a next best offer or you can understand their lives either full lifetime value for customer so on and so forth so that's how we see the world and that's how we help our customers in this really fragmented cloud world that's your primary value proposition it's a huge value proposition and again as I said always think customer first I met you got your big event coming up this spring so looking forward to seeing you there I want to get your take as now that you're looking at the next great chapter of informatica what is your vision how do you see that twenty miles stare out in the marketplace as you execute again your product oriented CEO because your product chops now you're leading the team what's your vision what's the 20 mile stair well as simple as possible we're gonna double the company our goal is to double the company across the board we have a great foundation of innovation we put together and we remain paranoid all the time as to where and we always start to look where the world is going serve our customers and as long as we have great customer loyalty which we have today have the foundations of great innovation and a great team and culture at the company which we fundamentally believe in we basically right now have the vision of doubling the company that's awesome well really appreciate you taking the time one final question I want to get your thoughts on you know it's looking valley and in the industry starting to see Indian American executives become CEOs you now see you have informatica congratulations Arvind over at IBM sathi natella this has been a culture of the technology for generations I remember when I broken the business in the late 80s 90s this is the pure love of tech and the and the meritocracy of Technology is at play here this is a historic moment it's been written about but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there future CEOs what's it take to get there what's it like what's your personal thoughts well first of all it's been a humbling moment for me to lead in from it's a great company and a great opportunity I mean I can say like it's the true Americans dream I mean I came here in 1998 I mean as a lot of immigrants Ted didn't have much in my pocket I went to business school I was deep in loans and and I believed in the opportunity and I think there is something very special about America and I would say something really special about Silicon Valley where it's all about at the end of the day value it's all about meritocracy the color of your skin and your accent and your those things don't really matter and I think we're such an embracing culture typically over here and my advice to anybody is that look believe and I genuinely use that word and I've gone through stages in my life where you sometimes doubt it but you have to believe and stay honest what you want and look there is no substitute to hard work sometimes luck does play a role but there is no substitute artwork and at the end of the day good things happen as we say that for the love of the game love attack your tech athlete love to love to interview and congratulate been great to follow your career get to know you and informatica it's great to see you at the helm thank you John pleasure being here I'm John 4 here is cube conversation in Palo Alto getting the update on the new CEO from informatics at MIT Walia friend of the cube and of course a great tech athlete and now running the great company I'm John forever here thanks for watching [Music] you [Music]

>> Narrator: From Burlingame, California, it's theCUBE, covering Sumo Logic Illuminate 2019! Brought to you by Sumo Logic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate at the Hyatt Regency, San Francisco Airport. Our second year here, about a thousand people, third year of the conference, a really good vibe. You know I think some of these cases where the marketers really come to Sumo Logic in terms of data and data monitoring, and there's so many applications that are business and security, and operations. We're excited to have our next guest. He is Lior Mechlovich. He's an architect at Informatica. Lior, great to see you. >> Great to see you, too. >> Absolutely, so you said you've been coming to this for a couple years, just kind of general impressions as it's grown. >> Sure, it's my third year, it's grown very nicely. Always exciting. I think there's a very nice vibe to this conference. I always learn new things so we've been with Sumo for more than four years now for Informatica. And excited as always. >> Yeah, and we've been covering the Informatica show. I think we've looked it up, since 2015 so, we've been doing a lot of work and you guys are right in the heart of this whole data thing, >> Right. >> And you been part of the kind of migration from pretty much pure on-prem, to Cloud. There's rush to public Cloud, and then now kind of this Hybrid model. So you kind of look at the data perspective you know, what's kind of your take as this thing has evolved over the last several years? >> Sure, so we have been around for 26 years. I think building a lot of on-pram, data platforms for being the enterprise Cloud data management that Informatica sells with basically getting your data inside our outside the organization from Clouds, on-pram, or whatever integration pattern you have and we decided four or five years ago to be a Cloud-first company and migrated most of our products to be on Cloud to provide them as a service. And for us, it was a huge journey, we needed to take some offering that we had in the Cloud, some products, and really revamp and building a new microscopic architecture and then slowly migrate all the customers. It took us over a year to make that. We currently run on all three Cloud providers. And really using Sumo mentoring tools to really understand the impact that we have on our customers during this migration. It was a very successful. They hardly noticed that-- >> Oh good. >> Only the nice UI, but they hardly noticed the problems. I mean we really changed a lot of things. >> What is some of the things you learned in that process that you can apply now with just some of your customers in terms of data migration and operating in a Cloud situation versus a traditional data center? >> Sure, so I would definitely highlight the need to be able to roll back and the need to always keep really good money to working it and understanding how the end user getting impacted. And so we really kept that in mind. Everything we do try do always do it side by side, and then when we migrate we're really sure that it is successful and there's no impact on the customer. So I think that's definitely too, harshly monitored everything and be able to roll back when you need to, because you will need to at some point. >> But the rollback is funny, because it use to be you had you know the release cadence was significantly slower than now. And now you've got all these kind of micro pushes that are going on multiple times a day. >> Yeah. >> So how does that impact kind of keeping that safety net? That rollback safety net? >> So it's interesting. So we actually don't deploy that many times a day. Where we can really impact the customer so we deploy the things that are not customer urgent impacting production more, but still the really heavy productions of the thrilling part of the customer; we try to minimize that and make it very customer aware okay. >> So basically they choose their own windows of maintenance and all that. But our customers again hospitals, all kinds of very important, then we are in charge of the data byte in those places. So we don't want to just push whatever we can. We really cannot take that, even a rollback of 1%, it can be very bad so we're a bit more conservative models of deployments, but actually that means we put a lot of effort in our monitoring. What is going on doing those deployments. >> Right. All right, so what are the big trends that happen? I mean containers have been around for awhile, but we really saw kind of the rise of containers in terms of the popular consciousness with Docker couple three or four years ago, and then a couple years ago the Cooper 90's coming in for the orchestration. From your point of view how had those things impacted your world, and how you do your job, and take care of your customers? >> Sure, so for us Cubernetics is really a great opportunity to standardize the way that we deploy across different products. So we have our platform, but we have also different products; different people across the globe. We're a very multi-globe organization, and to get a standard like Cubernetics to help us standardize to get more releases, more stable environments that really solves a lot of problems, because we had this migration that I talked about really left us with a lot of clusters across the globe, different time zones. It was really hard to standardize on the pipelines, and to deploy to really minimize the problems that we give to the end user at the end. >> So we really took that opportunity to use Cubernetics, to use containers to minimize the difference it has from the developer machine all the way to production. To automate the most we can so when it's really is excelling in this. Yeah, so that's where we really... took those containers apart. Today we are in migrating, so not all of that, but we truly see the benefits of standardization, of immutable infrastructure as that key component for us. >> This is just so great, because you have such a longitudinal point of view having been in. The company's been at it for awhile, and you've been at the company for awhile. So another topic I'd love to get your thought is just kind of this exponential explosion of data. I mean it would be curious not to know the numbers, but kind of the scale of data in which you guys are dealing with for you customers, and how that has changed over the last several years before you even really factor in IOT, and this next kind of machine to machine explosion? >> So we definitely see that explosion of data. It's not just the explosion. It's also the different types, and where data has been on-prem, now moving to Cloud. Where do people want to run off all those work loads? As of course a lot of feedback for us as well need to support all the Cloud providers when we use to do a lot of Hadoop on-prem, right? It's all changed now too. >> All the Cloud providers, the data it's theirs. So the data move, data locality is a big thing, Now we need to run all those things on the Cloud. So, I don't remember the exact numbers. I guess we're doing something of 2.5 billion transactions a