In most ways, Internet of Things (IoT) analytics are like any other analytics. IoT analytics use most of the same algorithms and tools as other kinds of business intelligence (BI) and advanced analytics. Even so, the IoT is creating unparalleled information management and analytics challenges.
During the Gartner Business Intelligence & Analytics Summit this week in Grapevine, Texas, Jim Hare, research director at Gartner, examined the difference between IoT analytics and traditional analytics.
What Makes IoT Analytics Different? By 2020, Gartner predicts that more than half of major new business processes and systems will incorporate some element of the IoT. Analytics are essential to the success of IoT systems. They are arguably the main point of the IoT as they support the decision-making process in operations that are created in business transformation and digital business programs.
Analytics are typically used for reporting, diagnostics, prediction and other cases of advanced analytics, but the IoT is requiring the combination of all of these types of analytics, including streaming data, to analyze and filter the fast-moving data.
“Just as we’re seeing a lot more complexity in the data, we’re seeing more complexity in how to manage that data,” said Mr. Hare.
More Data “The staple inputs for IoT analytics are streams of sensor data from machines, medical devices, environmental sensors and other physical entities,” said Mr. Hare.;
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