Implemented an analytics platform enabling the Customer to scale Brand launch analytics through Data Modernization Infrastructure

Built a robust data management process by implementing an analytics platform on cloud ecosystem for parallel and immediate analytics and reporting

to establish Data Lake

faster time to insights

“DDS Terra helped us establish our data lake in about 4 weeks. The UI was simple to use and easy to understand.”


The client wanted to move away from SAS platforms which were not compatible with many data sources thus drawing limitations to access data. In addition to that, the client required a comprehensive data analytics platform to power their pre-launch and launch analytics.


  • Deployed data management platform for end-to-end data management
  • Implemented a lite analytics platform on AWS for parallel and immediate analytics and reporting
  • Scaled up the existing CDW and developed functional data models that power enterprise-wide reporting, data science, self-service analytics platforms, and applications


  • Built and deployed a data management platform to replace the earlier SAS platform and hosted it on AWS cloud ecosystem to bring all data sources onto a single platform
  • The data infrastructure platform implemented on AWS was highly scalable and catered to both parallel and immediate analytics and reporting with end-to-end automated ingestion, processing, and publishing
  • Created a one-stop-shop for pre-launch reporting
  • Market Performance and Access
  • Source of Business, Pricing, and Promotional Spend
  • Created analytical data marts by using KPI libraries and use cases
  • Integrated available data marts with our pre-launch suite of dashboards thereby enabling end to end automation of reporting on Tableau
  • Provided launch specific analytics support ranging from Forecasting, segmentation, targeting, and patient-level analytics


  • Enabling analytics platform in less than 4 weeks ensured business continuity and ad hoc analytics
  • Availability of all datasets on a common platform helped to perform advanced analytics like forecasting, Segmentation, Targeting, and KoL mapping on an iterative basis
  • Data Ingestion process reduced the time to insights by 10 times as compared to the traditional methods used earlier
  • Reduced time for deployment of analytical data marts by leveraging our use case-specific KPI libraries and data models that catered to the majority of pharma data sources like MMIT, prescriber source, and PTD


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