Evolving Data Insights With Privacy at Mastercard

Summary

• Mastercard's privacy-first service: Mastercard operates 143 billion transactions annually and developed a novel service atop Databricks Clean Rooms for privacy-preserving data insights.
• Clean Rooms for trusted analytics: Secure computation environments enable customers to analyze Mastercard's transaction data while maintaining compliance and data privacy.
• Large-scale privacy governance: Managing data privacy for 3.4 billion cards requires sophisticated governance—Databricks and Clean Rooms provide the platform for secure, compliant insights.

Evolving Data Insights With Privacy at Mastercard

Talk by john Derrico, VP Data Strategy, Mastercard ; Spencer Cook, Lead Solutions Architect, Databricks
Mastercard is a global technology company whose role is anchored in trust. It supports 3.4 billion cards and over 143 billion transactions annually. To address customers’ increasing data volume and complex privacy needs, Mastercard has developed a novel service atop Databricks’ Clean Rooms and broader Data Intelligence Platform. This service combines several Databricks components with Mastercard’s IP, providing an evolved method for data-driven insights and value-added services while ensuring a unique standalone turnkey service. The result is a secure environment where multiple parties can collaborate on sensitive data without directly accessing each other’s information. After this video, attendees will understand how Mastercard used its expertise in privacy-enhancing technologies to create collaboration tools powered by Databricks’ Clean Rooms, AI/BI, Apps, Unity Catalog, Workflows and DatabricksIQ — as well as how to take advantage of this new privacy-enhancing service directly.
Here’s more to explore:
Economist Impact: Unlocking enterprise AI: https://www.databricks.com/resources/webinar/unlocking-enterprise-ai
The State of Data + AI: https://www.databricks.com/resources/ebook/state-of-data-ai
See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements

FAQs

What is Mastercard's role in global payments?

Mastercard processes 143 billion transactions annually, supporting 3.4 billion cards across global partners—requiring sophisticated data governance, security, and privacy infrastructure.

How do Clean Rooms enable privacy-preserving analytics?

Clean Rooms provide secure computation environments where customers can query Mastercard's data to derive insights without accessing raw transaction details—maintaining privacy and compliance.

What compliance requirements drive Mastercard's approach?

Global payment systems must comply with regulatory frameworks including data protection regulations, anti-money laundering rules, and industry standards—Clean Rooms help meet these requirements.

Can customers build custom analytics without data exposure?

Yes, customers can run proprietary analyses within the Clean Room to understand Mastercard's data landscape and opportunities, without Mastercard or other parties seeing their analytical logic.

