Executive Briefings

How Do You Mine Value From Tons of IoT-Generated Data?

IoT generates a tremendous amount of data - much more than people generate manually with their keyboards and cameras. And the volume of IoT data being generated will continue to increase at an exponential pace. How can companies extract the maximum value from that data? How should they think about it?

How Do You Mine Value From Tons of IoT-Generated Data?

Often the most useful data is not the direct sensor data, but information that is calculated or inferred from combinations of that data by an algorithm. An example is a real-time locating system. The raw reader data may provide strange results: a specific item seen in the back room and at various points while being brought to the store floor suddenly disappears from view without a trace. In fact, we can infer that the item is simply blocked from being read. Algorithms to decipher the meaning of low-level data are developed by experience in environments over time to create better and better higher-level inferences and intelligence. Data may also be federated from multiple sources to provide a broader view. For example, in an office building, data from the elevators, lighting systems, heating and A/C, security/surveillance, and other systems may be combined to provide a higher level of intelligence about the building. This may also include data from external sources such as weather, traffic, what time a big game is letting out, or other external events, to decide when to pre-heat, pre-cool, or take other actions in the building.

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Often the most useful data is not the direct sensor data, but information that is calculated or inferred from combinations of that data by an algorithm. An example is a real-time locating system. The raw reader data may provide strange results: a specific item seen in the back room and at various points while being brought to the store floor suddenly disappears from view without a trace. In fact, we can infer that the item is simply blocked from being read. Algorithms to decipher the meaning of low-level data are developed by experience in environments over time to create better and better higher-level inferences and intelligence. Data may also be federated from multiple sources to provide a broader view. For example, in an office building, data from the elevators, lighting systems, heating and A/C, security/surveillance, and other systems may be combined to provide a higher level of intelligence about the building. This may also include data from external sources such as weather, traffic, what time a big game is letting out, or other external events, to decide when to pre-heat, pre-cool, or take other actions in the building.

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How Do You Mine Value From Tons of IoT-Generated Data?