How a F500 Pharmaceutical Commercial Team achieved Data Democratization and reduced Drug Commercialization Cost by 30%

Agilisium used AWS services to rearchitect a commercial data lake and a data warehouse with end-to-end process automation


1.5M $
Cost Saved by new data management tools
Improved Data processing Time
Improved BI Reporting Time
Project delivery time saved

Client Profile

Amgen is one of the world’s leading biotechnology companies that is committed in unlocking the potential of biology for patients suffering from serious illnesses by discovering, developing, manufacturing, and delivering innovative human therapeutics.

Business challenges

• To migrate their on-premises system to AWS and to rearchitect a commercial data lake and a data warehouse with end-to-end process automation.

• To integrate data across the organization, improve data management, and ensure the availability and democratization of diverse data sets for stakeholders.

• Rigid data silos (CRM, database, and flat files) resulted in lack of view of product movement and sales progression

• Existing architecture was not conducive for near-real time Advanced Analytics

• Lack of Managed services delivery model and governance process for scope management

Agilisium Solution

• A Cloud One strategy to progressively transition to a globally accessible and scalable Big Data Analytics platform was devised.

• To rearchitect a commercial data lake, Amgen leveraged the capabilities of Amazon S3 as the single source of truth and constructed a data warehouse with Amazon Redshift.

• A Massively Parallel Processing (MPP) Analytics Data Warehouse in AWS Redshift that supports downstream BI & Advanced Analytics, at scale

• Faster processing of data with no connectivity issue for business users

• Reduced redundant pipelines to improve performance

• Stabilized the job scheduling using Airflow

• Implemented File Quality & Data Quality to reduce cost automated validation of data

Tech-stacks Used

AWS – S3
Glacier Databricks

Business outcome

  • Enabled Quicker, faster, and easy access of Data to different stakeholders.
  • Reduction of manual effort in data validation
  • Source file data validation before job run reduces the error & cost
  • Easy error handling & re-run of jobs in case of failures
  • Handle large volume of data without any latency
  • Quicker data on-boarding without any deviation or delays
  • Effective KPIs implemented in Tableau due to consolidated data availability.
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