Full transcript

So, you know, we always like to start with thank yous at data bricks. So, thank you all so much for coming. I uh actually see some people that I know came to one of my first talks on this topic like three years ago now. So, it's uh fun to be part of the journey with you guys and uh thanks for attending again and obviously thank you to for uh you know always being a great partner of these things and uh and participating again in the madness.
So, cheers. So, it's been three years. So, just a little bit of help. Who there's been some talk about like clean rooms. Oh, shoot. I fit into Mastercard. So, I'm on the strategy side of this. So, I get to play with for the last three years about this concept. And I'm the lead solution architect at data bricks on uh payments. So what's been fun about this is the bit of the journey and it's been really cool to see so many things happening with clean rooms and the whole concepts around private sharing of data just from a context to make this relevant for you because and I have done this a few times to try to get back to it like who here is currently using clean rooms or wants to use a clean room to sort of help me out a little.
Okay, that's awesome. Um the other thing is from this was if there were some pieces about this morning that who is our uh head of data and AI talked about some things. We're going to have time for questions at the end but also they're going to kick us out. So if you need have questions after you find I about it. Um I've got this really hard to find jacket instead of shoes. So like you'll never see me again in the crowd.
Yeah I had one too and um is now wearing it. So yeah, make sure your boss has the swag is what I've learned and I have a pen. Um, but in all seriousness, it there's been a big thing about this journey we've had about the people, the platform, and the partnership with data bricks. So if you haven't had that much of a chance to work with the teams, I cannot speak highly enough about it because this was a really fluid and dynamic deliverable that we made to put privacy capabilities in the clean room and we learned a lot about the journey which we're going to talk a bit about where that fits into the AI curve and everything else today.
But as we ground all this, if you think about the um the data bricks platform, it is the basis for our ethical data use and responsible AI. If that is of interest to you, you can just Google it. You will see Mastercard has principles and blueprints for all of that already. So we also talk about taking a privacy first approach to data governance and that helps enable private insights and that's the basis of what we have together here.
Yeah. So where and I kind of started uh taking yourself back you know three or four data and AI summits ago the biggest problems teams were having it wasn't necessarily getting the latest agentic system to completely replace all of your employees that was still like literally in the realm of science fiction right what Mastercard and tons of other people were looking at was basically how to get from BI tie to predictive analytics to having any sort of uh uh you know self-improving automated system for uh intelligence.
And so data bricks spent a lot of time trying to connect that chasm and basically it came down to those those three different gaps of like getting to you know what will happen, how should we respond and then making the best decision. And our point of view was that each one of those had kind of some unique characteristics of why teams failed at that particular chasm. And so what and I have kind of gone through at this point is like, sorry, you heard on stage, it's public, Mastercard is at the top of the curve and they're basically setting the standard for how to do these things.
And so when we were reflecting back, it's like, oh wow, what did we really do over the last 36 months to achieve this kind of result? And a lot of it came back to things that you might not necessarily expect. Things like governance, things like privacy controls, etc. that aren't necessarily part of some, you know, flashy chatbot, but turns out it's the actual stuff that makes all this possible. So with this, I'm assuming most of you are here familiar with the data maturity curve.
This is the basis for what we're going to talk about for an AI maturity curve as well. And when and I went into this, we didn't know how well this would play out. But the one, two, and three, that's the chasm. That's the gap that we're trying to cross along the way. And it's okay to be at certain parts of the sort of journey, but most people want to get to the top along the way. The view's nicer and you could have a heck of a lot more fun coming down.
And that's what set us up for the next piece here. So, I have another question for you. How many here like ski andor snowboard? Yes, this is going to make sense to you. Okay, so what's really cool about this? This is and if you've ever seen Juna's presentations, he uses all like AI generated images. That is not that is also not me because that's a skier. With that being said, when I'm not on stage doing this stuff with, I teach snowboarding.
I've been teaching it since college, so it's quite a while. And I the lessons that we have here is kind of like what I learned on the mountain. I talk about things what it's all about fundamentals. What you do on the trail, you take to the trees and you execute in the terrain park. Those fundamentals that the simple things that you do in some sports work. Now we're talking about gaps and chasms. Why not put a 30-foot tabletop?
Because sometimes that's what it's going to feel like. This works through. But along the way, there's information to be had. And for those of you who studied physics or at least hurled yourself in the air, that bottom picture may be familiar. But what's really fun about this is when you think about this brave new world and how it all converges, it's really interesting about how much information we take in and we use.
That information is context, metadata, and controls. So, if you've ever been through a terrain park, have seen it, you can imagine. And with that curve underneath, there's pieces about environment, information, and actions. That environment coming through. What's happening with the weather? What's happening there at that takeoff? What do you need to do for that information? Which way you going to lean one way or another?
