Informatica World 2024 | Neil Mendelson | Oracle & Rik Tamm-Daniels | Informatica

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Neil Mendelson

VP, Product Management

Oracle

Rik Tamm-Daniels

GVP, Technology Alliances

Informatica

Three insights you might have missed from theCUBE’s coverage of Informatica World

The battle for enterprise AI dominance is heating up, and companies such as Informatica Inc. have been seeking to capitalize. At this year’s Informatica World, executives, companies and partners weighed in on how best to stand out with data platform services.Planned announcements from Informatica were a key area of interest. The event was a chance to hear more from executives about the company’s vision, how important partners are and how customers are leveraging technology to move AI strategies forward, according to theCUBE Research principal analyst Rob Strechay.Rob Strechay and Rebecca Knight of theCUBE announced the latest announcements coming out of Informatica World 2024

Informatica and Oracle team up to deliver cloud data integration and governance

When Informatica Inc. announced a strategic partnership with Oracle Corp. two years ago, the focus was on delivering cloud data management, database integration and governance solutions for enterprise customers. Since then, the two firms have continued to bring Informatica’s artificial intelligence-powered Intelligent Data Management Cloud to Oracle Cloud Infrastructure as their customers increasingly build generative AI workloads.Oracle’s Neil Mendelson and Informatica’s Rik Tamm-Daniels talk with theCUBE about effective data management

play_circle_outline Partnership between Oracle and Informatica

play_circle_outline Enhancing Oracle Cloud and Informatica with AI, Machine Learning, and Analytics for Multi-Cloud Expansion and On-Premise Support

play_circle_outline Metadata visibility and cataloging for better data understanding

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Neil Mendelson | Oracle & Rik Tamm-Daniels | Informatica

Neil Mendelson

VP, Product Management Oracle

Rik Tamm-Daniels

GVP, Technology Alliances Informatica

search

Rebecca Knight

>> Hello everyone, and welcome back to theCUBE's Live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host and analyst, Rob Strechay. Rob, we're having so many great conversations. We're in the middle of day two of a 3-day conference here talking about data and AI innovation.

>> And I think, again, when you think about it, there's a lot of data out there that really needs to be joined together with the AI and with the algorithms and to really provide value because again, it's about context and it's about also security and privacy and all of the things that give you guardrails as well, which I think we'll get into a little bit during this conversation.

Rebecca Knight

>> Indeed, we will and indeed will. I'd like to welcome our next guest to the segment, both CUBE alumni, CUBE veterans, really Neil Mendelson VP of data and AI at Oracle. Thank you so much, Neil. And Rik Tamm-Daniels GVP, Global Vice President of Technology and Alliances at Informatica. Thank you so much.

Rik Tamm-Daniels

>> Great to be here.

Neil Mendelson

>> My partner.

Rebecca Knight

>> Well, let's start there, Neil. Talk us through this partnership and sort of the joint collaboration that goes on.

Neil Mendelson

>> Well, so a bit of a history. So Informatica was one of our first partners way back in the day when Oracle began in the data warehousing business. So many of the customers that we acquired during those early years are still with us today, both from an Informatica point of view and an Oracle point of view. And we're delighted to be able to bring those customers into the modern era, if you will, and give them the opportunity to take advantage of not only AI but machine learning and all the other things that are exciting.

>> Yeah, I think, again, that to me is, it has been a long relationship. I mean, having been a user of Informatica back in the probably early 2000 or mid-2000s, you start to look at how the relationship has just grown. And again, from a Informatica perspective, you're also part of Oracle Cloud now, and you're being able to be more than just, "Hey, it's where it started," which was On Premise because there was no such thing as Cloud even back in those days. So how has this evolved in what you see with, especially as we get towards GenAI?

Neil Mendelson

>> Well, one of the things that from my perspective makes Informatica a great partner is, I mean, you mentioned On Prem, right? We have a lot of data that's still On Prem, right? In fact, one would argue that a lot of the systems that run a business or a government are still On Prem, and many of those are still running Informatica and so forth. So we have that responsibility to try to take people forward. You mentioned OCI, which Oracle Cloud infrastructure. Most people don't realize Oracle have the Cloud, but indeed we do. But we're also as excited to expand into Azure now. So for our customers that are using combination of Oracle and other technologies, and Informatica brings that all together in a multi-Cloud world, but importantly can also reach into On Prem because it's a transition. You just don't turn over overnight. So it gives us that continuity.

