A Guide To Advanced Analytics
By Sean Tropsa
In the process manufacturing world, there’s been a significant increase in the accessibility into operational and equipment data. Teams now have visibility into both historical and near real-time data from their operation, and can even monitor this as it’s happening at remote locations. But the problem with this is that teams are drowning in data—”DRIP”—data rich, information poor.
Combining these colossal pools of data (for example, some chemical processing facilities have 20,000 to 70,000 signals, oil and gas working with 100,000 or more, and enterprise-wide networks reaching millions of sensors) with a lack of accurate organization, data cleansing, and contextualising, engineers face a major standstill.
It’s easy to be overwhelmed by these amounts of data, but implementing tactful refinement processes leads directly to transformational insights. Too many of today’s process engineers and SMEs are spending their valuable time sorting through spreadsheets in an attempt to wrangle their data, instead of analyzing patterns and models that lead to useful insights. By implementing advanced analytics, process manufacturing operations can seamlessly visualize all up-to-date data from multiple disparate sources and make data-driven decisions that will immediately improve outcomes.
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