Then lastly, the actions you're going to take, what you need to do in the air to stick the landing. And when you think about the AI curve, and we're going to talk about how do we stick that landing and get things to production. And this is what's really fun along the way. And I can totally geek out on this if you have other questions. I could talk to you all about the physics of snowboarding and not killing yourself on the tabletop.
But with this, there are controls and we talk about the context and the metadata to support it. Yeah. I mean, really what it comes down to me is everyone can uh strap on a set of skis like a bachelor party and aim down and it's probably going to work out. But if we want to repeat this stuff at scale, it turns out that there's like basically some common methodologies, some approaches you can take that make it safer, more successful, and like said, more fun because you're kind of, you know, leading your own journey.
So yeah, go ahead. No, you run. So when we think about that journey now, think about back to these gaps. , why don't you run with this a bit? Yeah. So it's it's basically like as uh as Genai became the type of AI that uh these conferences talked about four or five years ago and it kind of showed up overnight. We've really learned a lot just in the last 12 months. So really one of the first things that data bricks delivered for enterprises uh coming up on two years ago was rag and the ability to you know dump a huge context window into a chatbot.
Well, if you're listening to Tanin's talk today or or uh interview with Hanwind, we talked about how that's no longer sufficient for the types of use cases that most enterprises have. And so it turns out that there's a new set of chasms that we have to go through. Now I'll go ahead and brag again. As you heard on stage, Mastercard is actually deploying agentic systems at scale and they're creating agent factories. And so they're also when we kind of took a step back, it's like, oh, we're also at the peak already for uh AI.
This is fun. And so it's kind of like how did that uh happen? And it turns out that a lot of the same things that we had done strategically to get ready for the original data maturity curve put us in a really strategic position to accelerate our journey on the AI maturity curve. So that's where the data curve and the I curve makes sense. It's the same foundational elements that we're trying to hopefully you guys are starting to see of governance and lakeouses prepare you for both of those curves.
So to make a bit of a connection here, if you think about that context, that's the environment, right? And that environment, that's your operational reality. That's the practical things that happen, whether it be from the the people and the culture and the organization to make it happen. Now, you think about the metadata we talked about along the way. That's all the information that's happening within the systems, the system interactions that you need for the data and for AI along the way.
And it's that flow, that feedback of the people, the process, and the human in the loop. Then lastly, those controls. What are those actions you need to take to control as you work your way up the curve and the increasing complexity that need to happen? What's the strategic implementation you need to have for these controls, the deployment for the policies, the governance, the frameworks that help ethical and responsible deployment for scaling across the enterprise.
So if we think about it, those increasing levels of complexity of working your way through hopefully are starting to make some sense. Now the context, the metadata and the controls are needed for the privacy insights are more complex as well the further you go out. Yep. And so uh and I, it turns out I'll go ahead and do the build. They uh turns out we have a very different experience uh at the slopes. So when he's talking about launching himself off the curve, I I it made sense.
But to me, it's almost like uh the different levels of complexity of going from, you know, you start with greens on the bunny hill, you might do some steeper stuff. It's a learning development area. It's not Sorry, we practiced this uh the learning development area. Yes. So, once you progress past that, you can safely maybe take a more challenging blue if it's something you've seen before. But eventually we all want to get to the point where we're at the top of the mountain and we can kind of pick what we want to do.
So if you think about it visually, you start at the bottom and you're using things like business intelligence uh tools to solve certain use cases. We've been talking about for years the challenges and taking those to production and that's led us into uh advanced analytics which is awesome but it can be a little bit of a trough. I'll pick on myself here. I am uh stuck on the blues and don't have a ton of interest in going to the uh black diamonds. will help me.
Right. And a lot of AI companies or companies that want to be AI, they end up in the same spot where you kind of are doing advanced analytics. You have a cool story, but you're not able to really take control. And where we all want to get is to the summit of AI. Because what's beautiful is when you're at the top of the mountain, you can actually say, "Oh, I want to go see the view over there. I'm going to meet you at the middle of the mountain and get barbecue."
You are in control of your route and of your destiny. You really are. But but visually when I think about your architecture, because it's it's a platform, it's not built for a use case. someone coming in, they can kind of plan what they want to do in the same way I might plan a run where it's like I need to do this, this, and this. Do I have the right controls? Do I have the right context? And then they're basically uh, you know, picking this decision tree.
So to that point, if you think about if the rag's straightforward and the data is fixed, right, and the agents are more complicated, you can imagine the privacy constructs need to complete increase as well. And the complexity gets even more when you start to deal with the Gentic and have that multi-dimensional controls that need to happen in that distributed environment. So more sophisticated orchestration along with that comprehensive audit and consent along the way.
