Data Leakage

If the process of standardizing numeric data is prone to leakage, then why can't it be skipped? Equal Feature Importance -- Let's say we have two features: final_exam_score and SAT_score [USA college prep test]. On one hand, the final exam has a maximum score of 100, but, on the other hand, the SAT has a maximum score of 1600. If we don't normalize these two features based on their range of possible values, then an algorithm would initially be prone to prioritizing the SAT_score feature because of its larger values. However, if we normalize both features between 0 and 1, then they will be treated equally at the start of training. Help Prevent Gradient Explosion -- Neural networks learn better when input values are close to zero.