Normalize messy e-commerce data

Key findings and actionable guidance: - Start with a data inventory and mapping. Catalog every source of e-commerce data (Shopify, Amazon, marketplaces, POS, ERP, payment gateways, email/marketing platforms, analytics).

You must be able to locate personal data across systems to satisfy consumer rights and breach-notification obligations. - Define a canonical data model (Product, SKU, Variant, Order, OrderLine, Customer, Address, Payment, Fulfillment, Event).

Use stable primary keys (SKU/ProductID, OrderID, CustomerID) and document field semantics and required formats. - Build a mapping and transformation spec per source. For each upstream field, specify normalization rules: units (lbs→kg), currency, date format (ISO 8601), title standardization (strip noisy tokens), attribute harmonization (e.g., unify color/colour), and SKU normalization (case, punctuation removal, leading zeros).

Include example mapping templates. - Implement parsing, standardization, and enrichment in an ETL/ELT pipeline: ingest raw feeds, run parsers/cleaners (regex, normalization libraries), deduplicate (fingerprinting/hashing on normalized SKU+title or identifier sets), perform identity resolution (email normalization, phone E.164, address standardization via USPS APIs), enrich with reference data (GS1, product taxonomies) and load into master PIM or data warehouse. - Use data-quality tests and observability: implement validations (schema, nullability, ranges), automated tests (Great Expectations, Deequ), and monitoring/alerts for schema drift or quality regressions.

Backfill and reconciliation jobs are essential. - Protect PII and follow privacy laws: minimize collection, hash/encrypt identifiers at rest/in transit, implement role-based access, maintain retention schedules and deletion flows to honor rights (access, delete, correct, opt-out).

Implement a consumer request intake and verification workflow so you can find and delete/port records across systems within statutory timelines. - State privacy laws and CCPA/CPRA: many states now have privacy statutes (California, Virginia, Colorado, Connecticut, Utah, and many more).

CCPA/CPRA requires notices at collection, rights to know/delete/correct/limit, and honoring opt-out signals (GPC). Consider a single-program “highest standard” approach to simplify compliance across states. - Breach notification: every US state has breach-notification laws—maintain an incident response plan that identifies affected systems, data types, timelines, and reporter responsibilities to each state where residents are affected. - Sales tax and marketplace facilitator rules: marketplace facilitator laws shift sales tax collection to platforms for marketplace sales in many states—ensure your order normalization preserves flags for marketplace vs direct sales to apply correct accounting and tax flows.

Consult state DOR guidelines and vendors like Avalara for up-to-date lists. - Vendor contracts and DPAs: require Data Processing Addenda that specify permitted uses, security measures, consumer-request cooperation, and deletion/return requirements.

Practical checklist (for a US LLC / small-to-midsize e-commerce owner):

Key findings and actionable guidance:

8601), title standardization (strip noisy tokens), attribute harmonization (e.g., unify color/colour), and SKU normalization (case, punctuation removal, leading zeros). Include example mapping templates. - Implement parsing, standardization, and enrichment in an ETL/ELT pipeline: ingest raw feeds, run parsers/cleaners (regex, normalization libraries), deduplicate (fingerprinting/hashing on normalized SKU+title or identifier sets), perform identity resolution (email normalization, phone E.164, address standardization via USPS APIs), enrich with reference data (GS1, product taxonomies) and load into master PIM or data warehouse.

• Start with a data inventory and mapping. Catalog every source of e-commerce data (Shopify, Amazon, marketplaces, POS, ERP, payment gateways, email/marketing platforms, analytics). You must be able to locate personal data across systems to satisfy consumer rights and breach-notification obligations.
• Define a canonical data model (Product, SKU, Variant, Order, OrderLine, Customer, Address, Payment, Fulfillment, Event). Use stable primary keys (SKU/ProductID, OrderID, CustomerID) and document field semantics and required formats.
• Build a mapping and transformation spec per source. For each upstream field, specify normalization rules: units (lbs→kg), currency, date format (ISO
• Use data-quality tests and observability: implement validations (schema, nullability, ranges), automated tests (Great Expectations, Deequ), and monitoring/alerts for schema drift or quality regressions. Backfill and reconciliation jobs are essential.
• Protect PII and follow privacy laws: minimize collection, hash/encrypt identifiers at rest/in transit, implement role-based access, maintain retention schedules and deletion flows to honor rights (access, delete, correct, opt-out). Implement a consumer request intake and verification workflow so you can find and delete/port records across systems within statutory timelines.
• State privacy laws and CCPA/CPRA: many states now have privacy statutes (California, Virginia, Colorado, Connecticut, Utah, and many more). CCPA/CPRA requires notices at collection, rights to know/delete/correct/limit, and honoring opt-out signals (GPC). Consider a single-program “highest standard” approach to simplify compliance across states.
• Breach notification: every US state has breach-notification laws—maintain an incident response plan that identifies affected systems, data types, timelines, and reporter responsibilities to each state where residents are affected.
• Sales tax and marketplace facilitator rules: marketplace facilitator laws shift sales tax collection to platforms for marketplace sales in many states—ensure your order normalization preserves flags for marketplace vs direct sales to apply correct accounting and tax flows. Consult state DOR guidelines and vendors like Avalara for up-to-date lists.
• Vendor contracts and DPAs: require Data Processing Addenda that specify permitted uses, security measures, consumer-request cooperation, and deletion/return requirements. Practical checklist (for a US LLC / small-to-midsize e-commerce owner):

Data inventory and mapping.

Canonical schema and attribute dictionary.

Ingest -> clean -> normalize -> enrich -> dedupe -> validate -> load.

PII handling

encryption, access control, retention policy, deletion flows.

Consumer rights process and templates (verify identity, provide exports, honor opt-outs).

Breach response plan mapped to state notification rules.

Sales-tax flags and marketplace-facilitator reconciliation.

Vendor DPAs and privacy/security due diligence.

Monitoring, QA tests, and recurring data audits.