month for like number of records that we serve. That being we usually just see more work loads, more people, more use cases for onboarding more data from Cloud applications. The data became more dispersed not just more data, but the sources has become like everybody needs to integrate Salesforce or Workday with their on-prem that gives unique opportunities for this kind of data. >> Well, it's funny when you talk about the workloads, because it always use to be, do you bring the workload to the data or the data to the workload, and a knock on the Cloud is that you got to get all the data into the Cloud, and pay for the transport of the data. And there's data gravity that said once you have it in a central location like that the opportunity to put applications against that data is much much higher than if you're bringing the data to the application. You see and how are customers taking advantage of that opportunity? >> So for sure we saw they did that move to the Cloud. When we started from on naked Cloud 10 years ago our entire model was hybrid, so we can stick around on-prem, because the data was on-prem, and since then our hybrid model that you still run both on-prem and on Cloud, you can see the change right? You can see more of our agents. We have an agent based architecture to really being deployed much more on easy 2's, on AKS or whatever to run those workloads in the Cloud. >> Right, but I would imagine the number of workloads applied to each data set now have increased significantly, because now it's in that central repository. >> Yes, and definitely you can see those data legs being built, and mostly in the Cloud. That gives unique opportunity. >> So just get your perspective after a couple days here. I know you haven't been here for a couple of days. We're just getting started at this show. What does Sumo Logic bring to you and your team? What does it enable you to do that you couldn't otherwise do? Why are you happy to be a customer of Sumo? >> Sure, so four and foremost it's the democratization of data. I really like to say that internally. In an organization that's spread across the globe, really sharing insides based on data, it's very important. When you have many R&D centers that can just send this summary; send the data and show people what they mean saves so much time, and so we use it across. We use the customer success, product management to understand feature being used, SRE's, developers. All of those really can communicate based on data. In this Microsoft Office tool you cannot do it without that. You cannot do it without linking, because the different products that we onboard on the platform will not be able to communicate effectively without that. So that's very important, and giving that landing pages dashboard templates for onboarded services to have this kind of standard to follow to monitor how to operate that's very important for us. >> That's great. Go ahead. I'm sorry I interrupted you. >> Sorry, and the key place that we brought Sumo in is basically for instant management. So how to understand when something doesn't work just to try to understand the blast radius, which products are impacted. We have a variety of products, so just in minutes we minimize that in four hours to minutes trying to understand what exactly is going on. Who's impacted to update the customer  and all that. >> I love the part you talked about the democratization, because again I talk about it all the time, and I'll talk about it again, but to drive innovation in a company I think such a key piece of it is to enable more people to have more information, and the tools to manipulate that information, so they see opportunities to make improvements here, there, and otherwise and it sounds like you guys are really using it for that. >> Definitely. Definitely. >> In this case. >> You know when you get some people that you never knew that even though we have a customer support guys that did some crazy dash wars that we had no idea it's possible even, and they really getting chance to work with customers better to really tell the customer, "Oh, you just did that and that. "Maybe you'll try this option." And we found that even communicating, and really minimize the time it takes for them to figure out what's going on that it's been really impactful. >> With no call to It to help (laughs). >> And it was never the intent, so we wanted to allow dev's and off's to operate, and all of a sudden you're getting customer support without even telling them. >> Good, well Lior thanks for sharing your story and really appreciate you taking the time. >> Thank you. >> All right, he's Lior. I'm Jeff. You're watching theCube. We are at Sumo Logic Illuminate 2019. Thanks for watching. We'll see you next time. (upbeat techno music)

