Case Study Data quality & address validation

Data Quality & Address Validation to Support Large-scale Growth

For a fast-growing logistics company, new territories means more address data of varying structure, particularly difficult to validate. To replace a hard-to-scale data bottleneck of 30+ workers, the company invested in an automated solution built on CloverETL.

With expansion to new regions, manual validation of disparate address data was stalling growth for a logistics company. They sought a solution to replace the labor-intensive process. CloverETL built a data quality solution that validates, geo-locates, and repairs 80 to 90% of addresses instantly, while also providing self-learning tools for future use. Since the framework automatically validates addresses against AddressDoctor, GoogleMaps, HERE maps, and Baidu, now only exceptions, about 20% of addresses, require manual correction.

  • A scalable address validation and cleansing framework replaced the manual process
  • The solution includes customizable country‑specific rules for future expansion to new markets
  • Minimized human interactions down to 1/10th, a figure that’s still decreasing with the system’s self‑learning capability