You can’t have analytics without data. To deliver its promised optimizations, improved decisions and profitability, data analytics must be built on a solid foundation of networking and efficient information processing—but it must also have signals, readings, measurements and every other kind of input to process into useful results.
However, faced with sorting through increasingly huge information sources and repositories, these infrastructures and their users must contextualize and prioritize to find the few nuggets that can add value, and they’re relies on software and digitalization to remove former hurdles, make analytics less cumbersome, and streamline its capabilities for everyday use. Artificial intelligence (AI) can help, too, but only if it delivers solid, plant-floor benefits.
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Day 1: Société Le Nickel’s mines implement Rockwell’s FactoryTalk Analytics Pavilion8 MPC software. Read more.
Day 2: E Tech streamlines analytics for CPG and data center clients. Read more.
Day 3: British Sugar adds Seeq’s AI Assistant to dashboards, so it can ask them questions. Read more.
Day 4: Hargrove acquires data from advanced smart devices. Read more.
Day 5: Fluke’s Azima subsidiary standardizes and automates diagnostics, and uses AI to sort through results. Read more.
