How to Scale Industrial Data Operations Across Your Plant Network
Many organizations believe that “data availability” means unlocking and accessing OT data through a medley of solutions that must be managed individually (middleware/industrial connectivity + integration tools + cloud solutions).
In a small or single-plant environment, simply making OT data accessible may be enough. But attempting to replicate that data pipeline while different plants use different technologies and solutions quickly becomes too chaotic. Especially on a large scale, reliance on disparate, non-integrated components is unmanageable and inefficient. The result is a patchwork of plant-specific data collection models and machine learning models that are nearly impossible to manage, not to mention synchronize and harmonize.
Within a network of plants, you must have total control over your data along the entire pipeline—from native connectivity (OT source data) to native connectivity (the cloud), and vice versa—and it must be centrally administered.
This significantly accelerates enterprise data projects by providing direct access to the data source, reducing complexity by eliminating multiple in-between layers and ensuring ownership and consistency at every point along the data pipeline.
Too often in a multi-plant environment, new technologies are piloted at one or two locations. While your data-availability approach may work within those plants, it can become invalid when you attempt to scale to even three or four plants.
Piecemeal solutions create more problems than solutions
As they build an industrial data operations platform for each plant in their environment, many enterprises are finding that the “software medley” approach mentioned above becomes exponentially more complex and unmanageable. It can lead to problems that are not only very complex and unmanageable, but also extremely expensive to resolve in the cloud.
These problems include:
- Conflicting data standards, with plants using different naming conventions and data formats, which complicates data integration and analysis.
- High maintenance expenses, as continuous maintenance and updates divert resources from core business activities.
- Delayed decision-making, with precious time being wasted as data from multiple plants is aggregated and standardized.
What to include in your data management strategy
The industrial data operations journey must begin with enterprise-wide scalability in mind—and only then should the unlocking and enriching of data at the plant level begin.
While moving data to the cloud is crucial, it should not be the final goal. The goal instead should be data that is standardized, validated and enriched correctly, as close to the edge as possible, and consistently unlocked across all plants.
To scale efficiently and effectively, enterprises need a robust strategy that supports:
- Consistent data management practices across all plants
- Use cases in one plant to be easily transferred and applied to other plants
- Centralized management of template, firmware and new data tag updates
- The ability to rapidly adapt and manage data operations to maintain agility and consistency across plants
4 steps to scale data operations
To effectively scale industrial data operations strategies, your machine learning/artificial intelligence models must be easily and centrally managed and deployed. This involves four important steps.
1. Create a consistent data strategy
Develop a centralized strategy for unlocking data operations across all plants, including establishing common data collection standards and practices that ensure consistency and reliability.
2. Standardize and replicate
For successful data management, implement systems that can be replicated from one plant to all plants. Standardize data formats and processes for seamless integration and analysis.
3. Prioritize agility
Create an environment that supports rapid updates and the ability to manage data operations in a way that allows you to respond to changing business needs.
4. Minimize fragmentation
Instead of plant-specific solutions that create data silos and inefficiencies, focus on unified, scalable solutions that support enterprise-wide data operations.
The Future of industrial data operations
The ability to scale operational data across all plants is critical to success in the modern industrial landscape. Enterprises must move beyond simple data availability and focus on scaling and managing data streams on an enterprise-wide level.
By adopting a strategic approach that emphasizes centralization, standardization and agility, companies can use their data to drive real business value and sustain a competitive advantage.
To do this, you need a partner that understands OT and IT landscapes with a laser focus on scale. Belden can meet you wherever you are on your industrial data operations journey and create your roadmap to enterprise scale.
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