Address Data Clean-up
The situation
The client’s customer address data was inconsistent and could not be reliably analysed. As a result, the business had limited visibility into where guests were coming from or how location should inform decisions.
The opportunity
On Data already had deep familiarity with the client’s data stack. That made it possible to tackle data quality at source with a repeatable and automated approach.
How we made it happen
We built an automated application that cleaned and validated addresses directly from the Snowflake data warehouse. Using Python, raw address data was passed through address-validation APIs, standardised, and written back into Snowflake in a consistent, usable format.
We applied the same logic to phone numbers and email addresses, so the entire contact record became usable.
What changed
- Customer addresses are now clean, consistent, and ready for analysis
- Phone numbers and email addresses follow the same reliable standards
- Location and contact data can be trusted across the business
- Data quality is maintained automatically, not manually
Why it matters
By fixing contact data at source, the client removed a long-standing barrier to understanding their guests and made everyday analysis faster, easier, and more dependable.
