Data-driven from the beginning
RxDataScience began as a small team at a mid-market pharmaceutical manufacturer, led by Larry Pickett as CIO.
After a new CEO came on board, the team was tasked with finding a way to make the company more data-driven. They had a goal around developing foundational pharma big data use cases and having the analytical capabilities of a top-10 pharmaceutical company without the IT resources of one.
Finding talent outside of big pharma
The first step was looking outside of the pharmaceutical industry. The team was interested in what the financial services industry was using for their analytics since financial services had been using advanced analytics longer than the pharmaceutical industry. They were also interested in how they could build on top of existing financial services analytics solutions. The team included a few individuals from Wall Street. Their inclusion broadened the scope of what was possible. In fact, the underlying solution that RxDataScience chose was one heavily used in the financial services industry (kdb+ from Kx).
The use cases
The use cases were defined around handling APLD (anonymous patient level data), minimizing ETL work, fast processing of complex queries, and ease of building visuals. The solution the team created had to work for departments ranging from research and development to commercial marketing. Departments needed the ability to plug in their own data sources and get up and running very quickly with minimal IT involvement.
It needed to be agile and scalable, which is exactly what they achieved. They saw 100x better performance at 1/4 the cost of the next best solution they evaluated.
Belief in the solution
After creating the solution, the team was inspired by presenting at conferences to take their solution to market. Encouragement from other members of the industry, and a true belief and commitment to their solution, brought them to where they are now.
RxDataScience has since differentiated themselves based on their underlying technology and advanced visuals. They’ve minimized menial tasks experienced by the end user and developed a variety of apps around use cases such as patient journey, commercial analytics, and ETL.
Want to learn more about RxDataScience’s cost savings, underlying platform, and time-to-deployment? Click here to read the case study.