When Operational and IIoT Data Collide at the Edge

For industrial companies engaged in digital transformation, analytics are key to turning large volumes of data into business value for operational enhancements as well as improved customer experience.  Many data sets are available. Often, operational and IIoT data are considered separately, or they are both considered streams that simply feed Big Data industrial analytics.

In some situations, data and analytics need to be processed centrally, such as in a cloud, to drive strategic decisions.  In other instances, operational decisions need to be made instantaneously, meaning that centralized solutions cannot provide the analysis.

Decentralized analytics, otherwise known as “edge” analytics or computing, occur at or near the edge of the operational network. They are quite common in some consumer-facing industries, particularly those with highly mobile customers, such as retail, communications, and finance.

For industrial purposes, edge analytics that rely too heavily on data generated only by equipment and devices overlook some of the most valuable data and insight available to industrial companies: operational data, a portion of which is also generated at, or near, the operational edge.

Parsing Operational and IIoT Data

When considering data for edge analytics, a common misperception is that they only consist of streaming data, time stamped based on the input source. They are often referred to as Industrial Internet of Things (IIoT) data. The thinking here is that a combination of secure connection, automation, and rules/performance analysis, and workflow automation are key to getting value from the data. Real-time monitoring is an example.

Industrial operational and IIoT dataWhile true, this only paints a portion of the picture within the context of IIoT strategies. What’s missing is input of operational processes and their related data, some of which may be generated at the edge. Because these data are often generated and captured by subject matter experts (SMEs), they typically contain high-value information.

A good IIoT analytics strategy accounts for this set of operational data and knowledge, from records in maintenance to valuable notes and images generated by field service personnel.  These can inform edge analytics, along with IIoT data.

Correlations of Operational and IIoT Data

A considerable amount of operational data, particularly those generated at the edge, are often underutilized, if used at all. Unless a formal process exists as part of operational process, these data are rarely “systemized” into a source that can make them available as part of the overall pool of operational data. Images, videos, and notes captured and shared during events (and then forgotten) are examples.

These data often reflect observations of the most experienced workers or insights from the most valuable and loyal customers. They can include crucial information that can only be gained during events, provide critical information on best practices associated with optimized operations, or deliver direct input about product functionality and customer satisfaction.

The nature of edge operational data often means there is a high correlation with streaming, time-stamped data generated in parallel by equipment and sensors. The application of knowledge (i.e. operational context) is key to unlocking the value of these correlations. Combining operational and IIoT data is important to maximizing the value of edge analytics. Some examples include:

  • Observations noted in inspection reports from an engineer correlate to timestamp data, indicating issues such as dust in wind turbine, high level of salt on oil platforms, or unexpected pump failure.
  • Pictures taken during engine maintenance on a vessel by certain crews or notes in work orders by individuals who are following particular work practices correspond to future anomalies in asset performance.
  • Expert observations recorded about aroma or taste during a food product quality control inspection correlate to air measurements made in a production area. Combined with social media complaints, analysis pinpoints the source of quality assurance issues.

In short, you can’t always separate humans from IIoT data.

 

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