About Nataraj Dasgupta

Nataraj is the Vice President of Advanced Analytics at RxDataScience Inc. He is the author of "Practical Big Data Analytics", a comprehensive text on Data Science across multiple domains and technologies. Prior to his current role, he led the Data Science division at Purdue Pharma, L.P. and was responsible for the design, development and architecture of Purdue’s award-winning Big Data and Machine Learning Platform. Nataraj has been in the IT industry for more than 19 years and has worked in the technology divisions of Philip Morris, IBM, UBS Investment Bank and Purdue Pharma. At UBS, Nataraj held the role of Associate Director working with High Frequency & Algorithmic Trading technologies used by investment banks and hedge funds on Wall St and other financial centers across the world. The finance industry has been implementing “Big Data” systems for many decades and has had a mature set of tools that has permitted traders and quants to analyse large volumes of trading-related data in the order of milliseconds. Despite Pharma's ubiquitous reliance on more traditional enterprise systems, Nataraj and the Systems Development & Analytics group led by Sayee Natarajan, chose to instead leverage technologies that were exclusively used in the financial domain and never before in a healthcare setting to analyse terabyte-scale medical and pharmaceutical datasets. The effort led to a radically simple platform built using trading specific tools which were less expensive than conventional “Big Data” platforms, yet orders of magnitude faster in data processing capabilities facilitating tasks to complete in seconds compared to days in the existing and more popular Pharma enterprise systems. The platform received a wide recognition as one of the most advanced big data solutions for healthcare and led to several awards, including the recognition of Purdue Pharma as one of the top 25 most innovative technological divisions in the US by Information Week. In 2016, investors from Wall Street and the City of London provided funding for a new startup, RxDataScience Inc. responsible of commercialising the technology to the broader healthcare sector.

Top Use Cases for Machine Learning in Pharma


Real-World Use Cases for AI & ML in Pharma

For decades, Pharmaceutical data analytics has been a largely manual and tedious task conducted by the commercial research, health outcomes, R&D and Clinical Study groups at Pharma companies both small and large. With the emergence of machine learning, artificial intelligence and other disruptive innovations, Pharma, like other industries has also started its slow but sure transition to a more agile, data-driven model – one where in-house research is supplemented by intelligence gathered by applying algorithms across terabytes of Physician Rx, Patient Claims and other related datasets.

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