In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions sits down with Ameesh Paleja, Executive Vice President of Enterprise Platform Technology at Capital One. They explore how Capital One leverages modern data architecture, automation, and AI to improve customer interactions and experiences. The conversation delves into the importance of standardized data, the role of AI-driven personalized services, and the integration of marketing and AI strategies.
In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions sits down with Ameesh Paleja, Executive Vice President of Enterprise Platform Technology at Capital One. They explore how Capital One leverages modern data architecture, automation, and AI to improve customer interactions and experiences. The conversation delves into the importance of standardized data, the role of AI-driven personalized services, and the integration of marketing and AI strategies.
[00:00:00] Producer: Hello, and welcome to the Data Cloud Podcast. Today's episode features an interview with Ameesh Paleja, Executive Vice President of Enterprise Platform Technology at Capital One, hosted by Dana Gardner. Principal Analyst at Interarbor Solutions. They explore how Capital One leverages modern data architecture, automation, and AI to improve customer interactions and experiences.
[00:00:27] Producer: The conversation delves into the importance of standardized data, the role of AI driven personalized services, and the integration of marketing and AI strategies. So please enjoy this interview between Ameesh Paleja and your host, Dana Gardner.
[00:00:41] Dana Gardner: Welcome to the Data Cloud Podcast Ameesh. We're delighted to have you with us.
[00:00:45] Ameesh Paleja: Thank you so much. I'm happy to be here.
[00:00:47] Dana Gardner: Great. You know, customer interactions are among the most important elements of capturing loyalty and fostering positive brand awareness for today's businesses. Yet the complexity has never been greater in how end users receive communications across multiple channels and in conformance with a vast patchwork of constantly changing regulations, especially in the finance sector, the only way to manage and approve all these various ways that end users are reached
[00:01:14] Dana Gardner: and served is through effective and pervasive automation, and that automation, especially in real time, is only as good as the data it relies on and the intelligence that supports it. In today's discussion, we'll explore how Capital One leverages a modern data architecture to build and enhance customer interactions and experiences.
[00:01:33] Dana Gardner: Ameesh, tell us why it's so important to get the underlying systems and information right in order to better serve the users through the greater use of AI.
[00:01:46] Ameesh Paleja: Well, this is a, this is a loaded question. It's, there is such an immense number of new dimensions as you were talking about. The growth of data has been tremendous over the last, you know, almost decade.
[00:01:57] Ameesh Paleja: You know, in the last few years it's been between a 20 and 30% year, year on year growth. When you think about the sheer volume of information that's coming to us, the, the ways you can collect it, the channels, we can collect it, uh, it's, it's almost overwhelming if you don't just start and start thinking about standardization and automation, as you were saying.
[00:02:17] Dana Gardner: Well, first, let's also hear about your role at Capital One. Tell us about your background and how you got to be where you are.
[00:02:24] Ameesh Paleja: So my, my background is I'm an engineer by trade, studied computer science at UC San Diego. Worked at Microsoft at the Windows Kernel. I was at Amazon for over a decade, working on lots of different things, but Prime Video was my big baby there at the Amazon App Store.
[00:02:37] Ameesh Paleja: And then I ran my own startup and then I was right, right. Coming before, before Capital One. I was at Google writing Gmail, calendar and chat. And here at Capital One, I have an amazing charter. My job is basically to help build all of the multi-tenant platforms that support our different lines of businesses and provide leverage for Capital One to be successful, you know, in the long term.
[00:02:58] Ameesh Paleja: So things like our apps and our website, our next generation financial ledgers, our developer experience pipeline, our identity systems, so lots of of great infrastructure and systems that will power the next generation customer experiences for Capital One.
[00:03:15] Dana Gardner: Well, I'm sure most of our listeners have heard of Capital One, but let's revisit their mission, their role among financial services and why data science and AI are important, especially the latest and greatest in data science and a science and AI to help Capital One proceed and improve on what it's doing.
