Tackling Clinical Trial Data Overload with Data Lakes and Machine Learning

Overview

Study teams need to be able to access and analyze the diverse data generated in their trials in real time. That task is beyond the original remit of data warehouses but is right in the wheelhouse of data lakes.

Key Points

  • The key differentiator of data lakes is their ability to quickly ingest, aggregate and standardize diverse sets of data with minimal manual effort on the part of the user.
  • The emergence of machine learning points to a future in which automated systems rapidly analyze large datasets to extract benefits, value and insights.
  • ThoughtSphere created a clinical data and analytics solution that helps healthcare leaders leverage data science and machine learning to increase R&D efficiency by up to 30%.

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