Industrial DataOps: The Complete Guide for Manufacturers

Edge-first processing refers to the strategy of collecting, processing, and contextualizing data as close as possible to where it is generated — at the edge of the network, on the factory floor— rather than sending all raw data to a centralized system like a cloud or enterprise data warehouse for processing.

Compared to cloud-first approaches, Industrial DataOps prioritizes edge processing for several reasons:

• Latency reduction: Processing data close to its source minimizes delays in time-sensitive operations
• Bandwidth optimization: Only sending relevant, contextualized data reduces network traffic
• Security enhancement: Limiting exposure of raw industrial systems to external networks
• Resilience: Continuing to function even during network disruptions
• Cost efficiency: Reducing cloud storage and processing expenses by filtering at the source

For example, HighByte Intelligence Hub can be deployed on-premises, close to the data’s source, so operators most familiar with this data can contextualize, standardize, and model the data before it is streamed to the cloud. This approach ensures data lands in the cloud in an analytics-ready format. 

The edge-native approach represents a fundamental shift from early Industry 4.0 projects that attempted to move all data to the cloud before processing, which proved costly, slow, and often impractical.

A sophisticated Industrial DataOps solution uses a two-tier approach:

• Models define the structure, properties, and relationships for a class of assets
• Instances apply models to specific physical assets, mapping local tags to standardized properties

This approach allows organizations to create a model once and apply it hundreds of times. It also ensures consistency across similar equipment and sites. Models can be updated centrally, and changes will be automatically propagated. Models are not limited to equipment assets. Models may also represent systems, products, or processes.

Using an Industrial DataOps solution as an abstraction layer aids in deployment speed. Not every physical environment is laid out the same as another. For example, imagine you have sites in Tokyo and Atlanta—all the underlying hardware and software are completely different at these two sites. All units of measure are also different. A digital abstraction layer securely collects and organizes data into standard data models for distribution across on-premises and cloud-based applications. This abstraction layer makes Tokyo “look like” Atlanta. While the physical twins of the two plants are quite different, you can still compare “apples to apples.”

Manufacturing environments house a complex mix of systems spanning decades of technology evolution. A scalable Industrial DataOps solution must bridge these disparate technologies without requiring wholesale replacement of existing investments.

HighByte offers a system-agnostic approach with numerous connectors (e.g., OPC UA, SQL, REST, files) where data is modeled from legacy assets and transformed before it is published to a downstream application. This flexibility enables organizations to incorporate both decades-old equipment and cutting-edge technologies into a unified data architecture.

Vendor-specific challenges can be addressed systematically using Industrial DataOps, which creates a consistent data experience regardless of the underlying systems' age or vendor.

Industrial environments produce data in various cadences and patterns, from millisecond-level machine states to daily batch records. Industrial DataOps solutions must accommodate these diverse data flows while maintaining context and relationships.

Types of Industrial Data Flows

 By configuring these flows appropriately, manufacturers can ensure that data is delivered with the right frequency, completeness, and context for each application 

As industrial data becomes increasingly valuable, securing it while maintaining accessibility becomes a critical challenge. Industrial DataOps solutions should incorporate robust security and governance capabilities that satisfy both IT security requirements and operational needs.

For example, model validation is a vital governance tool because it only allows the data that the IT team requires to go to the cloud, preventing poor quality and non-compliant data.

A comprehensive security approach includes:

• Network segmentation: Maintaining separation between OT and IT networks
• Protocol security: Leveraging encryption and authentication in data transfers
• Access control: Granular permissions for users and applications
• Edge processing: Minimizing exposure of critical systems
• Audit trails: Logging all configuration changes and data access

Data Governance

Effective governance ensures data remains trustworthy and usable as it flows through the organization. It includes change management, version control, data lineage, quality monitoring, and role-based controls. Without these roles in place, companies could face steep fines in the event of an audit. 

Regulatory Compliance

For industries with specific regulatory requirements, an Industrial DataOps solution should provide:

• Traceability via complete records of data transformations
• Validation documentation with evidence of proper system operation
• Data integrity using controls to prevent unauthorized modifications

By addressing security and governance, Industrial DataOps solutions help organizations balance operational technology needs with information technology requirements, creating a secure but accessible data environment.

Data quality is measured by accuracy, completeness, consistency, reliability, and timeliness to meet business and analytical needs. High-quality data is essential for effective decision-making, compliance, and operational efficiency. Cleaning your data at the Edge will help manufacturers meet validation rules and achieve automated cleansing.