[00:03:35] Ameesh Paleja: Well, our mission is very simple. It's change banking for good, and that means a lot of things, but fundamentally what we're trying to do is we're trying to make financial accessibility to, to instruments, to credit, to, to independence for, for everybody. We wanna make it fair. We wanna make it a great experience.
[00:03:53] Ameesh Paleja: We want to leverage data in an enormous and thoughtful way to make personalized, real-time intelligent experiences. We really want to make sure that customers have a great experience and opportunity to build wonderful lives for themselves.
[00:04:09] Dana Gardner: And this, I think, involves more interaction. This is not a one-way street.
[00:04:13] Dana Gardner: The more you interact and learn about your customers, the more you can provide them with services and add-on services and from intelligence. The tighter the relationship, the more of a partnership they'll sense. So tell us a little bit about why this is an exciting time for applying what data and intelligence can do to improve that relationship and, and, and make it something that they'll
[00:04:36] Dana Gardner: find more, I guess, unique in, in working with your company than, than perhaps anyone else?
[00:04:44] Ameesh Paleja: I think, I think this is at the core of our mission, is that we really do want to make personalized experiences for everybody. And what that means is that we're, we're collecting, uh, not only feedback from customers subjectively, but we're also doing it objectively.
[00:04:58] Ameesh Paleja: The channels as we talked about, in which we can do that, whether it's through our apps or our websites, through whether it's our customer service agents, whether it's through our chat bots, whether it's through any of the pieces where customers touch and interact with. We want to make sure that we're understanding are we doing the right job for them?
[00:05:14] Ameesh Paleja: Are we servicing them in the way that they want to be serviced? And are we helping them achieve the goals that they're trying to get done? And all of that has to come back to kind of fundamental, you know, having great data infrastructure. Making sure that we have kind of closed loop processes that enable us to, we did this right, we did this wrong, we were ad test a X, or Y.
[00:05:35] Ameesh Paleja: This is what's working for our customers. And interesting enough, it's not down to cohorts. You know, in the past we would kind of generally bucket people and say, okay, well this is a group of X type of X type of customers or Y type of customers, but now we wanna be personalized down to the individual.
[00:05:50] Ameesh Paleja: Right? And that is a, a really powerful opportunity, but. It requires a lot of thoughtful and intentional engagement with the customer, and planning for where this data is gonna be stored and how we're gonna be using it.
[00:06:03] Dana Gardner: Sure. You know, it's refreshing to me whenever I interact with a, a company digitally that they can provide me with more and more over time.
[00:06:13] Dana Gardner: And I look for that and I expect that now, and it's sort of a quid pro quo. If you do good by me, I'll do good by you. But there's a lot of challenges in into that. So tell us why there are hurdles and challenges to getting down to that personalized one-to-one level.
[00:06:31] Ameesh Paleja: First and foremost, we wanna start off with kind of being very well managed and very thoughtful.
[00:06:36] Ameesh Paleja: You know, the collection of all of this information needs to be well regulated. It needs to be secure, and it needs to be very thoughtfully stored and organized so that we're supporting the customer's needs, but also with their consent. Right? So starting off with a position of understanding the regulatory environments and the customer preferences is kind of a at core of everything that we're doing.
[00:06:59] Ameesh Paleja: Second part of that is, is that, you know, the volume and collection of this information that's coming in, um, really necessitates standardization. This is, this is kind of one of Capital One's, uh, superpowers, is that we're willing to do the kind of ready work of organizing and standardizing information so that it's consistent so that we can then build automation on top of it.
[00:07:21] Ameesh Paleja: You know, standardization is kind of the unsung hero of. Our automation platform. Rich, our founder, often talks about this and it's been a, a huge motivator for a lot of us because it, it tends to drive outpaced results If we can stay standardized and our ability to automate on top of it becomes a lot simpler and a lot easier once that automation is in place, we just thinking about kind of the constant stream of information that's coming in, the growth and the volume of data that we're processing.
[00:07:50] Ameesh Paleja: It then enables us and enables all of our data analysts and data scientists, our engineers, our product leaders, our designers, basically every function in the company is now able to kind of leverage this with systems like Snowflake, with systems like Spark, with systems, like, you know, all, all of our, our, our kind of underlying infrastructure.
