Case Study

UST helps global apparel manufacturer overcome data challenges with 90% reduction in development efforts

The company wanted to consolidate its disparate data sources and streamline processes to analyze information. UST helped the company design and implement an Azure-based enterprise data lake, enabling 40 concurrent users to conduct analysis to gain reliable business insights.


Founded more than a century ago, this American multinational apparel company produces clothing for some of the most recognizable brands in the industry. With more than 50,000 employees, the company generates nearly $10 billion in revenue annually.


Disorganized data led to poor business insights

Our client had siloed systems with 20 years of historical data. Consolidating and analyzing data from the disparate systems was cumbersome, time-consuming, and fraught with issues, including duplicate or inaccurate information. While the data analysis team tried to manage through the challenges, shadow IT teams and business units created their own workarounds that led to inconsistent insights from the same KPIs. Often, poor business decisions became apparent when inventory reached the marketplace. The company needed help from data engineering experts to design and implement a solution that could seamlessly consolidate the siloed data, so analysts could uncover reliable insights to help company leaders make better business decisions.


Enterprise data lake consolidated data and resolved analysis issues

After a thorough analysis of the company’s supply chain, inventory, HR, finance, customer, and retail data systems, UST designed and implemented an end-to-end Azure-based enterprise data lake that included Azure Data Lake Storage Gen2, Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Analysis Service, Azure Automation, PowerShell, Power BI Premium, Azure Purview and Azure Monitor. The solution was designed with:


Data governance provided reliable information for more than 40 concurrent users

The company’s enterprise data lake can support more than 40 concurrent users as they analyze data to uncover insights across the organization. The solution uses horizontal CPU scaling, automated pauses for analysis services, and the scaling down of data warehouse units to keep costs in check. Dynamic data pipelines have contributed to a 90% reduction in development efforts.