Geo‑Standardization Service — Case Study

Last updated: Aug 13, 2025

This case study covers a backward‑compatible standardization of U.S./Canada State/Province fields and a bulk migration plan for 15–20M records. Proprietary details removed; approaches and code snippets are illustrative.

Problem

• Inconsistent State/Province values (free‑text, abbreviations, misspellings) caused joins and analytics issues.
• Need a service that standardizes geo fields while remaining compatible with existing consumers.
• Phase 1 targets US and CA; later phases extend globally.

Approach

1. Standardization Service: canonical mapping (e.g., CA ↔ California) with validated aliases.
2. Backward Compatibility: dual fields (state_raw, state_std) and read‑through logic.
3. Incremental Rollout: enable per‑country; monitor read/write ratios and error rates.
4. Bulk Migration: standardize 15–20M existing records with strong rollback guarantees.

Architecture

flowchart LR
A[Input: raw records] --> M[Mapper: alias → canonical]
M --> W[(DB: state_std)]
W --> S[Services & APIs]
S --> C[Downstream Consumers]

Loading

• Mapper: curated alias table + fuzzy match for common typos.
• DB: maintain both state_raw and state_std during transition; downstreams switch gradually.

Data Model (illustrative)

ALTER TABLE companies
  ADD COLUMN state_std TEXT,
  ADD COLUMN geo_version INT DEFAULT 1;

-- idempotent backfill procedure (batched)
UPDATE companies
SET state_std = geo_map(state_raw),
    geo_version = 2
WHERE country IN ('US','CA') AND state_std IS NULL
LIMIT 10000; -- looped in batches

Bulk Migration Plan

• Batching: process in chunks with checkpoints; record counts and mismatch rates per batch.
• Safety: snapshot before each major phase; maintain rollback SQL.
• Monitoring: dashboards for standardization rate, error rate, and downstream read success.

Results

• Cleaned US/CA state data at scale with no breaking changes to consumers.
• Prepared a repeatable playbook for additional countries.

Lessons Learned

• Dual‑write/dual‑read periods minimize risk.
• A curated alias list covers 80–90% of mess; the last 10% needs targeted fixes and owner feedback.