[00:08:11] Ameesh Paleja: And the core of it is start with great data standardization. And build that automation on top of that. And once we have that automation, there's a lot of option value that we get from it.
[00:08:23] Dana Gardner: You know, it's one thing, Ameesh, to have the right message and data and value to deliver, but then there's a second level of challenge around how to get it to the person in the way they want, and to do it in such a way that you're, you know, adhering to their, their preferences, but also to what they, you know, are able to get and should be getting and so forth.
[00:08:46] Dana Gardner: So, let's look at the challenge, not just through making sure that you're giving value, but you're getting it to them through the right way. At that scale and complexity, it sounds like quite, quite a task.
[00:08:58] Ameesh Paleja: It sure, it sure is. The, the real thing is, is that it's not just simply knowing about what needs to be delivered to the customers, but it's also where to deliver to them when to deliver to them.
[00:09:09] Ameesh Paleja: You know, if I send you a coupon for your shopping, but I send it to you first thing in the morning, and that's not when you're thinking about shopping, it's, we kind of miss the boat, right? We wanna do it in the afternoon. If you're looking for a car and something becomes available quickly. Our auto team wants to make sure that we're delivering the right offer and the right value to our customers at the right time.
[00:09:29] Ameesh Paleja: Right? When we think about all the other kind of internal systems and we think about kind of how our company operates, there's lots of other pieces too. What we thought, we talk about the end consumer as well, but you know, things like, you know, AI driven ops or our ability to enable our data analysts to.
[00:09:49] Ameesh Paleja: You know, you know, petabytes of data and smooth, you know, smoothly and easily manage the compute over such, you know, kind of massive amounts of resources, but it just wasn't possible in the past. Now we're like, we're able to actually build these tools and systems to be able to get, get the ability in the hands of our users as well as our customers.
[00:10:10] Dana Gardner: Yeah. So you're not just delivering capital, you're delivering convenience. And in doing so, you have to look at these latest and greatest technologies. So what is the confluence of, of these major technologies in your thinking? We've got cloud, we've got large language models, we've got pervasive data science.
[00:10:32] Dana Gardner: We've got different architectures for, for the data itself. What's, why, why is this a rather than a perfect storm a, a perfect tranquility when it comes to bringing these right technologies to, to this particular difficult problem?
[00:10:48] Ameesh Paleja: You know, this is, this is a, this is a complex part of the puzzle. There is a lot of different technology out there that serves lots of different needs.
[00:10:56] Ameesh Paleja: I think part of this starts with people, I. We need to make sure that we have the right people, the right skillset in the, in the company to be able to kind of activate and engage on these, on these frameworks. But as I think about the technology stacks in particular, I kind of think of it as a pyramid.
[00:11:10] Ameesh Paleja: You're building kind of fundamentally on core infrastructure like S3 that's provided by AWR partners at AWS, you know, and it provides us kind of infinite, uh, expandable storage capabilities. And then you think about kind of the compute layers. Whether it's serverless or you know, using EKS and you know, we're building on top of these, these particular systems and tools, and then you keep going even higher, then you have things like our data warehouse.
[00:11:37] Ameesh Paleja: So it's, you know, snowflake was one of our biggest decisions right when we first moved to the cloud, and that provided us kind of thoughtful, thoughtful, scalable compute power to enable all of our data analysts to kind of continue. And now, you know, even Capital One is now building on top of Snowflake.
[00:11:52] Ameesh Paleja: We have Capital One software building. Slingshot as a, as an offering. We have tokenization with data bolts happening right now. So I think about these things as kind of layered tactics where we keep extracting the problem, taking away these things. We automate them and we take them off the plate of our humans and really focus our energy for our human beings to be thought, you know, thinking about high order judgment problems.
[00:12:18] Ameesh Paleja: So. Coming back to that automation point is that if we standardize, we automate, we can keep moving up the tech stack so that the, the higher and higher leverage opportunities are focused on what our, our great staff and, you know, employees are, are working with.