Rik Tamm-Daniels

>> And as Neil said, I think that's a very unique aspect of the partnership, is just the existing customer base. And I look at it as back in the early days of a previous generations of partnership, it was all about helping customers succeed with their data and analytics and really building a trusted, robust foundation. And when we got together to figure out what does this partnership look like in the Cloud, that was a big piece of it, understanding what should the foundation look like in the Cloud for modern analytics. And one thing we did very uniquely is we got together and said, look, instead of giving customers dozens and dozens of choices, and they have to figure out what they're doing, we're going to put our heads together and say, what is the right solution for customers to get to the end state? And the great thing is, we've been now in the era of Generative AI, we've been doing exactly the same thing, building out those templates and roadmaps, if you will, to help customers on that journey.

Rebecca Knight

>> So how one size fits all is it? I mean, in the sense of, as you said, we wanted to make sure that we were together and on the same page and brought the unified approach of here's what we think our best practice is here, but how much are you saying, "Okay, this will work for you in this industry versus other functionalities and other industries?"

Neil Mendelson

>> Well, we focused our business from an industry perspective over the last few years. So at one point, if you Oracle, we are generic across the board now. We're very focused on individual industries. And we started this years ago with purchasing many Cloud-based applications that were industry-based. And from most hotels you might see on the kiosk, Micros, Oracle, those kinds of companies and so forth. So these things have begun for some time, and it's not just Oracle and Informatica, it's the entire ecosystem that's out there. And customers do have choice, and that's a good thing. And the more we can provide that, the better.

Rik Tamm-Daniels

>> I would say to your point that a great example though, if you look at the, so Oracle has this great website for often kinds of reference architectures. And when you look about modern Cloud analytics, Informatica's there, so it's one shared picture that we're bringing to customers. And that's not a one-size-fits-all though, right? It's more of a foundation and a platform and saying, these are the best practices for, you need governance, you need integration, all the different things that IDMC brings to the table. But again, giving customers that confidence that this is a shared vision, a joint vision, and it's vetted and tested from both Oracle and Informatica.

Neil Mendelson

>> It's not restricted to just analytics and analytics alone because the reality of it is that customers really don't want these abstract divisions. They don't see those abstract divisions. They just want their data. That's what they want. So the more that we can bring this together in a unifying fashion, the better off. And beyond analytics, there's data that is transmitted around the world on an ongoing basis. Banking systems, health systems and so forth. And Oracle's been there through, you're probably familiar with our Golden Gate product, one of our early acquisitions, right?

>> Oh, yeah. Oh yeah.

Neil Mendelson

>> So one of the really exciting things I think that's happening in Informatica talks, we've heard a lot about this at this conference, which is in order to get that AI, you need governance, you need these functions. And that's true not only for analytics, but for real-time data as well. So one of the recent things that we did with Informatica was we opened up our Golden Gate interface. So now we have the ability to manage Golden Gate metadata along with your other metadata from around the world, other vendors and so forth into Informaticas IDMC and we can bring that governance not just to the analytical workloads, but as you may know, Holden Gate is the infrastructure behind banking and governments around the world, and we need to bring visibility into that, not just the analytics side.

>> Yeah, I think that to me is, I mean, again, like you said, across all things, I mean, there's Cerner, there's a whole number of different verticals that Oracle has been into, and one of the things that we were just chatting beforehand is that people are getting to the point where the cognitive load on understanding where is your data. So having that catalog and centralized and governance is huge because most data teams want to just talk to their data to actually drive those insights. What are you seeing as this relationship grows, especially as we get into things like Clare and other places?

Neil Mendelson

>> One of the challenges I think that we kind of really attack together was that you've got all these systems that are out there, and they've been built over time, many of which have been acquired through acquisitions and selling off and things like that. So you kind of have a mixture of all kinds of different stuff that's out there, and you want to be able to bring that together on a more cohesive basis. So being able to do these kind of practical arrangements with Informatica and not only in our own Cloud and OCI, but extending that into Azure. I mean, we talk a lot about multi-Cloud because we believe it. I mean, we've been the backbone of most operational systems around the world and still are. And I remember years ago people said, "Well, decision support data," it's not mission-critical, so we don't need the kind of controls that you have on your operational system. It's kind of like if you talk to any CEO today and you ask them, "Do you think your analytics is mission-critical?" They'll say, "Absolutely." That's how we do surge pricing in the middle of the day. That's how we deal with our competition. It is now. So bringing that capability into the forefront so that people not only have the ability to speak human to their data, but they can be assured that the data has the right providence, that it has gone through the right inspections. And this is an area that we long focused on trying to make sure that only the people that are authorized to see can see.

