As a junior Data Scientist at a consulting firm, you're tasked with improving a machine learning model's performance. The initial analysis reveals that the dataset contains numerous missing values (NaN) across various fields, significantly impacting the model's accuracy. Your team lead emphasizes the importance of handling these missing values effectively during the data acquisition phase to ensure the model's reliability and performance. Considering the constraints of maintaining data integrity, minimizing bias, and ensuring scalability, which three strategies should you implement to address the missing values? (Choose three)