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Accelerating Insights from EDI & Patient Data on AWS

Impact Delivered

  • 80% Reduction in time-to-market
  • 70% Increase in user adoption
  • 60% Improvement in cross-team collaboration
  • 15% Overall cost reduction
  • The Customer

    The Customer

    One of the largest US healthcare insurance providers needing to process huge volumes of EDI and patient data quickly and empower data engineers/analysts with self-serve advanced analytics.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg
    • Very complex, siloed, time-consuming data processing with little automation
    • Difficult to value data due to limited data-quality assessment
    • Hard for analysts, scientists, engineers, and product to collaborate (no single pane of glass)
    • Need to scale from local machines to cloud to accelerate solutions and productivity
  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    Stand up a secure, cloud-native data/ML stack on AWS with governed workflows:

    • Configure secure connections to clusters/databases; connect to S3, DynamoDB, Redshift, RDS
    • Orchestrate 100+ daily jobs via Airflow; run distributed jobs on EMR for scale
    • Enable teams with low/no-code patterns, 450+ prebuilt functions, and 80+ ML algorithms
    • Embed data-quality checks and collaborative project/workflow sharing
  • Solution Components

    Solution Components

    EMR; S3; DynamoDB; Redshift; RDS; Airflow; AWS Data Lake

  • The Results

    The Results

    • Faster insights from EDI/patient data pipelines
    • Governed, scalable ingestion and transformation at petabyte scale
    • Rapid ML model development and deployment
    • Reusable patterns accelerating future use cases

Impact Delivered

  • 80% Reduction in time-to-market
  • 70% Increase in user adoption
  • 60% Improvement in cross-team collaboration
  • 15% Overall cost reduction

The Customer

One of the largest US healthcare insurance providers needing to process huge volumes of EDI and patient data quickly and empower data engineers/analysts with self-serve advanced analytics.

The Challenge

3D-Priniting-Challenges-Img.jpg
  • Very complex, siloed, time-consuming data processing with little automation
  • Difficult to value data due to limited data-quality assessment
  • Hard for analysts, scientists, engineers, and product to collaborate (no single pane of glass)
  • Need to scale from local machines to cloud to accelerate solutions and productivity

The Solution

the-solution-img_nextiva.jpg

Stand up a secure, cloud-native data/ML stack on AWS with governed workflows:

  • Configure secure connections to clusters/databases; connect to S3, DynamoDB, Redshift, RDS
  • Orchestrate 100+ daily jobs via Airflow; run distributed jobs on EMR for scale
  • Enable teams with low/no-code patterns, 450+ prebuilt functions, and 80+ ML algorithms
  • Embed data-quality checks and collaborative project/workflow sharing

Solution Components

EMR; S3; DynamoDB; Redshift; RDS; Airflow; AWS Data Lake

The Results

  • Faster insights from EDI/patient data pipelines
  • Governed, scalable ingestion and transformation at petabyte scale
  • Rapid ML model development and deployment
  • Reusable patterns accelerating future use cases
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