How an Enterprise Data Hub Enabled a Medical Device Manufacturer to Achieve 90% Faster Data Provisioning

Key Highlights: What This Case Study Covers

• Best practices/approaches in Data Modernization for Medical Device Manufacturing, including multi-zone structural orchestration to overcome enterprise data silos.
• Implementation/Deployment of an Azure-based Data Engineering compute cluster to manage high-volume transactional and telemetry data streams.
• How to build a scalable and automated Enterprise Data and Analytics hub that natively supports multi-format data storage with optimized schema unification.
• Real-world application of Snowflake, Databricks, and automated governance workflows to secure near-real-time data delivery to critical operational teams.

Client Overview

Our client is a prominent healthcare and medical device manufacturer renowned for their revolutionary auditory equipment. They operate across markets, and have adapted a tech-forward manufacturing and engineering approach to serve thousands of clinical practitioners and patients worldwide.

The Ask

The client wanted to establish an Enterprise Data Hub and a unified single source of truth through a robust Data and Analytics (DnA) platform. Their objective was to develop a system capable of capturing and processing structured, semi-structured, and unstructured data to enhance operational efficiency, minimize data latency, and support Near Real Time (NRT) data provisioning for downstream applications.

Challenges

• Siloed data across independent Enterprise Data Warehouses
• Execution delays in daily reporting and ETL jobs due to legacy compute systems
• Need for better infrastructure to ingest, parse, and process unstructured or semi-structured data profiles
• Room for improved standardizations to enhance metric reliability

Our Solution: Data & Analytics Platform

Migrated Architecture to Azure Cloud

Moved legacy enterprise storage boundaries into a high-performance Microsoft Azure Infrastructure, establishing logical isolation using secured virtual networks and localized gateway subnets.

Integrated Live Replication and Processing Change-Data-Capture

Leveraged Attunity Replicate over secure VPN channels to scan on-prem systems (Oracle, SQL Server, AS 400), automatically syncing operational deviations into the staging environment.

Orchestrated Structured Multi-Zone Storage

Designed and deployed an Azure Data Lake Store layered into Staging, Raw, and Curated zones to properly isolate unstructured, semi-structured, and production-ready elements.

Constructed Dual Analytics & Compute Clusters

Separated operational processing demands by setting up dedicated Azure Databricks data engineering clusters alongside a high-concurrency Snowflake Analytics engine to clear data latency.

Transitioned to Direct-to-Cloud Ingestion (Phase II)

Optimized long-term processing limits by building direct Databricks JDBC integrations and native Snowflake connectors, routing production transactional paths straight to analytics tiers.

Built Automated Data Governance Framework

Deployed a centralized Tableau management dashboard tracking 40 operational plant locations and over 1,200 database tables, triggering smart alerts for execution variances.

Impact Delivered

• 25X faster Query Engine Execution, boosting reporting performance from hours to minutes.
• 90% performance gain in Data Provisioning, accelerating multi-format asset provisioning to downstream business groups looking to fulfill highly strict SLAs.
• 75% deflection in System ETL Congestion, optimizing pipeline patterns across the ecosystem.
• Near-Real-Time data availability, enabling end-to-end telemetry ingestion frameworks to safely translate immediate field actions into direct dashboard visibility.
• Infrastructure Automation via ARM Templates, enabling automation for deployment & data-center replications (DR).