Rik Tamm-Daniels

>> Yeah, I would say with GenAI as well, I think has created even more demand for that metadata view centric view of the world, the catalog view, and not just the physical view of what's out there, but the semantic view. What does this data actually mean? What does it represent for my business? And when you think about conversational AI applications, it could be as simple as when you want to create an enterprise-facing app, when someone else look for customer information, is your customer a business or does a person, the public models don't know that they can't differentiate it, but when you have that rich metadata foundation that defines who is a customer, what does it mean? And you can add that to your context for inferencing, it does really ground the Generative AI capabilities in the enterprise information, the enterprise context of your business.

Neil Mendelson

>> And that's really a big part of our focus. We're not in the global LLM markets, so we're not producing the next version of search and getting involved in the Google whatever. It's like, cool, let them do that, right? We're on the enterprise side, and companies are really concerned about allowing their employees and so forth where literally the data that they've held closely is now made available for other people to build AI on. I mean, I'm sure there were a lot of people that woke up, whether it's contributing to GitHub or Reddit or others like that, where all of a sudden what you thought was your intellectual properties now being scooped up by others. So a lot of what we do is to focus on how do we make sure that we're all about the private side? How do you make sure that not only what you're building in your models are both industry-specific, which we're doing a lot of, is does it understand the key words, the terminologies of those industries, those fundamentals? And is it kept utterly and completely to yourselves? Fundamentally important. And that's completely different from what you're hearing about from a classic GenAI point of view, where in search, there's no concept of... Everything's open, but hat's not how enterprises work.

Rebecca Knight

>> Right.

>> But also, I mean, hallucinations are not a problem when you're doing search necessarily. When you start to get into healthcare or retail, like you said, pricing, you want kind of the answer to come back the same pretty much every time. And I think we were riffing on this earlier, is the fact that, again, it's not just about the prompt either. It's about building data products and data apps, like you talked about surge pricing. And we always talk about Uber for everybody and things like that, where you're building these data apps that are hugely... What do you see from your customers really going that direction and building data products and data apps?

Neil Mendelson

>> Well, we're seeing accelerate. Our customers have always built data products. I mean, you go back in this industry where retail started early on, there was a company IRI Nielsen that collected data retail and so forth, and these were instrumental. So now what we're seeing companies do is they've always wanted to talk human to their data. And I think people are confused because the quantitative result that they're looking for is not a function of GenAI. That's the human interface. The quantitative result is a statistical or mathematical model from a machine learning point of view. And that will be computed not in the GenAI context, but within your calculation engines and the like. So you may get a hallucination coming from the standpoint, from a text point of view, but we want to make sure that when you ask the question of "How many customers do I have?" Or this and that, your answer is still precise and it's not some hallucination that took place from some other factor.

Rik Tamm-Daniels

>> That grounding concept is so critical for enterprises. And so we see a lot of demand around, for example, our master data management capability. So we have that super high fidelity customer data, supplier data. It's high quality, it's trusted. And to your point, I think the generative piece is understanding the intention, the user, what kind of information they're looking for, and be able to route that query to the right underlying data to then do the computation rate, get the exact answer. So again, I think that's a big area of focus for folks is how do I, like I said, get the hallucinations out of it. And that also builds confidence and trust because critical, it's always key for analytics, especially true when people know there can be issues, they know there's this idea of hallucination. And so giving them a foundation that addresses that from the get-go is critical.

Rebecca Knight

>> Can you speak to any more specific use cases that customers are coming to you with their particular pain points or challenges that they're facing and ways that your innovations have helped?

Neil Mendelson

>> Sure. I mean, you'll see examples where simple examples where technical products have always had documentation of some sort or another, right? And search from a documentation point of view, we know what happens, right? I mean, it is what it is and it's great, but we can do better. So putting those kinds of projects into a GenAI interface where it's very contained and you can do it very quickly, and now instead of the old way of doing things, you can do it through the new way, right? Searching various things like that. And what we're trying to do is to make it as easy as possible, and that's why we're building the capabilities into the same infrastructure where your data lies. We're not the only ones. You see everybody's introducing Vector to go along with their data, and it's like, why would you have a separate vector store? So forth. And the same is true with everything that we asked today has a context related to geography, like, where are you? On this floor, on that floor? Where are you? So the spatial context, the relationships between people, right? I mean, what we need is answers that are quick. And if you've got this information all over the God's green Earth in these separate little niche systems, you're never going to gather it together to the point where you can provide real value. So we're just trying to make it easy, and we're asking customers again to say, "Hey, we're in the news again, so customers still were there and so forth, but we're back now and we've always been there, and we're here to help as much as we can." And meeting customers where they lie. So partnerships with Informatica and others are really important with Microsoft and others like that because at the end of the day, we have a responsibility to our customers and to governments and to industries around the world to treat their data with the ultimate amount of respect and security and to help in any way we possibly can.

