Our intelligent "data finds data" environment must contain eight essential building blocks:
The existence of, and availability of, observations
The ability to extract and classify features from the observations
The ability to efficiently discover related historical context
The ability to make assertions (same or related) about new observations
The ability to recognize when new observations reverse earlier assertions
The ability to accumulate and persist this asserted context
The ability to recognize the formation of relevance/insight
The ability to notify the appropriate entity of such insight
If there is no data, then there is no chance one can make sense of it. And if there is data, it has to be "sensed" (collected) by some sensor system for it to ever be of potential use. And even if data is collected, one must have access to it to have any hope of making sense of it.
For the sake of argument, let's say a grain of sand contains too few features to extract and classify. Grains of sand are frequently the same color, size, weight, shape, and so on. Therefore, the lack of discriminating features would prevent one from identifying the same piece (semantic reconciliation) later. The point being, for data to be placed into context, one must be able to extract and classify its key features. Structured data is rather easy when address information ...