For industrial companies engaged in digital transformation, analytics are key to turning large volumes of data into business value to enhance operations and improve the customer experience. Facing intense financial pressure and competition in rapidly changing global markets, companies need to think very carefully about where that data is and how best to leverage it. In some instances, data and analytics need to be processed centrally, such as in a cloud, to drive strategic decisions. In other situations, operational decisions will need to be made instantaneously, meaning that centralised solutions cannot provide the analysis.
Decentralised analytics, otherwise known as ‘edge’ analytics or computing, occur at, or near the edge of, the operational network. This is quite common in some consumer-facing industries. However, until recently, analytics at the industrial edge was not possible due to a mix of cost, complexity, security and technology barriers.
That is changing. Digitisation is occurring in all industrial environments. In brownfield infrastructure, intelligence is being added via devices such as sensors and gateways. In new infrastructure, we are seeing digitisation through embedded software and preconfigured intelligent equipment.
As this change has taken place, ARC has observed the market focus swinging away from centralised Big Data and analytics toward edge data management and analytics. This makes sense to some degree, as the growth of edge IoT devices and related data has skyrocketed and will continue to do so.
However, 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; plus process knowledge.
Cloud and edge redefine analytics
In industrial settings, hierarchical structures have traditionally been employed to capture, access and communicate data across an organisation. Operations personnel, whether in a field environment or on the plant floor, can certainly attest to processes and technology designed to capture, share, and use the data. Yet, limits on the use of the data were considerable, constrained rather tightly at times by business siloes and technology.
This data structure precedes the Internet. As the Internet becomes a ubiquitous part of business and operating environments, this traditional data structure is being replaced.
Organisations are now beginning to see the value of a more comprehensive view of data and analysis. This improved view includes centralised processing, such as in the cloud (or even on premises on a server), and extends seamlessly to and from the operational edge.
As business leaders wrestle with the ‘data explosion’, they see cloud computing as the solution for associated volume, speed and complexity issues.
The cloud can bring massive computational power to solve problems since it provides a viable solution for combining complex and large data sets – both structured and unstructured – with advanced analytics techniques.
Examples include applying machine learning to acoustics data to predict asset failure, integrating text analysis for process optimisation, or using image analysis for product assurance.
In reaction to the growth of the cloud, the concept of the ‘edge’ of an organisation has been defined as the furthest extensions of a businesses operating environment, whether physical infrastructure, distributed operational points, or customer engagements.
Edge analytics extends data processing and computing close to or at the data sources, which include equipment and devices. In industrial operations, analytics executed at the edge typically support tactical use cases for efficiency, reliability, unplanned downtime, safety and customer experience.
Often overlooked IIoT elements
When thinking about the 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 connection, automation, edge analysis and workflow automation are key to getting value from the data.
While true, this only paints a portion of the picture within the context of IIoT strategies. What is missing is an understanding of the value 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.
Operational data
Operational data, particularly those generated at the edge, are often underutilised, if used at all. Unless a formal process exists, these data are rarely ‘systemised’ into a source that can make them available as part of the overall pool of operational data.
Process knowledge
In addition to operational data, SMEs understand (and often design) operational processes and best practices. These high-value workers have specific knowledge of how to operate equipment, execute maintenance and ensure safety procedures. For example, crude oil engineers have expertise around impact of crude types on equipment failure during the refining process. This intellectual property is invaluable of course and organisations are fearful it will leave the business as workers retire or move on.
Technologies are now available that can mathematically model and capture that expertise as part of the analytics. In doing so, this process knowledge can be augmented with operational and IIoT data. This blending of knowledge and data can be used to drive the optimised decisions flows and equipment performance necessary for maximising IIoT strategies.
For more information contact Paul Miller, ARC Advisory Group, +1 781 471 1141, [email protected], www.arcweb.com
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