Building a Scalable Global Data Platform for One of the Largest Shingle Manufacturers

Background

As a global leader in shingle manufacturing, the client operates across multiple geographies with an expansive supply chain network. Their operations involve managing complex data from various sources such as order processing, demand forecasting, production planning, logistics, and capacity management. However, disparate systems and legacy infrastructures posed challenges in making timely, data-driven decisions.

To maintain their competitive edge and scale operations globally, they needed a unified, high-performance data platform that could centralize data access, ensure security, and support real-time analytics and advanced insights.

Challenges

  • Fragmented Data Systems: Multiple legacy systems storing inconsistent and siloed data, making it difficult to establish a single source of truth.
  • Scalability and Availability Issues: Existing infrastructure lacked the robustness needed for global scalability and high availability.
  • Slow Data Access and Decision-Making: Critical business functions like logistics, demand planning and production were hindered by delays in accessing reliable data.
  • Governance and Security Concerns: With data scattered across systems, ensuring compliance, governance and the security of sensitive information (e.g., PII) was a constant concern.
  • Limited Analytical Capabilities: Business units struggled to derive insights due to inconsistent data quality and limited integration.

Solution Delivered

To overcome the client’s challenges and enable global scale, we implemented a comprehensive Cloud-First Data Modernization Strategy, anchored by a centralized and high-performance data platform:

  • Built a Centralized Data Platform on AWS: Established a cloud-native architecture that unified all data operations, providing a scalable, secure, and high-availability foundation for global data access and analytics. This centralized platform served as a single source of truth across the enterprise.
  • Modernized Legacy Systems through Cloud Migration: Migrated disparate and outdated systems to AWS, reducing infrastructure complexity and enabling rapid elasticity, improved performance, and operational resilience.
  • Developed 100+ Robust Data Pipelines: Designed and deployed end-to-end data pipelines to ingest, transform, and harmonize data from diverse sources such as logistics, production, and forecasting systems, ensuring seamless data flow and interoperability.
  • Continuous Integration/Deployment: JenStandardized Data Access, Security, and Governance: Implemented enterprise-wide policies for secure, role-based access to sensitive data, aligned with regulatory standards and internal governance frameworks (e.g., PII compliance).kins, Docker, Kubernetes, etc.
  • Built Intuitive Enterprise Data Interfaces: Created user-friendly, self-service tools and dashboards tailored for various business functions, empowering users to interact with governed data in a consistent and insightful manner.
  • Enabled AI/ML and Data Analytics-Ready Infrastructure: Structured the platform to support advanced use cases including predictive analytics, intelligent demand forecasting, and data science initiatives by ensuring data quality, consistency, and availability.

Outcomes & Impact

  • Centralized Global Data Management: Unified platform enabled integration with global systems, fostering a cohesive data ecosystem.
  • Improved Data Accessibility and Governance: Reduced data access time from 2 days to real-time while improving security and policy compliance.
  • Eliminated Redundancy and Improved Data Quality: Consolidated 7 major data sources, enhancing insight accuracy and reducing duplication.
  • Enabled Real-Time Insights & Dashboards: Leveraged tools like Data Mirror to deliver high-impact analytics dashboards across the enterprise.
  • High-Performance Pipelines: Transformed data processing efficiency from 30 minutes to under 5 minutes, handling hundreds of gigabytes of data.
  • Future-Ready with AI/ML Integration: Positioned the platform to support intelligent forecasting and data science use cases.

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