Case Study: a pharma organization reduces data quality incidents with SEI

Pharma Organization reduces data quality incidents by 40% with SEI

The customer, a pharma organization, faced challenges with incomplete and inconsistent pharmaceutical sales data from a third-party aggregator, which was further impacted by internal pipeline errors and inconsistencies. These data quality issues posed a significant risk to SEI's customer, increasing the likelihood of incorrect compensation payouts and misguided marketing decisions while reducing confidence in reporting for their sales, marketing, and compensation teams.

SEI implemented a multi-pronged solution that included root-cause fixes, vendor collaboration, and the development of an automated quality engine built on a Databricks framework. This proactive approach allowed the organization to shift from reactive firefighting to preventive monitoring. As a result, SEI helped the pharma organization reduce reactive data quality incidents by approximately 40% within the first year, resolve issues before they reached users, and significantly increase trust in the accuracy of their enterprise information.