How do you deal with missing or inconsistent data during transformation?

Handling missing or inconsistent data during transformation involves identifying issues, applying cleaning strategies, and validating results. The first step is to detect problems using automated checks or manual inspection. For missing data, common approaches include removing rows with gaps (if the dataset is large enough), filling gaps with placeholder values (like “Unknown”), or imputing values using statistical methods (mean, median, or mode). For example, in a Python script using Pandas, df.dropna() removes incomplete rows, while df.fillna(df['column'].mean()) replaces missing numeric values with the column average. The choice depends on context: removing data risks losing insights, while imputation might introduce bias if not carefully applied.

Inconsistent data often requires standardization or transformation. For instance, dates formatted as “MM/DD/YYYY” and “DD-Mon-YY” in the same column can be converted to a uniform format using parsing libraries (e.g., Python’s datetime). Text fields with typos or variations (like “New York” vs. “NYC”) might need regex pattern matching or lookup tables to map values correctly. Outliers in numeric columns can be capped or flagged using domain-specific rules—for example, setting a maximum order value of $10,000 in an e-commerce dataset. Tools like SQL’s CASE statements or frameworks like Apache Spark’s DataFrame API help enforce consistency at scale by applying rules during transformation pipelines.

Validation ensures the cleaned data meets quality standards. Automated tests can check for non-null values, valid ranges, or expected formats post-transformation. For example, a unit test might verify that all email addresses in a column contain an “@” symbol, or that numeric fields fall within a predefined range. Integrating these checks into CI/CD pipelines or tools like Great Expectations ensures ongoing quality. Logging unresolved issues (e.g., unhandled outliers) and documenting decisions (like why certain imputation methods were chosen) adds transparency. By combining systematic cleaning, standardization, and validation, developers reduce errors and build reliable datasets for downstream use.