Absolutely. And it lets you do like if I saw you snowboarding, I probably would have a heart attack. But with you, it's like I'm safe. I'm in control. And it's because you put the right systems in place. So to this, we're beating this one up, but it's really that increasing levels of complexity is the reason why Mastercard was able to get things to prod. And I'll make you bet half this room has your data to the cloud faster than us.
And I can say that because the majority of the tech teams over at another talk be coming in soon. But the reason why we're able to get things to prod is actually having the controls and treating governance as value. Privacy first, privacy by design makes a fundamental difference. It's really hard to get your data to the cloud. It's even harder to get things launched into produc. All right. All right. So, Mastercard. So, for those of you who may or may not know us, we're grounded in trust.
If you think about us, financial services powering a global economy. Trust is the core of our business. And we've expanded in many different ways to help forward a digital economy for all. So, let's see. I got to back up for this. Oh, no. Or this. There we go. So, our expansion is having the expansion in payments, products and services as a trusted provider of insights. And part of these growth areas now supports our data strategy with evolving data uses.
And part of this is our data and AI innovation that you see here. And we talk about it in four ways that talked this morning about safer, smarter, more personalized, and stronger. But the challenge is with this happening also grows our data profile. So in the last 10 years we've expanded beyond the card realms. We've expanded exponentially and that expansion is in three ways. Volume, variety and velocity of the data of which it's accessed, processed and actioned.
And if you think about some of the things we're trying to do here, it's the strategies that manages these data risks for that expanded data profile. So part of this as we've grown through with the privacy first approach and the data intelligence platform that's helping making this possible because we don't know what's coming next but it's that investment in those controls a long time is the basis for how we've grown but now as we've grown here we go it worked through so have the challenges if I'm in front of this it just doesn't work so if you think about the challenges for us they fall into three areas and this is a simple build if you think about it from a governance deployment but then also alo the sharing that needs to happen along the way.
So those data combinations and that's why the context metadata and controls are needed and that's why governance is first. Yeah. Go ahead and do the data bricks build. So essentially when and I first started working on these things we kind of figured you know these are the major tent poles of problems that we needed to address and it mapped to three parts of the data brick. So it's now the data intelligence platform what was a lakehouse platform at the time and basically those areas were the unity catalog governance layer the mosaic AI uh AI layer and then uh clean rooms as sort of the tip of the spear for collaboration.
Obviously clean rooms uh has a ton of stuff going on below the surface like uh access to delta sharing etc. And so basically that family of capabilities kind of put us in a a really good position to try and address these problems holistically. So when we addressed a data governance problem, it was in support of a mosaic AI application and development. We were lifting all ships. when we would uh implement a privacy-enhancing control in the clean room, all of those worked consistently with both ML and data applications.
So, we're making sure that everything we're doing for the end AI platform worked with the secure uh data collaboration. And so, basically, you were able to get the data and AI governance, the secure delivery, the different combinations that you needed. And really that having all three of those addressed was where the greater than the sum of its parts piece came in because that was really the platform. Totally. And as mentioned earlier today and I didn't know we was going to be talking about this so it actually worked out really well.
So you guys have some context. He talked about the one of the middle ones we talked about. We have to rename it from Mosaic to their agent agent. No just Mosaic. That's the platform. Okay. Still going to call it that. So when we think about the Mastercard assistant um platform, it does three real things as he talked about. It's got that privacy first architecture because you think about it's got to do the federated data governance and quality controls at scale.
The second thing is that trust by design embedding of the sensitive information for all the compliance tagging and management. And the third is that enterprise consent capabilities at scale for that the usage aligns with the customer protections. The last circle there, we're going to talk about the clean rooms later on, but hopefully you see the way the AI piece follows the work that we did on the AI and governance. All right, journey falls into three C's.
Consumer, creator, culture. I'm going to walk through this pretty fast because some context is set. So for Mastercard, we are a consumer of data bricks. Okay? And let's assume that all of you are. That is the first thing. how the lakehouse is the hub of our hub and spoke efforts. Right? So if you think about it, we've taken the strategy and data and governance goals, matched it to the right infrastructure that you see here from a cloud perspective with that privacy first approach.
And this is where we think about how do you break down those silos of centralization because even though the data may be centralized, it still needs efforts to be governed, available, and consumable. Next falls into the creator. What are you all building with data bricks? Are you building for internal or external? So, we have some products and services that we're offering to internal teams and clients. One of it is that privacy preserving data clean room.
