Standardize customer codes across datasets

data cleanup

slug: recipe-data-cleanup-standardize-customer-codes-across-datasets

Recipe: Standardize customer codes across datasets

category: data cleansing and standardization

Problem

Customer identifiers often differ between tables:

• inconsistent formatting (e.g., C123 vs c-123)
• missing prefixes or leading zeros
• duplicates due to inconsistent naming

Solution

Follow these steps to standardize customer codes:

• load relevant datasets
• apply transformation rules to normalize identifiers
• optionally merge with a reference table to validate codes
• remove duplicates caused by inconsistent formatting

Step Sequence

load step -> calculate step -> keep step -> drop step

Input Datasets

• transactions_clean — cleaned transactional data
• customers_ref — reference table of valid customer codes
• Notes: customer codes may have inconsistent formatting or missing entries

Output Dataset

• transactions_standardized — dataset with uniform customer codes
• Notes: duplicates removed, all codes normalized, ready for analysis or merging

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load datasets	Includes transaction and reference tables
calculate step	Normalize customer codes	Apply rules: uppercase, remove special characters, pad numbers
keep step	Retain relevant columns	Keep only necessary fields for analysis
drop step	Remove redundant or invalid columns	Optional: drop original raw code columns

Variations & Extensions

• Combine with compare step to identify mismatched codes between datasets
• Apply lag step or calculate step to compute derived customer metrics
• Include combine step to merge standardized dataset with other data sources

Concepts Demonstrated

• Data standardization
• Identifier normalization
• Duplicate removal
• Sequencing transformation steps

Related Recipes

• Remove invalid records and handle missing values
• Detect and reconcile differences between tables

Notes & Best Practices

• Document the normalization rules applied
• Always validate against a reference table to catch anomalies
• Keep original identifiers in a separate column for traceability

Metadata

title: "Standardize customer codes across datasets"
category: "data cleansing and standardization"
difficulty: "Intermediate"
tags: [data-cleaning, standardization, customer-codes]
inputs: [transactions_clean, customers_ref]
outputs: [transactions_standardized]
steps: [step-load, step-calculate, step-keep, step-drop]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"