Data comes in different flavors, so many that there can be no one-size-fits-all approach to deriving value from it. Your data analytics capabilities have to be able to handle data arriving in different forms and in different speeds and sizes.
Traditional enterprise information management systems deal with structured (or at most semi-structured) data, small and slow data, data at rest, and internal data. This sort of material, residing in well-organized databases, is easy to process. The IoT, on the other hand, generates unstructured big data: all of the information indiscriminately churned out, in various formats, by sensors, satellites, cameras, and other connected devices. Such data, in its chaos and its abundance, represents a challenge.
A first step in confronting that challenge is to impose some discipline on your data flows. If a sensor buried deep inside a ship's engine is generating multiple temperature readings every second, you might do well to set your analytics capabilities so that they're ignoring 95 percent of them: that much information might just slow you down, with no perceptible upside. Recognizing patterns in IoT data will also be important – so that you can subsequent identify deviations from those patterns, deviations that will indicate that some component in a supply chain or an electricity grid, to give two examples, is on the blink.
Other challenges exist as well. To obtain actionable intelligence, you must integrate all of those different types of sensor data with each other. Receiving a data point that tells you that an engine part is overheating will be useful. But it will be more useful if it's accompanied, in real time, by a visual image that depicts what's happening to that part to make it so hot.
Meanwhile, you may well want to integrate all this unstructured data with your organization's structured internal data, a process that could yield great insights but will require you to define your data-related goals and choose the right tools and providers to achieve them.