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4 reasons to consider a multi-hub architecture for your Industrial DataOps deployment

“This story was originally published by HighByte
and has been republished here with permission.”

Manufacturers and other industrial companies adopting Industry 4.0 want to make industrial data available at scale across the enterprise to drive business decisions. Yet as these companies connect more processes, systems, and machines, their data modeling and integration needs have become more complex.

Industrial DataOps solutions like HighByte Intelligence Hub provide an answer to this complexity. The software provides a dedicated data modeling management and abstraction layer that helps users streamline their data architecture and reduce time to deploy new systems. In fact, as companies have expanded their usage of HighByte Intelligence Hub, they’ve begun to implement deployment architectures beyond a single hub.

In a recent poll of HighByte Intelligence Hub users, we asked how many instances they plan to run at a single site. The results validated the demand for a multi-hub architecture: Half of the respondents expect to deploy two to five hubs per site; nearly one-quarter said they plan to use six to 10 hubs per location.
In response to this demand, we released HighByte Intelligence Hub version 2.0 in July 2021 with multi-hub deployment in mind. Version 2.0 enables you to centrally manage all your Intelligence Hub instances from a single portal, making it easy to remotely configure, compare, and synchronize configurations at enterprise scale. Version 2.0 is available as a site license, so organizations can authorize it for unlimited deployments within a single facility.

If you’re thinking about investing in a data-modeling solution or upgrading your existing data infrastructure, you need to consider all possible scenarios before deciding which solution is ideal for your organization. Based on what we’re learning from our customers, deploying a scalable, multi-hub architecture will become more critical as your organization grows.

To further illustrate potential uses for a multi-hub DataOps deployment, here are four common reasons to consider this architecture for each site in your enterprise.

1. Segment Key Operations

You likely have multiple production lines, work cells, or machines that you’re working with. As systems have become more complex and interactions with other systems have increased, maintaining autonomy, agility, and resiliency are critical. We’re finding that many of our customers want to segment off key functions within their operating environment with unique Intelligence Hub deployments. That way, if they need to make changes to one line, they are assured they don’t impact the others. Or, if they must upgrade their operating system for one production cell, they only impact that particular work area instead of the entire facility. We’ve also seen some customers who want separate hubs for different use cases, such as predictive asset maintenance, production data, or quality information.

2. Close Network Security Gaps

With automation systems, it’s common for organizations to segment off different networks to minimize vulnerabilities. For example, it is typical to shield the controls network from your business and enterprise networks using firewalls, thereby limiting data and only allowing data to pass through in one direction. HighByte Intelligence Hub supports secure MQTT publishing and subscribing through a firewall. You can set up hubs in a federated architecture across multiple secure network zones. This architecture allows the Intelligence Hub to communicate with systems within that zone and either consume or publish data into the other zones securely.

3. Enable Change Management

Operational data is becoming critical for many business functions to perform their day-to-day jobs. Analytics-based systems require an iterative approach and frequently change as the factory floor is ever evolving with new assets and reprogrammed controllers. Many of the organizations we work with are establishing development and test hubs to enable rapid design and evaluation of changes for testing prior to production deployment. Within the Intelligence Hub, users can copy a configuration, push it to another system, make changes to the system, test it out, and then move it to production. That way, when they go live, they feel confident the system will work seamlessly.

4. Model Data at the Edge

Execution of real-time analytics in a factory is moving closer to the machinery and is often referred to as running at the Edge. In some cases, these analytics are applying machine learning or artificial intelligence techniques to the setpoint control of a machine where traditional logic-based control could not deliver the results. Furthermore, running closer to the machinery means there is lower latency and lower risk of missed data. The Intelligence Hub serves as the middleware between the device and the analytic and runs on an edge gateway. It ensures the data is collected from the work cell or machine in a consistent and standardized manner, allowing the deployment of the analytic across similar machines quickly and efficiently. By leveraging a site license and container-based deployment, you can cost-efficiently deploy a hub at an unlimited number of these Edge nodes within the factory.

If we’ve learned one lesson from the pandemic, it’s the need to be nimble in today’s uncertain environment. Changes are happening fast—whether you’re consolidating production, retooling product lines, or reconfiguring your operating environment. A multi-hub Industrial DataOps deployment minimizes overreliance on a single system, so organizations can implement changes faster with fewer disruptions to their operations.

/John Harrington

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