Your Data is a Mess. Let SQL Fix It.

• Your Data is a Mess. Let SQL Fix It.

Why Clean Data?

• Dirty data leads to inaccurate reports and analysis
• Ensures consistency across your entire dataset
• Builds a reliable foundation for machine learning models
• Saves time in the long run by preventing rework
• Essential for making confident business decisions
• Improves the performance of your database queries

Find Missing Values

• Use IS NULL to identify empty fields
• Use IS NOT NULL to find complete records
• Count nulls with COUNT(*) - COUNT(column_name)
• Use COALESCE to replace nulls with a default value
• Investigate why data is missing for critical fields
• Decide if rows with nulls should be kept or removed

Tame Text Data

• Use TRIM to remove extra spaces from strings
• Standardize case with UPPER() or LOWER() functions
• Replace substrings using the REPLACE function
• Check for inconsistent entries (e.g., 'USA', 'U.S.A.', 'US')
• Use LENGTH() to find abnormally long or short entries
• CONCAT strings to merge first and last names

Fix Data Types

• Ensure numbers are stored as INT or DECIMAL types
• Convert strings to proper DATE or DATETIME types
• Use CAST or CONVERT functions to change types
• Check for numbers mistakenly stored as text
• Validate that booleans are represented consistently
• Correct mismatched types before mathematical operations

Remove Duplicates

• Use GROUP BY and HAVING to find duplicate rows
• Identify duplicates based on key columns
• Use ROW_NUMBER() to rank and delete duplicates
• Keep the most recent or complete record
• Use SELECT DISTINCT for quick duplicate-free results
• Always validate counts before and after deduplication

Handle Outliers

• Use MIN() and MAX() to spot extreme values
• Calculate averages and standard deviations
• Filter data using WHERE and comparison operators
• Decide if outliers are errors or valid data points
• Use percentiles to identify top and bottom extremes
• Cap extreme values if they skew analysis

Standardize Formats

• Enforce consistent date formats (YYYY-MM-DD)
• Standardize phone numbers and country codes
• Apply a single currency format across financial data
• Create categories for free-text fields
• Use CASE statements to map values to a standard
• Ensure units of measurement are consistent

Validate Data

• Use CHECK constraints to prevent future errors
• Ensure foreign keys match primary keys in other tables
• Verify that numeric values fall within expected ranges
• Check that dates are logical (e.g., no future birth dates)
• Use LIKE with wildcards to find pattern violations
• Create data quality rules for ongoing checks

Your Cleaning Toolkit

• UPDATE to modify existing records
• ALTER TABLE to add, drop, or change columns
• DELETE to remove incorrect rows (use with caution)
• CASE statements for conditional logic and categorizing
• String functions (TRIM, REPLACE, SUBSTRING) for text
• Temporary tables to test changes before applying them
• Always backup your data before starting any cleaning process