For industrial organizations with vast business processes to manage, the large variety of machinery across value chains often results in high operational costs. Inefficiencies, such as production delays, inclement weather that slows deliveries, and unexpected machine failures can lead to millions in lost revenue and increase the cost of doing business.Analytics is the operational key to success. Minimize unscheduled downtime, optimize fleet activities, and improve customer experience by blending machine-generated internet of things (IoT) data with other contextualized data to feed predictive models. It may not be easy to do this across machines, lines, or plants, but there are tools that bring a simple no-code approach to help bring value quickly and easily through edge processing, helping you to overcome challenges and maximize return on investment. By hosting more applications and data management at the operating site level, cloud storage and compute costs can also be further optimized.You will learn how to:– Connect to machine data sources and perform univariate analytics like FFT, ML, and more at the edge.– Streamline data pipelines, and quickly create custom data sets through automation.– Machine learning orchestration makes data science teams more productive.– Embedded analytics allows insights to be delivered at the point of business impact.