Industrial data is generated in such volumes and high speeds that it’s difficult to capture accurately. Even if you could, its use is likely limited by lack of both context and consumability. With operational data locked in disparate devices or siloed in systems of record, data scientists expend significant effort on data preparation and are hard-pressed to extract actionable industrial performance insights.

FactoryTalk^® Edge Gateway^™ software addresses these challenges, unlocking actionable plant and enterprise-level insights to accelerate IIoT digital transformation. It simplifies and automates collection, contextualization, and organization of industrial equipment data across machines, devices and automation assets at the source itself—enabling high data integrity from the outset. It provides the right foundation to drive edge-to-cloud IT/OT convergence at the enterprise level so that everyone from the shop floor to the top floor can make smarter decisions.

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Unlock Industrial Performance Insights

With access to OT contextualized data, IT applications can help you to extract equipment insights across your enterprise. Quickly, easily and proactively identify process and production inefficiencies and product defects. With less required data preparation time, you can focus on using your data to detect and resolve equipment issues and improve production output.

Leverage FactoryTalk Smart Object Capability to Drive Data Contextualization

FactoryTalk Edge Gateway software automates data preparation, packaging it into a common information model powered by the FactoryTalk Smart Object™ capability. FactoryTalk Smart Object content is created by control system designers and they extend base Logix tags with additional, user-definable, configuration properties. Think of them as metadata that identify specific data points, like energy consumption, asset health, etc. When you configure these properties, you reference operational parameters to tell the software how to collect data with relevance to unique outputs.

The common information model is a logical data structure of related data grouped into a hierarchy that data scientists and others familiar with your process can easily understand and consume. For example, if you have an asset with sensors in multiple locations, you can group this data by sensor distribution, i.e., inlet vs outlet. This makes it easy to pull out relevant attributes for analysis.