Data Modeling for The Unified Namespace: Best Practices

Welcome to Part 4 of the blog series, An Advanced Guide to Building UNS for IIoT: Beyond the Basics. To effectively model data for a Unified Namespace (UNS), businesses must adopt a strategy that merges their domain-specific knowledge with their industrial data expertise into cohesive data products. These products are then shared across the entire organization through the UNS, promoting data reuse and enabling the rapid development of customized solutions. In essence, data modeling is essential for transforming isolated data silos into scalable, integrated systems.

However, physical industrial systems are inherently complex, and no single data representation can accommodate all the various ways data might be utilized. Often, a single data source serves multiple purposes. For example, a mechanical press might generate:

• Cycle data for traceability applications

• Downtime and defect information for tracking Overall Equipment Effectiveness (OEE)

• Time-series data for predictive maintenance

• Energy consumption data for energy efficiency initiatives

• Alarm data for maintenance scheduling

This data typically originates from multiple systems, including Programmable Logic Controllers (PLCs), databases, flat files, Manufacturing Execution Systems (MES), and Enterprise Resource Planning (ERP) systems—and will need to be contextualized for each specific use case.

Standardizing Base Data Models

To manage this complexity, it’s crucial to standardize a set of base data models. These models include common data elements while allowing for customization and the addition of unique data. Essentially, this means creating modular data models or building blocks that support a wide range of analytical techniques, such as:

• Visualization

• Asset Reliability KPIs 

• Digital Twins

• AI/ML applications

Once established, these base models can be replicated across similar assets, production cells, lines, and sites, ensuring consistency and scalability.

By packaging data into custom payloads using base data models, data consumers can more easily locate and utilize the appropriate data for each specific use case. This approach is key to:

• Accelerating the deployment of the Unified Namespace

• Enhancing usability for all users and applications

To implement this approach successfully, a robust data modeling framework is necessary. This framework should:

• Clearly define different perspectives of the same data

• Enable data reuse across various applications

Base Data Models for the UNS

Base Data Models are standardized frameworks that incorporate common data elements relevant to multiple use cases. These models serve as the foundation for organizing and managing data consistently across the enterprise. Key components of Base Level Data Models include:

• Job or Batch-Related Data:

  • Value-Adding Activities: Data captured when equipment performs tasks that add value, such as converting a flat sheet of aluminum into a formed door panel.

  • Batch Processes: Information related to the addition of ingredients or materials in batch manufacturing processes.

• Non-Job or Batch-Related Data:

  • Machine State: Data that reflects the operational status of machinery at any given moment, regardless of whether it is engaged in a specific job or batch process. This includes metrics like machine uptime, status indicators, and real-time performance parameters.

Example Base Data Models

Cycle Data

This Base Data Model for a mechanical press cycle systematically records comprehensive details of each production cycle. By standardizing this cycle data, the model enables consistent data collection across different machines and production lines. This uniformity supports various use cases, including performance monitoring, predictive maintenance, energy management, and quality assurance.

Downtime Data

This Base Data Model for a downtime event systematically records essential information whenever a mechanical press experiences downtime. By standardizing downtime event data, this model ensures consistent data collection across different machines and production lines. This uniformity supports various analytical applications, including performance monitoring, predictive maintenance, and operational optimization.

Customized Data Models 

Unlike Base Data Models, which focus on standardized and commonly used data elements applicable across multiple use cases, Customized Data Models are designed to address specialized requirements, ensuring that the Unified Namespace (UNS) can effectively support diverse and unique business processes.

They are designed to deliver precisely what the consuming application or user needs by transforming siloed data using the following techniques:

• Contextualization: Combining data from various sources to provide meaningful context.

• Structuring: Organizing data in a way that aligns with the requirements of the application or use case it serves.

Examples of Customized Data models include:

• Predictive Maintenance Data Model: Structured for training a specific predictive maintenance system

• Energy Efficiency Models: Designed to analyze and optimize energy consumption.

Example Customized Data Models

Predictive Maintenance Data 

This Customized Data Model is specifically designed to support predictive maintenance by capturing detailed operational metrics that are critical for forecasting machine failures and scheduling maintenance activities.

Energy Monitoring Data 

This Customized Data Model is tailored to support comprehensive energy monitoring and management for a mechanical press. It captures detailed energy-related metrics essential for analyzing and optimizing energy usage, promoting sustainability, and reducing operational costs.

Delivering Data through the Unified Namespace

After defining and creating the data models using the appropriate tools, the next step is to instantiate these models with actual data across different parts of the plant. Here’s how the process works:

1. Create Data Object Instances:

   • Generate instances of each data model at the specific locations within the plant where the information needs to be published to the (UNS).

