The potential value that we describe in Chapter 3 from IoT applications arises from how the data are analyzed and applied to decision making, improving operations, and enabling new lines of business. However, in most of the applications we evaluated, the data being generated are used in very limited ways.
In one oil platform application we studied, less than 1 percent of the data being gathered were used, largely because most of the data were never transferred from the rig (Exhibit 3). IoT should be a key source of big data that can be analyzed to capture value, and open data, which can be used by more than one entity.
Where IoT data are being used, they are often used only for anomaly detection or real-time control, rather than for optimization or prediction, which we know from our study of big data is where much additional value can be derived.
For example, in manufacturing, an increasing number of machines are “wired,” but this instrumentation is used primarily to control the tools or to send alarms when it detects something out of tolerance. The data from these tools are often not analyzed (or even collected in a place where they could be analyzed), even though the data could be used to optimize processes and head off disruptions.
The inability to capture and use relevant data from multiple streams generated by different IoT systems is the result of several organizational, technical, and commercial barriers. In some cases, a lack of understanding of the potential to use data has led to a failure to invest in deploying IoT-enabled solutions. But there are also technical challenges, including finding efficient ways to transmit and store data. The most fundamental challenges are in data transmission and storage.
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