Global enterprises operating at scale often struggle with fragmented data ecosystems created through regional autonomy and legacy modernization efforts. While local data warehouses enable speed, they also introduce silos, inconsistent governance, duplicated pipelines, and escalating operational costs, limiting an organization’s ability to generate trusted, real‑time insights.
To compete with data-mature peers, organizations are increasingly adopting cloud-native data mesh platforms that balance domain autonomy with federated governance, unlocking enterprise-wide visibility, compliance, and agility.
The Challenge
The client faced significant challenges in delivering unified, compliant, and timely insights across its global operations due to fragmented data warehouses and disconnected governance practices. Data products and ingestion pipelines varied by region, creating silos and inconsistent views of performance, compliance, and customer behavior.
Teams spent weeks reconciling data for reporting and analysis, slowing down product launches and compliance initiatives. Duplicate data products inflated operating costs, while rigid, centralized processes limited adaptability for high‑value use cases. At scale, data quality monitoring and regulatory adherence became complex and error‑prone, exposing the organization to increased risk, slower speed‑to‑market, and missed innovation opportunities.
Our Approach
Coforge implemented a unified, cloud‑native data mesh platform designed to modernize the data ecosystem, automate governance, and deliver trusted, real‑time insights at a global scale.
Cloud Native Data Mesh Foundation
Built a unified data platform on AWS and Snowflake, consolidating global data products into a single, trusted ecosystem while preserving domain ownership and autonomy.
Federated Governance & Real Time Integration
Enabled federated governance using the Hypernova Mesh artifact, ensuring consistent policies, compliance, and real time integration across more than 180 markets.
Automated Ingestion & Streaming
Deployed AWS Glue, Kafka, and Lambda to automate batch ingestion, streaming, and real time data processing pipelines.
AI Powered Data Quality & Classification
Introduced AI‑driven quality checks using the Agentic DQ Resolver artifact to autonomously detect and resolve data quality issues. Implemented an AI/ML‑powered AutoClassifier to automate metadata tagging, PII identification, and C1–C4 data classification, supported by Human‑in‑the‑Loop (HITL) validation.
Insight Enablement & Change Management
Delivered Power BI dashboards to provide leadership with real time visibility into cost, usage, and SLA performance. Supported adoption through an agile rollout model, structured change management, and a global data champions network.

Impact to Date
35% Faster
55% Lower
360° Customer View
Key Outcomes
- Enhanced Self‑Service Experience Consolidated fragmented data products into a single, enterprise‑wide platform with consistent governance.
- Improved Speed and Agility Reduced reconciliation cycles from weeks to near real-time, accelerating innovation and compliance reporting.
- Cost Optimization at Scale Eliminated duplication and optimized cloud usage, unlocking substantial annual savings.
- Future‑Ready Data Platform Established a federated, cloud‑native architecture ready to support AI, predictive analytics, and scalable innovation.
By implementing an AI-driven, cloud-native data mesh platform, Coforge enabled the healthcare enterprise to unify global data, reduce costs, and deliver real-time, trusted insights, creating a future-ready foundation for scalable innovation and data-driven decision-making.
