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MAY-JUN 2018

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the introduction of new advanced analytics capabilities, they will find a new set of features and experi - ences, far removed from the early days of the big data era. Applying these advanced analytics solutions to big data will improve the user ex - perience by accelerating the path to implementation. Contextualization, self-service for organizations, new platforms options, and advanced analytics capabilities benefit from years of vendor invest - ment and early adopter feedback. The one thing they do not guarantee, however, is success in the last mile of any analytics project, big or small, which is the landing or adoption of new insights into a conservative and questioning culture. That, always, is the largest obstacle for any analytics project, which no amount of technol - ogy innovation can paper over. But by embracing an analytics cul- ture and the innovation now available, organizations can seize the opportuni- ties to create value from their big data, allowing them to remain competitive. n ABOUT THE AUTHOR Michael Risse (michael.risse@seeq.com) is vice president and CMO at Seeq Corpora- tion, a company building advanced ana- lytics applications for engineers and ana- lysts to accelerate insights from process manufacturing data. He was formerly a consultant in big data applications and platforms, and prior to that worked with the Microsoft Corporation for 20 years. Risse is a graduate of the University of Wisconsin at Madison. View the online version at www.isa.org/intech/20180601. calculation offerings that have been used for years to accelerate insights for end users. As McKinsey and Company defines advanced analytics solutions: "These [advanced analytics solutions]— which provide easier access to data from mul tiple data sources, along with advanced modeling algorithms and easy-to-use visualization approaches— could finally give manufacturers new ways to control and optimize all process - es throughout their entire operations." What has happened is that vendors have recognized there is too much data from too many sensors, and potentially of too many types, for one person to simply solve problems manually with a spreadsheet. Therefore, through the introduction of machine learning or other analytics techniques, an engi- neer's efforts must be accelerated when seeking correlations, clustering, or any needle within the haystack of process data. With these features built on mul- tidimensional models and enabled by assembling data from different sources, engineers gain an order of magnitude in analytics capabilities, akin to moving from pen and paper to the spreadsheet. These advanced analytics innova- tions are not a black box replacement for the expertise of the engineers, but a complement and accelerator to their ex- pertise, with transparency to the under- lying algorithms to support a first prin- ciples approach to investigations. In this way, it is a natural next step in the his- tory of statistical and control processes, rather than a data science approach to investigations. At the same time, ad- vanced analytics recognizes the path to quicker insights must leverage innova- tions in adjacent areas to address the scope of data available for investigation. Same last mile As process manufacturers find an opportunity when their plant transi - tions or capital investments enable have cloud platforms and time-series data storage services—Cosmos DB, Bigtable, and Dynamo, respectively. All three have acquired IIoT platform companies (Solair, Xively, and 2lem- etry, respectively) to build out their manufacturing solutions. GE with Predix, Siemens with Mind- Sphere, and PTC and ThingWorx may have more industrial domain knowl- edge, and OSIsoft starts out with the best customer base and richest on- premise offering, but the deployment revolution offers flexibility in deploy- ment and service levels for how com panies license and run advanced analytics solutions. Advancing analytics The manufacturing industry would be well serviced by a marketing dictionary to define the large number of buzzwords, technology eras, and "marketectures" (marketing architectures that run on PowerPoint). In this dictionary of terms, big data would of course be included un der "B," but it would be preceded by "an alytics." Analytics: descriptive, predictive, diagnostic, interactive, pre- scriptive, basic, real-time, historical, root cause, and so forth. Analytics is now so over used that the word has lost specific meaning in a 30-year history of spread- sheets and in a 20-year role with the term marketed for "actionable insights." But now, the role of analytics has to change to address the volume, chal- lenges, and opportunity associated with massive data volumes, variety, etc. To the rescue comes a new entry to the diction- ary, "advanced analytics." Just as adding "smart" to a noun denotes a thing with sensors for telemetry and remote moni- toring services (e.g., smart refrigerator, smart parking lot), adding "advanced" to "analytics" brings analytics into a mod- ern framework for today's challenges. Specifically, advanced analytics speaks to the inclusion of cognitive computing technologies into the visualization and COVER STORY INTECH MAY/JUNE 2018 15 RESOURCES "Plant historians" www.isa.org/intech/20150201 "Big data analytics need new solutions" www.isa.org/intech/20170204 "Using the cloud to store and distribute manufacturing data" www.isa.org/intech/20160205 Analytics is now so over used that the word has lost specific meaning in a 30-year history of spreadsheets and in a 20- year role with the term marketed for "actionable insights."

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