Rebecca Knight

>> Well, a fine note to end on. Neil and Rik, thank you both so much for coming back on theCUBE. Especially, I like the "speak human to my data" I like that. I'm going to use that. I'm going to steal it. I'm Rebecca Knight Rob Strechay. Stay tuned for more of theCUBE's live coverage of Informatica World. You are watching theCUBE, the leader in enterprise tech coverage and analysis.

Neil Mendelson | Oracle & Rik Tamm-Daniels | Informatica

search

Rebecca Knight

>> Hello everyone, and welcome back to theCUBE's Live coverage of Informatica World here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host and analyst, Rob Strechay. Rob, we're having so many great conversations. We're in the middle of day two of a 3-day conference here talking about data and AI innovation.

>> And I think, again, when you think about it, there's a lot of data out there that really needs to be joined together with the AI and with the algorithms and to really provide value because again, it's about context and it's about also security and privacy and all of the things that give you guardrails as well, which I think we'll get into a little bit during this conversation.

Rebecca Knight

>> Indeed, we will and indeed will. I'd like to welcome our next guest to the segment, both CUBE alumni, CUBE veterans, really Neil Mendelson VP of data and AI at Oracle. Thank you so much, Neil. And Rik Tamm-Daniels GVP, Global Vice President of Technology and Alliances at Informatica. Thank you so much.

Rik Tamm-Daniels

>> Great to be here.

Neil Mendelson

>> My partner.

Rebecca Knight

>> Well, let's start there, Neil. Talk us through this partnership and sort of the joint collaboration that goes on.

Neil Mendelson

>> Well, so a bit of a history. So Informatica was one of our first partners way back in the day when Oracle began in the data warehousing business. So many of the customers that we acquired during those early years are still with us today, both from an Informatica point of view and an Oracle point of view. And we're delighted to be able to bring those customers into the modern era, if you will, and give them the opportunity to take advantage of not only AI but machine learning and all the other things that are exciting.

>> Yeah, I think, again, that to me is, it has been a long relationship. I mean, having been a user of Informatica back in the probably early 2000 or mid-2000s, you start to look at how the relationship has just grown. And again, from a Informatica perspective, you're also part of Oracle Cloud now, and you're being able to be more than just, "Hey, it's where it started," which was On Premise because there was no such thing as Cloud even back in those days. So how has this evolved in what you see with, especially as we get towards GenAI?

Neil Mendelson

>> Well, one of the things that from my perspective makes Informatica a great partner is, I mean, you mentioned On Prem, right? We have a lot of data that's still On Prem, right? In fact, one would argue that a lot of the systems that run a business or a government are still On Prem, and many of those are still running Informatica and so forth. So we have that responsibility to try to take people forward. You mentioned OCI, which Oracle Cloud infrastructure. Most people don't realize Oracle have the Cloud, but indeed we do. But we're also as excited to expand into Azure now. So for our customers that are using combination of Oracle and other technologies, and Informatica brings that all together in a multi-Cloud world, but importantly can also reach into On Prem because it's a transition. You just don't turn over overnight. So it gives us that continuity.

Rik Tamm-Daniels

>> And as Neil said, I think that's a very unique aspect of the partnership, is just the existing customer base. And I look at it as back in the early days of a previous generations of partnership, it was all about helping customers succeed with their data and analytics and really building a trusted, robust foundation. And when we got together to figure out what does this partnership look like in the Cloud, that was a big piece of it, understanding what should the foundation look like in the Cloud for modern analytics. And one thing we did very uniquely is we got together and said, look, instead of giving customers dozens and dozens of choices, and they have to figure out what they're doing, we're going to put our heads together and say, what is the right solution for customers to get to the end state? And the great thing is, we've been now in the era of Generative AI, we've been doing exactly the same thing, building out those templates and roadmaps, if you will, to help customers on that journey.

Rebecca Knight

>> So how one size fits all is it? I mean, in the sense of, as you said, we wanted to make sure that we were together and on the same page and brought the unified approach of here's what we think our best practice is here, but how much are you saying, "Okay, this will work for you in this industry versus other functionalities and other industries?"

