Big Data Pharma Case Study: High-Velocity Data Analytics with DataOps
Big Data Pharma Case Study: High-Velocity Data Analytics with DataOps


The software industry has advanced from releasing features every 6-12 months to every 11 seconds -- more than one million times improvement! In this session, learn how to apply the lessons learned from developing software to producing analytics and models. Learn about the latest practices called DataOps.

For background, we first look at challenges in analytics today and explain the dynamics of a new pharmaceutical launch. Next, we present the Seven Shocking Steps so you can learn how to set up a technical environment to support DataOps. We also show how we used Redshift and other Amazon Web Services (AWS) technologies and explain how to implement a Data Lake in AWS. The presenters then examine how to organize teams to partition the work and walk through examples on how to implement features quickly and with high quality.

The speakers are the founders of DataKitchen and the Senior Manager of Analytics at Celgene (Top 25 Global Pharma Company) and have decades of hands-on and executive management experience in data, analytics, and software development and are current practitioners of DataOps.


Gil Benghiat is a co-founder of DataKitchen, a company on a mission to enable analytic teams to deliver value quickly and with high quality. Gil’s career has always been data-oriented starting by collecting and displaying network data at AT&T Bell Laboratories, managing data at Sybase, collecting and cleaning clinical trial data at PhaseForward, integrating pharmaceutical sales data at LeapFrogRx, and liberating data at Solid Oak Consulting. Gil holds an MS in computer science from Stanford University and a BS in applied mathematics and biology from Brown University. He is working on hiking the 67 New England 4,000' peaks.

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