How to detect data quality issues?

Data quality issues detection includes several approaches:

• Data completeness check: analyze whether there are missing or empty values in critical fields.
• Accuracy check: compare data with reference values or business rules (for example, dates cannot be in the future).
• Consistency check: ensure that data is consistent across different sources or tables.
• Distribution analysis: identify anomalies in data distribution, such as sharp outliers or unusual values.
• Data profiling: automated tools (e.g., Great Expectations, Deequ) help identify anomalies and rule violations.

Example: if you have a "age" field, you can check that all values are within reasonable limits (for example, from 0 to 120), and if values outside this range are found — it signals a problem.