MIDAREL02-BP02 Verify data consistency and availability across OT/IT systems through redundancy and failover mechanisms

Manufacturing environments operate with complex interactions between OT and IT systems. When these systems fail, the impact can cascade through production lines, quality control processes, and enterprise planning systems. Successfully maintaining data consistency and system availability requires careful orchestration of data synchronization, communication paths, and recovery procedures across the manufacturing technology stack.

Desired outcome: Production operations continue with minimal disruption during system failures. Critical operational data remains available and consistent during disruptions, allowing for automated or manual fallback procedures to maintain production output while primary systems are restored.

Benefits of establishing the Best Practice:

Level of risk exposed if this best practice is not established: High

Implementation guidance

Real-time manufacturing data management

First, map your critical manufacturing data flows and establish requirements for data synchronization across OT/IT systems. Document recovery time objectives (RTOs), recovery point objectives (RPOs), and regulatory compliance needs.

Define data models that support consistent representation of equipment states and process parameters across systems.

For implementation, establish standardized data models and real-time state management systems for production equipment and processes. Verify data consistency through distributed databases with conflict resolution capabilities.

Consider using AWS IoT SiteWise for consistent equipment and process data models, AWS IoT Core's Device Shadow service for reliable state management, and Amazon DynamoDB global tables for distributed manufacturing data management.

Data validation and quality controls

Document data quality requirements for different manufacturing processes and establish validation rules. Define acceptable tolerance ranges for process parameters and identify critical quality metrics that must be maintained during system transitions.

Implement automated validation mechanisms that verify data integrity across systems, with standardized data quality rules and monitoring.

Set up continuous validation of data synchronization between OT/IT systems and establish regular backup points for critical manufacturing datasets.

Consider implementing AWS Glue Data Quality rules for automated validation, AWS IoT SiteWise Asset Models for standardized validation, and AWS Backup for consistent recovery points.

Recovery mechanisms and failover systems 

Document system dependencies and define acceptable failover scenarios. Establish clear procedures for both automated and manual recovery processes.

Determine sequence of operations for graceful system transitions that preserve data integrity. Design idempotent processing mechanisms to help prevent data duplication during recovery.

Implement point-in-time recovery capabilities for critical production systems and configure automated failover with health monitoring.

Consider using Amazon DynamoDB Point-in-Time Recovery for maintaining accurate operational states, Amazon Route 53 health checks for automated failover, and AWS Systems Manager for coordinated recovery automation.

Key AWS services

Resources