[00:12:33] Dana Gardner: Right. In order to get that right mix of what people do best and what the machines do best, that's gonna be an evolving equation to get out in front of that.
[00:12:42] Dana Gardner: I imagine you, like many companies are looking at what's going on with AI, with agents, with constellations of agents. Where do you see that going? So how do we build that pyramid up even higher?
[00:12:55] Ameesh Paleja: Yeah, so I think that this is, this is a fascinating question and a place where Capital One is already innovating.
[00:13:00] Ameesh Paleja: We already have a multi-agent solution in our concierge platform where, you know, we're able to, to orchestrate multiple agents together to enable great guest experiences. Whether it's finding a car, whether it's looking for my next travel itinerary, whether it's booking my flights, whether it's helping me shop, the idea is, is that we want to build great, specialized specialization in these agents and then help coordinate them across a group of them to to enable complex problem solving.
[00:13:30] Ameesh Paleja: That's where, that's where like the kind of magic of all of this happens. You know, I've been at the industry for, for multiple decades and I, I kind of, you know, think and watch and, and observe all that's going on. And if you showed me this even just five years ago, I would've been flabbergasted. I would've been like, this is magic.
[00:13:46] Ameesh Paleja: How is this even possible? But, you know, the advent of, you know, great machine learning technologies, you know, LLMs, transformers, all of that has become a sea change event across the entire industry. Now we see these kinds of new architectural patterns, these new agent approaches to solving the problem.
[00:14:05] Ameesh Paleja: And you know, I'm very pleased to say that Capital One is really kind of focused on leading the way in this space, particularly in a really well-managed, well-regulated environment like finance.
[00:14:15] Dana Gardner: Yeah, I've spoken to quite a few users and architects and people who are innovating with these technologies and what's becoming clear is that the higher up that pyramid as you described it, they go and the more they're able to avail themselves of automation and data, that they actually can create new services that were not possible before.
[00:14:39] Dana Gardner: And not just being in the capital, again, credit business, not just being in the convenience of banking and finance business, but you're able to now perhaps monetize and produce products and services that are of a data and intelligence nature, that you have insights into individuals one-on-one, but you also have insight into markets and trends.
[00:15:02] Dana Gardner: And for both the buy and sell side, you're able to come in and say, listen, we can provide you with all kinds of new services around data. How auspicious is that for you as well?
[00:15:14] Ameesh Paleja: I think it's, it's a, it's an opportunity to unlock so much value for our customer base. The, the simple, simple thing I would bring it all back to is that the democratization of, of this data, the democratization of the tooling, the ability to fly and experiment rapidly and frequently, has opened up lots of doors for us to continue to innovate in places where.
[00:15:38] Ameesh Paleja: We may have had to make a prioritization decision in the past where we said, ah, we just don't have the time or the engineers to go and build X now, if we can prompt it properly, if we can, you know, if we can enable the usage of, you know, coding assistance and, you know, AB testing assistance and all of the things that come with these modern tools and techniques, right?
[00:15:59] Ameesh Paleja: Our ability to innovate very quickly and very rapidly has just grown. It unlocks a whole new group of people to be much more productive in delivering these great customer experiences to our customers. And so it is a, it is a massively strong flywheel. It's, it's democratizing technology and the ability to build tools, products, features, and, and platforms.
[00:16:24] Ameesh Paleja: But then that enables great customer experiences, great new use cases that we wouldn't have never thought to prioritize, and now we actually have the capability to do that. It's very, very exciting.
[00:16:36] Dana Gardner: Yeah. And so again, the people can be innovating around, wow, what new business functions can we, can we provide, how can we invent ways of helping our suppliers and our customers and our ecosystem?
[00:16:50] Dana Gardner: So let's start to dig into how that's fomenting in the market. Now, you mentioned some customer use cases. Do you have a handful or a couple you could describe that help illustrate this new direction in, uh, in, in the capabilities of your systems and your people?
[00:17:05] Ameesh Paleja: Yeah, I mean, I think there's, there's quite a few.
