How Capital One empowers teams to manage and use data

The core of today’s world is data. Because of this, companies must have a strong data ecosystem that empowers users to leverage that data and rapidly innovate. At Capital One, we employ data management practices that ensure the right level of governance for data, while encouraging autonomy and agility.

This blog delves into our “You Build, Your Data" approach, which empowers developers to innovate with high development velocity while enforcing good governance—a model other enterprises can learn from as they modernize their data ecosystem in the cloud.

As part of our transition to the cloud, Capital One modernized its data ecosystem to take advantage of our new cloud infrastructure. In modernizing our data ecosystem, we knew that a self-service model was essential to ensuring our data was well-managed yet our data teams could move with speed and flexibility. 

As a result, we adopted a “You Build, Your Data” approach, a framework that empowers teams to manage data autonomously within defined boundaries. This approach balances governance requirements with the need for agile, rapid development. Our shift from centralized data management to a distributed model has enabled faster product development cycles, greater flexibility for our engineering teams and enhanced our ability to meet new customer demands. 

Capital One’s journey began with a shift away from the archaic monolithic structures most banks use today to a microservices-based architecture. Traditional banks tend to operate on centralized, monolithic systems that are difficult to scale and very costly to modernize. 

We recognized that this approach wouldn't work for executing on the vision we had developed as part of our multi-year tech transformation. As such, we transitioned to microservices, making it easier for individual product and development teams to innovate, scale and deploy their applications independently.

In order for this new approach to work, an API-first strategy—where all interservice interactions are governed by API contracts—was required. This API-driven strategy provides two key benefits. It allows different teams to work on their own schedules without disrupting each other, and it ensures all communication across these various services is consistent. Consistent communication is key as your infrastructure scales and becomes more loosely coupled over time.

Each microservice must register its API contract in a central catalog, creating a standardized method for interactions across the ecosystem. This central catalog serves as the map of the organization’s data and applications, allowing different services to communicate effectively and at scale. It's worth pointing out that banks have a naturally high number of individual data transactions happening at any point in time, so the infrastructure needs well-defined interactions that can scale up and down rapidly, efficiently and without errors.

Using a legacy method for these interactions results in multiple teams constantly working toward moving targets. Not only is this inefficient, but the lack of a proper API contract can cause teams to miss deliverables as they constantly have to refactor code due to shifts in the underlying architecture or integration points. API contracts provide solid anchor points with stable support structures around them. These function as solid targets for development teams as they develop microservices. 

API contracts are at the core of Capital One’s governance model. In a distributed environment, consistency in data exchange and interaction is critical, especially as you scale up within enterprise architectures. API contracts ensure all interactions follow standardized guidelines, helping maintain the integrity of data exchanges. For example, if a team builds a service to access customer insights, the contract specifies the exact data structure, methods and constraints, preventing unauthorized or inconsistent access. Rather than dictating that “you are not allowed” to do something, this is an explicit authorization within a fixed set of constraints—a permission to operate. Not only are you flipping the context of technical operation from exclusionary to explicit permission, you're changing the entire paradigm of how your development teams operate. They're now “empowered to operate” rather than “allowed to operate, but not in certain ways.”

By using API contracts as governance tools, we can enforce rigorous data governance practices without sacrificing flexibility. API contracts become the guiding light that ensures data adheres to the same quality and security standards, regardless of the service handling it. This creates a comprehensive data ecosystem that is flexible and adapts to different team requirements while maintaining strict, verifiable compliance. Similar to class libraries in object-oriented programming, API contracts ensure the reusability of the APIs, increasing the development velocity and efficiency of the teams working with them. Teams feel truly empowered and are allowed to make key decisions themselves without waiting on external approval. When “approval” is baked into the model, teams know where they need to go and exactly how to get there.

One of the core features of the “You Build, Your Data” approach is the self-service model, which empowers both data producers and data consumers within the platform. This self-service model covers a variety of tasks, from launching data pipelines and registering datasets to requesting data access.

Automation drives the self-service model, allowing product and development teams to rapidly complete tasks and removing bottlenecks in development workflows. For example, a data engineering team can independently set up a data pipeline connected to our data lake, with automation around registration and cataloging ensuring the process is aligned with our governance policies. Automation also plays a crucial role in security, with tools that automatically enforce access controls in a least-privilege context, manage data lineage and perform compliance checks on new datasets. Automation has a twofold benefit in this case. It enforces governance and mitigates the human error factor in development and testing. It also allows for automated gathering of data when required from internal and external teams and partners.

