JUL-AUG 2017

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24 INTECH JULY/AUGUST 2017 WWW.ISA.ORG FAST FORWARD l Big data analytics is valuable—but cannot be used for all industrial needs. l Edge analytics can help where big data falls short. l Executives need to know what each of the two technologies can and cannot do to improve their return on investment. When the cloud isn't fast enough The solution may be edge analytics T raditional industrial analytics brings gains in operating efficiency, machine uptime, and risk and hazard mitigation. But what if collecting and crunching millions of data points through a big data solution is too slow, too costly— or even impossible? Big data analytics in manu- facturing has become a well-established concept: Collect data from connected products, machines, factory lines, and entire plant functions, and en- able businesses to gain immediate insights into their ongoing operations to produce greater effi- ciency, quality, or safety. Also adopting "edge ana- lytics"—that is, technology that allows analysis at the "edge" of a network, without needing to send data back to the cloud at "the core"—can signifi- cantly further that goal. Large-scale big data analytics deployments can be useful. But, they are still few and far between. That is partly because collecting and crunching all that data can be a challenge. Big data requires industrial equipment to be connected. It requires the data from these machines to be collected and brought together for analyses. And it requires a lot of computing power to run the analyses, especial ly if sophisticated routines like machine learning are required. Therefore, almost all state-of-the-art big data solutions use high-bandwidth Internet con- nections and the cloud—two technologies that can be costly and are not always the best way to achieve the desired result. Consider, for example, a large network of gas sensors in a huge chemical production plant, installed to collect data that might help predict critical equipment failures. In most cases, the data points collected by these sensors will not indicate any failures that must be resolved—and yet, with big data technology, 100 percent of the data is streamed to, and analyzed by, expensive network, cloud, and big data infrastructure. Or think of large, underground mining equip- ment: Operational analysis for it is an obvious case for using data analytics, with applications ranging from early warning of minor maintenance issues to critical systems failure predictions. Unfortu-

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