And it follows a few things. The Delta Lake for the organization and unification, excuse me, the uni unity catalog for its governance, but then also the sharing for data access. In addition, you can throw in some AIBI for the reports. and you want to have some more fun, you could even use Mosaic for some chatbot experiences we have and it understands that a pet is not a cat or a dog, but a pet is a privacy-enhancing technology along the way.
So, part of this tooling provides some of the basics that we move beyond into now the culture. At Mastercard, we like to say we innovate by default. So, what does that really mean? Focusing on use cases where we have a distinct advantage with our first party data and the insights to make these frameworks available. So that gets back down to what I talked earlier about why we tend to work so well together is there's a match on the culture, but it's also the people, the platform, and the partnership that makes this happen.
And I think you had an experience as well that you wanted to share on this. Yeah. So uh I get to spend a lot of time in Mastercard's uh innovation centers and they are all very nice, which is lovely. There is one in Manhattan that uh always strikes me because it's dedicated to one of your ex CTO's and it basically talks about the way that Mastercard innovated in the '9s like back when you were seeing the priceless commercials and everything else.
We didn't have stuff like data bricks, we didn't have AI. So like how were you guys able to build that empire when you couldn't just ask chat GPT what to do, right? And one of the things that he he talks about in this little tribute is uh doing well by doing good, right? And so that really struck me in the way that approached designing this platform is that if we do the right things, I if we do good and we deliver things that work well for our customers, our clients, our partners, it tends to make us do well as a company.
And so they brought that culturally to the cloud when we were first trying to design some of these things. And again, like the outcomes are undeniable even for use cases that we wouldn't like put in the privacy-enhancing technology camp. They've been so positively influenced in a fundamental way by these principles and I give you guys so much credit for it being a part of your culture. So to that as well as a takeaway for you all, there's other three things we do well in the culture.
We have a set of data and AI responsibility principles and they're available on the web. The second thing is we have an AI governance framework that's continuing to evolve and modernize as we do. And the third which we just launched and that's actually a racetrack with the sponsorship we have with F1. We call them our data and I momentum sessions. So it's actually happening around the globe. We have teams of data and AI strategy leads presenting to the product teams about what data is available, what are the controls, what are the governance, what are the use cases.
And actually one of them is happening right this week in St. , our technology hub. So this goes round and round and these are things that you guys could take home as well about the culture that makes all of this really sort of happen by default. Cool. Yeah. All right. These I would say very cool. Uh so these uh these slides, believe it or not, I kind of introduced back when we were first uh uh announcing clean rooms over three years ago now.
And it was to kind of talk about organizationally. I'm actually going to do an adjustment real quick just to make my diagram bigger so I'm not craning my neck. what what we kind of talked about was this idea that data bricks gives you full dimensions of collaboration and trust and what we've kind of heard feedback on when I've shared this with other people and we've kind of uh worked with it uh over time internally within Mastercard is really it's less about trust and more about context like was mentioning there are times where I trust you but because of a failure of my own not delivering you the right context, our uh use case might still be a failure, right?
And so context becomes this this key piece. And so basically, if you haven't seen this before, the idea is that the data bricks data intelligence platform is able to provide the full set of capabilities across both axes. And that kind of going back to the mountain analogy means you're not headed in one direction or stuck on one part of the axis because of limited capabilities. You can essentially build use cases across a full gamut of privacy capabilities.
And that's basically what the Mastercard pets layer was is a way of bringing that to business users and making it uh simpler to build privacy enhancing applications. So if you look at this, when we first started, we had I want high collaboration, but the context it was trust. I don't trust you. Not in a bad way, but I don't better I don't know you. It just didn't fit on the slide. So we talk about that. If I'm going to walk around and go to our lawyers, all right, I'm going to share data, but we don't trust them.
All right, we're going to bring the data in. We're going to spin it up. We're going to do this. I'm going to send them a banded laptop, wait six months. It's like, dude, this is not going to work. So what we did with the clean room with data bricks is that we actually took some capabilities around privacy-enhancing tech one of our vendors is here today. You'll learn more about that in another slide and actually made it available.
So in the build, does this require anonymization? Does this require synthetic data? Does this require differential privacy? Just freaking run it in the clean room. It's you put in as a wheel file, execute it, whichever ones you need for your use case because that's the context of the use case that's needed for the controls I need to execute and the metadata to make it happen. So hopefully you could see that's why we have the clean room with pets is all the way up in the top left corner.
But the other solutions you may need as well. If I'm consuming data directly from the marketplace, the only thing I do is source thirdparty data from Mastercard because we use a lot of it to add context to our data and solutions. The marketplace is just fine. I can do data trials and commercialization right there. I don't always need a clean room. And that's where this these vectors come in really handy. Yep. Absolutely.
