Enterprise

Why Capital One CIO Mike Eason loves a visit to its big data lake

Protocol caught up with Capital One’s Mike Eason to talk about the credit card giant’s data setup, a new in-house data platform for building machine learning models, and why the company wants to automate how it explains its AI.

Capital One SVP of CIO Enterprise Data and Machine Learning Mike Eason.

Capital One's Mike Eason and his team of 1,800 engineers and technology staff are busy developing a self-service data pipeline and platform with tools for in-house staff to access data to build and train machine-learning models.

Photo: Capital One

Skittish, reluctant, hesitant — downright scared. Financial services companies have been all that and more when it comes to migrating their heavily regulated, data-heavy businesses from legacy systems to the cloud.

But while some banks and credit card providers are still just dipping their toes, Capital One has been “all in” on the public cloud since 2015, according to the company’s senior vice president of CIO Enterprise Data and Machine Learning at Capital One, Mike Eason. By 2020, Capital One had completed its full migration to AWS, even declaring, “We left our data centers behind” in a special website section.

“You see a bunch of our competitors following in our footsteps here,” Eason said, noting that choosing AWS as its primary cloud provider gives Capital One advantages. “We get to influence their roadmap,” he said.

But the company does have other data services partners including Snowflake and Databricks, and mixes and matches AWS cloud software such as SageMaker with other things such as open-source components to customize the tech it uses.

These days Eason and his team of 1,800 engineers and technology staff are busy developing a self-service data pipeline and platform with tools for in-house staff to access data to build and train machine-learning models. “Rather than having a whole bunch of different platforms, how do we invest in one that everyone can take advantage of?” Eason said.

The company’s data lake is the destination for all of the data that flows into that system. “We’ve got a big lake that’s in the cloud,” Eason said, like an excited kid bragging about summer vacation.

Protocol caught up with Eason this week to talk about why the data lake is making a difference, why the company wants to automate how it explains its AI models and its efforts to expand Capital One’s company-wide team of 11,000 engineers from the inside.

This interview was edited for clarity.

Capital One has a data lake. Why is there a need for that? What’s unique about what you can do in a cloud data lake environment?

There’s a couple of different things. One is just from a macro standpoint, the cost of data and compute is just dramatically reduced. When we were on prem, we were using the Teradatas of the world and others, and the cost of compute and space is dramatically different than it is today.

And then you're also just contained in what you can put in the four walls of your data center. And here [in cloud computing], it’s the elastic nature of it.

We're a big credit-card provider, and during the holidays, we can spin up more compute and more space and everything to handle the different loads as everyone's doing their holiday shopping, and so that aspect of the cloud has just been phenomenally important to us, and just a game changer.

From a lake standpoint, the amount of data that we can capture and utilize in our models is just tremendously different — like exponentially different. The lake provides that one copy of everything for us, and is the one place where all the data will be.

And so we use a combination of the lake and Snowflake for some more of the structured, traditional warehouse data.

What types of data points or data sources would be flowing into the lake versus a more structured environment?

Well, it would go into both. The lake is everything. It’s the receipt and the copy of all data from the company. So we've built a data pipeline to publish our data. And as an end user, you can then determine, I want to publish the data, so I’m gonna go to the lake, but I want to publish these attributes or this data to Snowflake.

Or – and this is something we just recently built – I might want to put data into a low-latency operational type of database that our operational systems can hit, or our models can hit.

So it's one pipeline that gets to publish to many different locations. It’s a simple, more self-service kind of platform for end users of publishing data. The lake is the copy of everything. And then there might be a subset of needs of things in Snowflake for reporting, doing some general analysis, the munging of data together.

And then there’s the low-latency environment for more back-end, really quick models, making a fraud decision in the moment, when you're using the data to determine if Kate’s transaction is going to go through.

What’s an example of a low-latency use for a data lake?

Fraud is a great case of that. You're swiping the card, we have less than 100 milliseconds of determining if this is a fraudulent transaction or not. And you want as much data and as [many] data points to be able to make that decision.

There’s increasing pressure on companies to audit, explain and monitor algorithmic and automated systems and provide reports on how they work or how they made decisions. What’s Capital One’s approach to this and how has that evolved?

This has been discussed in our world since the start of Capital One. We've used models since day one to predict credit loss and predict who we should give credit to based on the background of their financial performance and credit scores and all that. And then, like I said, making a call on a transaction. So obviously, they've progressed over the years [in] that you've got more data, more data points, more history. But we have a whole group that is focused on model governance, and so all of our models and the decisions are all vetted and monitored really closely.

