Data annotation is foundational for the development of machine learning (ML) systems, yet its specification from a Requirements Engineering (RE) perspective is often informal. This poster presents a preliminary investigation of data annotation within ML development, addressing challenges, mitigation strategies, and the potential role of RE. Through a non-systematic multivocal literature review, we identify key challenges including data quality, scalability, subjectivity, ethics, and process management. We found that establishing annotation guidelines is a common mitigation strategy. We then propose incorporating RE principles by formally defining “data annotation requirements”—specifying what to label and why—and conceptualize a traceability chain from system requirements to annotated data. While this approach appears promising for enhancing ML model quality, its practical necessity warrants further empirical investigation.