Automated AWS Data Pipeline for Avadel Pharmaceuticals Helps to Cut Data Processing Time by 95%

An 8-hour manual Salesforce CRM refresh cycle that did not scale.

• Updating prescriber targeting data in Salesforce required a long, repetitive workflow.
• The process depended on exporting Salesforce contacts, using that list as a filter, downloading 1–34 IMS files one-by-one, running pivots per product/market basket, then manually copying Contact IDs + NPI into a separate import file.

Complex filtering rules (activity window, targets, rep changes) that lived in people’s heads and spreadsheets

• Which records to keep/update depended on rules like target lists, recent activity windows (last ~1.5 years), and rep changes (left/terminated/territory changes).
• The workflow also required removing zero-activity prescribers per market basket and keeping the dataset consistent across quarters/products.

Data volume and access constraints

• Weekly IMS prescription data arrived as multiple large files and needed to be merged with mapping tables (e.g., Zip-to-territory).
• Data access was constrained by US-only portal restrictions, so ingestion had to be designed around that constraint.
• Quality checks had to move upstream to prevent bad inputs from contaminating Salesforce.

Salesforce loading requires safe, repeatable mechanics

• The existing approach relied on exporting an object, deleting outdated records via Data Loader, then uploading the new dataset.
• Automation needed to replicate this reliably, without breaking downstream reporting.