The text discusses the importance of data visibility and traceability in artificial intelligence (AI) development, particularly as organizations rush to digitize operations and boost efficiency. Data visibility refers to accessing and understanding data across its entire lifecycle, while traceability complements this by letting organizations track the flow and changes of vast amounts of data over time. Implementing robust data management practices can help businesses build reliable and trustworthy AI systems, enhance interpretability, and comply with ethical guidelines and legal standards. The benefits include increased trust, bias mitigation, enhanced regulatory compliance, faster debugging, and data management optimization. However, challenges such as increasing data complexity, data silos, model complexity, data privacy, and scalability must be addressed through best practices like aligning traceability with the entire data lifecycle, establishing metadata, implementing data governance, using robust storage solutions, and continuous monitoring. A third-party solution like Encord can help optimize visibility workflows by providing comprehensive data management capabilities tailored for diverse applications.