MIDAOPS03-BP03 Implement predictive analytics and proactive anomaly detection
Use machine learning capabilities to analyse historical data patterns and identify potential issues before they impact your industrial operations.
Desired outcome: Identifying anomalies early enabling organizations to take corrective action before problems escalate and disrupt operations.
Benefits of establishing this best practice: By implementing these real-time monitoring, anomaly detection, and centralized observability capabilities, industrial organizations can understand the health of their data-driven operations and maintain the reliability, security, and performance required to support their business objectives.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Develop real-time monitoring and alerting across your industrial data systems to proactively detect and resolve issues. Use machine learning and predictive analytics to identify anomalies before they impact operations.
Implementation steps
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Implement a centralized cloud data store as an enterprise historian using fully managed AWS services such as AWS IoT SiteWise and Amazon Timestream.
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Configure multi-tier, cloud-optimized services to store time-series data. Use AWS IoT SiteWise or Timestream for hot data and Amazon S3 for cold data.
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Use Amazon SageMaker AI AI to perform advanced AI/ML analyses of cold data for preventive maintenance, anomaly detection, and predictive quality insights. Use Amazon SageMaker AI Unified Studio as a single data and AI development environment where you can access and analyse your organization's data using the best tools across many use cases.
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Use integrated observability tools to provide a centralized view of the overall system health and performance, such as AWS X-Ray for distributed tracing. AWS X-Ray allows the team to quickly pinpoint the root cause of issues that span multiple components of the data infrastructure.
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For centralized observability and diagnostics, organizations can also use AWS CloudTrail to track API calls and configuration changes, providing an audit trail to understand the provenance of data and the lineage of operational changes.
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For proactive anomaly detection, integrate Amazon GuardDuty to identify suspicious activity or security threats, and use machine learning models (for example, through Amazon SageMaker AI AI) to detect unusual patterns in equipment telemetry data.
Key AWS services
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AWS IoT SiteWise
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Amazon Timestream
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Amazon Simple Storage Service (Amazon S3)
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Amazon SageMaker AI AI
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Amazon GuardDuty
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AWS CloudTrail
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AWS X-Ray
Resources
Related documents:
