Siloed data
Siloed data is a major challenge faced by many companies' supply chain operations. Data exists separately and is not accessible by other departments or business unit. As a result, decision making is made at each local level while lacking upstream and downstream indicators that would drive improved supply chain execution and risk mitigation.
A data fabric alleviates the frustration of hunting through data across multiple systems by creating a unified data architecture that supply chain teams can quickly access and trust. End users from procurement specialists to logistics managers can find, analyze, and act on real-time information through intuitive interfaces, replacing manual data gathering with automated insights. Further, by combining data across areas of the supply chain, leading indicators and downstream results can be correlated for greater performance management and risk avoidance.
Implementing a data fabric in the supply chain using the AWS Well-Architected Framework delivers self-service access to reliable data while maintaining enterprise-grade security and performance. Business analysts can directly connect to data from IoT devices, ERP systems, and external feeds through AWS services including Amazon S3 for storage, AWS Glue for ETL processing, and AWS DataSync for data movement and synchronization.
The Well-Architected Framework pillars provide users confidence as they follow efficient standards. Security controls protect sensitive data while allowing appropriate access. Reliability features help prevent disruptions to daily operations. Cost optimization keeps data storage and processing economical. Operational excellence means systems run smoothly without IT intervention. Performance efficiency delivers fast query responses and real-time analytics capabilities.
Supply chain teams gain powerful analytics capabilities through the data fabric. Business analysts run complex queries in Amazon Redshift. Planners and managers create their own visualizations and reports in QuickSight without waiting for IT support. These self-service tools enable predictive analytics, process optimization, and data-driven decisions across roles. The result is greater productivity, faster response times, and better outcomes through accessible, trusted data and analytics.
Reference architecture

Architecture description
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Ingest data from external and internal systems into Amazon S3.
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AWS Glue provides integration data flows. AWS DataSync to migrate data from on premises to AWS Cloud. Amazon DataZone to catalog and provide fine grained control of the data being integrated.
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Amazon Redshift provides the structured data store.
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For the application layer, Amazon Quicksight provides dashboards, reporting, and analytics.
Architecture objectives
Data accessibility and unification
Operational analytics
Business user empowerment
Supply chain performance enhancement
Operational efficiency
Data quality and trust
Metrics
Based on the given data fabric and AWS Well-Architected Framework scenario, the relevant metrics that provide valuable insights for measuring success and performance are:
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Data accuracy:
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Metric: Percentage of accurate data transfers.
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Rationale: Critical for supply chain operations where data quality directly impacts decision-making.
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Well-Architected pillar: Reliability.
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Aligns with the need for trusted data.
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System downtime:
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Metric: System uptime percentage.
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Rationale: Essential for maintaining continuous access to real-time supply chain data.
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Well-Architected pillar: Reliability.
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Critical for supporting uninterrupted supply chain operations.
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Response time:
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Metric: Average response time.
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Rationale: Crucial for real-time analytics and quick decision-making.
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Well-Architected pillar: Performance efficiency.
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Important for self-service analytics mentioned in the scenario.
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Security:
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Metric: Number of security incidents and compliance adherence.
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Rationale: Essential for protecting sensitive supply chain data.
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Well-Architected pillar: Security.
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Critical for maintaining enterprise-grade security mentioned in the scenario.
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