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JUL-AUG 2017

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26 INTECH JULY/AUGUST 2017 WWW.ISA.ORG SYSTEM INTEGRATION insights. One example for this is enhanc- ing "self-optimizing machines"—which cannot be realized with "slower," cloud- based big data analytics. Imagine a process manufacturing production line that attempts to self-optimize for high- est output with minimal wear through cloud-based big data analytics. The time lag between data generation and inter- pretation caused by bandwidth issues and network latency would very likely cause inaccurate measurements and ill- fitted instructions. Decentralized decision making, on the other hand, is an excellent way to mitigate some of the risks that come with "smart" equipment. Imagine 1,500 oil pumps being controlled by a cloud. If their con- nection to the cloud failed, or if the cloud ever ran into what IT experts call a "disas- ter," these 1,500 "smart" pumps would become ineffective in a matter of sec- onds, and remain in that state until the infrastructure was back up and running. This would also very likely affect safety and productivity in significant ways. Studies show that big data analytics in the cloud typically is not fast enough for many Industrial Internet of Things (IIoT) uses. Whether an analysis can be consid- ered "real time" greatly depends on its underlying use—and in many cases ma- chines need results fast. In a survey of 203 Internet of Things professionals, research firm Dimensional Research found that many experts think the "timely collection, processing, and analysis of data" was the chief technology challenge for IIoT im- plementation; 92 percent of the experts surveyed said they simply could not cap- ture data fast enough. And when asked about the business impact of "better" analytics, 86 percent of the respondents said that they believed that faster and more flexible analytics would increase the re turn on their IIoT investments. Benefits for manufacturers Performing sophisticated analysis at the edge means that targeted condition- or prediction-based outcomes can be trig- gered at the level of machines, compo- nents, or even parts very quickly, and with very short response times—without high-performance network and cloud in- frastructure. This means that analytics be- comes much more efficient, much faster, and therefore much more effective. By acquiring, monitoring, and interpret- ing data at the component level, edge ana- lytics can identify a cause before its effect materializes, enabling earlier and more specific reactions. So, rather than identify - ing and analyzing an effect (excessive mo- tor bearing vibration, for example), edge algorithms identify and act upon real causes at a more granular level. This could be, for example, a voltage leak causing a bearing temperature spike, degrading the other bearings, and causing vibration. All of this can be of tremendous value to industrial businesses. They can use edge condition and predictive analytics to improve equipment uptime much more effectively and efficiently than they could with big data analytics. They can reduce maintenance cost and planned down- times significantly by planning mainte- nance predictively and giving mainte- nance experts extremely granular, precise machine-specific status insights. Or they can package all these capabilities into ser- vice offerings, and begin to build entirely new business models around them. Edge analytics models can be tailored to the requirements of an individual de vice or system. This might mean reading sensors directly associated with certain components. Or it might mean inferring results based on known and validated calculations. The right sensor package for a piece of equipment will be guided by an organization's desired business value—the model can define how an asset or system should be opti - mally configured to achieve a business goal for the minimum cost. Putting edge analytics to work Edge analytics cannot, however, do every- thing, and they especially cannot replace big data and cloud computing when it comes to storing, analyzing, and inter- preting vast data sets or running resource- intensive technologies like machine learning. This is why businesses that plan to use edge analytics to their advantage should not think of the two technologies as either-or, but rather as complementary. In fact, both unlock the highest possible business value when used together. When integrated with a big data analytics cloud, edge technology brings precise insights and improvements on the component or machine level while relying on the cloud to do the same at the "collective" level. In addition, edge technology can reduce the volume of data sent to the cloud, while improving data quality. This makes for more efficient and effective analytics on all levels of a business operation. The way this works has much to do with the data models and analytics al- gorithms used in edge analytics. Be- cause routers' and switches' comput- ing power is nowhere near even that of a single server, let alone a cloud, most edge analytics solutions use models that are highly efficient—and very differ- ent from big data models—and execute them through algorithms that need rela- tively little computing power. These models are usually built to solve very specific analytics tasks or deliver very specific outcomes, which limits the data they need to only what is necessary to reliably run the model. This is almost the opposite of big data that analyzes large swathes of data until correlations or other patterns emerge. This "outcome- centered" approach is what unlocks the gains in data efficiency and quality. Only data that is relevant and insightful is being used and, if need be, pushed to the cloud. To be able to build these models in the first place, edge analytics experts usu - ally need expert knowledge, supported by historical data, and, in some cases, a cloud-based big data analytics resource to build, test, and optimize their statis - tics and algorithms. New business models Edge capabilities also can help shape new business models. Consider, for example, the potential impact of edge analytics on Edge analytics models can be tailored to the requirements of an individual device or system.

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