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Scaling Healthcare Analytics on Azure for Faster Decisions

Impact Delivered

  • 70% Increase in customer acceptance
  • 68% Improvement in advanced-analytics performance
  • 45% Reduction in external integration effort
  • 60% Faster solution delivery
  • The Customer

    The Customer

    A Fortune 500 healthcare organization on the US West Coast with petabytes of data on Azure needing to process complex datasets (e.g., drug and appointment adherence) and provide advanced analytics to business units.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg
    • Big-data analytics/pipelines are complex; heavy coding and configurations
    • Business units need out-of-the-box capabilities delivered seamlessly
    • Domain/organization-specific algorithms must be made available securely and at scale
  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    Operationalize a collaborative, Azure-native analytics environment with self-service:

    • Connect platform to HDInsight, ADLS, Kafka, Ranger, Hive for governed big-data processing
    • Provide business units AD-based access to apply NLP on very large datasets
    • Build ML models for use cases like appointment/drug adherence and diabetes prediction
  • Solution Components

    Solution Components

    HDInsight; ADLS; Kafka; Ranger; Hive; Azure AD

  • The Results

    The Results

    • Scalable, governed analytics on Azure for petabyte data
    • Business-unit self-service with secure access
    • Reduced external integration reliance
    • Faster adoption of advanced analytics

Impact Delivered

  • 70% Increase in customer acceptance
  • 68% Improvement in advanced-analytics performance
  • 45% Reduction in external integration effort
  • 60% Faster solution delivery

The Customer

A Fortune 500 healthcare organization on the US West Coast with petabytes of data on Azure needing to process complex datasets (e.g., drug and appointment adherence) and provide advanced analytics to business units.

The Challenge

3D-Priniting-Challenges-Img.jpg
  • Big-data analytics/pipelines are complex; heavy coding and configurations
  • Business units need out-of-the-box capabilities delivered seamlessly
  • Domain/organization-specific algorithms must be made available securely and at scale

The Solution

the-solution-img_nextiva.jpg

Operationalize a collaborative, Azure-native analytics environment with self-service:

  • Connect platform to HDInsight, ADLS, Kafka, Ranger, Hive for governed big-data processing
  • Provide business units AD-based access to apply NLP on very large datasets
  • Build ML models for use cases like appointment/drug adherence and diabetes prediction

Solution Components

HDInsight; ADLS; Kafka; Ranger; Hive; Azure AD

The Results

  • Scalable, governed analytics on Azure for petabyte data
  • Business-unit self-service with secure access
  • Reduced external integration reliance
  • Faster adoption of advanced analytics
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