Data Analytics and Data Science Platforms for Life Sciences

By analyzing the most significant and most complex data sets, the pharma and life sciences firms are seeking to achieve a higher resolution perspective of each patient so that they can have very customized and successful care.

FREMONT, CA: Pharma firms are following the ultimate goal of improving the way drugs are produced so that the correct medication can be given to the right patient at the right time. This aspect facilitates the delivery of deeply customized treatment that impacts pharma, life science, and healthcare verticals.

Pharma and Life Sciences are dedicated to delivering customized healthcare through access to millions of deep, diverse, and loosely related patient-years of data from a range of sources (Electronic Health Record (HER), Digital Data, Imaging, and Genomics) to offer practical insights. By analyzing the most significant and most complex data sets, the pharma and life sciences firms are seeking to achieve a higher resolution perspective of each patient so that they can have very customized and successful care.

Several companies have developed a network of accelerators that can deploy a massively scalable, modular, stable, and compliant data analytics and data science framework that can be implemented in a few days so that pharmaceutical companies can concentrate on providing personalized healthcare and transforming the way drugs are developed.

The data platforms are highly scalable and can handle various types of data, streaming vs. batch, text, images, different kinds of data, video files, audio files, and others. Data can be checked for accuracy, labeled for Protected Health Information (PHI)/ Personally Identifiable Information (PII) and tokenized or anonymized as needed, cataloged and source collected.

This micro service-driven architecture is orchestrated to create an integrated end of data pipelines, using APIs. Data scientists can run their markdown documents in a containerized setting that enables them to co-resident with the data, allowing a massive volume of data to be evaluated. The platform features are made accessible by Application Programming Interfaces (APIs), so data scientists and data engineers can simplify the whole data pipeline from ingestion to review.