Data Cleaning Checklist for Beginners

Overview

A data cleaning checklist is the backbone of any analytics or engineering workflow. It codifies the individual steps you must follow—such as validating column types, handling missing values, and standardizing units—so that downstream consumers can trust the numbers they see. In this glossary entry, you’ll learn why the checklist matters, what goes into one, and how to implement it in SQL, Python, or your preferred tooling.

Why a Checklist Beats Ad-Hoc Cleaning

Data scientists and engineers often rush into analysis, relying on intuition to “fix” issues as they appear. Unfortunately, ad-hoc cleaning creates inconsistent logic, hidden biases, and hours of rework. A documented checklist solves these problems by:

• Providing repeatability. Everyone applies the same rules every time.
• Reducing human error. Written steps act as guardrails, catching common pitfalls before they cause defects.
• Accelerating onboarding. New team members see exactly what "clean" means at your organization.
• Facilitating automation. Once codified, steps can be converted into SQL, dbt tests, Airflow DAGs, or Galaxy Collections.

Core Components of a Beginner-Friendly Checklist

1. Source Validation

Before touching the data, confirm you’re pulling from the correct tables or files, and that row counts match expectations. Mismatched sources can propagate systemic errors.

2. Schema Conformance

• Verify data types align with the data catalog: integers are integers, timestamps are timestamps.
• Enforce naming conventions—e.g., snake_case for column names—to avoid case-sensitive bugs.

3. Duplicate Detection

Identify duplicate rows based on natural keys. Decide whether to DELETE them, create a DISTINCT view, or aggregate.

4. Missing Value Handling

For each nullable column, choose one of four strategies:

1. Remove rows (listwise deletion).
2. Replace with defaults (e.g., 0 or "Unknown").
3. Impute using statistical methods (mean, median, model-based).
4. Leave as NULL but flag downstream.

5. Range and Domain Checks

Validate that numeric values fall within realistic bounds (ages >= 0 && < 120) and categorical values match controlled vocabularies (ISO country codes, for example).

6. Unit Standardization

Ensure all measurements use consistent units (meters vs. feet, USD vs. EUR) and document currency dates when exchange rates apply.

7. Text Normalization

Trim whitespace, fix encoding issues (UTF-8), and standardize capitalization. Regular expressions help clean phone numbers, ZIP codes, and IDs.

8. Outlier Treatment

Isolate extreme values with z-scores or IQR and decide whether to cap, transform, or exclude. Always keep a record of the original value for reproducibility.

9. Consistency Across Tables

Foreign keys must exist in dimension tables; reference data should use the same spelling and IDs across all datasets.

10. Final Documentation

Update the data dictionary with changes, list known limitations, and version your cleaning scripts in Git.

Implementing the Checklist in SQL

Below is a simple SQL workflow you could execute in Galaxy’s desktop app. Galaxy’s AI Copilot will auto-suggest column names and spot missing WHERE clauses, cutting your query time in half.

-- Step 1: Source Validation
SELECT COUNT(*) AS raw_row_count
FROM raw.sales;-- Step 2: Remove duplicates based on order_id + line_item
CREATE OR REPLACE TABLE cleaned.sales AS
SELECT DISTINCT ON (order_id, line_item)
*
FROM raw.sales;-- Step 3: Null handling for amount
UPDATE cleaned.sales
SET amount = 0
WHERE amount IS NULL;-- Step 4: Domain check for status
DELETE FROM cleaned.sales
WHERE status NOT IN ('COMPLETE','PENDING','CANCELLED');

Best Practices

• Automate early. Convert manual steps into repeatable scripts as soon as they stabilize.
• Write assertions. Use unit tests or dbt test blocks to catch regressions.
• Use version control. Treat data cleaning scripts like application code—pull requests, reviews, CI.
• Separate staging vs. production. Never clean data directly in prod tables. Instead, write to staging_ schemas or temp tables.
• Leverage lineage tools. Tools like Galaxy (with future catalog features) or OpenLineage help visualize dependencies.

Common Misconceptions

“I can clean later.”

Early decisions compound. The cost of retrofitting cleanliness rises exponentially as pipelines grow.

“Nulls are always bad.”

Sometimes NULL conveys legitimate missingness. Replacing with 0 can skew averages.

“One checklist fits all.”

Industries differ. Healthcare data may require HIPAA-compliant de-identification steps absent in e-commerce datasets.

Galaxy & Data Cleaning

Because Galaxy is a SQL-first IDE, it naturally becomes the cockpit for executing and sharing your checklist scripts. Key integrations:

• AI Copilot flags columns that violate data types and recommends fixes in-line.
• Collections let teams endorse the canonical cleaning query, preventing rogue forks in Slack.
• Access Controls ensure only data stewards can edit cleaning logic, while analysts have read-only visibility.

Next Steps

Begin with a lightweight checklist containing the ten components above. Iterate as your data grows, and bake the logic into version-controlled SQL using a tool like Galaxy so your team can collaborate efficiently and confidently.