Overview

Engagement overview

A leading global healthcare enterprise began modernizing its data ecosystem to support international commercial operations and U.S. patient services. The objective was to establish a secure, cloud-native data foundation capable of supporting analytics, operational reporting, and regulatory compliance across regions.

As part of this initiative, AWS-based data pipelines and governed data lakes were introduced to serve multiple business functions. These platforms support commercial analytics for international teams and enable patient services use cases in the U.S., including adherence risk monitoring and operational reporting for a mission-critical application integrated with several third-party systems.

Our client

Healthcare United States
The client is a global healthcare and life sciences organization focused on improving patient outcomes through data-driven decision-making. With expanding international operations, the organization defined a strategic roadmap to unify data across regions and build a scalable, compliant platform for advanced analytics and predictive modeling.
our client

Business objectives

The client defined a roadmap to unify and modernize its data landscape, enabling consistent analytics while aligning with evolving regulatory requirements.

01

Establish a centralized and standardized data foundation across international and domestic operations

02

Enable business users and data scientists to derive actionable insights from multiple internal and external sources

03

Embed GDPR and regulatory compliance into the data platform by design

04

Improve operational efficiency through automation and proactive monitoring

05

Build a scalable, low-cost, and flexible cloud environment to support long-term growth

Business solution

An AWS-based data engineering ecosystem was implemented to support both commercial and patient services use cases.

  • Cloud-native pipelines were built using AWS services to ingest data from Salesforce, sFTP, relational databases, and APIs
  • Automated quality checks and business transformations were applied using Apache Spark
  • A self-service environment was established for machine learning, predictive analytics, and BI reporting
  • Architecture design, ingestion, transformation, and analytics workflows were aligned with internal data teams
  • Secure data handling and GDPR compliance were embedded across pipelines
  • The environment was designed to remain flexible and scalable as data volumes and use cases expanded
  • Near real-time data access reduced time-to-insight for business users
  • Process automation reduced manual monitoring and service requests

Business impact

The modernized data platform supported measurable improvements across commercial operations and patient services.

Improved patient adherence risk monitoring, supporting faster operational decision-making

99.9% application uptime achieved across mission-critical systems

Reduced operational costs by migrating to scalable AWS cloud services

Expanded data initiatives, including Profile 360, to strengthen long-term data strategy

Enabled near real-time analytics, accelerating time-to-insight and supporting data-driven customer and patient engagement

Tech stack

  • Data Engineering
  • Cloud
  • Integration
  • DevOps
  • Data Quality
App Development
Marketing & Experience Tools
Data
Cloud & DevOps
QE & Observability