Data — its collection, preparation and curation — is a crucial need in the artificial intelligence (AI) lifecycle. Ensuring that data is consistent, correct and representative for the intended use is critical to the efficacy of an AI-enabled system. Data verification, validation, and accreditation (VV&A) should address this need, but it is often used in an ad-hoc manner that may limit its support to developing and testing AI-enabled capabilities. This DATAWorks presentation highlights how existing frameworks for data VV&A are applicable and important to the AI lifecycle, outlining concerns and best practices.