Data Quality and Address Validation

How a self-learning address validation solution repairs 90% of addresses instantly without the need for human intervention.

If you’re a logistics company, one of your most valuable assets is your address data. If your address data is poor quality, you can’t optimize routes effectively, your drivers waste time finding where to drop off packages, and, in the worst case, you can’t deliver at all.

A manual address validation bottleneck

Address data can be notoriously tricky to validate and interpret, especially in emerging markets where language, alphabets, address structure and rules can vary dramatically. For one logistics company, the only way they could find to manage data quality was to have a team of 30 people manually verifying the data and working shifts to meet overnight delivery deadlines. But as the company expanded, this manual process was becoming a serious bottleneck.

The CloverDX services team came in to solve the company’s address data problems, paving the way for business growth.

A data validation and cleansing framework

Working with multiple third-party systems (including Google Maps, HERE maps and Baidu), we created a data validation and cleansing framework, along with a user interface so that teams could interact easily with the solution. The framework can be tailored to country-specific address structures, validation rules and legal restrictions using automated rules that can be easily modified to support additional countries without the need for expensive coding.

The solution validates, geo-locates and repairs almost 90% of addresses instantly, so the hard-to-scale bottleneck of 30 people has been removed. Human interactions in the address validation process have been reduced by 90% (and are still decreasing with the system’s self-learning capability) and as a result,  the company can now readily expand their business.