2. Map to Data Sources:

   • Link each instantiated data object to its corresponding real-time data sources, such as sensors, machines, or databases, ensuring that the data is accurately represented within the UNS.

3. Route to MQTT Topics:

   • Deliver the mapped data objects to relevant users by routing them to the appropriate MQTT topics within the UNS. This ensures that the right data reaches the right stakeholders and applications.

4. Utilize Delivery Mechanisms:

   • Change-Based Notifications: Provide real-time updates by sending data whenever changes occur, ensuring users have the most current information.

   • Scheduled Intervals: Deliver data at predefined frequencies, suitable for applications that do not require immediate updates.

Best Practices for Data Modeling in a Unified Namespace

Effective data modeling is crucial for the success of a Unified Namespace (UNS) as it ensures that data is organized, accessible, and usable across the entire enterprise. To achieve this, organizations should adhere to several best practices, which include the following:

1. Thoroughly Understand and Incorporate User Requirements

Engage with end-users and stakeholders early in the data modeling process to fully understand their needs and expectations. Conduct comprehensive requirements gathering to identify the practical demands of those who will be utilizing the data models. By incorporating user requirements into the design, you ensure that the data model is not only technically sound but also highly relevant and useful for its intended audience. This user-centric approach enhances the effectiveness of the UNS, promoting higher adoption rates and greater satisfaction among users.

2. Model Data Appropriately to Avoid Unnecessary Complexity

Instead of attempting to capture every conceivable detail, focus on modeling data that is essential and relevant to your specific use cases. Aim for simplicity by including only the necessary data elements that provide meaningful insights and support your business objectives. This approach prevents excessive complexity, making the data model more manageable and efficient. By avoiding over-modeling, you ensure that the data structure remains practical and aligned with the intended purposes, facilitating easier maintenance and better performance.

3. Design Data Models with Flexibility and Adaptability

Create data models that are inherently flexible and can adapt to changing business requirements and technological advancements. Incorporate modular and scalable structures that allow for easy modifications, such as adding new data elements, adjusting sample frequencies, or changing units of measurement. A flexible data model can seamlessly accommodate future enhancements and evolving use cases without necessitating significant redesigns. This adaptability ensures that the UNS remains resilient and capable of supporting the organization’s growth and dynamic needs.

4. Utilize a Modular Design Approach

Adopting a modular design allows for the creation of interchangeable and reusable components within the data model. This approach simplifies the process of updating and extending data models, as individual modules can be modified or replaced without affecting the entire system. A modular design promotes agility, enabling organizations to respond quickly to changing business needs and technological advancements.

5. Promote Data Reusability

Encouraging data reusability is essential for maximizing the value of data assets. By designing data models that can be utilized across multiple applications and departments, organizations can reduce redundancy, minimize data silos, and enhance efficiency. Reusable data models also simplify maintenance and updates, as changes need to be implemented in only one place.

6. Ensure High Data Quality and Consistency

Prioritize data quality and consistency to maintain the reliability and integrity of the UNS. Implement robust data validation, cleansing, and standardization processes to ensure that all data inputs are accurate and uniform across different sources and systems. High-quality data is crucial for generating trustworthy analytics and supporting sound decision-making. Consistent data standards facilitate seamless integration, reduce the likelihood of errors, and enhance the overall usability of the data within the UNS.

7. Establish Clear Data Standards and Naming Conventions

Consistent data standards and naming conventions are fundamental to creating a cohesive data environment. By defining and adhering to standardized naming conventions, organizations can ensure that data elements are easily identifiable and understood across different departments and applications. This consistency reduces confusion, facilitates data integration, and enhances communication among stakeholders.

8. Continuously Monitor and Refine Data Models

Data modeling is an ongoing process that requires regular monitoring and refinement to remain effective. Organizations should establish mechanisms for continuously assessing the performance of their data models, identifying areas for improvement, and incorporating feedback from users. Regular updates and iterations ensure that the data models remain relevant, efficient, and aligned with the evolving needs of the business.

Conclusion

Mastering data modeling for the Unified Namespace (UNS) is essential for transforming disparate data sources into a cohesive and scalable framework that drives enterprise-wide insights and innovation. By strategically combining domain-specific knowledge with industrial data expertise, organizations can create standardized Base Data Models that ensure consistency and scalability across various assets and sites, while Customized Data Models address specific application needs.

Other Blogs from the Series 

Part 1: The Business Value of Unified Namespace for Industry 4.0

Part 2: Why a Data Warehouse Can’t Be the Unified Namespace

Part 3: UNS Semantic Data Hierarchy with MQTT: Explained with an Example

Part 5: Automating Manufacturing Business Processes with the Unified Namespace