The AI revolution shows no signs of slowing down, transforming industries at an unprecedented pace. The fuel powering this change is high-quality, well-managed data. As companies adopt AI, the demand for reliable data is also growing rapidly. To stay competitive, organizations must do the hard work of building scalable data ecosystems that provide a foundation for strong data management, which is necessary to handle the complexity of data inherent with the AI revolution.
At Capital One, we’ve long recognized the crucial role data plays in driving our business forward. As a technology and data-first company, we’ve invested in modernizing our data ecosystem in the cloud and have implemented data management principles to ensure this ecosystem can scale with the pace of innovation. We’ve learned valuable lessons about what it takes to create a scalable and well-governed data ecosystem where data is easy to locate, understand, and use across the company – all of which is necessary for responsible AI readiness and acceleration.
Building Data Muscle for AI Innovation
As AI becomes ubiquitous, the models powering it require more high-quality data with increasing complexity. The amount of global data being produced is accelerating, predicted to expand to 180 zettabytes by 2025, which is nearly double what it was in 2022. This explosion of data—both structured and unstructured—presents organizations with data management challenges that must be tackled head-on to unlock the power of AI.
To address these challenges, organizations should focus on building data products, processes, and platforms that ensure data is well-governed, yet accessible and usable for those who need it. For Capital One, this has meant a focus on three key principles: standardization, automation, and centralization.
Driving data standardization from the point of creation involves establishing clear definitions, rules, and governance for metadata and data to ensure data consistency, which in turn leads to better reliability in the downstream experiences the data is powering. We’re developing modular, reusable data management capabilities that can be embedded into our platforms and pipelines, making it easier to enforce standards at every stage.
But no matter how well your data adheres to standards put in place, managing massive amounts of data can strain existing manual processes. To adapt, data quality needs to increase while reducing the time spent managing data by producers, owners, and users. It will become increasingly important to identify redundancies and automate recurring tasks that are the most labor-intensive—for example, data registration, metadata management, and ownership updates—so data users can spend their time innovating.
We’re automating data governance and quality assurance wherever possible. Assisted by machine learning techniques, we can discover sensitive data, manage metadata, and perform quality checks at scale. By establishing policies and thresholds upfront, we can automate the enforcement of our data standards, freeing up our teams to focus on more strategic tasks. For example, our data scanning tooling can automatically detect data problems across our environment and notify the right teams to take action. With millions of potential alerts, manual remediation alone is unsustainable, but by combining automated detection and remediation tooling with clear governance policies and workflows, we can handle data issues proactively.
Finally, we’ve centralized our data platforms and tools, moving away from siloed, bespoke solutions. By consolidating onto shared enterprise platforms and embracing product design principles like multi-tenancy, we enable standardization across the organization, which means all data users can enjoy the benefits of a well-governed data platform and reduce the redundancies that come with maintaining multiple custom tools. By fostering an environment where data is easily accessible and reliable, we remove barriers to collaboration and empower teams to work together more effectively and innovatively.
Ensuring AI Integrity Through Data Quality
The success of any AI initiative also requires high-quality data with context. Simply put, AI is only as good as the data fueling it, and the ability of users and models to understand it. This requires ensuring the completeness and consistency of metadata across all systems, and involves capturing a full picture of all attributes of data and maintaining uniformity in how this metadata is recorded across various platforms. For instance, if a dataset is identified as containing sensitive data, this attribute should be consistently reflected in every system where the dataset resides. Equally crucial is the validity of data, which hinges on adherence to the standards that are implemented in a well-managed data ecosystem.
It is also important to understand how and where data is being used. This requires tracking the end-to-end lineage of data—from its source through every transformation and movement to its final use. This visibility can enable data management tooling to more effectively identify and rectify potential issues. For example, by maintaining detailed lineage records, if unprotected, sensitive data is found in one location, organizations can efficiently identify and address all other instances of that data, mitigating potential risks.
Producing and managing high-quality data also requires being prepared for an evolving digital landscape. In this context, it’s even more crucial to ensure the data products, processes, and platforms being built are capable of managing data across various technologies. This requires developing modular and flexible tools that can seamlessly adapt as technology changes and new data environments emerge. By doing so, organizations can maintain data quality, consistency, and accessibility, regardless of this evolution.
Perhaps the most important ingredient in building and maintaining an ecosystem full of high-quality data is establishing a curious and data-driven culture. Encouraging continuous learning and upskilling among product leaders and data managers is crucial in having talent that is abreast of the latest technologies and best practices. As is similar to the need for flexible tooling, this is especially important given the rapid pace of change in data management and AI.
Data: A Key to AI Success
It’s clear that the winners in the race to make the best use of AI will be those who can effectively harness the power of data at scale. To address the data demands of an AI-driven future, it’s not enough to simply “go bigger” – it requires a thorough understanding of the complexities of the challenge, and a sophisticated approach that emphasizes data quality as much as volume.
While the future of AI evolves on a daily basis, it’s clear that it will have a profound impact on how we manage data. Those who evolve their organizations to meet the challenge will be the ones best positioned to take advantage of the new frontier.