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Data Engineering

Data Management & Analytics Platform for R&D Support

Fragmented and unstructured data across manufacturing and development systems limited visibility, slowed analysis, and delayed decision-making. The absence of an integrated data ecosystem and reliance on manual processes created inefficiencies in data access, quality control, and analytics, preventing effective utilization of critical R&D data

Cloud based R&D data management platform architecture

R&D intelligence is no longer delayed—it is real-time, unified, and actionable

The Challenge

Disconnected Data Ecosystems and Inefficient Analytics Workflows

Manufacturing and development data existed in fragmented, unstructured systems with no centralized mechanism for ingestion, storage, or analysis. The lack of an integrated data ecosystem and standardized data models led to high manual effort, inconsistent data quality, and limited visibility across workflows. These challenges slowed analytics, delayed decision-making, and restricted the ability to generate actionable insights from critical R&D data

Solution

  • Designed and implemented a cloud-based data warehouse (AWS RDS/Postgres) for centralized data ingestion and storage

  • Developed a universal data model integrating production data, analytical data, and metadata across workflows

  • Built an event-driven architecture using AWS Lambda, Python, and R for automated data ingestion and processing

  • Enabled data curation and transformation through a federated architecture to improve data consistency and quality

  • Created a custom web-based UI for capturing raw manufacturing data and metadata with REST API integration

  • Integrated advanced analytics and visualization via BI tools (TIBCO Spotfire) for trend analysis and insights

  • Implemented secure access controls including SSO, IAM, VPC isolation, and WAF for governance and compliance

  • Provided post-production support, monitoring, and continuous optimization

Research analytics dashboard for scientific workflows

Impact

Enhanced Data Quality, Accelerated Analytics, and Operational Efficiency

  • Improved data accessibility and enabled faster, insight-driven decision-making

  • Enhanced data quality, consistency, and analytical efficiency across teams

  • Accelerated R&D analytics by enabling integrated and structured data workflows

  • Reduced dependency on manual processes for data ingestion and validation

  • Established a scalable platform supporting continuous data operations and analytics

Measurable Impact

50–60%

 improvement in data accessibility and retrieval efficiency

40–50%

reduction in manual effort for data processing and validation

30–40%

faster analytics and
reporting cycles

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