Data Management That’s Truly Next-Gen

A large pharma company might require highly customized data warehouses with reporting capabilities to support a multitude of teams. A smaller company about to launch an asset might require a subset of these capabilities while maintaining the functionality to scale effortlessly.


reduction time to ingest all files with automated ETL process


Manual intervention

“D Cube Analytics played a key role in the commercial success of our new drug launch mainly through the speed with which they stood up and delivered our project. We see the value they continue to deliver in optimizing our commercial success further.”


Information gleaned from organized data is one of a pharmaceutical business’s most valuable resources. The key word here is “organized,” as free-flowing data is significantly different from useful information. Thus, the need is great for organizing data within a data warehouse – to enable retrieving of information necessary for analysis, interpretation, and trend identification, regardless of whether objectives focus on sales operations, marketing, pricing and contracting clinical trials, or regulatory compliance. The definition of a data warehouse can vary greatly from one organization to another. For instance, sometimes data is retained in elegant, highly-customized data warehouses – driving Business Intelligence, with the original cost in the tens of millions of dollars, while others might be simple data storage systems. There is the added complexity of “standardization” of data entering a warehouse. While in theory “standardization” may seem like a reasonable approach, we know the inherent complexity and cost associated. There are several strategic decisions that a pharma company needs to make depending on the stage of a product’s lifecycle they are at. For example, what analytics capabilities are essential now? What is the audience? Who will it benefit?


A client, on the verge of launching their asset, approached us with a need to build a data warehousing solution. It was imperative for the client to maintain a high benefit-cost ratio where the solution caters to their pre-launch needs with the capability to easily scale into an enterprise-wide data warehouse post-implementation of its go-to-market strategy. The client had approached a majority of the big consulting companies but their run-of-the-mill approach to building a typical enterprise data warehouse easily exceeded their current requirements and ran into high development costs whereas a custom-built subset of it entailed less flexibility, compromised in functionality and performance, and forewent the ease of scalability.


Combining our domain knowledge and productized approach while keeping in mind the client’s requirements, we deployed our cloud-based data management module DDS Foundations to deliver a next-generation data lake.
DDS Foundations deployed data extractors that extract data from different source systems and load it to cloud storage services. The extractor module comes bundled with a UI interface and is hosted on an Amazon EC2 instance. The extractors loads data to the raw layer built on Amazon Simple Storage Service and post running a suite of data quality algorithms data is loaded to Amazon Redshift.

Post-deployment DDS Foundation delivered a next-generation data lake for the client that enabled

  • Completely automated ETL process with the time required to ingest all files reduced by 80%
  • Multiple data sources standardized with no overhead of manual intervention
  • Modular architecture made it extremely easy to upgrade when scaling up
  • Integration of our suite of Pre-Launch dashboards with the data lake


D Cube Analytics was able to deploy a solution that not only caters to their current pre-launch preparation needs but also serves as a base for a launch/post-launch analytics and data management solution when they go live. Request a demo to find out how D Cube Analytics can help you leverage a data processing and management solution that is truly next-gen.

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