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.

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