Beginner’s Data-Cleaning Checklist

Overview

Cleaning is the unglamorous but indispensable work that turns messy, real-world data into trustworthy information. A data-cleaning checklist gives beginners a repeatable sequence of tasks—validation, formatting, enrichment, and documentation—so they can produce analysis-ready datasets with confidence.

Why a Checklist Matters

Data-science folklore says that 80% of a project’s time is spent cleaning data. While the precise number is debatable, every experienced analyst agrees on the pain: missing values, inconsistent units, stray characters, duplicate rows—the list goes on. A checklist does three things for beginners:

• Consistency: Guarantees nothing critical is forgotten when deadlines loom.
• Quality: Surfaces hidden issues that derail models and dashboards later.
• Communication: Makes cleaning steps explicit so teammates, auditors, and future you know exactly what happened to the raw data.

The Standard Beginner’s Data-Cleaning Checklist

1. Understand the Data Source

Before writing a single line of code or SQL, answer:

1. What system produced this data? (Application DB, CSV export, third-party API)
2. What time window does it cover? (Yesterday’s orders, all-time history, daily snapshots)
3. What granularity is expected? (One row per customer? One row per order line?)

These questions prevent later surprises like duplicate customer IDs caused by a poorly documented incremental load.

2. Validate Schema and Types

Compare column names, data types, and constraints to source-of-truth documentation (ERDs, API specs, or data contracts). Common tasks:

• Cast strings that should be dates (2024-01-31) or numbers ('123' → 123).
• Flag unexpected or null primary keys.
• Verify enumerations (e.g., status IN ('pending','paid','failed')).

3. Standardize Formats and Units

People love formatting data in creative ways. Bring order:

• Dates: Use ISO-8601 (YYYY-MM-DD) or your org’s canonical format.
• Currencies & Units: Convert to a base (e.g., cents, meters) to avoid floating-point rounding.
• Strings: Trim whitespace, normalize casing (UPPER or lower), remove control characters.

4. Identify and Handle Missing Values

Missingness is information! Determine:

• Is the null legitimate (e.g., optional coupon code) or a data pipeline failure?
• Should you drop, impute, or flag the record?

Document the chosen strategy so subsequent analyses don’t unwittingly double-count imputed data.

5. Detect and Remove Duplicates

Use primary keys or composite keys to find exact duplicates. Deduplication strategies:

• Keep First/Last: Useful when later rows reflect updates.
• Aggregate: Merge numerical columns (sum, avg).
• Manual Review: For fuzzy duplicates (e.g., john.doe@gmail.com vs john_doe@xyz.com).

6. Handle Outliers and Anomalies

Outliers can signal data errors or genuine business events. Start by:

• Visualizing distributions (histograms, box plots).
• Flagging values outside logical bounds (e.g., negative ages).
• Setting alert thresholds for live pipelines.

Decide whether to cap, remove, or leave extreme values, and justify the choice.

7. Enrich and Derive Features

Once the raw data is trustworthy, derive helpful columns:

• order_total = quantity * unit_price
• signup_to_purchase_days
• Geocode zip_code → state, country

Feature engineering during cleaning keeps downstream models simpler and more reproducible.

8. Document Every Transformation

The final (and most skipped) step: write down what you did.

• Use comments in SQL or notebooks.
• Commit code to version control.
• Update data dictionaries or a README in the dataset folder.

Documentation converts one-off cleaning scripts into institutional knowledge.

Best Practices for Beginners

• Automate Early: Even a messy bash script is better than manual Excel cleaning.
• Fail Fast: Add assertions (CHECK sum(amount) > 0) so pipelines break loudly.
• Work Incrementally: Load a sample of 1,000 rows, perfect the logic, then scale up.
• Version Raw Data: Store an untouched copy so you can rerun cleaning if logic changes.

Common Misconceptions

1. “Cleaning is a one-time task.” In reality, data drifts; schedules and schemas change. Invest in reusable pipelines.
2. “We can fix it in the BI tool.” Quick dashboard hacks accumulate tech debt. Clean once near the source.
3. “AI will handle it automatically.” Large-language models help generate code, but humans must still define rules and validate outputs.

Practical SQL Example

-- Example: Cleaning an orders table
WITH raw AS (
SELECT * FROM staging.orders_2024_05
),
validated AS (
SELECT *
FROM raw
WHERE order_id IS NOT NULL -- Primary key check
AND status IN ('pending','paid','failed')
),
standardized AS (
SELECT
order_id,
customer_id,
CAST(order_date AS DATE) AS order_date,
CAST(unit_price AS NUMERIC) AS unit_price_cents,
quantity,
UPPER(TRIM(currency)) AS currency,
created_at,
updated_at
FROM validated
),
deduped AS (
SELECT DISTINCT ON (order_id) *
FROM standardized
ORDER BY order_id, updated_at DESC
)
SELECT
*,
quantity * unit_price_cents AS order_total_cents
FROM deduped;


This query walks through several checklist steps: validation, standardization, deduplication, and feature derivation.

How Galaxy Fits In

Galaxy’s modern SQL editor accelerates checklist execution:

• AI Copilot: Autocompletes cleaning SQL, explains errors, and suggests constraints.
• Collections: Store the canonical cleaning queries so teammates reuse, not retype, them.
• Endorsements: Mark queries as “clean” to avoid shadow copies floating in Slack.

While Galaxy doesn’t clean data for you, it removes friction from writing, sharing, and iterating on cleaning code.

Troubleshooting & Common Mistakes

1. Ignoring Type Mismatches

Why it’s wrong: Silent casting may truncate data (e.g., '123.45' → 123).
Fix: Explicitly cast and add TRY_CAST checks to detect failures.

2. Over-Aggressive Deduplication

Why it’s wrong: Removing all but one record may erase legitimate distinct rows.
Fix: Deduplicate by the true primary key (e.g., order_id and item_id).

3. Treating Nulls and Zeroes the Same

Why it’s wrong: A null revenue means “unknown,” while zero revenue means “free.”
Fix: Use CASE statements or separate flags to distinguish.

Putting It All Together

Start every data project with the checklist in mind. Automate each step, version-control your code, and document decisions. You’ll gain cleaner inputs, faster iterations, and far fewer late-night bug hunts.