JUL-AUG 2019

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by the cloud. Large process manufac- turers will likely utilize a mix of public and private cloud offerings, as well as on-premise components, for analytics. The trend is in its infancy, though some industries are ahead. Chemical manufacturers, for example, are begin- ning to embrace the cloud, for analytics as well as other use cases. As a result, Microsoft, Amazon, and Google have specifically focused on the oil and gas sector as a starting point for their efforts. This is clearly a sign of market interest, and it is also a sign of the ma turity of the cloud offerings: Amazon brought out AWS in 2002, and then introduced S3 (storage) and EC2 (virtual machines) in 2006. Cloud computing competition then increased with Microsoft's and Google's cloud platform introductions in 2008. Storing large volumes of data in the cloud is increasing, and it is already a "when" and not an "if" question for most companies. Consequently, the big public cloud platforms are paying more attention to the largest sources of data, with manufacturing leading all sectors of the economy. What this means for process manufacturing customers is faster time to deployment and a lower price for analytics access. Dominant for decades as the analytics tool of choice, spreadsheets are not up to the task of performing advanced analyt- ics on ever-larger datasets, yet their accessibility to engineers is a re- quirement for any future analytics of - fering. Insights that take too long to discover languish because they can - not easily be pub- lished and shared with others. Ad - vanced analytics applications con - nect with data from a wide array of sources and surface insights much more quick - ly in a format that is easy to share, enabling actions to im - prove business results and profitability. Here is an example showing advanced analytics in action. Cloud-based analytics A chemical company took advantage of a browser-based advanced analytics ap- plication running in the cloud to connect back to its on-premise data via a secure HTTPS connection and a remote con- nection agent. The solution was deployed and accessible in a matter of hours, and the data stayed where it was, enabling in- sight in days rather than months. Another option is to make the cloud the destination for datasets collected from remote or IIoT end points. This is a more natural and easier option than trying to reroute data from carriers and wireless systems back into IT systems and then to the cloud, because data "born on the cloud" is a popular option for many monitoring applications. In this case, end users can then access the data by either running analytics on the cloud or by running the analytics solution on premise with a remote connection to the cloud-based data. In either scenario, the monitoring data may be complemented or contextualized by connecting the analytics solutions to other data sources—historians, manu- facturing execution systems, etc.—to get a complete view of all data. For chemical PROCESS AUTOMATION INTECH JULY/AUGUST 2019 21 companies, this scenario can be used for new insights into supply chain and opera- tions by complementing existing data with data from wireless or cellular networks. A third scenario is accessing multiple sites from a cloud deployment of analyt- ics software. Although moving or copying the data to the cloud also could facilitate cross-plant comparisons for yields, qual- ity, etc., a simple remote connection for occasional queries and comparisons may suffice, depending on the frequency and requirements of the end user. Measurable value Analytics is not new, and neither are the unrealized promises that have sur- rounded the field. But technical ad- vancements, cloud computing and ma- chine learning for instance, along with the massive explosion in data from sensors and other sources, have come together to create new opportunities. There is now reason to believe analytics will finally generate measurable value for process manufacturers by rapidly bringing shareable insights to light. n ABOUT THE AUTHOR Michael Risse ( is the CMO and vice president at Seeq Cor- poration, which builds advanced analytics applications for engineers and analysts for insight into industrial process data. Risse was formerly a consultant with big data platform and application companies and before that worked with Microsoft for 20 years. He is a graduate of the University of Wisconsin at Madison. View the online version at RESOURCES "Empowering an effective PAT methodology" "What's next for big data in process manufacturing" "Big data analytics need new solutions" Cloud Scale-out Big data ML Deep learning Figure 5. Software takes advantage of advances in a range of technologies, including machine learning, empowering engineers to create insights.

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