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Implementation of Data Mesh Architecture for Enhanced Data Accessibility and Security in Healthcare
  1. case
  2. Implementation of Data Mesh Architecture for Enhanced Data Accessibility and Security in Healthcare

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Implementation of Data Mesh Architecture for Enhanced Data Accessibility and Security in Healthcare

intechhouse.com
Medical
Information technology
Healthcare

Challenges in Data Management and Accessibility

The organization faced significant challenges including delayed access to critical data, inefficient management of concurrent data workflows across teams, and limited integration of data into business processes. These issues hindered agile decision-making and operational efficiency while maintaining compliance with healthcare industry regulations.

About the Client

A global pharmaceutical company specializing in research-driven innovative treatments for critical health conditions

Strategic Objectives for Data Modernization

  • Accelerate data delivery timelines for improved decision-making
  • Implement scalable data management infrastructure
  • Enhance data security while maintaining accessibility
  • Create reusable data products for cross-functional efficiency
  • Establish robust data governance framework compliant with healthcare regulations

Core System Capabilities

  • Domain-driven data product creation
  • Self-service data access interface
  • Automated data quality validation
  • Role-based access control system
  • Data lineage tracking and audit trails
  • Cross-platform data integration pipelines

Technology Stack

Snowflake
AWS SDK
Python
DBT
DataOPS Live

System Integrations

  • Legacy clinical research databases
  • Regulatory compliance systems
  • Enterprise analytics platforms
  • Cloud storage infrastructure

Operational Requirements

  • Enterprise-grade data encryption at rest/in transit
  • 99.95% system availability SLA
  • Horizontal scalability for petabyte-scale data growth
  • Real-time monitoring and alerting
  • HIPAA/GDPR compliance framework

Anticipated Business Outcomes

The implementation is expected to reduce data processing times by 60-70%, enable 30% faster market response cycles through improved analytics, achieve 40% reduction in data management costs, and create a scalable foundation supporting 5x data volume growth over five years while maintaining strict regulatory compliance.

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