Case Study: One of the World’s Largest Retailers achieves improved data quality and cost savings with UST Global

Leveraging Machine Learning And Analytics To Improve Data Quality

World’s Largest Retailer, a global leader in retail with massive store and supply chain operations, was spending enormous manual effort trying to maintain accurate operational, inventory, and product data. The company needed a better way to detect, correct, and prevent data errors while improving reliability, customer service, and cost efficiency.

UST Global implemented a customized, cloud-based machine learning and analytics solution that used a self-learning engine plus automated data quality rules and correction processes. UST Global’s approach improved speed and accuracy, identified and corrected errors in 30% of records during test runs, filled in missing data in 5% of records, and delivered higher scalability, better data quality, and significant operational efficiency and cost savings.