- Common issue is null or missing values in mandatory fields.
- We handled by replacing defaults or excluding invalid records.
- Duplicate records often appear due to multiple source loads.
- We identified using primary keys and removed duplicates.
- Incorrect data types like text dates were converted to proper formats.
- Inconsistent naming such as “NY”, “New York” standardized into one value.
- Outliers like negative sales amounts were flagged and corrected.
- Invalid relationships between tables were fixed using mapping tables.
- Extra spaces and special characters were cleaned in text columns.
- These steps ensured accurate KPIs and reliable reporting.
What are common data quality issues addressed during transformation?
Updated on February 9, 2026
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