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MAY-JUN 2018

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model might be a data lake for data ag gr egation, on-premise or in the cloud, or a comprehensive IIoT solution—like a next-generation data storage plat - form. At a minimum, current process historian vendors need to introduce road maps with safe passage for data from on-premise offerings to the cloud. As a vendor of advanced analytics so lutions, here are examples of what this means to the end users we work with daily. Three years ago, we had cus- tomer requests for sales engineers to visit them on site to work with their on- premise and air-locked data sets. Today, in contrast, we have customers shar- ing five-year road maps that integrate cloud-based offerings, and specifically asking for context on some of the open source offerings, such as Hortonworks and InfluxData. The assumption that data can never, or will never, move to the cloud is increasingly uncommon, and has changed quite quickly in process manufacturing over the past few years. Not only will the services and de ploy- ment models change, but new vendors will enter the market for data man - agement and analytics. In particular, Microsoft, Google, and Amazon all wherever it is; it is the analytics that needs attention. That said, there are many good rea- sons to leverage cloud computing, and it certainly has momentum in its favor, although it is impossible to generalize proposed benefits versus specific costs for every organization. Costs can in clude security, data governance, time to gain approval, and actual cost of deploy- ment. If one couples the cloud, or not, with innovations in both open source data management and Industrial Inter- net of Things (IIoT) cloud platform in- vestments, the result is a host of tempt- ing elements for deployment of big data solutions. Consider that in just 90 days in late 2017 and early 2018, eight com- panies received more than $250 million in investment capital for open source data storage, IIoT cloud platform, and IIoT analytics—and one gets a sense of the interest in advanced analytics. What this means is that the current model for big data storage in process manufacturing—which is on-premise, historian-based, and proprietary— is undergoing a transition, enabling new alternatives for how and where advanced analytics are run. The new 14 INTECH MAY/JUNE 2018 WWW.ISA.ORG COVER STORY use these new capabilities to enable the distribution of benefits throughout a plant and a company. Revolution in deployment "We tend to overestimate the effect of a technology in the short run and un- derestimate the effect in the long run," observed Roy Amara, past president of The Institute for the Future. If big data is not new, then certainly the cloud is not either. Some popular picks for the start of cloud computing include the introduction of the first big "SaaS" application (Salesforce) in 1999; the in- troduction of AWS by Amazon in 2002, and then S3 and EC2 in 2006; and when cloud computing competition got interesting with Microsoft's and Google's cloud platform introductions in 2008. So conservatively, like big data, there is a decade of history and innova- tion to leverage for advanced analytics. To be clear, the cloud is not a require- ment for big data implementations. If someone says a cloud deployment is required for advanced analytics with big data, he or she is likely a cloud salesperson seeking quota fulfillment. In our experience, the data is just fine, Figure 4. Advanced analytics software provides self-service capabilities for engineers to create various views of data.

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