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CorrelateIQ reveals hidden relationships among many data streams at once, catching hidden patterns, early warning signs and operating shifts that single-variable monitoring overlooks. Built on analysis techniques from high-energy physics, this software from Los Alamos National Laboratory turns existing sensor and process data into practical insight that helps organizations prevent failures, protect quality and reduce downtime. Because the method makes no assumptions about where the data comes from, it applies to a wide range of numeric data, from manufacturing sensors and equipment logs to financial records, market data and scientific measurements. Overview Factories, laboratories, infrastructure networks, financial and real-estate markets now produce huge volumes of linked data from sensors, equipment logs and transactions. Most analytics tools, however, judge each variable separately or depend on fixed thresholds that trigger an alert only after a single reading crosses a limit. These methods have trouble spotting effects that arise from subtle interactions among many variables, such as gradual equipment wear, a drifting manufacturing process or a market entering a new phase. As a result, organizations often miss the earliest warning signals, react to failures only after they happen and leave much of the value in their data untapped. Teams in every data-reach fields need a practical way of understanding how an entire system behaves as a whole, not merely how each part perfo

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