(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, we're here with theCUBE at the MIT Chief Data Officer Information Quality Conference. I'm Dave Vellante with my co-host Paul Gillin. Susan Wilson is here, she's the vice president of data governance and she's the leader at Informatica. Blake Anders is the corporate vice president of data governance at New York Life. Folks, welcome to theCUBE, thanks for coming on. >> Thank you. >> Thank you. >> So, Susan, interesting title; VP, data governance leader, Informatica. So, what are you leading at Informatica? >> We're helping our customers realize their business outcomes and objectives. Prior to joining Informatica about 7 years ago, I was actually a customer myself, and so often times I'm working with our customers to understand where they are, where they going, and how to best help them; because we recognize data governance is more than just a tool, it's a capability that represents people, the processes, the culture, as well as the technology. >> Yeah so you've walked the walk, and you can empathize with what your customers are going through. And Blake, your role, as the corporate VP, but more specifically the data governance lead. >> Right, so I lead the data governance capabilities and execution group at New York Life. We're focused on providing skills and tools that enable government's activities across the enterprise at the company. >> How long has that function been in place? >> We've been in place for about two and half years now. >> So, I don't know if you guys heard Mark Ramsey this morning, the key-note, but basically he said, okay, we started with enterprise data warehouse, we went to master data management, then we kind of did this top-down enterprise data model; that all failed. So we said, all right, let's pump the governance. Here you go guys, you fix our corporate data problem. Now, right tool for the right job but, and so, we were sort of joking, did data governance fail? No, you always have to have data governance. It's like brushing your teeth. But so, like I said, I don't know if you heard that, but what are your thoughts on that sort of evolution that he described? As sort of, failures of things like EDW to live up to expectations and then, okay guys over to you. Is that a common theme? >> It is a common theme, and what we're finding with many of our customers is that they had tried many of the, if you will, the methodologies around data governance, right? Around policies and structures. And we describe this as the Data 1.0 journey, which was more application-centric reporting to Data 2.0 to data warehousing. And a lot of the failed attempts, if you will, at centralizing, if you will, all of your data, to now Data 3.0, where we look at the explosion of data, the volumes of data, the number of data consumers, the expectations of the chief data officer to solve business outcomes; crushing under the scale of, I can't fit all of this into a centralized data at repository, I need something that will help me scale and to become more agile. And so, that message does resonate with us, but we're not saying data warehouses don't exist. They absolutely do for trusted data sources, but the ability to be agile and to address many of your organizations needs and to be able to service multiple consumers is top-of-mind for many of our customers. >> And the mind set from 1.0 to 2.0 to 3.0 has changed. From, you know, data as a liability, to now data as this massive asset. It's sort of-- >> Value, yeah. >> Yeah, and the pendulum is swung. It's almost like a see-saw. Where, and I'm not sure it's ever going to flip back, but it is to a certain extent; people are starting to realize, wow, we have to be careful about what we do with our data. But still, it's go, go, go. But, what's the experience at New York Life? I mean, you know. A company that's been around for a long time, conservative, wants to make sure risk averse, obviously. >> Right. >> But at the same time, you want to keep moving as the market moves. >> Right, and we look at data governance as really an enabler and a value-add activity. We're not a governance practice for the sake of governance. We're not there to create a lot of policies and restrictions. We're there to add value and to enable innovation in our business and really drive that execution, that efficiency. >> So how do you do that? Square that circle for me, because a lot of people think, when people think security and governance and compliance they think, oh, that stifles innovation. How do you make governance an engine of innovation? >> You provide transparency around your data. So, it's transparency around, what does the data mean? What data assets do we have? Where can I find that? Where are my most trusted sources of data? What does the quality of that data look like? So all those things together really enable your data consumers to take that information and create new value for the company. So it's really about enabling your value creators throughout the organization. >> So data is an ingredient. I can tell you where it is, I can give you some kind of rating as to the quality of that data and it's usefulness. And then you can take it and do what you need to do with it in your specific line of business. >> That's right. >> Now you said you've been at this two and half years, so what stages have you gone through since you first began the data governance initiative. >> Sure, so our first year, year and half was really focused on building the foundations, establishing the playbook for data governance and building our processes and understanding how data governance needed to be implemented to fit New York Life in the culture of the company. The last twelve months or so has really been focused on operationalizing governance. So we've got the foundations in place, now it's about implementing