[00:17:07] Ameesh Paleja: I think a simple one is our, our marketing efforts. Capital One has one of the, the best brands out there in the space. You know, what's in your wallet. And, you know, we want to be able to engage with our customers with the right message at the, you know, on the right channel at the right time. When we think about, you know, our, our chatbots and our ability to kind of service customers in a really thoughtful way.
[00:17:33] Ameesh Paleja: Customers come in and they, they present us with the question. NLP can kind of break that down and understand semantically what, what is going on or what is the, the root of the, the issue. And help our customer service agents find the right answer to the question with the right tools right in front of them.
[00:17:50] Ameesh Paleja: And so our ability to kind of resolve their issues quickly and effectively have, have dramatically improved. You know, when we think about even simple things like. AI operations. You know, oftentimes we focus on customer facing features, but you know, underneath the covers, when things go wrong, when servers go down, when systems are failing, you know, having great ML models.
[00:18:10] Ameesh Paleja: Because we have standardized and organized this vast amount of data, it helps us not only improve our meantime to detection, but our meantime to resolution as well. So that ultimately, you know, creates an amazing experience for our customers where they, they don't even see problems because we catch 'em before they, they become fire, right?
[00:18:28] Ameesh Paleja: We catch 'em when we see smoke.
[00:18:30] Dana Gardner: Yeah, let's just dig one step deeper into that marketing, 'cause I know you guys do so much with that. And we see you at live events, we see you at big sporting events. You've got a really good way of understanding your market, and you spend and you invest and you innovate in marketing.
[00:18:47] Dana Gardner: So how in particular are you using the data and the AI in that interaction between what your audience tells you and what you can then provide back to them?
[00:18:58] Ameesh Paleja: So I, I would say, I would start off with, we have amazing master craftsmen when it comes to our marketing campaigns. They are some of the best I've ever seen.
[00:19:08] Ameesh Paleja: They're very thoughtful, organized, they have great brand guidelines. They've like, they've really thought through all the things that they, they need to be thinking about and doing to engage our customers in the most thoughtful way. You know, my goal with our infrastructure and our tools. Really to provide them with the best possible options and tooling to help them accelerate their journey.
[00:19:30] Ameesh Paleja: Right. If they were able to create, I know pick a number, a hundred campaigns a week, can we help them experiment to create 10,000 campaigns a week? Can we help them do that on a personalized basis or specific individuals? Let's say there's a cohort of individuals that speak Spanish. Can we have AI help us translate?
[00:19:50] Ameesh Paleja: Our campaigns into Spanish and, and Hindi and Mandarin and all the other languages that are spoken across the United States and make it a really personalized experience. These are things that like, we haven't had the structural capability to give to our amazing marketers. They have the kind of ideas, the creativity and the, and the analytics.
[00:20:13] Ameesh Paleja: We want to give them basically a better tool bench to be, to be much more effective. And so as we think about kind of the infrastructure that we're building, whether it's helping them experiment and ideate new ideas with AI, because there's lots of creative potentials to kind of explore and flesh out.
[00:20:30] Ameesh Paleja: To the times where we flight those, those experiments and understand all the conversion data, the, you know, the click through rates, all the things that happen with, you know, great marketing campaigns. And then eventually looking at the kind of fundamental like, was this a long-term impact? Did it create great LTV for our customers and for our business?
[00:20:51] Ameesh Paleja: And all of these things are fundamentally tied to kind of core infrastructures of being, being able to say. We have the right data in the right place. Can they, can they focus and, and engage with it? You know, you know, particularly when we think about programmatic versus non-programmatic interfaces, it's important to know that our data scientists are kind of attaching to the data, you know, from a programmatic way.
[00:21:13] Ameesh Paleja: And our data analysts are using their kind of thoughtfulness and judgment and creativity to actually go back and, and look at what's happening. Leveraging tools like Snowflake, leveraging tools like, you know, Spark and, and TensorFlow and all, all the other areas that we're kind of focused on, Flink, et cetera, you know, helps us understand not only what's happening right now, what's happened in the past, but then we can also start doing things about real time analysis.
