Data contracts have gained traction as a solution to address data quality challenges that impede organizations from maximizing business value from their data. These contracts apply software development principles such as versioning, service level agreements, and continuous integration to data management, aiming to prevent disruptions caused by unexpected changes. The fragmented nature of data platforms and the increasing complexity of data systems, especially with the rise of AI and data mesh, necessitate such contracts to ensure data integrity across functions. At Foundational, data contracts are categorized as either existing dependencies or custom constraints, leveraging a mix of manual and automated processes to manage changes efficiently. Manual contracts typically include schema definitions and may specify values and freshness requirements, while automated contracts focus on flagging potential issues without predefined definitions. Foundational’s approach integrates data contracts with git, automating enforcement and monitoring code changes across repositories, thus aiming to unify data management and preemptively address issues like data quality, privacy, and cloud costs, streamlining data operations across the organization.