JUL-AUG 2019

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By Michael Risse M erriam-Webster's online dictionary says the first known use of "analytics" was in 1590 when it was defined as "the meth- od of logical analysis"; whereas "analysis" was first used in 1581 and was defined as "the separation of a whole into its component parts." Fast forward 430 years, and analytics is now defined in many ways, including data visualization, machine learn- ing, business intelligence, dashboards, and key performance indicators (KPIs). The pressure to gain insight from data is so pervasive that analytics has become a throwaway term in marketing mate- rials for all types of software. But whatever analytics is called or supposed to mean, process manufacturers have too much data and not enough insights. Most process in dus- try companies have collected years of time-series historical data but are unable to quickly surface and share critical insights leading to improve- ments in efficiency and innovation. Addition- ally, it is difficult to determine value or affect in- process batches or processes, because it takes so long to find the insights. Further, this "data rich, information poor" (DRIP) (figure 1) situation is only getting worse with the exponential increase in data as the Indus - trial Internet of Things (IIoT) takes hold. IIoT fore- casts correlate to the amount of data expected, and market intelligence firm IDC is expecting world - wide spending on IoT to reach $745 billion in 2019, led by the manufacturing sectors. That represents a massive amount of sensor data, and it will go to waste absent robust analytics and a flexible, cost- effective way to store and process it. If business and production insights are going to be faster, better, and easier to achieve—then something will need to change by bridging com- puter science innovation with the expertise and 18 INTECH JULY/AUGUST 2019 WWW.ISA.ORG New approaches handle more data volume and perform predictive analytics Analytics next: Beyond spreadsheets

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