I have a dataset that contains the following issues: [LIST ISSUES e.g., 20% missing values in column X, inconsistent date formats, categorical data with high cardinality, and several outliers in column Y]. Act as a Senior Data Scientist and outline a comprehensive, step-by-step strategy to clean and preprocess this data for a regression model. Provide code snippets using Python and Pandas to illustrate the most complex steps, specifically focusing on handling missing data without dropping rows and encoding high-cardinality categorical variables.