Common data quality issues in ETL workflows often stem from inconsistencies, errors, or incompleteness in source data, which can lead to unreliable downstream analytics or reporting. These issues typically arise during extraction, transformation, or loading phases and require proactive detection and mitigation.
One frequent problem is schema or format mismatches. For example, source systems might store dates as strings in varying formats (e.g., MM/DD/YYYY vs. YYYY-MM-DD), leading to parsing errors during transformation. Similarly, numeric fields might contain non-numeric characters (e.g., "123A" in a ZIP code field), causing type conversion failures. These mismatches often occur when integrating data from legacy systems, third-party APIs, or unstructured files. Without proper validation or standardization, such issues can halt pipelines or produce incorrect data in the target system. Tools like data profiling or schema validation checks during extraction can help identify these discrepancies early.
Another common issue is missing or incomplete data. For instance, required fields like customer_id might be null in source records, or entire rows could be omitted due to extraction errors. In one scenario, an ETL process pulling sales data might fail to account for optional fields in a source API response, leading to gaps in revenue calculations. Duplicate records—such as a customer appearing twice due to a flawed merge operation—also fall into this category. Implementing deduplication logic, default value assignments, or null checks during transformation can mitigate these risks. However, resolving missing data often requires collaboration with source system owners to enforce data entry standards.
Finally, data validity and accuracy problems can undermine trust in the output. Examples include outdated records (e.g., inactive product codes still referenced in sales data), invalid ranges (e.g., a user_age value of -1), or business rule violations (e.g., a shipment date preceding an order date). These issues might originate from source system bugs, manual data entry errors, or misconfigured transformation rules. For example, a transformation step that incorrectly rounds decimal values could distort financial metrics. Automated validation rules, such as range checks or referential integrity tests, are critical to flagging invalid data. However, addressing root causes often requires auditing source systems or refining transformation logic to align with business requirements.