Neil Mendelson

>> Well, we focused our business from an industry perspective over the last few years. So at one point, if you Oracle, we are generic across the board now. We're very focused on individual industries. And we started this years ago with purchasing many Cloud-based applications that were industry-based. And from most hotels you might see on the kiosk, Micros, Oracle, those kinds of companies and so forth. So these things have begun for some time, and it's not just Oracle and Informatica, it's the entire ecosystem that's out there. And customers do have choice, and that's a good thing. And the more we can provide that, the better.

Rik Tamm-Daniels

>> I would say to your point that a great example though, if you look at the, so Oracle has this great website for often kinds of reference architectures. And when you look about modern Cloud analytics, Informatica's there, so it's one shared picture that we're bringing to customers. And that's not a one-size-fits-all though, right? It's more of a foundation and a platform and saying, these are the best practices for, you need governance, you need integration, all the different things that IDMC brings to the table. But again, giving customers that confidence that this is a shared vision, a joint vision, and it's vetted and tested from both Oracle and Informatica.

Neil Mendelson

>> It's not restricted to just analytics and analytics alone because the reality of it is that customers really don't want these abstract divisions. They don't see those abstract divisions. They just want their data. That's what they want. So the more that we can bring this together in a unifying fashion, the better off. And beyond analytics, there's data that is transmitted around the world on an ongoing basis. Banking systems, health systems and so forth. And Oracle's been there through, you're probably familiar with our Golden Gate product, one of our early acquisitions, right?

>> Oh, yeah. Oh yeah.

Neil Mendelson

>> So one of the really exciting things I think that's happening in Informatica talks, we've heard a lot about this at this conference, which is in order to get that AI, you need governance, you need these functions. And that's true not only for analytics, but for real-time data as well. So one of the recent things that we did with Informatica was we opened up our Golden Gate interface. So now we have the ability to manage Golden Gate metadata along with your other metadata from around the world, other vendors and so forth into Informaticas IDMC and we can bring that governance not just to the analytical workloads, but as you may know, Holden Gate is the infrastructure behind banking and governments around the world, and we need to bring visibility into that, not just the analytics side.

>> Yeah, I think that to me is, I mean, again, like you said, across all things, I mean, there's Cerner, there's a whole number of different verticals that Oracle has been into, and one of the things that we were just chatting beforehand is that people are getting to the point where the cognitive load on understanding where is your data. So having that catalog and centralized and governance is huge because most data teams want to just talk to their data to actually drive those insights. What are you seeing as this relationship grows, especially as we get into things like Clare and other places?

Neil Mendelson

>> One of the challenges I think that we kind of really attack together was that you've got all these systems that are out there, and they've been built over time, many of which have been acquired through acquisitions and selling off and things like that. So you kind of have a mixture of all kinds of different stuff that's out there, and you want to be able to bring that together on a more cohesive basis. So being able to do these kind of practical arrangements with Informatica and not only in our own Cloud and OCI, but extending that into Azure. I mean, we talk a lot about multi-Cloud because we believe it. I mean, we've been the backbone of most operational systems around the world and still are. And I remember years ago people said, "Well, decision support data," it's not mission-critical, so we don't need the kind of controls that you have on your operational system. It's kind of like if you talk to any CEO today and you ask them, "Do you think your analytics is mission-critical?" They'll say, "Absolutely." That's how we do surge pricing in the middle of the day. That's how we deal with our competition. It is now. So bringing that capability into the forefront so that people not only have the ability to speak human to their data, but they can be assured that the data has the right providence, that it has gone through the right inspections. And this is an area that we long focused on trying to make sure that only the people that are authorized to see can see.

Rik Tamm-Daniels

>> Yeah, I would say with GenAI as well, I think has created even more demand for that metadata view centric view of the world, the catalog view, and not just the physical view of what's out there, but the semantic view. What does this data actually mean? What does it represent for my business? And when you think about conversational AI applications, it could be as simple as when you want to create an enterprise-facing app, when someone else look for customer information, is your customer a business or does a person, the public models don't know that they can't differentiate it, but when you have that rich metadata foundation that defines who is a customer, what does it mean? And you can add that to your context for inferencing, it does really ground the Generative AI capabilities in the enterprise information, the enterprise context of your business.

