Data Half-Life is not an indication of the importance of a particular piece of information. It is actually a measure of how long a piece of information is relevant. Relevance is not a substitute for importance. It is dependent on context and the information itself. So a low data half-life means that the piece of information will quickly lose its relevancy. A high data half-life means the relevancy will drop slowly.
Consider the story that Clay Shirky related in his keynote at Web 2.0 Expo in New York. In this story someone changed their relationship status from engaged to single. This information is highly relevant to some people and not very relevant to most others. Given that data half-life reflects the broader relevance of the information to a person’s network, it has a low data half-life. It is generally not relevant to most of the people in the network.
Now they many want to know or feel the need to know, that does not mean it is relevant to them. It is easy to mistake the desired to know or the need to know as relevant. Desire to know has no bearing of the information’s data half-life.
By having a low data half-life the relationship status will only travel only so far through the person’s network, thereby avoiding the result in Clay Shirky’s story. Data Half-life is represents how time dependent the information is. The more time dependent some data is, the lower the half-life and the less time dependent the higher the half-life.
Tags: Filters
