Data quality issues can arise from various sources, including:
Duplicate Data: Redundant entries that can skew analysis and lead to inaccurate conclusions. Incomplete Data: Missing values that can compromise data integrity and lead to biased results. Inconsistent Data: Variations in data formats, naming conventions, or units of measure that can cause confusion and errors. Outdated Data: Information that is no longer relevant or has changed over time, leading to incorrect insights.