This chapter explores an integrated framework for managing and interpreting performance data in sport scienceSport science workflow. It outlines the complete data lifecycle, starting with ingestion and labeling, then cleaning, processing, statistical considerations, visualization, and interpretation. Particular attention is given to challenges commonly encountered in applied environments, including device variability, contextual complexity, and data quality assurance. The chapter draws from real-world scenarios and illustrates how multiple data sources can be synthesized to inform decision-making regarding training loadTraining load, readiness, and athlete monitoringAthlete monitoring. Readers are encouraged to adopt workflows that reflect sport's dynamic and non-linear nature, emphasizing reproducibility, relevance, and the translation of data into context-aware performance decisions.