And a key thing to keep in mind uh that we talked about, you know, a couple years ago is that before the clean rooms, that entire low context column was essentially impossible. And so there were entire use cases that depended on building trust starting with low context so you could mature into high context. But because that was missing, we were just stuck. And so that was one of the chasms. And so basically having this full gamut of capabilities starting with low context going to high is uh is really remarkable.
When I went to uh go check on, you know, how we had presented this a couple years ago, I was really struck by uh how little of this has really changed on the data side. It's actually been kind of cool to see these things all go GA get significant use. But this kind of blueprint I think is is you know pretty sturdy for data. Now what we wanted to talk about uh this year that we've realized you know throughout this journey together responding to Genai everything else is what is really the uh version of this matrix for AI systems because they're very different from analytical systems BI systems and so one of the things that uh you know we focus on here is replacing uh the capability or level of collaboration with modality Because AI is autonomous.
It doesn't necessarily need interactivity. So what AI needs is to be multimodal where you can have an agent that isn't just retrieving relational data, it can also talk to the internet or maybe instead of just generating a paragraph, it can also generate an image. And so that's what we mean by modality. The DI platform like you heard all morning with our announcements is a really rich uh multimodal platform for AI. So it gives you really rich AI interfaces and then we're using PETs within Mastercard to continue bringing the privacy enhanced focus to AI.
So what we see just as an example is in a situation where you have low context and low modality I might be handing you just a bucket of data with an instance profile and say hey I promise there's a model here good luck and for most of my career that was kind of the state of affairs for data science what we realize is that as we deliver more more modality like when I've seen teams delta share models they spend so much less time in the PC proof of value phase they're able to actually go and say oh the model works just like you said I see the schema I registered it what next right they kind of are moving on to innovation and then an example of full modality is like we saw this morning with model serving if instead of even making you responsible for infrastructure access management etc if I just serve it to you all of a sudden you can build complicated applications on top of my platform and so Those are some of the things that this PEP platform has has allowed us to do for both data and AI.
Now, going back down to low modality, what we've seen is when we add context like with rag serving, that's what originally allowed for the product onboarding assistant that talked about this morning that went live before uh agents were really, you know, in vogue. And so we were able to get to that kind of amazing capability through just adding more context to the same model. And so as you go further and take that to a snatch drill conclusion, data bricks last year introduced the data intelligence platform concept and basically made it so that all these different uh uh pieces of context that are floating around in Unity catalog can be turned into a product and consumed through Genie.
And so now all of a sudden you have high context, high control, high structure. And what we can basically see is that as you continue to build out this structure, the value increases. So when we add things like marketplace models, when we look at things like models through a clean room, they're all basically giving me more modality through preserving privacy, through preserving context. And so yeah, go ahead. So the if you think about the clean rooms again, it's not just intersections of data.
You can do model training and model serving in a clean room. It works. So when everybody else talks about clean rooms intersect data, think of think beyond what else you can do with that trusted serverless compute. Yes, there are some limitations based upon the size of the nodes, but really the art of imagination is up to you of what you can try to pull off and if you get stuck, let them know. True. That's what we've done.
So, I really say it's not just data. There's a lot you can do in a clean room that you're going to be hearing more and more about probably from many other teams as well as they start to dig in and explore. Yep. I think it's going to become a fairly standard practice for model collaboration in the same way that clean room, you know, products are are really starting to gain traction. What's awesome is because of this architecture, we don't have to change anything.
Basically, this this pattern that we made for data work for AI. And as data bricks has added capabilities like unity catalog tool uh uh capabilities, you can define really contextrich interfaces that add modality to your data and AI platform. And then you can have an agentic system that ties all these different pieces together, dynamically applies privacy, dynamically brings in context, but it basically gives you the complete picture for uh having the AI capabilities you need.
And the beauty is that it mirrors what we found for data AI and analytics. So you're not on a different mountain. you're kind of at the peak and you can still do analytics, you can still do BI, but you're doing it in this, you know, enhanced way. So, I want to get into the architecture, but uh go ahead. No, I'm just looking at the time. So, yeah, totally. That's what I mean. So, in closing, I wanted to kind of talk through literally what this kind of, you know, estate looks like from the bottom up.
So when and I first started working on this, it was how to put Mastercard data and client data together in a privacy uh uh enhancing or privacy preserving way, but that also created value for both parties. Totally. And so what we did was we brought together Delta Sharing, what was at the time data bricks new serverless capabilities and Unity catalog. What was cool about serverless is it allows you to kind of do compute in a trusted environment. data bricks is kind of playing the trusted party because the other two don't necessarily know each other yet.
