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Data Cleaning Checklist Basics
Prep datasets before analysis
This guide provides a stepwise data cleaning checklist: schema validation, missing data handling, outlier review, deduplication, and documenting transformations.
- data cleaning
- datasets
- analytics
- quality
- etl
Validate schema
Confirm column types, ranges, and required fields; fail fast on violations.
Handle missing data
Quantify missingness, decide on drop, impute, or flag; record rationale.
Review outliers and duplicates
Use simple profiling to spot outliers; deduplicate using keys and fuzzy checks if needed.
Document steps
Log every transformation, assumptions, and QA checks to keep analyses reproducible.