Modernization of Commercial Data Warehouse for US Pharmaceutical company
Data Warehousing for Commercial Operations can be a challenge to manage; having the right partner is key. A US-based pharmaceutical company had implemented a SaaS-based Commercial Data Warehouse system and was facing a variety of challenges from a solution, partner, cost, and delivery perspective. To assist, Coforge, with our deep Data & Analytics expertise, migrated the client off the SaaS platform to a new, modern platform, implemented a managed service model, and initiated introduction of features from the backlog requests.
About the Client
US Pharmaceutical company with a focus on CNS market
The client was having numerous challenges with a SaaS-based Commercial Data Warehouse solution that was supporting all core Commercial Operations business processes. Challenges included:
Fully outsourced model with no ability to implement enhancements outside of SaaS vendor.
Long lead times and high costs for enhancements
Lack of basic functional capabilities necessary for a Life Science company
Limited architecture and data model that did not support all business use cases.
High number of defects with lack of transparency around root cause and resolution
Data integration issues with providing and consuming partners/applications
Slow response for information and service requests
Significant time/cost required for vendor management.
Lack of complete and proper documentation
Phase I (complete)
Quick migration to new client-owned, modern, high performance Coforge Data Warehouse platform
Platform technology architecture (high-level):
Microsoft Azure Data Factory infrastructure
PySpark data pipelines
Expanded, flexible data model.
Qlik for Reporting & Analytics
Migration to Coforge for managed services to provide full DevOps support.
Increase support coverage including onsite presence.
New base QC framework to address data integration/quality issues.
Phase II (in process)
Iterative build-out of required feature backlog.
High performing, modern Commercial Data Warehouse
Significantly reduced turnaround time and cost for enhancements
Reduced time for service requests and incidents resolution.
Regular updates on service requests, incidents, enhancements to key stakeholders
Increased communication and reduced issue resolution time