Success Story.

How a Global Medical Device Manufacturer Unified Enterprise Data and Standardized Analytics with a Cloud-Native Snowflake Platform

The client is a global medical device manufacturer with complex operations across manufacturing, supply chain, quality, and commercial functions. Critical enterprise information—products, customers, and organizational hierarchies—was scattered across SAP, S/4HANA, MS Dynamics, AS400, DB2, and SharePoint. This fragmentation resulted in inconsistent data, downtime, and limited visibility into operational performance. The organization needed a unified, reliable, and scalable cloud platform to support analytics and decision-making.

Business Need

The manufacturer struggled with siloed systems and the absence of a centralized master data platform. Manual consolidation and inconsistent data definitions made reporting slow and error prone. Leadership lacked standardized KPIs and had limited ability to monitor quality and service performance. The business required a cloud-based single source of truth, improved governance, and automated data pipelines to enable consistent enterprise-wide analytics.

Solution

YASH Technologies implemented a cloud-native Snowflake unified data platform, designed to centralize and govern enterprise master and transactional data. Key solution elements included:

Multi-source ingestion from SAP, S/4HANA, MS Dynamics, AS400, DB2, flat files, and SharePoint via AWS S3
SAP data extraction using SNP Glue supporting both full and delta loads
Data transformation and modeling using Informatica Cloud and dbt
A scalable enterprise data warehouse with standardized metrics and hierarchies
Role-based access controls implemented directly in Snowflake
Power BI dashboards with drill-down analytics tailored for managers and operators

This architecture created a consistent, governed, and analytics-ready data foundation.

Business Benefits

Improved data reliability

through a single source of truth for enterprise master data

Higher uptime and platform stability

via automated ingestion and standardized pipelines

Better operational visibility

with consistent KPIs across manufacturing, quality, and service

Stronger governance

through role-based access and controlled data stewardship

Faster reporting cycles

due to automated data extraction and transformation

Future-ready scalability

enabling advanced analytics and IoT-driven use cases

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