Data management is foundational in developing artificial intelligence-based algorithms and solutions for the healthcare industry. This chapter provides a comprehensive overview of clinical data management, beginning with an examination of various healthcare data types and their corresponding collection protocols. It emphasizes the critical importance of data security, privacy, regulatory compliance, and the necessary agreements to protect sensitive information. A strong focus is placed on assessing data quality and completeness, which are essential for the success of AI applications in healthcare. The chapter also details key data preprocessing and manipulation techniques required to prepare raw data for analysis. It further explores the Data Quality Lifecycle and Data Quality Management Lifecycle frameworks, as introduced by EU AI Act-aligned standards, highlighting their role in maintaining data quality and integrity throughout the training and validation of AI algorithms. The chapter concludes by emphasizing the need for AI solutions in healthcare to consistently meet the rigorous standards set by regulatory bodies.