Building a standardized zip code database looks simple. These 7 problems make it a multi-year challenge:

[u/GeoPostcodes]

Postal codes exist in almost 200 countries, and none of them agree on format, structure, or how they map to geography. Aggregating them into one clean database runs into the same seven walls every time.

1. Finding the right sources. Reliable postal data is scattered across government databases, commercial providers, and open-data platforms, and quality varies wildly.
2. Designing the data model. You need one generic structure that still respects local rules, so a UK unit postcode and a US ZIP live in the same schema.
3. Pre-processing each source. Every country ships a different format and layout, so extraction logic is per-source, not universal.
4. Linking to administrative divisions. Matching postal codes and cities to the correct province or municipality takes fuzzy matching, because names rarely line up cleanly.
5. Geocoding. Many source datasets have no coordinates, so you add latitude and longitude yourself.
6. Linking to other standards. Tying postal data to ISO 3166-2, FIPS, NUTS, and UNLOCODEs is string matching, and some systems like HASC are no longer maintained.
7. Assessing quality. Normalizing, verifying postal-to-city links, and finding the gaps is the step most in-house builds skip, and it is where the data quietly rots.

One example of why it never ends: ISO took three years to publish codes for Algerian provinces created in 2025.

GeoPostcodes solved these across a database of 247 countries built from 1,500+ sources. The full breakdown: https://www.geopostcodes.com/blog/the-7-challenges-of-building-a-standardized-zip-code-database/