tools to further augment those capabilities and help assist our data stewards and give them a better skill set and a better tool set to do their jobs. >> Are you, sort of, crowdsourcing the process? I mean, you have a defined set of people who are responsible for governance, or is everyone taking a role? >> So, it is a two-pronged approach, we do have dedicated data stewards. There's approximately 15 across various lines of business throughout the company. But, we are building towards a data democratization aspect. So, we want people to be self-sufficient in finding the data that they need and understanding the data. And then, when they have questions, relying on our stewards as a network of subject matter experts who also have some authorizations to make changes and adapt the data as needed. >> Susan, one of the challenges that we see is that the chief data officers often times are not involved in some of these skunkworks AI projects. They're sort of either hidden, maybe not even hidden, but they're in the line of business, they're moving. You know, there's a mentality of move fast and break things. The challenge with AI is, if you start operationalizing AI and you're breaking things without data quality, without data governance, you can really affect lives. We've seen it. In one of these unintended consequences. I mean, Facebook is the obvious example and there are many, many others. But, are you seeing that? How are you seeing organizations dealing with that problem? >> As Blake was mentioning often times what it is about, you've got to start with transparency, and you got to start with collaborating across your lines of businesses, including the data scientists, and including in terms of what they are doing. And actually provide that level of transparency, provide a level of collaboration. And a lot of that is through the use of our technology enablers to basically go out and find where the data is and what people are using and to be able to provide a mechanism for them to collaborate in terms of, hey, how do I get access to that? I didn't realize you were the SME for that particular component. And then also, did you realize that there is a policy associated to the data that you're managing and it can't be shared externally or with certain consumer data sets. So, the objective really is around how to create a platform to ensure that any one in your organization, whether I'm in the line of business, that I don't have a technical background, or someone who does have a technical background, they can come and access and understand that information and connect with their peers. >> So you're helping them to discover the data. What do you do at that stage? >> What we do at that stage is, creating insights for anyone in the organization to understand it from an impact analysis perspective. So, for example, if I'm going to make changes, to as well as discovery. Where exactly is my information? And so we have-- >> Right. How do you help your customers discover that data? >> Through machine learning and artificial intelligence capabilities of our, specifically, our data catalog, that allows us to do that. So we use such things like similarity based matching which help us to identify. It doesn't have to be named, in miscellaneous text one, it could be named in that particular column name. But, in our ability to scan and discover we can identify in that column what is potentially social security number. It might have resided over years of having this data, but you may not realize that it's still stored there. Our ability to identify that and report that out to the data stewards as well as the data analysts, as well as to the privacy individuals is critical. So, with that being said, then they can actually identify the appropriate policies that need to be adhered to, alongside with it in terms of quality, in terms of, is there something that we need to archive. So that's where we're helping our customers in that aspect. >> So you can infer from the data, the meta data, and then, with a fair degree of accuracy, categorize it and automate that. >> Exactly. We've got a customer that actually ran this and they said that, you know, we took three people, three months to actually physically tag where all this information existed across something like 7,000 critical data elements. And, basically, after the set up and the scanning procedures, within seconds we were able to get within 90% precision. Because, again, we've dealt a lot with meta data. It's core to our artificial intelligence and machine learning. And it's core to how we built out our platforms to share that meta data, to do something with that meta data. It's not just about sharing the glossary and the definition information. We also want to automate and reduce the manual burden. Because we recognize with that scale, manual documentation, manual cataloging and tagging just, >> It doesn't work. >> It doesn't work. It doesn't scale. >> Humans are bad at it. >> They're horrible at it. >> So I presume you have a chief data officer at New York Life, is that correct? >> We have a chief data and analytics officer, yes. >> Okay, and you work within that group? >> Yes, that is correct. >> Do you report it to that? >> Yes, so-- >> And that individual, yeah, describe the organization. >> So that sits