Distributed data ownership is another key aspect of the “You Build, Your Data” approach. Rather than centralizing all responsibilities, we can assign data ownership at the domain level, where each team manages its own data assets and ensures quality and governance across specific data domains. This structure allows data teams to operate independently while adhering to company-wide governance policies.

This distributed model means each team is responsible for specific data domains and must approve new data created within its domain. For example, if a customer experience team wants to create a new data pipeline for customer feedback analytics, it would need to secure permission from the specific owner of that data domain. This ensures each dataset complies with our standards, while distributed ownership prevents data silos and improves data quality across the organization. Data silos are a huge problem for traditional banks and large enterprises in general, resulting in large storage inefficiencies and a lack of visibility into things like data residency and data loss prevention. 

With multiple teams producing and consuming data in a self-service model, managing data growth and data redundancy or sprawl can become a challenge that steadily increases with scale. We address this by maintaining a centralized catalog that provides visibility into all registered datasets, helping teams avoid data duplication and improving overall data management efficiency.

The central catalog offers visibility into data assets across the organization and serves as a single source of truth. By cataloging all datasets, we can identify and consolidate similar data assets created by different teams, reducing data redundancy and ensuring accuracy. When there are multiple copies of the same data within an organization, a natural “data drift” happens. Consolidating data to reduce or eliminate duplicate data removes the risk of this data drift in an enterprise. This data approach builds a collaborative environment where teams can leverage shared data resources.

The centralized catalog also encourages data reuse rather than redundant dataset creation. For example, when team members want to create a new dataset, they can first check the catalog to see if similar data already exists. If so, they can use that data rather than create a redundant data set. This reduces the infrastructure load and cost of managing data. With data at the core of any enterprise, small increases in efficiency within data management can have outsized results in efficiency as that data scales to multiple use cases.

Automated tooling streamlines governance, making it possible to monitor data lineage, enforce access controls and maintain data quality without manual intervention. For example, automated processes ensure each dataset undergoes scanning to detect sensitive information and see if it's located in the right place with the right controls around the data. 

Automation also helps with anomalous behavior detection. While anomalous behavior is generally difficult to detect, automation sets a consistent baseline from which to derive behavior modeling. That behavior modeling greatly simplifies anomalous behavior detection.

Overall, an emphasis on automation ensures governance requirements are consistently met and in an efficient manner. By automating essential tasks, we can also reduce the operational burden on developers and data engineers and allow them to focus on high-impact projects.

We’ve also developed tooling to support consistent, verifiable data governance, including a centralized Continuous Integration/Continuous Deployment (CI/CD) pipeline, data lineage tracking systems and API gateways.

These tools provide guardrails that allow for autonomy while ensuring all teams work within the same standards. For example, our CI/CD pipeline includes standardized templates and preconfigured security checks, which ensures each production deployment meets our security standards. This approach minimizes configuration errors and security risks across production deployments.

By offering these shared tools, we’re able to promote a culture of collaboration and knowledge sharing across and between various teams. Common tools and templates allow teams to learn from each other, avoid redundant development efforts and improve overall efficiency. In addition, as the teams become more familiar with shared tools, standardized templates and how other teams are leveraging them, a core set of best practices is automatically being created through the normal course of business. 

For companies looking to adopt a similar approach, clear and well-defined standards, especially for API contracts and data interactions, are of the utmost importance. Well-defined, consistent standards prevent different types of fragmentation in a distributed environment and help maintain data integrity.

It’s also important to invest in automation from the outset. Automation is much more difficult to add on after the fact, but when it's integrated from the beginning, many efficiencies are gained as a result. Automating governance tasks ensures consistency, reduces manual intervention and allows teams to focus on high-value-added work. In addition, establishing a centralized catalog to provide visibility and transparency into data assets and other types of digital assets is key. This approach helps teams identify opportunities for collaboration and data reuse within a well-defined set of guidelines.  

Automation is critical to addressing many data management challenges, and it allows smaller teams to gain leverage when they need to be agile. The combination of automation and a central catalog also has a natural symbiotic growth mechanism that results in best practices shared across all product and development teams.

By empowering teams to own their data while adhering to a shared governance framework, you can foster a culture of responsibility and innovation across teams. Assigning ownership at the domain level as opposed to micromanaging lower levels, drives accountability and encourages teams to take responsibility for data integrity. With this approach, each product and development team understands its role in safeguarding data and contributing to the organization’s data ecosystem in a well-managed manner. Giving teams ownership empowers them to do their best work with confidence and clarity.

By investing early in automation, establishing well-defined standards and empowering teams to manage their data, you can unlock the value of your data. 

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