 Hey everyone, welcome back to theCUBE. Lisa Martin here with Dave Vellante. We're covering Snowflake Summit 22. This is day two of our wall-to-wall CUBE coverage of three days. We've been talking with a lot of customers, partners, we've got some more partners to talk with us next. Inframanica, two of our guests are back with us on the program. Rick Tam Daniels joins us, the GVP Global Ecosystems and Technology at Inframanica. I'm Peter Kuh, vice president and chief strategist, banking and financial services. Welcome guys. Thank you. Yes, 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, 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 at Inframanica 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 in 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 is 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. 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? You're seeing increasingly that line of businesses are leaning in, owning the data, 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 to make decisions and to run their business, whether it's to improve customer experience, whether it's to grow wallet share, whether it's to comply with regulations, manage risks in today's environment, and of course, being agile. Business knows that data is important. They have ownership of it. And technology and data organizations help facilitate the 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 want to 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 the 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 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 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 was an IDC study for last year that said that 84% of executives, no surprise, right? They want to 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 in creating trust around data, and not only creating trust, but delivering data to end users. So that's one big trend. The other one is departmental user adoption. We're seeing a huge push towards agility and rapid startup of new projects, new data-driven transformations that are happening at the departmental level. Individual contributors, that sort of thing. So Informatica, we made an announcement yesterday with Snowflake of a whole host of innovations that are really targeting those two big trend areas. I want to get into the announcements, but the point about governance and business users being reluctant, it's kind of chicken and egg, isn't it? If I don't have the governance, I'm afraid to use it. But even if I do have it, there's the architecture of my company, my data organization may not facilitate that, and so I'm going to change the architecture, but then it's a wild west, so it has to be governed. Isn't that a challenge that companies face? Absolutely, and governance is a lot more than just technology, right? It's 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 to be 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 want to work. You 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've really focused on creating a holistic, single pane of glass view with our cloud data governance and catalog offering that really takes all the way from the raw technical data and actually delivers data in a shopping cart-like experience for actual enterprise users, right? And so I think that's when data governance goes from, historically data governance 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. Let's talk about the news. So data loader, what does that do? Well, it's all in the name, isn't it? No, the data loader, it's a free utility that we announced here 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 want to get started quickly. Yeah, so, I mean, that's a key point in getting data into the Snowflake data cloud, right? It's like any cloud, you got to get data in. How does it work with customers? I mean, you guys are known, you have a long history of extract, transform, ETL. How does it work in the Snowflake world? Is it different? Is it, you remember the Hadoop days, it was ELT, right? How are customers doing that today in this environment? Yeah, it's different. I mean, there are a lot of the same patterns are still in play. There's a lot more of 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 a flavor of ELT, right? But it's really pushing down to the Snowflake data cloud as opposed to Hadoop with Spark or something like that, right? So that's definitely how customers are using it and a 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 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, 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? 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 and ones that no one knows what to do with. So we talk about data governance holistically to 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. So, well, 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 cloud platform, we have what's called our Clare AI Engine, right? So we use AI techniques, machine learning, recommendations to accelerate with the knowledge of the metadata of what's going on the organization. For example, when we discover data assets, we 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 them down to weeks and days and even hours. 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 Informatica and Snowflake, together we help customers modernize their data architecture and 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 I'm going to 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 data architecture. Yeah, so a lot with so many customers, right? They built data warehouses, core data and analytics systems on-premises, right? They're using ETL technology, using those either warehouse appliances or databases. And what they're looking for is they want to 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 to do with our modernization programs 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 also on a consumption-based model that we introduced about a year and a half ago. So they're looking for all those elements of a cloud-native platform, 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 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-of-breed tools and stitching them together and hoping that it will actually work. And Informatica's value proposition is 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, our Intelligent Data Management Cloud for financial services, retail, CPG, and healthcare and life sciences, in addition to our core capabilities and our Clare AI machine learning engine, we also have industry accelerators, pre-built data quality rules for certain regulations within banking. We've got master data management and customer models for healthcare, insurance industry, all pre-built. So these are accelerators that we've actually built over the years and we're now making available to our customers who adopt Informatica's Intelligent Data Management Cloud for their data management and governance needs. And then the other part of the statement that's interesting is provide AI-driven data governance. You know, we are seeing a move toward, you know, decentralized data architectures and organizations. And we talked to Snowflake about that. They go, yeah, we're globally distributed cloud. Okay, great, so that's decentralized. But when we see a lot of customers doing this, they say, okay, we're going to give lines of business responsibility for data. We're going to argue about who owns what. And then once we settle that, here's your own data lake. Maybe they've tried to cobble together a catalog or a super catalog. And then they'll try to figure out some algorithms to determine data quality, you know, best, okay, don't use. Right, 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 and 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'd add to that as well. I think you're talking a lot about data mesh architectures, right, that are really kind of popular right now. And I think those kind of, they live or die on data governance, right? If you don't have data governance, the shared taxonomy, these things, it's very hard to, I think, scale those individual working groups. But if you have a platform where 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 once you figure out those two, you just created two more problems, which is the other two parts of the principle, 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 a 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. I think data mesh is more of an organizational construct than it is. I'm not quite sure what data fabric is. I think Gartner confused the issue there. Data fabric was an old NetApp term. You probably work in NetApp at the time. And it made sense in the NetApp context. And then I think Gartner didn't like the fact that Jamak Degani co-opted this cool term so they created data fabric, but whatever. But my point being, I think when I talk to customers that they're trying to get more value out of 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 the only way to do that is to distribute responsibility, get more people involved in the process. And that kind of dovetails with the announcements we made on data governance for Snowflake, you're taking these operational controls in the Snowflake layer that are typically managed by SQL and that decentralized architecture, data owner doesn't know how to set those patterns and things like that. So we're saying, all right, we're creating this 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 going to 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 involved. Thanks so much for joining Dave and 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. Thank you for having us. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE's coverage at Snowflake Summit Day Two of theCUBE's coverage. Stick around, Dave and I will be right back with our next guest.