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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

Business Challenge

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

Our Solution

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
    • Snowflake database
    • 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
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