As you get into a space of now having more data, being able to make more decisions more quickly, how do you ensure that you can grow that kind of model governance piece with this process? And this gets into explainable AI and ML, which is a top focus within our whole ML strategy — ensuring that as we start to take advantage of more data that we're doing that in a very non-biased, explainable way. Today, even though I think we are very progressed on our models, nothing is totally self-learning AI, like hands off. It’s still all modeled and governed and there's human eyes on it; that's going through the whole explainability piece.

What we’re focusing on internally is how do you automate all the documentation around this and prove out the bias aspect and other pieces where you can kind of prove it out, but do it in a way that it's all done much more from an automated standpoint. Especially as you're using more and more attributes in the model, it's incredibly time-consuming to try to do all this manually. And so how do you show all this explainability in a much more automated way? That's a big focus of a lot of our research that we're doing with colleges right now. And, there's a bunch of small companies that are focusing on this.

Are you exploring partnering with vendors that either help do monitoring to watch for problems like model drift or to help automate things like documenting explainability?

Yeah, we've done a bunch of this discovery with some of the players in the space. Even though there's a lot of companies out there that are focusing on it, it's still kind of greenfield. And so we're working with a bunch of universities as well, from a research standpoint specifically in this space. So we haven't picked one company that is gonna do the explainability for us. There's still many that we're investigating. And I think it's gonna be for different situations, different models. It won't be one size fits all.

Speaking of universities, it’s not easy to find people with engineering and machine-learning expertise.

Yeah, there’s a whole talent piece to this. We created the MLE job family to attract more talent, have specialization in the ML space, which has gone well for us. [Capital One categorizes employees and jobs into “families.”] And so trying to hire and retain and develop is a huge focus for us. We have a big tech college internal to Capital One that we've invested millions in. I happen to be the accountable executive for our college, and a big focus this year is a whole ML training program to develop people within the company to have this expertise, both from an engineering standpoint, but also from a data science standpoint, and for a general business leader: How do you need to know more about ML, and where's the leverage?

Where are you getting people for that program? What types of roles do they have in Capital One?

We have a tech college; they are folks that develop the curriculum and they go outside to partners to get some of that curriculum, or we might just develop it ourselves. Engineers are taking time to actually create a module that others would learn from. And so it's a combination of things.

What about on the flip side — the students, the people who are doing that training? They already have some sort of job at Capital One. What kinds of jobs might they have?

More of your general software engineer. They might be doing front-end or back-end development, database or UI or mobile. And I'm assuming that over time, we'll probably do that more in the ML space. Right now, it's a general software-engineering-type program. But as ML becomes more commonplace, there will be modules specific to that.

Elon Musk's influence over Twitter was clear at its annual meeting

Even though executives tried not to talk about Musk's deal to buy the company, they couldn't help but address his agenda.

Elon Musk loomed over Twitter's annual shareholder meeting.

Photoillustration: Getty Images; Unsplash; Protocol

In his opening remarks at Twitter's annual shareholder meeting on Wednesday, CEO Parag Agrawal said he wouldn't discuss the pending acquisition bid from Elon Musk, which wasn't on the agenda. That didn’t matter much: Musk’s fingerprints were all over the event, even overshadowing the expected if still-emotional news that Jack Dorsey would step away from Twitter’s board at the meeting's conclusion.

Keep Reading Show less
Hirsh Chitkara

Hirsh Chitkara ( @HirshChitkara) is a reporter at Protocol focused on the intersection of politics, technology and society. Before joining Protocol, he helped write a daily newsletter at Insider that covered all things Big Tech. He's based in New York and can be reached at hchitkara@protocol.com.

Sponsored Content

Why the digital transformation of industries is creating a more sustainable future

Qualcomm’s chief sustainability officer Angela Baker on how companies can view going “digital” as a way not only toward growth, as laid out in a recent report, but also toward establishing and meeting environmental, social and governance goals.

Three letters dominate business practice at present: ESG, or environmental, social and governance goals. The number of mentions of the environment in financial earnings has doubled in the last five years, according to GlobalData: 600,000 companies mentioned the term in their annual or quarterly results last year.

But meeting those ESG goals can be a challenge — one that businesses can’t and shouldn’t take lightly. Ahead of an exclusive fireside chat at Davos, Angela Baker, chief sustainability officer at Qualcomm, sat down with Protocol to speak about how best to achieve those targets and how Qualcomm thinks about its own sustainability strategy, net zero commitment, other ESG targets and more.

Keep Reading Show less
Chris Stokel-Walker

Chris Stokel-Walker is a freelance technology and culture journalist and author of "YouTubers: How YouTube Shook Up TV and Created a New Generation of Stars." His work has been published in The New York Times, The Guardian and Wired.

Workplace

Netflix’s layoffs reveal a larger diversity challenge in tech

Netflix just laid off 150 full-time employees and a number of agency contractors. Many of them were the company’s most marginalized employees.

It quickly became clear that many of the laid-off contractors possessed marginalized identities.

Illustration: Christopher T. Fong/Protocol

After Netflix’s first round of layoffs, there was a brief period of relief for the contractors who ran Netflix’s audience-oriented social media channels, like Strong Black Lead, Most and Con Todo. But the calm didn’t last.

Last week, Netflix laid off 150 full-time employees and a number of agency contractors. The customary #opentowork posts flooded LinkedIn, many coming from impacted members of Netflix’s talent and recruiting teams. A number of laid-off social media contractors also took to Twitter to share the news. It quickly became clear that similar to the layoffs at Tudum, Netflix’s entertainment site, many of the affected contractors possessed marginalized identities. The channels they ran focused on Black, LGBTQ+, Latinx and Asian audiences, among others.

Keep Reading Show less
Lizzy Lawrence

Lizzy Lawrence ( @LizzyLaw_) is a reporter at Protocol, covering tools and productivity in the workplace. She's a recent graduate of the University of Michigan, where she studied sociology and international studies. She served as editor in chief of The Michigan Daily, her school's independent newspaper. She's based in D.C., and can be reached at llawrence@protocol.com.

Fintech

Crypto doesn’t have to be red or blue

Sens. Cynthia Lummis and Kirsten Gillibrand are backing bipartisan legislation that establishes regulatory clarity for cryptocurrencies. This is the right way to approach a foundational technology.

"Crypto doesn’t neatly fall along party lines because, as a foundational technology, it is — or should be — inherently nonpartisan," says Diogo Mónica, co-founder and president of Anchorage Digital.

Photo: Anchorage Digital

Diogo Mónica is president and co-founder of Anchorage Digital.

When I moved from Portugal to the United States to work at Square, it was hard to wrap my head around the two-party system that dominates American politics. As I saw at home, democracies, by their very nature, can be messy. But as an outsider looking in, I can’t help but worry that the ever-widening gap between America’s two major parties looms over crypto’s future.

Keep Reading Show less
Diogo Mónica
Diogo Mónica is the co-founder and president of Anchorage Digital, the premier digital asset platform for institutions. He holds a Ph.D. in computer science from the Technical University of Lisbon, and has worked in software security for over 15 years. As an early employee at Square, he helped build security architecture that now moves $100 billion annually. At Docker, he helped secure core infrastructure used in global banks, governments and the three largest cloud providers.
Fintech

What downturn? A16z raises $4.5 billion for latest crypto fund

The new fund is more than double the $2.2 billion fund the VC firm raised just last June.

A16z general partner Arianna Simpson said that despite the precipitous drop in crypto prices in recent months, the firm is looking to stay active in the market and isn’t worried about short-term price changes.

Photo: Andreessen Horowitz

Andreessen Horowitz has raised $4.5 billion for two crypto venture funds. They’re the industry’s largest ever and represent an outsized bet on the future of Web3 startups, even with the industry in the midst of a steep market downturn.

The pool of money is technically two separate funds: a $1.5 billion fund for seed deals and a $3 billion fund for broader venture deals. That’s more than other megafunds recently raised by competitors such as Paradigm and Haun Ventures.

Keep Reading Show less
Tomio Geron

Tomio Geron ( @tomiogeron) is a San Francisco-based reporter covering fintech. He was previously a reporter and editor at The Wall Street Journal, covering venture capital and startups. Before that, he worked as a staff writer at Forbes, covering social media and venture capital, and also edited the Midas List of top tech investors. He has also worked at newspapers covering crime, courts, health and other topics. He can be reached at tgeron@protocol.com or tgeron@protonmail.com.

Latest Stories
Bulletins