How Capital One Leverages the Power of Data in the Cloud

Capital One is a big customer of Snowflake and we were recently joined by Biba Helou, Capital One’s Senior Vice President of Enterprise Data, for an episode of our podcast, Rise of the Data Cloud. She described how the bank relies on Snowflake to scale its data operations and stay ahead of its ever-changing customer needs.

Like other industries, the financial services sector is undergoing a massive digital transformation, with the goal to better serve and retain customers by offering them a broader range of more personalized services. That work relies on the intelligent use of customer data.

Most people know Capital One as one of the largest banks in the U.S., offering credit cards, checking and savings products to consumers, small businesses, and commercial enterprises. “What most people probably don’t know is that we’ve also built a massive in-house tech workforce of nearly 11,000 people, and 85% of those are engineers,” Helou explained.

Those teams use big data and machine learning to drive insights and deliver real-time, personalized solutions to millions of customers, to make banking simple and accessible. In effect, Capital One is a technology company that also happens to be one of the largest banks.

The bank reached an important milestone in November 2020, when it completed a nine-year tech transformation. Capital One was one of the first U.S. banks to go all-in on the cloud, after closing all eight of its on-premises data centers.

“Our primary driver for moving to the cloud was the need for agility and giving our software engineers and our business partners the ability to innovate at the pace that they wanted,” Helou said.

Elasticity and Scalability Within a Highly Regulated Environment

In moving to the cloud, Capital One sought an elastic and scalable platform that could seamlessly support, with no degradation in performance, thousands of analysts running complex models involving millions of queries. The platform also had to comply with the banking industry’s strict security and regulatory requirements. Capital One chose the Snowflake Data Cloud because it met all these needs.

“We really rely on the elastic capabilities of the Data Cloud to leverage as much data and as much compute as we need,” Helou said. Snowflake allows the bank to store and access larger, more varied data sets than it ever could with its own on-premises data centers. For example, Capital One is using the Data Cloud to help train machine learning models for Eno, its intelligent assistant, which uses AI-driven insights to notify customers about any unusual transactions and also helps them track their spending.

“We have really appreciated the partnership with Snowflake,” Helou said. “The relationship has really worked. Snowflake has incorporated a lot of our requirements into their roadmap and worked with Capital One to understand what a big complex enterprise needs.”

Keeping Up with Customer Needs While Also Controlling Costs

Capital One uses the Snowflake Data Cloud to understand the changes happening in the world and react quickly to address its customers’ evolving needs. That includes using Snowflake Data Marketplace, which provides quick access to third-party data sets to help generate valuable insights.

“We can also account for world events such as the COVID-19 pandemic to make sure that our decisions are accurate and beneficial to our customers,” said Helou. She mentioned Capital One’s adoption of a test-and-learn philosophy to how it uses data and continually builds and refines data models to meet the bank’s business objectives and metrics.

The bank has also been able to control costs, thanks in part to Snowflake’s consumption-pricing model, which allows customers to expand their usage over time. The Data Cloud gives Capital One insight into metrics so it can track consumption, control any waste or sprawl, and analyze which internal groups are using which resources. Capital One pays only for the resources it needs and has the option to spin up additional compute power if it’s needed.

Looking ahead, Helou expects an acceleration of what’s already happening in the analytics arena, so predictions can be made immediately rather than waiting for analytics and looking at historical trends before making a decision.

“I actually see data science becoming much more of a ‘data science-as-a service’ or ‘business intelligence-as-a-service’ over time,” Helou said. “I think more and more products are going to become self-service and give end users the ability to do what those data scientists typically had to spend a lot of time on doing centrally.”

Rise of the Data Cloud is a podcast hosted by award-winning author and journalist Steve Hamm. For each episode, he speaks with a data leader to learn how they leverage the cloud to manage, share, and analyze data to drive business growth, fuel innovation, and disrupt their industries. You can listen to more episodes here.