| | Dec/Jan 20178CIOReviewManaging Big Analog Data from the Internet of ThingsData volumes have exploded over the last few years. More data has been created in the past two years than in the entire previous history of the human race. The rate at which data is growing requires dramatic changes in the way data is extrapolated and analyzed. According to Oxford Economics Ltd, the Industrial Internet of Things (IIoT) is a major approach to deriving insight from growing data volumes, and extracting data from previously untapped sources, such as pumps, turbines, manufacturing equipment, vehicles, buildings, and even people. The IIoT will have significant implications for the global economy, as it spans industries representing 62 percent of gross domestic product (GDP), among G20 nations, including manufacturing, mining, agriculture, oil and gas, and utilities. In general, I like to portion the sources of big data into three categories:1. Traditional IT data sources: stock ticks, medical data, inventories, sales data, events process and control.2. New and emerging data sources: social data, behaviors, sentiments, tweets, blogs, and comments.3. "Things" data, analog and natural world sources: machines, people, tools, cars, wearables, buildings, wind farms, crop fields, etc.It's this third source of things, big data, which I refer to as big "analog" data, that's the dominant data source in the IIoT. It's referred to as analog since its sourced from the physical world and must be digitized to condition it for use in IT computer systems, networks, and storage. Some industries, such as manufacturing and processing are collecting and processing big analog data from equipment such as pipes, pumps, dams, and turbines for years through sensors. Acquiring data isn't always their top challenge, but rather to analyze and take immediate action based upon the insights from the data, right at the IIoT edge. Let's review a sample scenario.In an industrial factory sits an entire area of pumps that supply water to a small city. The pumps are instrumented with sensors to collect several types of data, including data that can help predict system failures. However, the information that is collected is usually sent back to the cloud or data center, delaying Tom BradicichIN MY OPINIONTom Bradicich, VP & GM-Servers and IoT Systems, Hewlett Packard Enterprise [NYSE:HPE]By
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