The strategic importance of industrial data fabrics

Recent outages have disrupted cloud services across a wide range of industries. From streaming services to smart water filters, each incident is an eye-opening demonstration of just how pervasive cloud services have become.

For businesses that rely on cloud-hosted applications, the impact is immediate: Dashboards become inaccessible, analytics pipelines stall, and data flow is interrupted.

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But for many industrial companies using a modern data infrastructure with an industrial data fabric, disruption does not need to result in data loss or operational downtime. A lot can be learned from the role of industrial data fabrics in supporting operational resiliency when it’s needed most.

What’s an industrial data fabric?

An industrial data fabric is a unified architecture that connects and contextualizes data from across OT and IT systems. It enables secure, scalable data movement from the edge to the cloud, while maintaining visibility and control across the entire data lifecycle.

These systems are used to support real-time monitoring, AI initiatives, digital twin, predictive analytics, ERP systems, and enterprise-wide decision-making often by facilitating the hydration of cloud technologies with OT data. But their ability to maintain continuity during infrastructure failures is where their resilience is truly tested.

Resilience at the edge: store-and-foward in action

Industrial data fabrics must be able to maintain operations when cloud connectivity is interrupted. While many platforms claim hybrid capabilities, few offer true edge resilience. Some rely heavily on cloud infrastructure for processing and orchestration, which can leave them exposed during outages.

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A modern industrial data fabric includes distributed data collectors deployed on-premise or at the edge. These collectors acquire data directly from industrial sources like OPC servers, PLCs, and historians. When upstream systems become unavailable, they buffer data locally with each data point timestamped and queued for delivery.

This is made possible by tightly integrated store-and-forward (SaF) logic. The system retains data during transmission failures and forwards it in correct chronological order once connectivity resumes.

This ensures that no gaps appear in the historical record, which is critical for time-series data used in compliance reporting, performance analysis, and root cause investigations.

While some platforms offer partial buffering or limited offline capabilities, modern industrial data fabrics are purpose-built to operate in environments where network reliability cannot be assumed.

They support high-throughput environments without compromising data integrity, making them particularly well-suited for industrial operations that span remote sites, constrained networks, or critical infrastructure.

Decoupled system design

Some industrial data fabrics support hybrid deployments, where local components manage data acquisition and processing on-site, while centralized systems, often cloud-hosted, handle analytics, reporting, and long-term storage.

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To maintain true data continuity, it’s important for industry to identify data fabrics that are well-suited to be deployed in this hybrid model, where local components can operate autonomously during upstream disruptions.

This allows use cases such as MES integration, calculations, and logic execution to continue without interruption, even when connectivity to cloud services is unavailable.

This design reflects the operational realities of industrial environments. Systems must be able to function independently of centralized infrastructure, especially in remote or bandwidth-constrained locations.

Prepared for more than just outages

While cloud outages represent one scenario to be prepared for, effective industrial data fabrics are designed for resilience across a range of scenarios:

• Network instability: Whether caused by hardware faults or bandwidth limitations, the system must be able to buffer data and ensure delivery once conditions stabilize.
• Cybersecurity threats: Secure data flows and authentication mechanisms are crucial in helping to protect against unauthorized access and tampering. It is critical that data is encrypted both in motion and at rest, and cybersecurity threat surfaces must be limited by minimizing the number of ports through which data flows.
• Operational diagnostics: Industrial data fabrics can provide comprehensive performance counters to provide insight into system health to assist support teams in responding to anomalies quickly.

These capabilities are crucial for systems that operate in industrial environments where conditions can change rapidly and where data continuity is critical.

Industrial data’s critical role

Industrial data plays a central role in maintaining safe, efficient, and compliant operations. Losing access to this data, even temporarily, can lead to gaps in historical data records, compromised data integrity, operational blind spots, delayed decisions, and regulatory risk.

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By maintaining data continuity during outages, industrial data fabrics help organizations avoid these consequences. They ensure that data is always collected, always protected, and always ready for use regardless of what happens upstream.

Cloud platforms offer scalability and flexibility, but they are not immune to failure. The industry’s most recent cloud outages weren’t the first, and they won’t be the last. Industrial data fabrics provide a layer of protection for critical infrastructure next time an organization’s resiliency is threatened.