[00:21:41] Ameesh Paleja: And as things are happening in the moment, oh, we see customer clicking this button and looking here and searching for a flight, they're actually interested in X, Y, and z. Let's start changing the experience of our clients and our marketing efforts to really focus and help them solve their problems that they're, they're trying to adjust to.
[00:21:58] Ameesh Paleja: So there's an amazing, amazing opportunity in flywheel here that just grows better and better. But fundamentally, my goal in marketing and for Capital One is continue to upgrade the tools to enable the amazing people that have already built a tremendous brand. I wanna just super charge, I wanna put rocket fuel in there.
[00:22:17] Dana Gardner: Right, right. It, it seems pretty obvious to me that the marketing organization and the AI organization should be getting closer and closer at many companies. You're a great example of that. So I hope some others learn from that. If you haven't brought your AI and marketing people together, now is the time to do it.
[00:22:35] Dana Gardner: You also mentioned earlier the culture and the people, so I wonder if you have any advice for other organizations that are. Perhaps trying to bring, you know, hither two, four disparate organizations and units closer or to break down silos, both technically and culturally. What advice do you have for organizations to try to become the AI first and AI ready company when it comes to the people and how they're organized?
[00:23:01] Ameesh Paleja: I think the, I think this is one of the great things about Capital One's culture is that we're incredibly data centric. Our company was founded on our kind of information based strategy. We were literally doing AB tests with mailers. You know, rich started off this company just to, to say, Hey, there's a great way to use data to really understand what offers on to land well, which products will be deliver.
[00:23:23] Ameesh Paleja: And as time went on, the company really in, you know, put that into our, our core part of our culture. We are very data-driven. We are very truth seeking, and this is an important one. It's easy to say that, but it's not easy to live it. And I think that the, the, the third part of of this is that you have to be open to the information leading you down past, that you were anticipating, right?
[00:23:48] Ameesh Paleja: So, you know, with AB tests, with any kind of experimentation with the, the, the ideas of I need to try X before I actually roll it out to more and more people. It has to be a fundamental part of your culture. Once you have that kind of foundational piece in place to say, I'm looking for the truth, and I'm okay if it takes me in unexpected places, right?
[00:24:11] Ameesh Paleja: If everybody is kind of bought into that overall perspective, it really helps drive the kind of further investment and the further, you know, intentionality around the standardization, the automation, the collection of information it, because all those things are, they're expensive and painful, right? They have a big payoff.
[00:24:34] Ameesh Paleja: But it takes a, it takes a lot of grit and fortitude to actually take the time and investment to make all of those things happen. So it's not something that you enter in lightly. It's something that you really have to be mindful of and say, I really do want the end goal here. I want to have those personalized, realtime experiences.
[00:24:53] Ameesh Paleja: I want to be able to leverage my data in every aspect of my business, whether it's, you know, software operations, whether it's customer service, whether it's marketing, whether it's fraud detection, whether it's cybersecurity. All these places will be able to get value if you take the time and, and, and space to actually do this work.
[00:25:15] Ameesh Paleja: But you can't do that work unless you fundamentally believe in it. You don't. So I would say the first and foremost thing is you gotta build it right from the top down is, is establish a culture where this is important. And then once you know that it's, that's really in people's, not only brains, but also hearts.
[00:25:33] Ameesh Paleja: And you can start taking the time to invest in the technology to infrastructure and the capabilities that will enable you to have all of these different use cases unlocked.
[00:25:43] Dana Gardner: Great. I think that's a wonderful place to end it. Thank you for pulling that together and the concept of the pervasiveness of how these technologies will impact and are impacting businesses.
[00:25:53] Dana Gardner: So thanks so much to our latest Data Cloud podcast guest, Ameesh Paleja, Executive Vice President, Enterprise Platform Technology at Capital One. We so much appreciate your sharing your time, your thoughts, your experience and expertise.
[00:26:08] Ameesh Paleja: Thank you so much, Dana. I appreciate you having me.
[00:26:11] Dana Gardner: It was a pleasure.
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