Neil Mendelson

>> And that's really a big part of our focus. We're not in the global LLM markets, so we're not producing the next version of search and getting involved in the Google whatever. It's like, cool, let them do that, right? We're on the enterprise side, and companies are really concerned about allowing their employees and so forth where literally the data that they've held closely is now made available for other people to build AI on. I mean, I'm sure there were a lot of people that woke up, whether it's contributing to GitHub or Reddit or others like that, where all of a sudden what you thought was your intellectual properties now being scooped up by others. So a lot of what we do is to focus on how do we make sure that we're all about the private side? How do you make sure that not only what you're building in your models are both industry-specific, which we're doing a lot of, is does it understand the key words, the terminologies of those industries, those fundamentals? And is it kept utterly and completely to yourselves? Fundamentally important. And that's completely different from what you're hearing about from a classic GenAI point of view, where in search, there's no concept of... Everything's open, but hat's not how enterprises work.

Rebecca Knight

>> Right.

>> But also, I mean, hallucinations are not a problem when you're doing search necessarily. When you start to get into healthcare or retail, like you said, pricing, you want kind of the answer to come back the same pretty much every time. And I think we were riffing on this earlier, is the fact that, again, it's not just about the prompt either. It's about building data products and data apps, like you talked about surge pricing. And we always talk about Uber for everybody and things like that, where you're building these data apps that are hugely... What do you see from your customers really going that direction and building data products and data apps?

Neil Mendelson

>> Well, we're seeing accelerate. Our customers have always built data products. I mean, you go back in this industry where retail started early on, there was a company IRI Nielsen that collected data retail and so forth, and these were instrumental. So now what we're seeing companies do is they've always wanted to talk human to their data. And I think people are confused because the quantitative result that they're looking for is not a function of GenAI. That's the human interface. The quantitative result is a statistical or mathematical model from a machine learning point of view. And that will be computed not in the GenAI context, but within your calculation engines and the like. So you may get a hallucination coming from the standpoint, from a text point of view, but we want to make sure that when you ask the question of "How many customers do I have?" Or this and that, your answer is still precise and it's not some hallucination that took place from some other factor.

Rik Tamm-Daniels

>> That grounding concept is so critical for enterprises. And so we see a lot of demand around, for example, our master data management capability. So we have that super high fidelity customer data, supplier data. It's high quality, it's trusted. And to your point, I think the generative piece is understanding the intention, the user, what kind of information they're looking for, and be able to route that query to the right underlying data to then do the computation rate, get the exact answer. So again, I think that's a big area of focus for folks is how do I, like I said, get the hallucinations out of it. And that also builds confidence and trust because critical, it's always key for analytics, especially true when people know there can be issues, they know there's this idea of hallucination. And so giving them a foundation that addresses that from the get-go is critical.

Rebecca Knight

>> Can you speak to any more specific use cases that customers are coming to you with their particular pain points or challenges that they're facing and ways that your innovations have helped?

Neil Mendelson

>> Sure. I mean, you'll see examples where simple examples where technical products have always had documentation of some sort or another, right? And search from a documentation point of view, we know what happens, right? I mean, it is what it is and it's great, but we can do better. So putting those kinds of projects into a GenAI interface where it's very contained and you can do it very quickly, and now instead of the old way of doing things, you can do it through the new way, right? Searching various things like that. And what we're trying to do is to make it as easy as possible, and that's why we're building the capabilities into the same infrastructure where your data lies. We're not the only ones. You see everybody's introducing Vector to go along with their data, and it's like, why would you have a separate vector store? So forth. And the same is true with everything that we asked today has a context related to geography, like, where are you? On this floor, on that floor? Where are you? So the spatial context, the relationships between people, right? I mean, what we need is answers that are quick. And if you've got this information all over the God's green Earth in these separate little niche systems, you're never going to gather it together to the point where you can provide real value. So we're just trying to make it easy, and we're asking customers again to say, "Hey, we're in the news again, so customers still were there and so forth, but we're back now and we've always been there, and we're here to help as much as we can." And meeting customers where they lie. So partnerships with Informatica and others are really important with Microsoft and others like that because at the end of the day, we have a responsibility to our customers and to governments and to industries around the world to treat their data with the ultimate amount of respect and security and to help in any way we possibly can.

Rebecca Knight

>> Well, a fine note to end on. Neil and Rik, thank you both so much for coming back on theCUBE. Especially, I like the "speak human to my data" I like that. I'm going to use that. I'm going to steal it. I'm Rebecca Knight Rob Strechay. Stay tuned for more of theCUBE's live coverage of Informatica World. You are watching theCUBE, the leader in enterprise tech coverage and analysis.