in our lines of business. Originally, our data governance office sat in technology. And then, our early 2018 we actually re-orged into the business under the chief data and analytics officer when that role was formed. So we sit under that group along with a data solutions and governance team that includes several of our data stewards and also some others, some data engineer-type roles. And then, our center for data science and analytics as well that contains a lot of our data science teams in that type of work. >> So in thinking about some of these, I was describing to Susan, as these skunkworks projects, is the data team, the chief data officer's team involved in those projects or is it sort of a, go run water through the pipes, get an MVP and then you guys come in. How does that all work? >> We're working to try to centralize that function as much as we can, because we do believe there's value in the left hand knowing what the right hand is doing in those types of things. So we're trying to build those communications channels and build that network of data consumers across the organization. >> It's hard right? >> It is. >> Because the line of business wants to move fast, and you're saying, hey, we can help. And they think you're going to slow them down, but in fact, you got to make the case and show the success because you're actually not going to slow them down to terms of the ultimate outcome. I think that's the case that you're trying to make, right? >> And that's one of the things that we try to really focus on and I think that's one of the advantages to us being embedded in the business under the CDAO role, is that we can then say our objectives are your objectives. We are here to add value and to align with what you're working on. We're not trying to slow you down or hinder you, we're really trying to bring more to the table and augment what you're already trying to achieve. >> Sometimes getting that organization right means everything, as we've seen. >> Absolutely. >> That's right. >> How are you applying governance discipline to unstructured data? >> That's actually something that's a little bit further down our road map, but one of the things that we have started doing is looking at our taxonomy's for structured data and aligning those with the taxonomy's that we're using to classify unstructured data. So, that's something we're in the early stages with, so that when we get to that process of looking at more of our unstructured content, we can, we already have a good feel for there's alignment between the way that we think about and organize those concepts. >> Have you identified automation tools that can help to bring structure to that unstructured data? >> Yes, we have. And there are several tools out there that we're continuing to investigate and look at. But, that's one of the key things that we're trying to achieve through this process is bringing structure to unstructured content. >> So, the conference. First year at the conference. >> Yes. >> Kind of key take aways, things that interesting to you, learnings? >> Oh, yes, well the number of CDO's that are here and what's top of mind for them. I mean, it ranges from, how do I stand up my operating model? We just had a session just about 30 minutes ago. A lot of questions around, how do I set up my organization structure? How do I stand up my operating model so that I could be flexible? To, right, the data scientists, to the folks that are more traditional in structured and trusted data. So, still these things are top-of-mind and because they're recognizing the market is also changing too. And the growing amount of expectations, not only solving business outcomes, but also regulatory compliance, privacy is also top-of-mind for a lot of customers. In terms of, how would I get started? And what's the appropriate structure and mechanism for doing so? So we're getting a lot of those types of questions as well. So, the good thing is many of us have had years of experience in this phase and the convergence of us being able to support our customers, not only in our principles around how we implement the framework, but also the technology is really coming together very nicely. >> Anything you'd add, Blake? >> I think it's really impressive to see the level of engagement with thought leaders and decision makers in the data space. You know, as Susan mentioned, we just got out of our session and really, by the end of it, it turned into more of an open discussion. There was just this kind of back and forth between the participants. And so it's really engaging to see that level of passion from such a distinguished group of individuals who are all kind of here to share thoughts and ideas. >> Well anytime you come to a conference, it's sort of any open forum like this, you learn a lot. When you're at MIT, it's like super-charged. With the big brains. >> Exactly, you feel it when you come on the campus. >> You feel smarter when you walk out of here. >> Exactly, I know. >> Well, guys, thanks so much for coming to theCUBE. It was great to have you. >> Thank you for having us. We appreciate it, thank you. >> You're welcome. All right, keep it right there everybody. Paul and I will be back with our next guest. You're watching theCUBE from MIT in Cambridge. We'll be right back. (techno music)