Enforce Strict Data Type Conversion: Ensure numbers are numbers, dates are dates, booleans are true/false, and text is text.
Standardize Text (Case, Abbreviations): Apply consistent capitalization (UPPER, LOWER, PROPER), expand abbreviations (e.g., 'NY' to 'New York'), and correct common misspellings.
Handle Missing Values Strategically: Decide how to treat NULLs or blanks: remove rows, impute (fill in) values (with caution!), or assign a specific "N/A" marker. Document your decision.
Deduplicate with Precision: Use multiple fields (not just one) to identify true duplicates. Set rules for which duplicate to keep if there are minor variations.
Implement Data Validation Rules: Build rules into your process (e.g., numbers must be positive, email format must be valid, categories must be from a predefined list) to flag or reject bad data.
Transform Data for Consistency: Convert units (e.g., Celsius to Fahrenheit), currencies, or list to data timezones to a single standard if your LIST has mixed units.
Create Unique Identifiers (Keys): Ensure every record and entity has a primary key. Use foreign keys to link related data across different LISTS (tables).
IV. Augmentation & Enrichment (Adding Value to DATA)
Derive New Features/Attributes: Create new, insightful fields from existing ones (e.g., Age from Date of Birth, Quarter from Date, Profit Margin from Revenue and Cost).
Join with External Data Sources: Merge your newly structured DATA with other relevant datasets (e.g., combine customer feedback with sales history, or product data with market trends) using common identifiers.
Categorization & Tagging: Add new fields to classify records into meaningful groups (e.g., "High-Value Customer," "Logistics Issue," "Product Bug"). This can be manual or AI-assisted.