Databricks for the Life Sciences Industry

Bringing new treatments to patients in need with data analytics and AI

Data analytics and AI are critical for improving the success of drug discovery and ensuring the efficient delivery of new treatments to market.

Databricks helps life science organizations consolidate massive volumes of data and apply powerful analytics so they can realize benefits across the entire drug lifecycle — to achieve lowered costs and better patient outcomes.
See our solutions for life sciences

See how leaders in the life sciences are using Databricks to improve patient outcomes

CUSTOMER STORY

Identifying new drug targets with cloud-based AI

CUSTOMER STORY

Using predictive analytics to improve medication adherence

CUSTOMER STORY

Improving research with the world’s largest genomics database

AstraZeneca Databricks Customer Story

CUSTOMER STORY

Empowering researchers with a research knowledge graph

AstraZeneca Databricks Customer Story
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Latest blog posts, webinars, and case studies

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Why Databricks for life sciences

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Single View of the Drug Lifecycle

Bring together all your structured and unstructured data across the drug lifecycle — such as genomics, imaging, EHR and clinical trial data — with a unified Lakehouse Platform in the cloud

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Real-time Insights on Real-world Data

From health wearables to IoT sensors embedded in the supply chain, reliably ingest streaming data to unlock real-time insights that power the development and efficient delivery of new therapeutics

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Personalized Care with Predictive Analytics

Enhance your ability to develop and recommend the right treatment to the right patient at the right time with a single platform for all your analytics and machine learning

Use cases

Data and AI are powering innovation across the entire drug development and commercialization lifecycle

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Drug Discovery

Enhance the ability to discover new drugs and therapeutics faster and cheaper.

  • Genomics-based target identification
  • Lakehouse for Cancer Cell Line Encyclopedia (CCLE)
  • Improved QSAR workflows using ML

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Clinical Trial Design

Optimize clinical trial protocols for speed and success.

  • Optimize trials with real-world data
  • Compute complex biomarkers with ML
  • Manage clinical trial supply chains

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Efficient Manufacturing

Improve operational efficiencies to boost time-to-market and profitability.

  • Forecast seasonal demand
  • Predictive maintenance
  • Identify bottlenecks in fulfillment

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Drug Commercialization

Leverage actionable insights to augment the performance of marketing and sales.

  • Recommend next-best steps for sales
  • Identify underdiagnosed patients
  • Improve ad spend efficiency

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Drug Safety and Effectiveness

Ensure the safe and effective delivery of treatments to patients in the real world.

  • Monitor real-world effectiveness
  • Automate signal detection

Additional resources

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