So what uh what we basically launched uh announced three years ago I think went G two years ago is the collaboration ecosystem that includes things like marketplace clean rooms etc. Basically that layer made it simple to use things like serverless compute because you aren't designing your own API to you know shred the uh data every time. It happens automatically and then what we worked closely on with team and then also with partners like Rearch who are super awesome in terms of you know uh turning some of these things into systems and frameworks.
We were able to build out a ton of unique capabilities like the pets ecosystem, like a bunch of self-service tools to run all this. Go ahead. Yeah. So, so the orange is what data bricks going to give you. The green is what you can build on top. Yes. Thank you. So, if you think about that as well and this journey set us so when and I look back, we're like, dude, this actually kind of fits to the curve because you've got the context of whether it be your first party data or your client's data or you're taking thirdparty data from a marketplace and you're jamming it in for that use case.
That's the base. And then you think about the metadata, all that information that's going to happen along the way power the controls I need in this next layer. Do I need to do and you could talk a little bit about some of the pets and other ones we have there in a moment. But with this, it's really cool with the logging capabilities. You've got multiple Unity catalogs going on and based upon the privacy risk and transparency you need, you can actually have full audit trails here.
So I think there's one more build on this. Oh yeah. Yeah. I well I had a a point to make as well where basically well one of the things we talked about in designing this is it tells the uh uh consumer creator story as well basically on the bottom is when Mastercard was consuming things like you know their data using ED catalog over time you started to create net new capabilities and that's what these green boxes are and then essentially you developed this complete self-service layer that gives you full access to modalities and context text and the current plan is to deliver that through an app, have it be consumed through an app.
That's some of the things that we demoed last year. And this is all top to bottom integrated with master cards uh data bricks controls for things like workflows, approval, you know, uh governance and uh in the more traditional way. So with this you can think about based upon the use cases that you're having and you could do this even internally. So we'll use this with some of our acquisitions with moving data around.
Use the clean room internally. It doesn't always need to be excellent. It could be between your own partners. This is a really easy way to get the insights. And you can think about you could use those privacy tech on the way in. Do you need to synthesize or anonymize that we have with TRDA and synthesize? One of our partners is here. How so? As well synthesize on the way in, synthesize on the way out. What rules do I want to fire based upon the use case?
That context makes so much so helpful. and Rearch's been great with building out the different solution accelerators you all are going to see in months coming up for this based upon the flavors you have and I've been pushing back and you guys have been really helpful is how do we make this so a business analyst can do this that self-service layer and then the privacy lawyers come back and they're used to saying no if you ever see a privacy lawyer say yes take a picture because now they're saying yes and because and depends upon the use case and the context for what I need to do and our lawyers love saying yes because they're always saying no.
And this is so much easier for us. We have hundreds of use cases that are going on because the lawyers now understand that they could finally say yes and get the transparency. But really where you guys want to push this, you really can. Call to action. Call okay wrapping this stuff up. Um three things when you think about invest in the fundamentals. You better have a governance and privacy strategy. And the third one is how do you utilize that context, metadata and controls?
I mean, the governance and controls is not an afterthought. It's not to get through security. It's not to pacify your lawyer. If you treat the governance and controls as a value function, you're going to get ready for AI. And I don't know what's coming next, but if you use the same enthusiasm of like AI bricks and everybody going crazy for governance. I mean, you may look a little psychotic if your lawyers start to geek out on all that, but it's really important that we do as well because at the end of the day, I mean, yeah, sorry, from a snowboarder.
I have no idea what's coming next. We sure know we're going to be able to tackle it because you've got your house in order and you understand enough about the data, the use cases and whether this be even bringing data into Unity. I mean, we've got manifests running that have you can use if you you familiar with Unity, you can sit there and do the AI descriptions for the table. How do you have the context at the column and the row level for everything for every data set that comes in your data usage rights controls loaded into Unity and then you start to ask questions on it.
Now imagine you can ask questions of what use cases can I do and then have it recommend this is better for a clean room with these privacy controls. You're not far off. this gets really really fun getting ready for the future and as I talked about here just having those three things use the right system at the right time. Absolutely. I think that whatever you and I end up uh doing next is going to involve CMC. I like just I would there's no way it won't.
And so invest in this now and you're going to be ready for the future I think is the story. Cool. Absolutely. So thank you so much everyone. Emails there. Yeah. And I have to do the plug. Yes. To complete your surveys. Thank you so much everyone. Cheers.

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